test/test_jit.py

# -*- coding: UTF-8 -*-
from __future__ import division
import torch

# TODO: remove this global setting
# JIT tests use double as the default dtype
torch.set_default_dtype(torch.double)

# This is how we include tests located in test/jit/...
# They are included here so that they are invoked when you call `test_jit.py`,
# do not run these test files directly.
from jit.test_recursive_script import TestRecursiveScript  # noqa: F401
from jit.test_type_sharing import TestTypeSharing  # noqa: F401
from jit.test_logging import TestLogging  # noqa: F401
from jit.test_list_dict import TestList, TestDict  # noqa: F401
from jit.test_async import TestAsync  # noqa: F401
from jit.test_data_parallel import TestDataParallel  # noqa: F401
from jit.test_models import TestModels  # noqa: F401
from jit.test_autodiff_subgraph_slicing import TestAutodiffSubgraphSlicing  # noqa: F401
from jit.test_custom_operators import TestCustomOperators  # noqa: F401
from jit.test_export_modes import TestExportModes  # noqa: F401
from jit.test_class_type import TestClassType  # noqa: F401
from jit.test_builtins import TestBuiltins  # noqa: F401
from jit.test_unsupported_ops import TestUnsupportedOps  # noqa: F401

# Torch
from torch import Tensor
from torch._C import TensorType, BoolType, parse_ir, _propagate_shapes
from torch._six import inf, PY2, PY37, StringIO
from torch.autograd import Variable, Function
from torch.jit.annotations import BroadcastingList2, BroadcastingList3, Any  # noqa: F401
from torch.jit.frontend import NotSupportedError
from torch.onnx import OperatorExportTypes
from torch.testing import FileCheck
import torch.cuda
import torch.jit
import torch.jit._logging
import torch.jit.frontend
import torch.jit.quantized
from torch.jit._recursive import wrap_cpp_module
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.quantized.modules.linear import LinearPackedParams
from torch.quantization import QConfig
from torch.quantization._quantize_script import ConvPackedParams
from torch.quantization._quantize_script import script_qconfig
from torch.quantization import default_observer
from torch.quantization import default_weight_observer
from torch.quantization import default_per_channel_weight_observer
from torch.quantization import default_qconfig

from torch.quantization import quantize
from common_quantization import SingleLayerLinearModel, AnnotatedSingleLayerLinearModel
from common_quantization import ConvModel, AnnotatedConvModel
from common_quantization import test_only_eval_fn as _test_only_eval_fn


# Testing utils
import jit_utils
from common_utils import run_tests, IS_WINDOWS, TEST_WITH_UBSAN, \
    skipIfRocm, suppress_warnings, IS_SANDCASTLE, GRAPH_EXECUTOR, ProfilingMode, \
    freeze_rng_state, set_rng_seed, slowTest, TemporaryFileName, skipIfCompiledWithoutNumpy, \
    enable_profiling_mode
from jit_utils import JitTestCase, enable_cpu_fuser, disable_autodiff_subgraph_inlining, \
    _trace, enable_cpu_fuser_if, do_input_map, \
    execWrapper, _inline_everything, _tmp_donotuse_dont_inline_everything, \
    get_forward, get_forward_graph, get_module_method, \
    RUN_CUDA, RUN_CUDA_MULTI_GPU
from common_nn import module_tests, new_module_tests, criterion_tests
from common_methods_invocations import method_tests as autograd_method_tests
from common_methods_invocations import create_input, unpack_variables, \
    exclude_tensor_method, non_differentiable, EXCLUDE_GRADCHECK, EXCLUDE_FUNCTIONAL
from common_device_type import instantiate_device_type_tests

# For testing truediv in python 2
from test_module.future_div import div_int_future, div_float_future
from test_module.no_future_div import div_int_nofuture, div_float_nofuture

# Standard library
from collections import namedtuple, OrderedDict
from copy import deepcopy
from functools import wraps
from itertools import product, chain
from textwrap import dedent
from typing import List, Dict, Optional, Tuple, Union
import copy
import inspect
import math
import numpy as np
import io
import os
import pickle
import pickletools
import random
import shutil
import sys
import tempfile
import types
import unittest
import warnings
import zipfile
import re

RUN_CUDA_HALF = RUN_CUDA
if torch.cuda.is_available():
    CUDA_VERSION = torch._C._cuda_getCompiledVersion()
    for d in range(torch.cuda.device_count()):
        major = torch.cuda.get_device_capability(d)[0]
        if (major < 6):
            RUN_CUDA_HALF = False


PY35 = sys.version_info >= (3, 5)

def canonical(graph):
    return torch._C._jit_pass_canonicalize(graph).str(False)

def LSTMCellF(input, hx, cx, *params):
    return LSTMCell(input, (hx, cx), *params)


def doAutodiffCheck(testname):

    if GRAPH_EXECUTOR == ProfilingMode.SIMPLE:
        return False

    if GRAPH_EXECUTOR == ProfilingMode.LEGACY:
        return True


    # these tests are disabled because BailOut nodes
    # inserted by ProfilingExecutor interfere with
    # subgraph slicing of Differentiable Graphs
    test_exceptions = [
        # functional
        'test_nn_dropout',
        'test_nn_log_softmax',
        'test_nn_relu',
        'test_nn_softmax',
        'test_nn_threshold',
        'test_nn_lp_pool2d',
        'test_nn_lp_pool1d',
        'test_nn_gumbel_softmax_hard',
        'test_nn_gumbel_softmax',
        'test_nn_multilabel_soft_margin_loss',
        'test_nn_batch_norm',
        # AutogradJitGenerated
        'test___rdiv___constant',
        'test___rdiv___scalar_constant',
    ]

    if testname in test_exceptions:
        return False
    return True

torch._C._jit_set_profiling_executor(GRAPH_EXECUTOR != ProfilingMode.LEGACY)
# even though FULL_PROFILER should be our default
# we haven't tested every single test in this file
# but we enable FULL_PROFILER for a large subset
# of the tests with "with enable_profiling_mode"
torch._C._jit_set_profiling_mode(False)

def LSTMCell(input, hidden, w_ih, w_hh, b_ih=None, b_hh=None):
    hx, cx = hidden
    gates = F.linear(input, w_ih, b_ih) + F.linear(hx, w_hh, b_hh)

    ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1)
    ingate = torch.sigmoid(ingate)
    forgetgate = torch.sigmoid(forgetgate)
    cellgate = torch.tanh(cellgate)
    outgate = torch.sigmoid(outgate)

    cy = (forgetgate * cx) + (ingate * cellgate)
    hy = outgate * torch.tanh(cy)
    return hy, cy


def LSTMCellC(*args, **kwargs):
    hy, cy = LSTMCellF(*args, **kwargs)
    return torch.cat((hy, cy))


def LSTMCellS(x, hx, cx, w_ih, w_hh, b_ih, b_hh):
    gates = x.mm(w_ih.t()) + hx.mm(w_hh.t()) + b_ih + b_hh
    ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1)
    ingate = torch.sigmoid(ingate)
    forgetgate = torch.sigmoid(forgetgate)
    cellgate = torch.tanh(cellgate)
    outgate = torch.sigmoid(outgate)
    cy = (forgetgate * cx) + (ingate * cellgate)
    hy = outgate * torch.tanh(cy)
    return hy, cy


# Code reference: https://github.com/pytorch/translate/blob/master/pytorch_translate/rnn_cell.py#L27:44
def MiLSTMCell(x, hx, cx, w_ih, w_hh, alpha, beta_i, beta_h, bias):
    Wx = x.mm(w_ih.t())
    Uz = hx.mm(w_hh.t())
    # Section 2.1 in https://arxiv.org/pdf/1606.06630.pdf
    gates = alpha * Wx * Uz + beta_i * Wx + beta_h * Uz + bias
    # Same as LSTMCell after this point
    ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1)
    ingate = ingate.sigmoid()
    forgetgate = forgetgate.sigmoid()
    cellgate = cellgate.tanh()
    outgate = outgate.sigmoid()
    cy = (forgetgate * cx) + (ingate * cellgate)
    hy = outgate * cy.tanh()
    return hy, cy



def get_lstm_inputs(device, training=False, seq_length=None):
    input_shape = (3, 10) if seq_length is None else (seq_length, 3, 10)
    input = torch.randn(*input_shape, dtype=torch.float, device=device, requires_grad=training)
    hx = torch.randn(3, 20, dtype=torch.float, device=device, requires_grad=training)
    cx = torch.randn(3, 20, dtype=torch.float, device=device, requires_grad=training)
    module = nn.LSTMCell(10, 20).to(device, torch.float)  # Just to allocate weights with correct sizes
    if training:
        params = tuple(module.parameters())
    else:
        params = tuple(p.requires_grad_(False) for p in module.parameters())
    return (input, hx, cx) + params


def get_milstm_inputs(device, training=False):
    minibatch = 3
    input_size = 10
    hidden_size = 20
    x = torch.randn(minibatch, input_size, device=device, dtype=torch.float)
    hx = torch.randn(minibatch, hidden_size, device=device, dtype=torch.float)
    cx = torch.randn(minibatch, hidden_size, device=device, dtype=torch.float)

    ih = torch.randn(4 * hidden_size, input_size, device=device, dtype=torch.float, requires_grad=training)
    hh = torch.randn(4 * hidden_size, hidden_size, device=device, dtype=torch.float, requires_grad=training)
    alpha = torch.randn(4 * hidden_size, dtype=torch.float, device=device, requires_grad=training)
    ibeta = torch.randn(4 * hidden_size, dtype=torch.float, device=device, requires_grad=training)
    hbeta = torch.randn(4 * hidden_size, dtype=torch.float, device=device, requires_grad=training)
    bias = torch.randn(4 * hidden_size, dtype=torch.float, device=device, requires_grad=training)
    return x, hx, cx, ih, hh, alpha, ibeta, hbeta, bias


def get_fn(file_name, script_path):
    import importlib.util
    spec = importlib.util.spec_from_file_location(file_name, script_path)
    module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(module)
    fn = module.fn
    return fn


def get_execution_plan(graph_executor_state):
    execution_plans = list(graph_executor_state.execution_plans.values())
    num_plans = len(execution_plans)
    if num_plans != 1:
        raise RuntimeError('This test assumes this GraphExecutor should '
                           'only have one execution plan, got: {}'.format(num_plans))
    return execution_plans[0]


def get_grad_executor(plan_state, diff_graph_idx=None, skip_check=False):
    if diff_graph_idx is None:
        nodes = list(plan_state.graph.nodes())

        if not skip_check:
            nodes = list(filter(lambda n : n.kind() != "prim::BailOut" and n.kind() != "prim::BailoutTemplate", nodes))
            if len(nodes) == 1 or (len(nodes) == 2 and nodes[1].kind() == "prim::TupleConstruct"):
                pass
            else:
                raise RuntimeError("Can't get a grad_executor for a non-differentiable graph")
    grad_executors = list(plan_state.code.grad_executor_states())
    return grad_executors[diff_graph_idx or 0]


def all_backward_graphs(script_module, diff_graph_idx=None):
    # Note: for Python 2 the order seems to be unstable
    ge_state = script_module.get_debug_state()
    fwd_plan = get_execution_plan(ge_state)
    grad_executor_state = get_grad_executor(fwd_plan, diff_graph_idx=diff_graph_idx)
    bwd_plans = list(grad_executor_state.execution_plans.values())
    return [p.graph.copy() for p in bwd_plans]


def backward_graph(script_module, diff_graph_idx=None, skip_check=False):
    ge_state = script_module.get_debug_state()
    fwd_plan = get_execution_plan(ge_state)
    grad_executor_state = get_grad_executor(fwd_plan, diff_graph_idx=diff_graph_idx, skip_check=skip_check)
    bwd_plan = get_execution_plan(grad_executor_state)
    # Running JIT passes requires that we own the graph (with a shared_ptr).
    # The debug state struct does not own its graph so we make a copy of it.
    return bwd_plan.graph.copy()


# helper function to get sum of List[Tensor]
def _sum_of_list(tensorlist):
    s = 0
    for t in tensorlist:
        s += t.sum()
    return s


# has to be at top level or Pickle complains
class FooToPickle(torch.nn.Module):  # noqa T484
    def __init__(self):
        super(FooToPickle, self).__init__()
        self.bar = torch.jit.ScriptModule()

class TestJit(JitTestCase):
    @unittest.skipIf(not RUN_CUDA, "requires CUDA")
    def test_large_nbr_kernel_args(self):
        class Recurrence(nn.Module):
            def __init__(self, seq_len):
                super(Recurrence, self).__init__()
                self.seq_len = seq_len

            def forward(self, input):
                input = input.transpose(0, 1)

                # Main loop
                output = []
                for i in range(self.seq_len):
                    b = input[i] * 2
                    output.append(b)

                output = torch.cat(output, 0).view(input.size(0), *output[0].size())
                output = output.transpose(0, 1)
                return output

        input_size = 8
        batch_size = 2
        seq_len = 130

        rec = Recurrence(seq_len)
        input = torch.rand(batch_size, seq_len, input_size)

        torch.cuda.set_device(0)
        rec = rec.cuda()
        input = input.cuda()

        traced_rec = torch.jit.trace(rec, (input))

    def test_trace_legacy_ctor(self):
        class MyModule(nn.Module):
            def forward(self, x):
                return (x + 1, torch.FloatTensor([0]))

        traced_rec = torch.jit.trace(MyModule(), torch.randn(2, 2))

    @unittest.skip("Requires a lot of RAM")
    def test_big(self):
        m = torch.jit.ScriptModule()
        gig = int(1024 * 1024 * 1024 / 4)
        # a small tensor in the first 4GB
        m.v0 = nn.Parameter(torch.full((2,), 1, dtype=torch.float))
        # a large tensor in the first 4GB that ends outside of it
        m.v1 = nn.Parameter(torch.full((5, gig), 2, dtype=torch.float))
        # a small tensor in >4GB space
        m.v2 = nn.Parameter(torch.full((2,), 3, dtype=torch.float))
        # s large tensor in the > 4GB space
        m.v3 = nn.Parameter(torch.full((5, gig), 4, dtype=torch.float))

        m2 = self.getExportImportCopy(m)

        self.assertEqual(tuple(m.parameters()), tuple(m2.parameters()))

    def test_simple(self):
        x = torch.tensor([0.4], requires_grad=True)
        y = torch.tensor([0.7], requires_grad=True)

        def f(x, y):
            return torch.sigmoid(torch.tanh(x * (x + y)))

        self.checkTrace(f, (x, y))

    def test_trace_aliased_parameter(self):
        class M(nn.Module):
            def __init__(self, x):
                super(M, self).__init__()
                self.x = nn.Parameter(x)

            def forward(self, y):
                return self.x + y

        m = M(torch.rand(3, 4))
        r = torch.jit.trace(m, m.x)
        t2 = torch.rand(3, 4)
        self.assertEqual(r(t2), m.x + t2)

    def test_constants_pkl(self):
        # This test asserts that the serialization archive includes a `constants.pkl`
        # file. This file is used by `torch.load` to determine whether a zip file
        # is a normal eager-mode serialization zip or a jit serialization zip. If
        # you are deleting `constants.pkl`, make sure to update `torch.serialization.load`
        # so it is still able to figure out which is which.
        @torch.jit.script
        def fn(x):
            return x

        buf = io.BytesIO()
        torch.jit.save(fn, buf)
        buf.seek(0)

        files = zipfile.ZipFile(buf).filelist
        self.assertTrue(any(['archive/constants.pkl' == f.filename for f in files]))

    def test_trace_nested_fn(self):
        class TracedInlineDecision(torch.nn.Module):
            def forward(self, x, flag):
                @torch.jit.script
                def make_decision(flag, x):
                    if flag:
                        return x
                    else:
                        return torch.zeros_like(x)
                x = torch.neg(x)
                return make_decision(flag, x)


        decision = TracedInlineDecision()
        torch.jit.trace(decision, (torch.rand(3, 4), torch.tensor([True], dtype=torch.bool)), check_trace=True)

    def test_restore_device(self):
        class M(torch.jit.ScriptModule):
            def __init__(self, cpu_device_str):
                super(M, self).__init__()
                self.p0 = nn.Parameter(torch.tensor([0.3], dtype=torch.float,
                                       device=cpu_device_str))
                self.b0 = torch.tensor([0.9], dtype=torch.float,
                                       device=cpu_device_str)

        # main purpose is checking map_location works
        m = M("cpu")
        m2 = self.getExportImportCopy(m)
        self.assertEqual(tuple(m.parameters()), tuple(m2.parameters()))
        self.assertEqual(tuple(m.buffers()), tuple(m2.buffers()))
        self.assertFalse(m2.p0.is_cuda)
        self.assertFalse(m2.b0.is_cuda)

    def test_model_save_error(self):
        with TemporaryFileName() as fname:
            with self.assertRaisesRegex(pickle.PickleError, "not supported"):
                torch.save(FooToPickle(), fname)

    def test_single_tuple_trace(self):
        x = torch.tensor(2.)

        def f2(x):
            return (x,)
        jit_f2 = torch.jit.trace(f2, x)
        assert f2(x) == jit_f2(x)  # fails

    def test_trace_namedtuple(self):
        Point = namedtuple('point', ['x', 'y'])

        def f(p):
            if type(p) is tuple:
                p = Point(*p)
            return p.x + p.y

        p = Point(torch.randn(1), torch.randn(1))
        traced = torch.jit.trace(f, (p,))
        self.assertEqual(f(p), traced(p))

    @unittest.skipIf(not RUN_CUDA, "restore device requires CUDA")
    def test_restore_device_cuda(self):
        class MyModule(torch.jit.ScriptModule):
            def __init__(self):
                super(MyModule, self).__init__()
                self.register_buffer('b0', torch.randn(1, 3))
                self.p0 = nn.Parameter(torch.randn(2, 3))

            @torch.jit.script_method
            def forward(self, x):
                return x + self.b0 + self.p0

        m = MyModule()
        m.cuda(torch.cuda.device_count() - 1)
        cuda_device_str = 'cuda:' + str(torch.cuda.device_count() - 1)

        self.assertTrue(m.p0.is_cuda)
        self.assertTrue(m.b0.is_cuda)

        # restore to the saved devices
        m2 = self.getExportImportCopy(m)
        self.assertEqual(tuple(m.parameters()), tuple(m2.parameters()))
        self.assertEqual(tuple(m.buffers()), tuple(m2.buffers()))
        self.assertEqual(str(m2.p0.device), cuda_device_str)
        self.assertEqual(str(m2.b0.device), cuda_device_str)

        # restore all to cpu using string
        cpu_device_str = 'cpu'
        m3 = self.getExportImportCopy(m, map_location=cpu_device_str)
        self.assertEqual(str(m3.p0.device), cpu_device_str)
        self.assertEqual(str(m3.b0.device), cpu_device_str)

        # restore all to first gpu using device
        m4 = self.getExportImportCopy(
            m3, map_location=torch.device('cuda:0'))
        self.assertEqual(str(m4.p0.device), 'cuda:0')
        self.assertEqual(str(m4.b0.device), 'cuda:0')

        # compute and compare the results
        input = torch.rand(2, 3).cuda(torch.cuda.device_count() - 1)
        origin_result = m(input)
        self.assertEqual(origin_result, m2(input))
        self.assertEqual(origin_result, m3(input.cpu()))
        self.assertEqual(origin_result, m4(input.cuda(0)))

    @unittest.skipIf(not RUN_CUDA, "restore device requires CUDA")
    def test_restore_shared_storage_on_cuda(self):
        class Foo(torch.jit.ScriptModule):
            def __init__(self):
                super(Foo, self).__init__()
                whole_tensor = torch.randn(4, 5, dtype=torch.float, device='cpu')
                self.p0 = nn.Parameter(whole_tensor.narrow(0, 0, 1))
                self.register_buffer('b0', whole_tensor.narrow(0, 3, 1))

        m = Foo()
        m2 = self.getExportImportCopy(m, map_location=torch.device('cuda:0'))
        self.assertEqual(tuple(m.parameters()), tuple(m2.parameters()))
        self.assertEqual(tuple(m.buffers()), tuple(m2.buffers()))
        self.assertTrue(m2.p0.is_cuda)
        self.assertTrue(m2.b0.is_cuda)
        self.assertTrue(m2.p0.is_shared())
        self.assertTrue(m2.b0.is_shared())
        self.assertEqual(m2.b0.storage().data_ptr(), m2.p0.storage().data_ptr())

    def test_typeas_trace_check(self):
        a = torch.tensor([0.4], requires_grad=True)
        b = torch.tensor([0.7], requires_grad=True)

        def f(x, y):
            return x.type_as(y)

        trace = torch.jit.trace(f, (a, b))

    def test_peephole_with_writes(self):
        def test_write(x):
            s = 0
            s += x
            s += x
            return s

        self.checkScript(test_write, (torch.ones(4, 4),))


    def test_peephole_with_non_output_writes(self):

        @torch.jit.ignore
        def nomnom(x):
            pass

        def test_write(x):
            t = torch.ones_like(x)
            z = x.clone()
            y = z + 0
            z.add_(t)
            # this makes sure z isn't blasted out of existence
            # because it isn't returned or used in a side-effectful
            # way
            nomnom(z)
            return y + y

        a = torch.ones(4, 4)
        j = self.checkScript(test_write, (a,))

    def test_peephole_no_output_aliasing(self):
        def test_peephole(x):
            y = x + 0
            return x, y

        a = torch.ones(4, 4)
        j = self.checkScript(test_peephole, (a,))
        r1, r2 = j(a)
        self.assertNotEqual(r1.data_ptr(), r2.data_ptr())

    def test_peephole(self):
        a = torch.tensor([0.4])
        b = torch.tensor([0.7])
        c = torch.tensor([0], dtype=torch.int32)

        def f(x, y):
            return x.type_as(y)

        tf = torch.jit.trace(f, (a, b))
        FileCheck().check("type_as").run(str(tf.graph))
        self.run_pass('peephole', tf.graph)
        FileCheck().check_not("type_as").run(str(tf.graph))
        tf2 = torch.jit.trace(f, (a, c))
        s = str(tf2.graph)
        self.run_pass('peephole', tf2.graph)
        self.assertEqual(s, str(s))

    def test_peephole_dynamic(self):
        def f(x, y):
            return x.type_as(y)

        fn = torch.jit.script(f)
        s = str(fn.graph)
        torch._C._jit_pass_peephole(fn.graph)
        self.assertEqual(s, str(fn.graph))

    @unittest.skipIf(not RUN_CUDA, "cpp tests require CUDA")
    def test_peephole_cuda(self):
        a = torch.tensor([0.4], device='cpu')
        b = torch.tensor([0.7], device='cuda')
        c = torch.tensor([0.7], device='cuda')

        def f(x, y):
            return x.type_as(y)

        trace = torch.jit.trace(f, (a, c))
        s = str(trace.graph)
        self.run_pass('peephole', trace.graph)
        self.assertEqual(s, str(trace.graph))
        trace = torch.jit.trace(f, (b, c))
        self.run_pass('peephole', trace.graph)
        self.run_pass('dce', trace.graph)
        FileCheck().check_not("type_as").run(str(trace.graph))

    @unittest.skipIf(GRAPH_EXECUTOR == ProfilingMode.SIMPLE, "Simple executor doesn't have shape information")
    def test_peephole_optimize_shape_ops(self):
        def test_input(func, input, result):
            # if result == 2 we will trigger a bailout and
            # the unprofiled graph should return the correct result
            self.assertEqual(func(input, profile_and_replay=True), result)
            gre = func.graph_for(input)
            if GRAPH_EXECUTOR == ProfilingMode.PROFILING:
                FileCheck().check("prim::Constant").check_next("prim::BailoutTemplate").run(gre)
            else:
                FileCheck().check_not("prim::If").run(gre)

        def test_dim():
            @torch.jit.script
            def func(x):
                if x.dim() == 1:
                    return 1
                else:
                    return 2

            test_input(func, torch.tensor([0.5]), 1)
            test_input(func, torch.tensor([[0.5]]), 2)
        test_dim()

        def test_dtype():
            @torch.jit.script
            def func(x):
                if x.dtype == torch.float32:
                    return 1
                else:
                    return 2

            test_input(func, torch.tensor(0.5, dtype=torch.float32), 1)
            test_input(func, torch.tensor(0.5, dtype=torch.int64), 2)
        test_dtype()

        def test_device():
            @torch.jit.script
            def func_1(x):
                if x.device == torch.device('cuda:0'):
                    a = 0
                else:
                    a = 1
                return a

            @torch.jit.script
            def func_2(x):
                if x.is_cuda:
                    a = 0
                else:
                    a = 1
                return a

            test_input(func_1, torch.tensor(0.5), 1)
            test_input(func_2, torch.tensor(0.5), 1)

            if RUN_CUDA:
                test_input(func_1, torch.tensor(0.5, device="cuda:0"), 0)
                test_input(func_2, torch.tensor(0.5, device="cuda:0"), 0)

        test_device()

    def test_attrs(self):
        def foo(x):
            return (
                # x.dtype, TODO: dtype long -> instance conversion
                x.device,
                x.shape,
                x.is_cuda,
                x.is_mkldnn,
                x.is_quantized,
                x.requires_grad,
                # x.layout TODO: layout long -> instance conversion
            )

        scripted = torch.jit.script(foo)
        x = torch.rand(3, 4)
        self.assertEqual(scripted(x), foo(x))

    def test_layout(self):
        @torch.jit.script
        def check(x, y):
            return x.layout == y.layout

        x = torch.rand(3, 4)
        y = torch.rand(3, 4)

        self.assertTrue(check(x, y))

    def test_index(self):
        x = torch.tensor([0.4], requires_grad=True)
        y = torch.tensor([0], dtype=torch.int64)

        def fn(x, y):
            return x[y]

        fn_traced = torch.jit.trace(fn, (x, y,))

        self.assertEqual(fn(x, y), fn_traced(x, y))

    def test_disabled(self):
        torch.jit._enabled = False
        try:
            def f(x, y):
                return x + y

            self.assertIs(torch.jit.trace(f, (torch.randn(2, 2), torch.randn(2, 2))), f)
            self.assertIs(torch.jit.script(f), f)

            class MyModule(torch.jit.ScriptModule):
                @torch.jit.script_method
                def method(self, x):
                    return x

            # XXX: Unfortunately ScriptModule won't simply become Module now,
            # because that requires disabling the JIT at startup time, which
            # we can't do in here.
            # We need to or those two conditions to make it work with all versions of Python
            self.assertTrue(inspect.ismethod(MyModule.method) or inspect.isfunction(MyModule.method))
        finally:
            torch.jit._enabled = True

    def test_train_eval(self):
        class Sub(nn.Module):
            def forward(self, input):
                if self.training:
                    return input
                else:
                    return -input

        class MyModule(torch.jit.ScriptModule):
            def __init__(self, module):
                super(MyModule, self).__init__()
                self.module = module

            @torch.jit.script_method
            def forward(self, input):
                return self.module(input) + 1

        m = MyModule(Sub())
        input = torch.rand(3, 4)
        self.assertEqual(input + 1, m(input))
        m.eval()
        self.assertEqual(-input + 1, m(input))

        # test batchnorm and dropout train/eval
        input = torch.randn(6, 10)
        batchnorm = nn.BatchNorm1d(10)
        dropout = nn.Dropout(p=0.2)

        m_batchnorm = MyModule(batchnorm)
        self.assertEqual(batchnorm(input) + 1, m_batchnorm(input))
        batchnorm.eval()
        m_batchnorm.eval()
        self.assertEqual(batchnorm(input) + 1, m_batchnorm(input))

        m_dropout = MyModule(dropout)
        dropout.eval()
        m_dropout.eval()
        self.assertEqual(dropout(input) + 1, m_dropout(input))

    def test_script_autograd_grad(self):
        def test_simple_grad(x, y):
            # type: (Tensor, Tensor) -> List[Optional[Tensor]]
            z = x + 2 * y + x * y
            return torch.autograd.grad((z.sum(), ), (x, y))

        def test_simple_grad_with_grad_outputs(x, y):
            # type: (Tensor, Tensor) -> List[Optional[Tensor]]
            z = x + 2 * y + x * y
            grad_outputs = torch.jit.annotate(List[Optional[torch.Tensor]], [torch.ones((2, 2)), ])
            return torch.autograd.grad((z, ), (x, y), grad_outputs)

        def test_one_output_not_requires_grad(x, y):
            # type: (Tensor, Tensor) -> List[Optional[Tensor]]
            z = 2 * y + y
            return torch.autograd.grad((z.sum(),), (x, y), allow_unused=True)

        def test_retain_graph(x, y):
            # type: (Tensor, Tensor) -> None
            z = x + 2 * y + x * y
            torch.autograd.grad((z.sum(), ), (x, y), retain_graph=True)
            torch.autograd.grad((z.sum(), ), (x, y))

        x = torch.randn(2, 2, requires_grad=True)
        y = torch.randn(2, 2, requires_grad=True)
        self.checkScript(test_simple_grad, (x, y), inputs_requires_grad=True)
        self.checkScript(test_simple_grad_with_grad_outputs, (x, y), inputs_requires_grad=True)
        self.checkScript(test_one_output_not_requires_grad, (x, y), inputs_requires_grad=True)
        self.checkScript(test_retain_graph, (x, y), inputs_requires_grad=True)

    def test_script_backward(self):
        def checkBackwardScript(fn, inputs):
            scripted_fn = torch.jit.script(fn)
            FileCheck().check("torch.autograd.backward").run(scripted_fn.code)
            recording_inputs = do_input_map(lambda t: t.detach().requires_grad_(), inputs)

            fn(*inputs)
            scripted_fn(*recording_inputs)

            for inp1, inp2 in zip(inputs, recording_inputs):
                self.assertEqual(inp1.grad, inp2.grad)

        def test_tensor_backward(input):
            # type: (Tensor) -> None
            output = torch.relu(input)
            output = output.softmax(0)
            sum_out = output.sum()
            sum_out.backward()

        def test_torch_autograd_backward(input):
            # type: (Tensor) -> None
            output = torch.relu(input)
            output = output.softmax(0)
            torch.autograd.backward(output.sum())

        def test_torch_autograd_backward_with_grad_tensors(input):
            # type: (Tensor) -> None
            output = torch.relu(input)
            output = output.softmax(0)
            grad_outputs = torch.jit.annotate(List[Optional[torch.Tensor]], [torch.ones((2, 2)), ])
            torch.autograd.backward((output,), grad_outputs)

        inp = torch.randn(2, 2, requires_grad=True)
        checkBackwardScript(test_tensor_backward, (inp,))
        checkBackwardScript(test_torch_autograd_backward, (inp,))
        checkBackwardScript(test_torch_autograd_backward_with_grad_tensors, (inp,))

    def test_diff_subgraph_clones_constants(self):
        @torch.jit.script
        def f(x, y):
            return x + x + y + x + y + x + y + x + y + x

        def count_constants(graph):
            return sum(node.kind() == 'prim::Constant' for node in graph.nodes())

        graph = f.graph.copy()
        self.run_pass('cse', graph)
        self.run_pass('create_autodiff_subgraphs', graph)
        nodes = list(graph.nodes())
        self.assertEqual(count_constants(graph), 1)
        self.assertEqual(count_constants(nodes[1].g('Subgraph')), 1)

    # Backwards tracing was broken for indexing by a constant,
    # because it's internally implemented using as_strided,
    # and we attempted to trace its derivative (which is not
    # currently supported.)  It currently works because
    # slice() is now not marked as traceable.
    def test_index_constant(self):
        x = torch.tensor([0.4], requires_grad=True)

        def fn(x):
            return x[0]

        def run(f):
            y = f(x)
            grad = torch.autograd.grad(y, x)[0].clone()
            return y, grad

        traced_fn = torch.jit.trace(fn, torch.ones(1))
        self.assertEqual(run(fn), run(traced_fn))

    def test_canonicalize_tensor_iterator(self):
        x = torch.randn(4, 4)

        def f(x):
            x = x + 2
            x = x - 4
            x = x * 6
            x = x / 8
            return x

        traced = torch.jit.trace(f, (x,))
        f(x)
        graph = traced.graph_for(x)
        # There should be 4 int constants for the right sides of operators, plus one
        # for the alpha argument for add and sub
        self.assertTrue(str(traced.graph_for(x)).count(': int = prim::Constant') == 5)

    # TODO: adapt this test to check that GraphExecutor treats them differently
    @unittest.skip("Need to be adjusted to Graph Executor")
    def test_arg_configurations(self):
        """Different arg configurations should trigger different traces"""
        x = Variable(torch.FloatTensor(4, 4).uniform_())
        x_double = Variable(x.data.double())
        x_grad = Variable(x.data.clone(), requires_grad=True)
        y = Variable(torch.randn(4))

        configurations = [
            (x,),
            (x_double,),
            (x_grad,),
            (y,),
            ([x, x],),
            ([x, y],),
        ]
        if torch.cuda.is_available():
            x_cuda = Variable(x.data.cuda())
            configurations += [
                (x_cuda,),
                ([x, x_cuda],),
                ([x_cuda, x],),
                ([[x_cuda, x]],),
            ]
            if torch.cuda.device_count() > 1:
                x_cuda_1 = Variable(x.data.cuda(1))
                configurations += [
                    (x_cuda_1,),
                    ([x_cuda, x_cuda_1],),
                ]

        @torch.jit.compile(nderivs=0)
        def fn(*args):
            in_vars, _ = torch._C._jit_flatten(args)
            return in_vars[0] + 1

        for i, config in enumerate(configurations):
            self.assertFalse(fn.has_trace_for(*config))
            fn(*config)
            self.assertTrue(fn.has_trace_for(*config))
            for unk_config in configurations[i + 1:]:
                self.assertFalse(fn.has_trace_for(*unk_config))
        self.assertEqual(fn.hits, 0)

    def test_cse(self):
        x = torch.tensor([0.4, 0.3], requires_grad=True)
        y = torch.tensor([0.7, 0.5], requires_grad=True)

        def fn(x, y):
            w = (x + y) * (x + y) * (x + y)
            t = torch.tanh(w) + torch.tanh(w)
            z = (x + y) * (x + y) * (x + y) + t
            return z

        g, _ = torch.jit._get_trace_graph(fn, (x, y))
        self.run_pass('cse', g)
        do_exactly = True
        FileCheck().check_count("add", 1).check_count("mul", 2, do_exactly) \
            .check_count("tanh", 1, do_exactly).check_count("add", 2, do_exactly).check_next("return")  \
            .run(str(g))

        self.assertExportImport(g, (x, y))

    def test_cse_not_introduce_aliasing(self):
        @torch.jit.script
        def tensor_alias_outputs(x):
            return x + x, x + x

        self.run_pass('cse', tensor_alias_outputs.graph)
        FileCheck().check_count("aten::add", 2).run(tensor_alias_outputs.graph)

        @torch.jit.script
        def ints_alias_outputs(x):
            # type: (int) -> Tuple[int, int]
            return x + x, x + x

        # non-aliasing types can be CSEd
        self.run_pass('cse', ints_alias_outputs.graph)
        FileCheck().check_count("aten::add", 1, exactly=True).run(ints_alias_outputs.graph)

    def test_recursive_cse(self):
        input_str = """
graph(%x : Tensor,
      %y : Tensor,
      %20 : int):
  %2 : int = prim::Constant[value=1]()
  %3 : Tensor = aten::add(%x, %y, %2)
  %4 : int = aten::add(%2, %20)
  %5 : bool = aten::Bool(%4)
  %z : int = prim::If(%5)
    # CHECK: block
    block0():
      # CHECK-NOT: aten::add
      %z.1 : int = aten::add(%2, %20)
      -> (%z.1)
    block1():
      -> (%2)
  return (%z)
"""
        graph = parse_ir(input_str)
        self.run_pass('cse', graph)
        FileCheck().run(input_str, graph)

    @_tmp_donotuse_dont_inline_everything
    def test_insert_observers(self):
        class M(torch.nn.Module):
            def __init__(self):
                super(M, self).__init__()
                self.conv = torch.nn.Conv2d(3, 5, 3)

            def forward(self, x):
                return self.conv(x)

        m = torch.jit.script(M())
        observer = torch.jit.script(default_observer())
        qconfig_dict = {
            '':
            QConfig(
                activation=observer._c,
                weight=observer._c)
        }
        torch._C._jit_pass_insert_observers(m._c, "forward", qconfig_dict, True)
        assert len([x for x, _ in m._modules._c.items()
                    if x.startswith('_observer_')]) == 0, \
            'Expected to have 0 observer submodules'
        FileCheck().check_not('Observer = prim::GetAttr[name="_observer_') \
                   .check('Conv2d = prim::GetAttr[name="conv"](%self)') \
                   .check_next('Tensor = prim::CallMethod[name="forward"]') \
                   .check_not('Observer = prim::GetAttr[name="_observer_') \
                   .run(str(get_forward_graph(m._c)))
        assert len([x for x, _ in m.conv._modules._c.items()
                    if x.startswith('_observer_')]) == 3, \
            'Expected to have 3 observer submodules'
        FileCheck().check('Observer = prim::GetAttr[name="_observer_') \
                   .check_next('prim::CallMethod[name="forward"](%_observer_') \
                   .check('Observer = prim::GetAttr[name="_observer_') \
                   .check_next('prim::CallMethod[name="forward"](%_observer_') \
                   .check('Tensor = aten::conv2d') \
                   .check('Observer = prim::GetAttr[name="_observer_') \
                   .check_next('prim::CallMethod[name="forward"](%_observer_') \
                   .run(str(m._c.getattr("conv")._get_method('_conv_forward').graph))

    @_tmp_donotuse_dont_inline_everything
    def test_insert_observers_child_qconfig(self):
        class Sub(torch.nn.Module):
            def __init__(self):
                super(Sub, self).__init__()
                self.linear = torch.nn.Linear(5, 5)

            def forward(self, x):
                return self.linear(x)

        class M(torch.nn.Module):
            def __init__(self):
                super(M, self).__init__()
                self.conv = torch.nn.Conv2d(3, 5, 3)
                self.sub = Sub()

            def forward(self, x):
                return self.sub(self.conv(x))

        def check_observed(s):
            FileCheck().check('Observer = prim::GetAttr[name="_observer_') \
                       .check_next('prim::CallMethod[name="forward"](%_observer_') \
                       .check('Observer = prim::GetAttr[name="_observer_') \
                       .check_next('prim::CallMethod[name="forward"](%_observer_') \
                       .check('Observer = prim::GetAttr[name="_observer_') \
                       .check_next('prim::CallMethod[name="forward"](%_observer_') \
                       .run(str(s))

        def check_not_observed(s):
            FileCheck().check_not('Observer = prim::GetAttr[name="_observer_') \
                       .check_not('prim::CallMethod[name="forward"](%_observer_') \
                       .run(str(s))

        m = torch.jit.script(M())
        observer = torch.jit.script(default_observer())

        torch._C._jit_pass_constant_propagation(get_forward_graph(m._c))
        qconfig = QConfig(
            activation=observer._c,
            weight=observer._c)
        qconfig_dict = {
            'conv': qconfig,
            'sub.linear': qconfig
        }
        torch._C._jit_pass_insert_observers(m._c, "forward",
                                            qconfig_dict,
                                            True)
        # check m is not observed
        check_not_observed(get_forward_graph(m._c))
        # check conv.forward is observed
        check_not_observed(get_forward_graph(m._c.getattr('conv')))
        # check conv._conv_forward is observed
        check_observed(get_module_method(m, 'conv', '_conv_forward').graph)
        # check sub is not observed
        check_not_observed(get_module_method(m, 'sub', 'forward'))
        # check forward of sub.linear is observed
        check_observed(get_forward(m._c.getattr('sub').getattr('linear')).graph)

    @_tmp_donotuse_dont_inline_everything
    def test_insert_observers_skip_values(self):
        import torch.nn.functional as F

        class M(torch.nn.Module):
            def __init__(self):
                super(M, self).__init__()
                self.conv = torch.nn.Conv2d(3, 5, 3)

            def forward(self, x):
                return F.relu(self.conv(x))

        class M2(torch.nn.Module):
            def __init__(self):
                super(M2, self).__init__()
                self.conv = torch.nn.Conv2d(3, 5, 3)
                self.relu = torch.nn.ReLU()

            def forward(self, x):
                return self.relu(self.conv(x))

        def test_module(module, relu_call, num_observers):
            m = torch.jit.script(module())
            # TODO: this is because right-now the InsertObservers is in-place.
            # When we change the implementation to clone the module before
            # inserting observers, we can remove this copy
            m = m.copy()
            observer = torch.jit.script(default_observer())
            qconfig_dict = {
                '':
                QConfig(
                    activation=observer._c,
                    weight=observer._c)
            }
            torch._C._jit_pass_insert_observers(m._c, "forward", qconfig_dict, True)
            assert len([x for x, _ in m._modules._c.items()
                        if x.startswith('_observer_')]) == num_observers, \
                'Expected to have ' + str(num_observers) + ' observer submodules'
            c = FileCheck().check('Conv2d = prim::GetAttr[name="conv"]') \
                           .check_next('prim::CallMethod[name="forward"]') \
                           .check_not('Observer = prim::GetAttr[name="_observer_') \
                           .check(relu_call)
            if num_observers == 1:
                c = c.check('Observer = prim::GetAttr[name="_observer_') \
                     .check_next('prim::CallMethod[name="forward"](%_observer_')
            c.run(str(get_forward_graph(m._c)))
            # TODO: add checks for conv and relu later, graph looks correct but this pr
            # has too many changes already
        test_module(M, 'prim::CallFunction(', 1)
        test_module(M2, 'prim::CallMethod[name="forward"]', 0)

    @_tmp_donotuse_dont_inline_everything
    def test_insert_observers_weight_dtype(self):
        class M(torch.nn.Module):
            def __init__(self):
                super(M, self).__init__()
                self.conv = torch.nn.Conv2d(3, 5, 3)

            def forward(self, x):
                return F.relu(self.conv(x))

        m = torch.jit.script(M())
        observer = torch.jit.script(default_observer())
        weight_observer = torch.jit.script(default_weight_observer())
        qconfig_dict = {
            '':
            QConfig(
                activation=observer._c,
                weight=weight_observer._c)
        }
        torch._C._jit_pass_insert_observers(m._c, "forward", qconfig_dict, True)
        print()
        dtypes = set([obs.getattr('dtype') for x, obs in m.conv._modules._c.items()
                      if x.startswith('_observer_')])
        assert len(dtypes) == 2, 'Expected to have 2 different types of dtype'

    def test_insert_observers_shared_class_type(self):
        class M(torch.nn.Module):
            def __init__(self):
                super(M, self).__init__()
                self.conv1 = torch.nn.Conv2d(3, 5, 3).float()
                self.conv2 = torch.nn.Conv2d(3, 5, 3).float()

            def forward(self, x):
                return self.conv2(self.conv1(x))

        m = torch.jit.script(M())
        qconfig_dict = {'': script_qconfig(default_qconfig)}
        torch._C._jit_pass_insert_observers(m._c, "forward", qconfig_dict, True)
        # conv1 and conv2 shares the same type, we need to
        # make sure we didn't quantize the type twice
        assert len([x for x, _ in m.conv1._modules._c.items()
                    if x.startswith('_observer_')]) == 3, \
            'Expected to have 3 observer submodules'
        assert len([x for x, _ in m.conv2._modules._c.items()
                    if x.startswith('_observer_')]) == 3, \
            'Expected to have 3 observer submodules'

    def test_insert_quant_dequant(self):
        class M(torch.nn.Module):
            def __init__(self):
                super(M, self).__init__()
                self.conv = torch.nn.Conv2d(3, 5, 3).float()

            def forward(self, x):
                return self.conv(x)

        for is_per_channel in [True, False]:
            m = torch.jit.script(M())
            observer = default_per_channel_weight_observer.with_args(ch_axis=1) \
                if is_per_channel else default_observer
            qconfig = QConfig(activation=observer, weight=observer)
            qconfig_dict = {
                '': script_qconfig(qconfig)
            }
            m._c = torch._C._jit_pass_insert_observers(m._c, "forward", qconfig_dict, False)
            data = torch.randn(1, 3, 10, 10, dtype=torch.float)

            get_forward(m._c)(data)
            m._c = torch._C._jit_pass_insert_quant_dequant(m._c, "forward", False)
            assert len(m._modules._c.items()) == 1, \
                'Expected to have single submodule of conv'

            get_forward(m._c)(data)
            quant_func = "aten::quantize_per_channel" if is_per_channel \
                else "aten::quantize_per_tensor"
            FileCheck().check_not(quant_func) \
                       .check("prim::CallMethod[name=\"forward\"]") \
                       .check_not(quant_func) \
                       .check("return") \
                       .run(str(get_forward_graph(m._c)))
            FileCheck().check(quant_func) \
                       .check_next("aten::dequantize") \
                       .check("aten::conv2d") \
                       .check(quant_func) \
                       .check_next("aten::dequantize") \
                       .check("return") \
                       .run(str(get_module_method(m, 'conv', '_conv_forward').graph))

    def test_insert_quant_dequant_multi_uses(self):
        class M(torch.nn.Module):
            def __init__(self):
                super(M, self).__init__()

            def forward(self, x, w0, w1, w2):
                a = F.conv2d(x, w0)
                b = F.conv2d(a, w1)
                c = F.conv2d(a, w2)
                return b + c

        m = torch.jit.script(M())
        observer = torch.jit.script(default_observer())
        qconfig_dict = {
            '':
            QConfig(
                activation=observer._c,
                weight=observer._c)
        }
        torch._C._jit_pass_insert_observers(m._c, "forward", qconfig_dict, True)
        torch._C._jit_pass_insert_quant_dequant(m._c, "forward", True)

        # we just check we have one dequant on every op input, even input
        # is sharded as multi uses
        FileCheck().check_count("aten::dequantize", 8, exactly=True) \
                   .run(str(get_forward_graph(m._c)))

    def test_insert_prepack_unpack(self):
        # Module with linear and per tensor/channel quantized weight
        class L(torch.nn.Module):
            def __init__(self):
                super(L, self).__init__()
                self.weight = torch.nn.Parameter(torch.rand((5, 5), dtype=torch.float))
                self.bias = torch.nn.Parameter(torch.rand(5, dtype=torch.float))

            def forward(self, x):
                xq = torch.quantize_per_tensor(x, 0.2, 1, torch.quint8)
                wq = torch.quantize_per_tensor(self.weight, 0.2, 1, torch.qint8)
                r = torch.nn.functional.linear(xq.dequantize(), wq.dequantize(), self.bias)
                rq = torch.quantize_per_tensor(r, 0.2, 1, torch.quint8)
                return rq

        class L2(torch.nn.Module):
            def __init__(self):
                super(L2, self).__init__()
                self.weight = torch.nn.Parameter(torch.rand((5, 5), dtype=torch.float))
                self.bias = torch.nn.Parameter(torch.rand(5, dtype=torch.float))

            def forward(self, x):
                xq = torch.quantize_per_tensor(x, 0.2, 1, torch.quint8)
                wq = torch.quantize_per_channel(self.weight,
                                                torch.tensor([2], dtype=torch.float),
                                                torch.tensor([0], dtype=torch.long),
                                                0,
                                                torch.qint8)
                r = torch.nn.functional.linear(xq.dequantize(), wq.dequantize(), self.bias)
                rq = torch.quantize_per_tensor(r, 0.2, 1, torch.quint8)
                return rq

        for M in [L, L2]:
            m = torch.jit.script(M())
            torch._C._jit_pass_insert_prepack_unpack(m._c)
            FileCheck().check("quantized::linear_prepack") \
                       .check("quantized::linear_unpack") \
                       .run(get_forward_graph(m._c))

        # Module with conv2d and per tensor/channel quantized weight
        class C(torch.nn.Module):
            def __init__(self):
                super(C, self).__init__()
                self.weight = torch.nn.Parameter(torch.rand((5, 5), dtype=torch.float))
                self.bias = torch.nn.Parameter(torch.rand(5, dtype=torch.float))

            def forward(self, x):
                xq = torch.quantize_per_tensor(x, 0.2, 1, torch.quint8)
                wq = torch.quantize_per_tensor(self.weight, 0.2, 1, torch.qint8)
                r = torch.conv2d(xq.dequantize(), wq.dequantize(), self.bias)
                rq = torch.quantize_per_tensor(r, 0.2, 1, torch.quint8)
                return rq

        class C2(torch.nn.Module):
            def __init__(self):
                super(C2, self).__init__()
                self.weight = torch.nn.Parameter(torch.rand((5, 5), dtype=torch.float))
                self.bias = torch.nn.Parameter(torch.rand(5, dtype=torch.float))

            def forward(self, x):
                xq = torch.quantize_per_tensor(x, 0.2, 1, torch.quint8)
                wq = torch.quantize_per_channel(self.weight,
                                                torch.tensor([2], dtype=torch.float),
                                                torch.tensor([0], dtype=torch.long),
                                                0,
                                                torch.qint8)
                r = torch.conv2d(xq.dequantize(), wq.dequantize(), self.bias)
                rq = torch.quantize_per_tensor(r, 0.2, 1, torch.quint8)
                return rq

        for M in [C, C2]:
            m = torch.jit.script(M())
            torch._C._jit_pass_insert_prepack_unpack(m._c)
            FileCheck().check("quantized::conv2d_prepack") \
                       .check("quantized::conv2d_unpack") \
                       .run(get_forward_graph(m._c))

    def test_quant_fusion(self):
        input_strs = [
            # aten::conv2d --> quantized::conv2d
            """
graph(%packed_params_module, %a, %a_scale, %a_zero_point, %a_dtype,
%r_scale, %r_zero_point, %r_dtype, %stride, %padding, %dilation, %groups):
        %a_quant = aten::quantize_per_tensor(%a, %a_scale, %a_zero_point, %a_dtype)
        %a_dequant = aten::dequantize(%a_quant)
        %packed_params = prim::GetAttr[name="_packed_params"](%packed_params_module)
        %w_quant : Tensor, %b : Tensor? = quantized::conv2d_unpack(%packed_params)
        %w_dequant = aten::dequantize(%w_quant)
        # CHECK: quantized::conv2d
        # CHECK-NOT: aten::conv2d
        %r = aten::conv2d(%a_dequant, %w_dequant, %b, %stride, %padding, %dilation, %groups)
        %r_quant = aten::quantize_per_tensor(%r, %r_scale, %r_zero_point, %r_dtype)
        %r_dequant = aten::dequantize(%r_quant)
        return (%r_dequant)""",
            # addmm -> quantized::linear
            """
graph(%packed_params_module, %a, %a_scale, %a_zero_point, %a_dtype, %r_scale, %r_zero_point, %r_dtype, %4):
        %a_quant = aten::quantize_per_tensor(%a, %a_scale, %a_zero_point, %a_dtype)
        %a_dequant = aten::dequantize(%a_quant)
        %packed_params = prim::GetAttr[name="_packed_params"](%packed_params_module)
        %w_quant : Tensor, %b : Tensor? = quantized::linear_unpack(%packed_params)
        %w_dequant = aten::dequantize(%w_quant)
        %w_dequant_t = aten::t(%w_dequant)
        # CHECK: quantized::linear
        # CHECK-NOT: aten::addmm
        %r = aten::addmm(%b, %a_dequant, %w_dequant_t, %4, %4)
        %r_quant = aten::quantize_per_tensor(%r, %r_scale, %r_zero_point, %r_dtype)
        %r_dequant = aten::dequantize(%r_quant)
        return (%r_dequant)""",
            # matmul(with bias) -> quantized::linear
            """
graph(%packed_params_module, %a, %a_scale, %a_zero_point, %a_dtype, %r_scale, %r_zero_point, %r_dtype, %4):
        %a_quant = aten::quantize_per_tensor(%a, %a_scale, %a_zero_point, %a_dtype)
        %a_dequant = aten::dequantize(%a_quant)
        %packed_params = prim::GetAttr[name="_packed_params"](%packed_params_module)
        %w_quant : Tensor, %b : Tensor? = quantized::linear_unpack(%packed_params)
        %w_dequant = aten::dequantize(%w_quant)
        %w_dequant_t = aten::t(%w_dequant)
        # CHECK: quantized::linear
        # CHECK-NOT: aten::addmm
        %output = aten::matmul(%a_dequant, %w_dequant_t)
        %r = aten::add_(%output, %b, %4)
        %r_quant = aten::quantize_per_tensor(%r, %r_scale, %r_zero_point, %r_dtype)
        %r_dequant = aten::dequantize(%r_quant)
        return (%r_dequant)""",
            # matmul(without bias) -> quantized::linear
            """
graph(%packed_params_module, %a, %a_scale, %a_zero_point, %a_dtype, %r_scale, %r_zero_point, %r_dtype):
        %a_quant = aten::quantize_per_tensor(%a, %a_scale, %a_zero_point, %a_dtype)
        %a_dequant = aten::dequantize(%a_quant)
        %packed_params = prim::GetAttr[name="_packed_params"](%packed_params_module)
        %w_quant : Tensor, %b : Tensor? = quantized::linear_unpack(%packed_params)
        %w_dequant = aten::dequantize(%w_quant)
        %w_dequant_t = aten::t(%w_dequant)
        # CHECK: quantized::linear
        # CHECK-NOT: aten::matmul
        %r = aten::matmul(%a_dequant, %w_dequant_t)
        %r_quant = aten::quantize_per_tensor(%r, %r_scale, %r_zero_point, %r_dtype)
        %r_dequant = aten::dequantize(%r_quant)
        return (%r_dequant)"""
        ]
        for input_str in input_strs:
            graph = parse_ir(input_str)
            torch._C._jit_pass_quant_fusion(graph)
            FileCheck().run(input_str, graph)

    @_tmp_donotuse_dont_inline_everything
    def test_foldbn_trivial(self):
        # Test trivial case
        class TestModule(torch.nn.Module):
            def __init__(self):
                super(TestModule, self).__init__()
                self.conv = torch.nn.Conv2d(1, 20, 5, 1)
                self.bn = torch.nn.BatchNorm2d(num_features=20)
                self.bn.eps = 0.0023

            def forward(self, x):
                x = self.conv(x)
                x = self.bn(x)
                return x

        eager = TestModule()
        scripted = torch.jit.script(eager)
        eager.eval()
        scripted.eval()

        # Check that in the original script module's forward we have two
        # CallMethod nodes. One of them should be for conv.forward and the other
        # for bn.forward.
        FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 2, exactly=True) \
            .run(str(get_forward(scripted._c).graph))

        # Run FoldConvBatchnorm2d pass.
        torch._C._jit_pass_fold_convbn(scripted._c)

        # Check that after the pass one of the CallMethods is gone (supposedly,
        # the bn.forward).
        FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 1, exactly=True) \
            .run(str(get_forward_graph(scripted._c)))

        # Check that the transformation doesn't change numerics
        x = torch.rand(1, 1, 6, 6)
        self.assertAlmostEqual(eager(x), scripted(x), delta=1e-5)

    @_tmp_donotuse_dont_inline_everything
    def test_foldbn_trivial_nobias(self):
        # Test trivial case
        class TestModule(torch.nn.Module):
            def __init__(self):
                super(TestModule, self).__init__()
                self.conv = torch.nn.Conv2d(1, 20, 5, 1, bias=False)
                self.bn = torch.nn.BatchNorm2d(num_features=20)

            def forward(self, x):
                x = self.conv(x)
                x = self.bn(x)
                return x

        eager = TestModule()
        scripted = torch.jit.script(eager)
        eager.eval()
        scripted.eval()

        # Check that in the original script module's forward we have two
        # CallMethod nodes. One of them should be for conv.forward and the other
        # for bn.forward.
        FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 2, exactly=True) \
            .run(str(get_forward_graph(scripted._c)))

        # Run FoldConvBatchnorm2d pass.
        torch._C._jit_pass_fold_convbn(scripted._c)

        # Check that after the pass one of the CallMethods is gone (supposedly,
        # the bn.forward).
        FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 1, exactly=True) \
            .run(str(get_forward_graph(scripted._c)))

        # Check that the transformation doesn't change numerics
        x = torch.rand(1, 1, 6, 6)
        self.assertAlmostEqual(eager(x), scripted(x), delta=1e-5)

    @_tmp_donotuse_dont_inline_everything
    def test_foldbn_in_submodule(self):
        # Test that we find Conv-BN patterns in submodules
        class SubModule(torch.nn.Module):
            def __init__(self):
                super(SubModule, self).__init__()
                self.conv = torch.nn.Conv2d(1, 20, 5, 1)
                self.bn = torch.nn.BatchNorm2d(num_features=20)

            def forward(self, x):
                x = self.conv(x)
                x = self.bn(x)
                return x

        class TestModule(torch.nn.Module):
            def __init__(self):
                super(TestModule, self).__init__()
                self.sub = SubModule()

            def forward(self, x):
                x = self.sub(x)
                return x

        m = torch.jit.script(TestModule())
        FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 2, exactly=True) \
            .run(str(get_forward_graph(m.sub._c)))

        torch._C._jit_pass_fold_convbn(m._c)

        FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 1, exactly=True) \
            .run(str(get_forward_graph(m.sub._c)))

    def test_fuse_linear(self):
        input_strs = ["""
graph(%input, %weight, %bias, %4):
    # CHECK-NOT: aten::t
    # CHECK-NOT: aten::addmm
    # CHECK: aten::linear
    %weight_t = aten::t(%weight)
    %res = aten::addmm(%bias, %input, %weight_t, %4, %4)
    return (%res)""", """
graph(%input, %weight, %bias, %4):
    # CHECK-NOT: aten::t
    # CHECK-NOT: aten::matmul
    # CHECK-NOT: aten::add_
    # CHECK: aten::linear
    %weight_t = aten::t(%weight)
    %output = aten::matmul(%input, %weight_t)
    %res = aten::add_(%output, %bias, %4)
    return (%res)""", """
graph(%input, %weight):
    # CHECK-NOT: aten::t
    # CHECK-NOT: aten::matmul
    # CHECK: aten::linear
    %weight_t = aten::t(%weight)
    %output = aten::matmul(%input, %weight_t)
    return (%output)"""]
        for input_str in input_strs:
            graph = parse_ir(input_str)
            torch._C._jit_pass_fuse_linear(graph)
            FileCheck().run(input_str, graph)

    @_tmp_donotuse_dont_inline_everything
    def test_fold_quantize(self):
        class M(torch.nn.Module):
            def __init__(self):
                super(M, self).__init__()
                self.weight = torch.nn.Parameter(torch.tensor([2], dtype=torch.float))

            def forward(self, x):
                return torch.quantize_per_tensor(self.weight, 2.0, 0, torch.quint8)

        m = torch.jit.script(M())
        torch._C._jit_pass_fold_quantize(m._c, 'forward')
        self.assertTrue(m._c.hasattr('_quantized_weight'))
        FileCheck().check_not('GetAttr[name="weight"]') \
                   .check('GetAttr[name="_quantized_weight"]') \
                   .run(m._c._get_method('forward').graph)

    @unittest.skipUnless(
        'fbgemm' in torch.backends.quantized.supported_engines,
        " Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs"
        " with instruction set support avx2 or newer.",
    )
    @unittest.skip("Skip for now since we changed scale/zero_point to attributes."
                   "We'll enable this in a separate PR")
    def test_fold_prepack(self):
        def copy_weights(name, m, ref_m):
            if name == 'linear':
                m.fc1.weight = torch.nn.Parameter(ref_m.fc1.module.weight.detach())
                m.fc1.bias = torch.nn.Parameter(ref_m.fc1.module.bias.detach())
            else:
                m.conv.weight = torch.nn.Parameter(ref_m.conv.weight.detach())

        for is_per_channel in [True, False]:
            for name, M, ref_M, data in [
                    ('linear',
                     SingleLayerLinearModel,
                     AnnotatedSingleLayerLinearModel,
                     torch.randn((5, 5), dtype=torch.float)),
                    ('conv',
                     ConvModel,
                     AnnotatedConvModel,
                     torch.randn((1, 3, 7, 7), dtype=torch.float))]:
                qconfig = QConfig(
                    activation=default_observer,
                    weight=default_per_channel_weight_observer if is_per_channel else default_weight_observer)
                # eager mode
                ref_m = ref_M()
                m = M()
                copy_weights(name, m, ref_m)
                ref_m.qconfig = qconfig
                ref_m = quantize(ref_m, _test_only_eval_fn, [(data, torch.randint(0, 1, (5,), dtype=torch.long))])
                ref_res = ref_m(data)
                # script mode
                m = torch.jit.script(m)
                qconfig_dict = {
                    '': script_qconfig(qconfig)
                }
                m._c = torch._C._jit_pass_insert_observers(m._c, 'forward', qconfig_dict, False)
                get_forward(m._c)(data)
                m._c = torch._C._jit_pass_insert_quant_dequant(m._c, 'forward', False)
                torch._C._jit_pass_insert_prepack_unpack(m._c)
                linear_packed_params = torch.jit.script(LinearPackedParams())._c
                conv_packed_params = torch.jit.script(ConvPackedParams())._c
                torch._C._jit_pass_fold_prepack(m._c,
                                                linear_packed_params,
                                                conv_packed_params)
                res = get_forward(m._c)(data)
                # check result
                self.assertEqual(res, ref_res)

                # check attributes
                # construct a RecursiveScriptModule
                m = wrap_cpp_module(m._c)
                mod_to_inspect = m.fc1 if name == 'linear' else m.conv
                packed_module_list = [x for x, _ in mod_to_inspect._modules._c.items()
                                      if x.startswith('_' + name + '_packed_params_module')]
                assert len(packed_module_list) == 1, \
                    'Expected to have one packed_params_module'
                packed_module_name = packed_module_list[0]
                # check values
                w, _ = mod_to_inspect._c.getattr(packed_module_name)._get_method('_weight_bias')()
                original_w = mod_to_inspect.weight
                if is_per_channel:
                    ref_w = torch.quantize_per_channel(original_w,
                                                       w.q_per_channel_scales(),
                                                       w.q_per_channel_zero_points(),
                                                       w.q_per_channel_axis(),
                                                       w.dtype).dequantize()
                else:
                    ref_w = torch.quantize_per_tensor(original_w, w.q_scale(), w.q_zero_point(), w.dtype).dequantize()
                self.assertEqual(ref_w, w.dequantize())

                # test serialization
                buffer = io.BytesIO()
                torch.jit.save(m, buffer)
                buffer.seek(0)
                loaded_mod = torch.jit.load(buffer)
                loaded_res = loaded_mod(data)
                self.assertEqual(ref_res, loaded_res)

    def test_dedup_module_uses(self):
        class M(torch.nn.Module):
            def __init__(self):
                super(M, self).__init__()
                self.relu = torch.nn.ReLU()

            def forward(self, x):
                x = self.relu(x)
                x -= 0.5
                return self.relu(x)

        data = torch.randn((2, 2))
        m = torch.jit.script(M())
        ref_res = m(data)
        assert len([x for x, _ in m._modules._c.items()
                    if x.startswith('relu')]) == 1, \
            "Expected to have 1 relu modules after dedup module uses"
        torch._C._jit_pass_dedup_module_uses(m._c)
        m = torch.jit._recursive.wrap_cpp_module(m._c)
        res = m(data)
        assert len([x for x, _ in m._modules._c.items()
                    if x.startswith('relu')]) == 2, \
            "Expected to have 2 relu modules after dedup module uses"
        self.assertEqual(res, ref_res)

    def test_pattern_based_rewrite(self):
        # mul(mul(mul(mul(x,y),z),x),y) --> mul(mul(mulmul(x,y,z), x), y) -->
        # --> mulmul(mulmul(x,y,z), x, y)
        input_str = """
graph(%x, %y, %z):
    # CHECK-NOT: aten::mul
    # CHECK: my::fused_mulmul
    %t = aten::mul(%x, %y)
    %p = aten::mul(%t, %z)
    # CHECK: my::fused_mulmul
    %u = aten::mul(%p, %x)
    %o = aten::mul(%u, %y)
    return (%o)"""
        graph = parse_ir(input_str)
        torch._C._jit_pass_custom_pattern_based_rewrite_graph("""
graph(%a, %b, %c):
  %q = aten::mul(%a, %b)
  %r = aten::mul(%q, %c)
  return (%r)""", """
graph(%a, %b, %c):
  %r = my::fused_mulmul(%a, %b, %c)
  return (%r)""", graph)
        FileCheck().run(input_str, graph)

        # Check that overlapping matches are handled correctly
        # mul(mul(mul(x,y),z),x) --> mul(mulmul(x,y,z), x)
        input_str = """
graph(%x, %y, %z):
    # CHECK-NOT: aten::mul
    # CHECK: my::fused_mulmul
    %t = aten::mul(%x, %y)
    %p = aten::mul(%t, %z)
    # CHECK-NEXT: aten::mul
    %u = aten::mul(%p, %x)
    return (%u)"""
        graph = parse_ir(input_str)
        torch._C._jit_pass_custom_pattern_based_rewrite_graph("""
graph(%a, %b, %c):
  %q = aten::mul(%a, %b)
  %r = aten::mul(%q, %c)
  return (%r)""", """
graph(%a, %b, %c):
  %r = my::fused_mulmul(%a, %b, %c)
  return (%r)""", graph)
        FileCheck().run(input_str, graph)

        # Check add(mul(x,y),z) --> muladd(x,y,z) replacement
        input_str = """
graph(%x, %y, %z):
    # CHECK-NOT: aten::mul
    # CHECK-NOT: aten::add
    %c = prim::Const[value=1]()
    %t = aten::mul(%x, %y)
    %p = aten::add(%t, %z, %c)
    # CHECK: my::muladd
    # CHECK-NEXT: return
    return (%p)"""
        graph = parse_ir(input_str)
        torch._C._jit_pass_custom_pattern_based_rewrite_graph("""
graph(%a, %b, %c, %d):
  %q = aten::mul(%a, %b)
  %r = aten::add(%q, %c, %d)
  return (%r)""", """
graph(%a, %b, %c, %d):
  %r = my::muladd(%a, %b, %c, %d)
  return (%r)""", graph)
        FileCheck().run(input_str, graph)

        # Check add(mul(x,y),z) --> sub(add(x,y),z) replacement
        input_str = """
graph(%x, %y, %z):
    # CHECK-NOT: aten::mul
    %c = prim::Const[value=1]()
    # CHECK: aten::add
    %t = aten::mul(%x, %y)
    # CHECK-NEXT: aten::sub
    %p = aten::add(%t, %z, %c)
    # CHECK-NOT: aten::add
    # CHECK-NEXT: return
    return (%p)"""
        graph = parse_ir(input_str)
        torch._C._jit_pass_custom_pattern_based_rewrite_graph("""
graph(%a, %b, %c, %d):
  %q = aten::mul(%a, %b)
  %r = aten::add(%q, %c, %d)
  return (%r)""", """
graph(%a, %b, %c, %d):
  %q = aten::add(%a, %b, %d)
  %r = aten::sub(%q, %c, %d)
  return (%r)""", graph)
        FileCheck().run(input_str, graph)

        # Check mul(x,y) --> x replacement
        input_str = """
graph(%x, %y, %z):
    %c = prim::Const[value=1]()
    # CHECK-NOT: aten::mul
    %t = aten::mul(%x, %y)
    # CHECK: aten::add(%x, %z
    %p = aten::add(%t, %z, %c)
    # CHECK-NEXT: return
    return (%p)"""
        graph = parse_ir(input_str)
        torch._C._jit_pass_custom_pattern_based_rewrite_graph("""
graph(%Pa, %Pb):
  %Pq = aten::mul(%Pa, %Pb)
  return (%Pq)""", """
graph(%Ra, %Rb):
  return (%Ra)""", graph)
        FileCheck().run(input_str, graph)

    @_tmp_donotuse_dont_inline_everything
    def test_pattern_based_module_rewrite(self):
        # Check match::module behavior
        class Test(torch.nn.Module):
            def __init__(self):
                super(Test, self).__init__()
                self.conv = torch.nn.Conv2d(1, 20, 5, 1)
                self.bn = torch.nn.BatchNorm2d(num_features=20)

            def forward(self, x):
                x = self.conv(x)
                x = self.bn(x)
                return x
        m = torch.jit.script(Test())
        torch._C._jit_pass_custom_pattern_based_rewrite_graph("""
        graph(%self, %x):
                %conv = match::module[name="Conv2d"](%self)
                %y = prim::CallMethod[name="forward"](%conv, %x)
                %bn = match::module[name="BatchNorm2d"](%self)
                %z = prim::CallMethod[name="forward"](%bn, %y)
                return (%z)""", """
        graph(%self, %x):
          %z = my::matched_conv_bn(%self, %x)
          return (%z)""", m._c._get_method("forward").graph)

        FileCheck().check("my::matched_conv_bn").run(m._c._get_method("forward").graph)

    def test_expand_quantlint(self):
        pass

    def test_expand_fold_quant_inputs(self):
        pass

    def test_shape_analysis_broadcast(self):
        def broadcast(a, b):
            return a + b

        x = torch.randn(3, 1, 5, requires_grad=True)
        y = torch.randn(4, 1, 8, 5, requires_grad=True)

        graph = torch.jit.script(broadcast).graph
        torch._C._jit_pass_complete_shape_analysis(graph, (x, y), False)
        FileCheck().check("Double(4, 3, 8, 5)").run(str(graph))

    # TODO: update verify to work with GraphExecutors
    @unittest.skip("verify needs to be updated to work with GraphExecutors")
    def test_verify(self):
        x = torch.tensor([0.4], requires_grad=True)
        y = torch.tensor([0.7], requires_grad=True)

        @torch.jit.compile
        def f(x, y):
            z = torch.sigmoid(x * (x + y))
            w = torch.abs(x * x * x + y) + Variable(torch.ones(1))
            return z, w

        torch.jit.verify(f, (x, y), loss_fn=lambda z, w: z * w, devices=[])

    @suppress_warnings
    def test_constant(self):
        x = torch.randn(2, 2, requires_grad=True)

        def f(x):
            return x.matmul(torch.diag(torch.tensor([2., 2.])))

        self.checkTrace(f, (x,), (torch.ones(2, 2, requires_grad=True),))

    def test_legacy_fail(self):
        class MyLegacyFn(Function):
            def forward(self, x):
                return x

            def backward(self, grad_output):
                return grad_output

        x = torch.tensor([0.], requires_grad=True)
        with warnings.catch_warnings(record=True):
            with self.assertRaisesRegex(RuntimeError, "MyLegacyFn"):
                torch.jit._get_trace_graph(lambda x: MyLegacyFn()(x), (x,))

    def test_inplace_transplant(self):
        x = torch.tensor([0.], requires_grad=True)

        def fn(x):
            y = x.clone()
            y.add_(2)
            y.add_(3)
            return y

        g, _ = torch.jit._get_trace_graph(fn, (x,))
        self.run_pass('dce', g)
        FileCheck().check_count("aten::clone", 1, exactly=True) \
            .check_count("aten::add_", 2, exactly=True) \
            .check_next("return").run(str(g))
        self.assertExportImport(g, (x,))

    def test_inplace_flags(self):
        class InplaceFn(Function):
            @staticmethod
            def forward(ctx, x):
                ctx.mark_dirty(x)
                return x.add_(1)

            @staticmethod
            def backward(ctx, go):
                return go

        class RegularFn(Function):
            @staticmethod
            def forward(ctx, x):
                return x.add(1)

            @staticmethod
            def backward(ctx, go):
                return go

        x = torch.tensor([0.], requires_grad=True)

        def fn(x):
            y = RegularFn.apply(x)
            y = InplaceFn.apply(y)
            y = InplaceFn.apply(y)
            y = RegularFn.apply(y)
            return y

        trace_graph, _ = torch.jit._get_trace_graph(fn, (x,), _force_outplace=True)
        self.run_pass('dce', trace_graph)
        ops = [n for n in trace_graph.nodes()]
        for op in ops:
            self.assertTrue(op.hasAttribute('inplace'))
        inplace_flags = [False, True, True, False]
        for op, is_inplace in zip(ops, inplace_flags):
            self.assertEqual(op.i('inplace'), is_inplace)

    def test_inplace_check(self):
        class MyInplaceFn(Function):
            @staticmethod
            def forward(self, x):
                x.add_(1)
                self.mark_dirty(x)
                return x

            @staticmethod
            def backward(self, grad):
                return grad

        def fn(x):
            return MyInplaceFn.apply(x)

        x = torch.randn(5, 5)
        ge = torch.jit.trace(fn, (x,), _force_outplace=True, check_trace=False)
        with self.assertRaisesRegex(RuntimeError, 'inplace MyInplaceFn'):
            ge(x)

    def test_force_outplace_check_fill(self):
        def f(x):
            return torch.empty(x.shape).fill_(7)
        x = torch.randn(10, 15)
        ft = torch.jit.trace(f, x, _force_outplace=True)
        self.assertEqual(f(x), ft(x))

    def test_force_outplace_check_zero(self):
        def f(x):
            return torch.empty(x.shape).zero_()
        x = torch.randn(10, 15)
        ft = torch.jit.trace(f, x, _force_outplace=True)
        self.assertEqual(f(x), ft(x))

    def do_trace_size(self, requires_grad):
        def fn(x):
            return x.view(x.shape[1] * 2, x.size(0), 2)

        x = torch.randn(5, 2, 4, requires_grad=requires_grad)
        y = torch.randn(4, 8, 4, requires_grad=requires_grad)

        # Check that it behaves as expected
        traced_fn = torch.jit.trace(fn, x)
        self.assertEqual(traced_fn(y), fn(y))
        self.assertEqual(traced_fn(x), fn(x))

    def test_trace_size(self):
        self.do_trace_size(False)

    # test the different graph_executor path that happens when
    # gradients are required and sizes are involved
    def test_trace_size_with_grad(self):
        self.do_trace_size(True)

    def do_trace_arange(self, requires_grad):
        def arange(x):
            return torch.arange(x.shape[0])

        def arange_scalar(x):
            return torch.arange(12)

        def arange_start_end(x):
            return torch.arange(start=x.shape[0], end=x.shape[0] + 5)

        x = torch.randn(5, 3, 2, requires_grad=requires_grad)
        y = torch.randn(8, 2, 4, requires_grad=requires_grad)

        # Check that it behaves as expected
        traced_arange = torch.jit.trace(arange, x)
        self.assertEqual(traced_arange(y), arange(y))
        self.assertEqual(traced_arange(x), arange(x))

        traced_arange_scalar = torch.jit.trace(arange_scalar, x)
        self.assertEqual(traced_arange_scalar(y), arange_scalar(y))
        self.assertEqual(traced_arange_scalar(x), arange_scalar(x))

        traced_arange_start_end = torch.jit.trace(arange_start_end, x)
        self.assertEqual(traced_arange_start_end(y), arange_start_end(y))
        self.assertEqual(traced_arange_start_end(x), arange_start_end(x))

    def test_trace_arange(self):
        self.do_trace_arange(False)

    # test the different graph_executor path that happens when
    # gradients are required and sizes are involved
    def test_trace_arange_with_grad(self):
        self.do_trace_arange(True)

    # Test that a trace of torch.full(x.shape) doesn't store the shape as a constant
    def test_trace_full_dynamic_shape(self):
        def full_with_shape_like(x):
            return torch.full(x.shape, 2)

        x = torch.randn(3, 4)
        ge = torch.jit.trace(full_with_shape_like, example_inputs=x)
        y = torch.randn(2, 7)
        self.assertEqual(ge(y).shape, y.shape)
        self.assertEqual(ge(x).shape, x.shape)

    def do_trace_slice(self, requires_grad):
        def slice(x):
            results = []
            for i in range(4):
                results.append(x[:x.size(0) - i, i:x.size(2), i:3])
            return tuple(results)

        def slice_select(x):
            results = []
            for i in range(4):
                results.append(x[:, i:, x.size(2) - 5])
            return tuple(results)

        x = torch.randn(5, 6, 7, requires_grad=requires_grad)
        y = torch.randn(7, 8, 9, requires_grad=requires_grad)

        # Check that it behaves as expected
        traced_slice = torch.jit.trace(slice, x)
        self.assertEqual(traced_slice(y), slice(y))
        self.assertEqual(traced_slice(x), slice(x))

        traced_slice_select = torch.jit.trace(slice_select, x)
        self.assertEqual(traced_slice_select(y), slice_select(y))
        self.assertEqual(traced_slice_select(x), slice_select(x))

    def test_trace_slice(self):
        self.do_trace_slice(False)

    # test the different graph_executor path that happens when
    # gradients are required and sizes are involved
    def test_trace_slice_with_grad(self):
        self.do_trace_slice(True)


    def test_trace_casts(self):
        casts = [
            lambda x: x.byte(),
            lambda x: x.float(),
            lambda x: x.cpu(),
            lambda x: x.to(device='cpu'),
            lambda x: x.to(dtype=torch.int64),
            lambda x: x.to(device='cpu', dtype=torch.float),
            lambda x: x.to(x)
        ]

        def assertContainsCast(trace):
            self.assertEqual(sum(n.kind() == 'aten::to' for n in trace.graph.nodes()), 1)

        for cast in casts:
            trace = torch.jit.trace(cast, torch.randn(2, 2))
            assertContainsCast(trace)
            x = torch.randn(2, 2)
            self.assertEqual(trace(x), cast(x))

        def to_tensor(x, y):
            return x.to(y)

        to_tensor_trace = torch.jit.trace(to_tensor, (torch.randn(2, 2), torch.randn(1, 8)))
        assertContainsCast(to_tensor_trace)
        x, y = torch.randn(2, 2), torch.randn(1, 10)
        self.assertEqual(to_tensor_trace(x, y), to_tensor(x, y))

    @skipIfCompiledWithoutNumpy
    def test_trace_warn(self):
        def fn(x):
            int(x)  # Warning 1.
            y = x * 1
            if y:   # Warning 2.
                pass
            q = [x, x * 4]
            z = q[y]  # Warning 3.
            float(z)  # Warning 4.
            z.tolist()  # Warning 5.
            z.numpy()  # Warning 6.
            for _ in torch.ones(4, 4):  # Warning 7.
                pass
            return z + 4

        with warnings.catch_warnings(record=True) as warns:
            traced_fn = torch.jit.trace(fn, torch.tensor([1]))
        warns = [str(w.message) for w in warns]
        self.assertIn('a Python integer', warns[0])
        self.assertIn('a Python boolean', warns[1])
        self.assertIn('a Python index', warns[2])
        self.assertIn('a Python float', warns[3])
        self.assertIn('a Python list', warns[4])
        self.assertIn('a NumPy array', warns[5])
        self.assertIn('Iterating over', warns[6])

    def test_trace_tuple(self):
        def fn(x, y):
            return x, (x * y[1], x * y[0])

        x, y = torch.randn(2, 2), (torch.ones(2, 2), torch.randn(2, 2))
        traced_fn = torch.jit.trace(fn, (x, y))
        self.assertEqual(traced_fn(x, y), fn(x, y))
        # should be a tuple nested within another tuple
        FileCheck().check_count("prim::TupleConstruct", 2, exactly=True).check_next("return") \
            .run(str(traced_fn.graph))
        self.assertExportImport(traced_fn.graph, (x, y))

    def test_trace_random(self):
        def f(mean, std):
            return torch.normal(mean, std)

        traced = torch.jit.trace(f, (torch.zeros(2, 3), torch.ones(2, 3)), check_trace=False)
        mean, std = torch.zeros(5, 5), torch.ones(5, 5)
        with torch.random.fork_rng(devices=[]):
            output = f(mean, std)
        traced_output = traced(mean, std)
        self.assertEqual(output, traced_output)

    def test_trace_tensor_factory(self):
        def run(**kwargs):
            inputs_require_grads = kwargs.pop('inputs_require_grads', True)

            def fn(x):
                return x + torch.ones(2, 3, **kwargs)

            input_kwargs = kwargs.copy()
            if 'out' in input_kwargs:
                del input_kwargs['out']
            input = torch.ones(2, 3, **input_kwargs)
            self.checkTrace(fn, (input,), inputs_require_grads=inputs_require_grads)
            # check we recorded 'ones' and did not just record a constant
            tfn = torch.jit.trace(fn, input)
            self.assertTrue("ones" in str(tfn.graph))
        run()
        run(dtype=torch.int, inputs_require_grads=False)
        run(out=torch.tensor([]))
        if RUN_CUDA:
            run(device="cuda:0")
        if RUN_CUDA_MULTI_GPU:
            run(device="cuda:1")

    def test_trace_indexed_assignment(self):
        def stuff(x, y):
            x = x.clone()
            x[0] = y
            return x
        example = torch.rand(3, 4)
        self.checkTrace(stuff, (example, example[0] + 1))

    # TODO: implement
    @unittest.expectedFailure
    def test_output_unflatten(self):
        """Check that outputs of traced functions retain the original structure and nesting"""
        def fn(x):
            return (x * 2, (x ** 2, x + 4, (x + 2,), ), x * 4)

        self.checkTrace(fn, (torch.randn(2, 2),))

    def test_input_flatten(self):
        """Check that inputs to traced functions are flattened"""

        def fn(x, t):
            y, z = t
            return x * y * z

        inputs = (torch.randn(1), (torch.randn(1), torch.randn(1)))
        self.checkTrace(fn, inputs)

    def test_input_dict_empty(self):
        def test(d):
            pass

        with self.assertRaises(RuntimeError):
            self.checkTrace(test, {})

    def test_input_dict_flattens(self):
        class Test(torch.nn.Module):
            def forward(self, d):
                return d['x'] + d['y']

        inputs = {'x': torch.rand(3, 4), 'y': torch.rand(3, 4)}
        module = torch.jit.trace(Test(), inputs)

        FileCheck().check('aten::values').check('prim::ListUnpack').run(str(module.graph))

    def test_input_dict_flattens_recursive(self):
        class Test(torch.nn.Module):
            def forward(self, d):
                # Use both to avoid getting optimized away
                a = d['x'][0]
                b, c = d['y']
                return a + b

        inputs = {'x': (torch.rand(2, 2), torch.rand(2, 2)), 'y': (torch.ones(1, 1), torch.ones(2, 1))}
        module = torch.jit.trace(Test(), inputs)
        FileCheck().check('aten::values') \
                   .check('prim::ListUnpack') \
                   .check_count('prim::TupleUnpack', 2) \
                   .run(str(module.graph))

    def test_input_dict_checkTrace_mut(self):
        def test(d):
            d['x'].tanh_()
            return d['x']
        inputs = {'x': torch.rand(3, 4), 'y': torch.rand(3, 4)}
        self.checkTrace(test, (inputs,), inputs_require_grads=False)

    def test_input_dict_unify(self):
        def test(d):
            return d['int'], d['float']
        inputs = {'int': torch.ones((2, 2), dtype=torch.int32),
                  'float': torch.ones((2, 2), dtype=torch.float32)}
        self.checkTrace(test, (inputs,), inputs_require_grads=False)

    def test_input_tuple_of_dicts(self):
        def test(t):
            d = t[0]
            return d['x']['y']
        inputs = {'x': {'y': torch.rand(2, 3)}}
        self.checkTrace(test, ((inputs, inputs),), allow_unused=True)

    def test_input_dict_of_dicts(self):
        def test(d):
            return d['x']['y']
        nested_input = {'y': torch.rand(2, 3)}
        unified_nested = {'y': torch.rand(3, 2)}
        inputs = {'x': nested_input, 'force_unify': unified_nested}
        self.checkTrace(test, (inputs,), allow_unused=True)

    def test_input_dict_of_lists(self):
        def test(d):
            return d['x'][0]

        inputs = {'x': [torch.rand(3, 2)]}
        self.checkTrace(test, (inputs,))

    def test_input_list_toplevel_flatten(self):
        def test(t1, t2):
            return torch.add(t1, t2)

        inputs = [torch.ones(2, 2), torch.rand(2, 2)]
        self.checkTrace(test, inputs)

    def test_input_list_toplevel_flatten_direct(self):
        class Test(torch.nn.Module):
            def forward(self, t1, t2):
                return torch.add(t1, t2)
        inputs = [torch.ones(2, 2), torch.rand(2, 2)]
        torch.jit.trace(Test(), inputs)

    def test_input_list_of_tuples(self):
        def test(l):
            return l[0][0]
        inputs = [(torch.ones(2, 2),)]
        self.checkTrace(test, (inputs,))

    def test_input_dict_empty_list(self):
        def test(d):
            pass
        inputs = {1: []}
        with self.assertRaisesRegex(RuntimeError, 'List trace'):
            self.checkTrace(test, (inputs,))

    def test_input_list_mixed_type(self):
        def test(d):
            pass
        inputs = [torch.rand(2, 3), (torch.ones(2), torch.ones(2))]
        with self.assertRaisesRegex(RuntimeError, 'consistent'):
            self.checkTrace(test, (inputs,))

    # TODO: adapt to a GraphExecutor test
    @unittest.skip("Need to instrument GraphExecutors a bit more")
    def test_flags(self):
        x, y = torch.randn(2, 2)
        y = Variable(torch.randn(2, 2))

        @torch.jit.compile
        def fn(x, y):
            return (x * x + y * y + x * y).sum()

        grads = {}
        for rx, ry in product((True, False), repeat=2):
            x.requires_grad = rx
            y.requires_grad = ry

            self.assertFalse(fn.has_trace_for(x, y))
            out = fn(x, y)

            self.assertFalse(fn.has_trace_for(x, y))
            for v, name, compute in [(x, 'x', rx), (y, 'y', ry)]:
                if not compute:
                    continue
                grad_v, = torch.autograd.grad(out, v, retain_graph=True)
                expected_grad = grads.setdefault(name, grad_v)
                self.assertEqual(grad_v, expected_grad)
            self.assertEqual(fn.has_trace_for(x, y), rx or ry)

    def test_python_ir(self):
        x = torch.tensor([0.4], requires_grad=True)
        y = torch.tensor([0.7], requires_grad=True)

        def doit(x, y):
            return torch.sigmoid(torch.tanh(x * (x + y)))

        g, _ = torch.jit._get_trace_graph(doit, (x, y))
        self.run_pass('dce', g)
        self.run_pass('canonicalize', g)
        g2 = torch._C.Graph()
        g_to_g2 = {}
        for node in g.inputs():
            g_to_g2[node] = g2.addInput()
        for node in g.nodes():
            n_ = g2.createClone(node, lambda x: g_to_g2[x])
            g2.appendNode(n_)
            for o, no in zip(node.outputs(), n_.outputs()):
                g_to_g2[o] = no

        for node in g.outputs():
            g2.registerOutput(g_to_g2[node])

        t_node = g2.create("prim::TensorTest").t_("a", torch.ones([2, 2]))
        self.assertEqual(t_node.attributeNames(), ["a"])
        g2.appendNode(t_node)
        self.assertTrue(torch.equal(torch.ones(2, 2), t_node.t("a")))
        for node in g.nodes():
            self.assertTrue(g2.findNode(node.kind()) is not None)

    @unittest.skipIf(IS_WINDOWS, "TODO: need to fix this test case for Windows")
    @unittest.skipIf(IS_SANDCASTLE, "gtest runs these in sandcastle")
    @unittest.skipIf(RUN_CUDA, "covered by test_cpp_cuda")
    @unittest.skipIf(not torch._C._jit_has_cpp_tests(), "Tests were not built, use BUILD_TEST=1")
    @skipIfRocm
    def test_cpp(self):
        from cpp.jit import tests_setup
        tests_setup.setup()
        torch._C._jit_run_cpp_tests(run_cuda=False)
        tests_setup.shutdown()

    @unittest.skipIf(IS_WINDOWS, "TODO: need to fix this test case for Windows")
    @unittest.skipIf(not RUN_CUDA, "cpp tests require CUDA")
    @unittest.skipIf(not torch._C._jit_has_cpp_tests(), "Tests were not built, use BUILD_TEST=1")
    @skipIfRocm
    def test_cpp_cuda(self):
        from cpp.jit import tests_setup
        tests_setup.setup()
        torch._C._jit_run_cpp_tests(run_cuda=True)
        tests_setup.shutdown()

    def test_batchnorm(self):
        x = torch.ones(2, 2, 2, 2)
        g, outputs, inputs = torch.jit._get_trace_graph(nn.BatchNorm2d(2), x,
                                                        _force_outplace=True, return_inputs=True)
        m = self.createFunctionFromGraph(g)
        self.assertEqual(outputs, m(*inputs))

    def test_dropout(self):
        x = torch.ones(2, 2)
        with torch.random.fork_rng(devices=[]):
            g, outputs, inputs = torch.jit._get_trace_graph(nn.Dropout(0.6), x, return_inputs=True)
        with torch.random.fork_rng(devices=[]):
            m = self.createFunctionFromGraph(g)
            self.assertEqual(outputs, m(*inputs))

    @slowTest
    @unittest.skipIf(GRAPH_EXECUTOR == ProfilingMode.SIMPLE, 'Testing differentiable graph')
    def test_dropout_module_requires_grad(self):
        with enable_profiling_mode():
            class MyModule(torch.nn.Module):
                def __init__(self, M):
                    super(MyModule, self).__init__()
                    self.dropout = torch.nn.Dropout(0.5)
                    self.linear = torch.nn.Linear(M, M)

                def forward(self, input):
                    input = self.dropout(input)
                    output = self.linear(input)
                    return output

            def profile(func, X):
                with torch.autograd.profiler.profile() as prof:
                    func(X)
                return [e.name for e in prof.function_events]

            M = 1000
            scripted = torch.jit.script(MyModule(M))
            # To reduce confusion about expected behaviors:
            #   requires_grad controls whether dropout is symbolically differentiated.
            #   training controls whether bernoulli_ is called inside symbolic differentiation of dropout.
            # * When requires_grad == training, the expected behaviors are obvious.
            # * When requires_grad=True and training=False, bernoulli_ might still show up in the graph.
            #   But it's in a branch that's not called. That's why we have separate checks for autograd
            #   profiler to make sure it's not run.
            # * When requires_grad=False and training=True, bernoulli_ must be run since it's the expected
            #   behavior for the dropout layer in training mode. It's independent of whether graph requires
            #   gradient. In fact bernoulli_ comes from autograd instead of autodiff in this case.
            for training in (True, False):
                if training:
                    scripted.train()
                else:
                    scripted.eval()
                for requires_grad in (True, False):
                    X = torch.randn(M, M, requires_grad=requires_grad)
                    if requires_grad:
                        FileCheck().check("aten::bernoulli_").run(scripted.graph_for(X, profile_and_replay=True))
                    self.assertEqual(training, 'bernoulli_' in profile(scripted, X))

    @unittest.skipIf(GRAPH_EXECUTOR == ProfilingMode.SIMPLE, 'Testing differentiable graph')
    def test_dropout_func_requires_grad(self):
        def dropout_training(input):
            return F.dropout(input, 0.5, training=True)

        def dropout_eval(input):
            return F.dropout(input, 0.5, training=False)

        def profile(func, X):
            with torch.autograd.profiler.profile() as prof:
                func(X)
            return [e.name for e in prof.function_events]

        M = 1000
        scripted_training = torch.jit.script(dropout_training)
        scripted_eval = torch.jit.script(dropout_eval)
        # See comments in test_dropout_module_requires_grad.
        for requires_grad in (True, False):
            X = torch.randn(M, M, requires_grad=requires_grad)
            if requires_grad:
                FileCheck().check("aten::bernoulli_").run(scripted_training.graph_for(X, profile_and_replay=True))
            self.assertIn('bernoulli_', profile(scripted_training, X))
            self.assertNotIn('bernoulli_', profile(scripted_eval, X))

    @unittest.skipIf(not RUN_CUDA, "test_dropout_cuda require CUDA")
    def test_dropout_cuda(self):
        # Dropout AD is dispatched to _fused_dropout in CUDA case,
        # which is not included in TestJitGeneratedFunctional
        x = torch.ones(4, 4).cuda().requires_grad_()

        with enable_profiling_mode():
            @torch.jit.script
            def func(x):
                return torch.nn.functional.dropout(x)

            with freeze_rng_state():
                out_ref = torch.nn.functional.dropout(x)
                grad_ref = torch.autograd.grad(out_ref.sum(), x)

            with freeze_rng_state():
                out = func(x)
                grad = torch.autograd.grad(out.sum(), x)

            self.assertEqual(out, out_ref)
            self.assertEqual(grad, grad_ref)

    def test_conv(self):
        x = torch.ones(20, 16, 50, 40)
        g, outputs, inputs = torch.jit._get_trace_graph(nn.Conv2d(16, 13, 3, bias=False), x, return_inputs=True)
        m = self.createFunctionFromGraph(g)
        self.assertEqual(outputs, m(*inputs))

    def test_max_pool(self):
        x = torch.rand(20, 16, 10, 10)

        def max_pool2d(x):
            return F.max_pool2d(x, 2) + 2

        trace = torch.jit.trace(max_pool2d, (x))
        graph = trace.graph_for(x)
        FileCheck().check("aten::max_pool2d(").run(graph)
        self.assertEqual(max_pool2d(x), trace(x))

    def test_repeated_input(self):
        def fn(a, b):
            return a + b

        ge = self.checkTrace(fn, [torch.randn(2, 2)] * 2)
        inputs = set(ge.graph.inputs())
        # three instead of 2 because the export/import in checkTrace adds a
        # `self` module argument
        self.assertTrue(len(inputs) == 3)

    def test_repeated_output(self):
        def fn(a, b):
            z = a + b
            return z, z

        ge = self.checkTrace(fn, [torch.randn(2, 2) for _ in range(2)])
        tuple_output = list(ge.graph.outputs())[0]
        tuple_inputs = list(tuple_output.node().inputs())
        self.assertTrue(tuple_inputs[0] == tuple_inputs[1])

    def test_inplace_copy(self):
        x = torch.randn(4, 4, requires_grad=True)

        def f(x):
            out = Variable(torch.zeros(x.size()))
            out.copy_(x)
            return out

        g, outputs, inputs = torch.jit._get_trace_graph(f, (x, ), return_inputs=True)
        self.run_pass('dce', g)
        m = self.createFunctionFromGraph(g)
        self.assertEqual(outputs, m(*inputs))
        self.assertExportImport(g, (x,))

    def test_shared_param(self):
        class MyModule(torch.nn.Module):
            def __init__(self):
                super(MyModule, self).__init__()
                self.b = self.a = nn.Parameter(torch.randn(2, 2))

            def forward(self, x):
                return x * self.a + self.b

        m = MyModule()
        g, _ = torch.jit._get_trace_graph(m, (torch.randn(2, 2),))
        self.run_pass('dce', g)
        self.assertEqual(len(list(g.inputs())), 2)
        FileCheck().check("mul").check("add").run(str(g))

    def test_trace_c10_ops(self):
        try:
            _ = torch.ops._caffe2.GenerateProposals
        except RuntimeError:
            self.skipTest("Skip the test since c2 ops are not registered.")

        class MyModel(torch.nn.Module):
            def __init__(self):
                super(MyModel, self).__init__()

            def forward(self, scores, bbox_deltas, im_info, anchors):
                a, b = torch.ops._caffe2.GenerateProposals(
                    (scores), (bbox_deltas), (im_info), (anchors),
                    2.0, 6000, 300, 0.7, 16, True, -90, 90, 1.0, True,
                )
                return a, b
        model = MyModel()
        A = 4
        H = 10
        W = 8
        img_count = 3
        scores = torch.ones(img_count, A, H, W, dtype=torch.float32)
        bbox_deltas = torch.linspace(0, 10, steps=img_count * 4 * A * H * W,
                                     dtype=torch.float32)
        bbox_deltas = bbox_deltas.view(img_count, 4 * A, H, W)
        im_info = torch.ones(img_count, 3, dtype=torch.float32)
        anchors = torch.ones(A, 4, dtype=torch.float32)
        inputs = (scores, bbox_deltas, im_info, anchors)
        traced_model = torch.jit.trace(model, inputs)
        self.assertEqual(traced_model(*inputs), model(*inputs))
        self.assertExportImportModule(traced_model, (scores, bbox_deltas, im_info, anchors))

    def test_nested_inplace(self):
        x = torch.randn(2, 2)
        g, outputs, inputs = torch.jit._get_trace_graph(
            lambda x: F.threshold(x, 0, 0, inplace=True), (x, ), return_inputs=True)
        m = self.createFunctionFromGraph(g)
        self.assertEqual(outputs, m(*inputs))
        FileCheck().check("threshold_").run(str(g))
        self.assertExportImport(g, (x,))

    def run_ge_tests(self, optimize, use_cuda):

        with enable_profiling_mode():
            with torch.jit.optimized_execution(optimize):
                def rand(*args):
                    t = torch.rand(*args).float()
                    if use_cuda:
                        t = t.cuda()
                    return t
                self.checkTrace(lambda a, b: a * b + b,
                                [rand(1), rand(1)], [rand(2, 3), rand(2, 3)])
                # trivial identity
                self.checkTrace(lambda a, b: (b, a), [rand(1), rand(1)])

                def foo(a):
                    t = a * a
                    return t * t, 4 * t
                self.checkTrace(foo, [rand(1)])
                # unused input
                self.checkTrace(
                    lambda a, b: a * a, [rand(1), rand(1)], allow_unused=True)
                # test outputs that do not get used in grad
                self.checkTrace(foo, [rand(1)], drop=1)
                # test autograd fallback
                self.checkTrace(lambda a, b: a * b /
                                (a - 2 * b) + b, [rand(1), rand(1)])

    def test_ge_unoptimized(self):
        self.run_ge_tests(False, False)

    @unittest.skipIf(IS_SANDCASTLE, "NYI: fuser support for Sandcastle")
    @enable_cpu_fuser
    def test_ge_optimized(self):
        with enable_profiling_mode():
            self.run_ge_tests(True, False)

    @unittest.skipIf(not RUN_CUDA, "requires CUDA")
    def test_ge_cuda(self):
        self.run_ge_tests(True, True)

    # more manual test of graph executor that can be used as a scratchpad
    def test_ge(self):
        def foo(a, b):
            return a * b / (a - b) + b
        V = Variable
        a, b = V(torch.rand(1)), V(torch.rand(1))
        ge = torch.jit.trace(foo, (a, b))
        a, b = V(torch.rand(1), requires_grad=True), V(
            torch.rand(1), requires_grad=True)
        r, = ge(a, b)
        da, db = torch.autograd.grad(r + 3, [a, b], create_graph=True)

        l2 = (da * db + db * db)
        g2result = torch.autograd.grad(l2, [da, db])

        r = foo(a, b)
        da2, db2 = torch.autograd.grad(r + 3, [a, b], create_graph=True)
        self.assertEqual(da, da2)
        self.assertEqual(db, db2)
        l3 = (da2 * db2 + db2 * db2)
        g2result2 = torch.autograd.grad(l3, [da2, db2])
        self.assertEqual(g2result, g2result2)

    def test_trace_annotation(self):
        @_trace(torch.rand(1))
        def foo(a):
            return a + a + a

        x = torch.randn(5, 5)
        self.assertEqual(foo(x), x + x + x)

    def test_trace_script(self):
        @torch.jit.script
        def func1(x):
            # type: (Tuple[Tensor, Tensor]) -> Tensor
            return x[0] + x[1]

        @torch.jit.script
        def func2(x):
            # type: (List[Tensor]) -> Tensor
            return x[0] + x[1]

        a = torch.randn(5)
        b = torch.randn(5)

        self.checkTrace(func1, ((a, b),))
        self.checkTrace(func2, ((a, b),))

        @torch.jit.script
        def func3(x, method='bilinear', align_corners=True):
            # type: (Tensor, str, bool) -> Tensor
            hw = x.shape[2:4]
            return F.interpolate(x, hw, mode=method, align_corners=align_corners)

        inp = torch.rand(1, 3, 6, 6)
        self.checkTrace(func3, (inp,))

        @torch.jit.script
        def func4(x, a):
            # type: (Tensor, List[str]) -> Tensor
            if len(a) == 2:
                return x + 2
            else:
                return x

        def invalid_constant_baking(x):
            a = ["hello", "world"]
            return func4(x, a)

        with self.assertRaisesRegex(RuntimeError,
                                    "Tracer cannot get value trace for type"):
            self.checkTrace(invalid_constant_baking, (inp,))


    def test_einsum(self):
        def outer(x, y):
            return torch.einsum('i,j->ij', (x, y))

        traced = torch.jit.trace(outer, (torch.randn(4), torch.randn(5)))
        script = torch.jit.script(outer)
        fns = [traced, script]
        x, y = torch.randn(10), torch.randn(2)
        for fn in [traced, script]:
            self.assertGraphContains(fn.graph, kind='aten::einsum')
            self.assertEqual(fn(x, y), outer(x, y))

    @unittest.skipIf(not RUN_CUDA, "calls .cuda()")
    def test_traced_module_cuda(self):
        class Model(nn.Module):
            def __init__(self, num_features, num_layers):
                super(Model, self).__init__()
                self.num_layers = num_layers
                layers = [[nn.Linear(num_features, num_features), nn.Sigmoid()]
                          for _ in range(num_layers)]
                self.submodule = nn.Sequential(*chain(*layers))

            def forward(self, x):
                for i in range(self.num_layers):
                    x = self.submodule[i](x) + x
                return x

        model = Model(5, 3)
        x = torch.randn(2, 5)
        traced_model = torch.jit.trace(model, x)

        # We're missing some attributes these modules had initially. Make sure we can
        # still get the __repr__()
        model.__repr__()

        # XXX: indexing sequentials is broken
        linear_submodule = next(iter(traced_model.submodule._modules.values()))

        # All attributes that aren't parameters should raise
        with self.assertRaises(AttributeError):
            linear_submodule.in_features
        linear_submodule.weight
        linear_submodule.weight = nn.Parameter(torch.randn(linear_submodule.weight.shape))
        with self.assertRaises(RuntimeError):
            del linear_submodule.weight

        # Submodules can't be called
        with self.assertRaises(RuntimeError):
            linear_submodule(x)

        # Type casts
        linear_submodule.cuda()
        traced_model.float().cuda()
        cuda_out = traced_model(x.float().cuda())
        traced_model.cpu()
        cpu_out = traced_model(x.float())
        self.assertEqual(cpu_out, cuda_out)
        traced_model.to('cuda')
        cuda_out = traced_model(x.float().cuda())
        traced_model.to('cpu')
        cpu_out = traced_model(x.float())
        self.assertEqual(cpu_out, cuda_out)
        traced_model.double()

        # state_dict + load_state_dict
        state = {k: v.clone() for k, v in traced_model.state_dict().items()}
        new_state = {k: v.clone().fill_(1) for k, v in state.items()}
        out = traced_model(x)
        traced_model.load_state_dict(new_state)
        out_ones = traced_model(x)
        traced_model.load_state_dict(state)
        out_state = traced_model(x)
        self.assertEqual(out, out_state)
        self.assertNotEqual(out, out_ones)

    def test_export_no_reorder(self):
        def func(a, b):
            return a * b / (a - 2 * b) + b

        recording_inputs = [torch.tensor([0.55619788169860839844], dtype=torch.float32, requires_grad=True),
                            torch.tensor([0.25947844982147216797], dtype=torch.float32, requires_grad=True)]

        ge1 = torch.jit.trace(func, recording_inputs)
        ge2 = self.getExportImportCopy(ge1)

        outputs_ge1 = ge1(*recording_inputs)
        outputs_ge2 = ge2(*recording_inputs)

        grad_ge1 = torch.autograd.grad(outputs_ge1, recording_inputs)
        grad_ge2 = torch.autograd.grad(outputs_ge2, recording_inputs)
        self.assertTrue(outputs_ge1 == outputs_ge2)
        self.assertTrue(grad_ge1 == grad_ge2)

    def test_python_function(self):
        class MyFn(Function):
            @staticmethod
            def forward(ctx, x):
                return x + 1

            @staticmethod
            def backward(ctx, grad_output):
                return grad_output

        @_trace(torch.zeros(2))
        def fn(x):
            return MyFn.apply(x + 2) + 3

        x = torch.tensor([1., 2., 3.])
        y = torch.randn(2, 2, requires_grad=True)
        fn(x)
        fn(y)

    def test_python_function_tup(self):
        class MyFn(Function):
            @staticmethod
            def forward(ctx, x):
                return x + 1, x - 1

            @staticmethod
            def backward(ctx, grad_output):
                return grad_output, grad_output

        @_trace(torch.zeros(2))
        def fn(x):
            a, b = MyFn.apply(x + 2)
            return a + b + 3
        x = torch.tensor([1., 2., 3.])
        y = torch.randn(2, 2, requires_grad=True)
        fn(x)
        fn(y)

    def test_decompose_addmm(self):
        def does_decompose():
            @torch.jit.script
            def addmm(mat, mat1, mat2):
                a = mat.addmm(mat1, mat2)
                b = mat.addmm(mat1, mat2, alpha=1.0, beta=1.0)
                return a + b

            mat = torch.randn(2, 2)
            mat1 = torch.randn(2, 4)
            mat2 = torch.randn(4, 2)

            out_ref = addmm(mat, mat1, mat2)
            self.run_pass('decompose_ops', addmm.graph)
            out_test = addmm(mat, mat1, mat2)
            self.assertEqual(out_ref, out_test)
            FileCheck().check_not("addmm").run(str(addmm.graph))

        def doesnt_decompose():
            @torch.jit.script
            def addmm(mat, mat1, mat2, alpha, beta):
                a = mat.addmm(mat1, mat2, alpha=4.20, beta=2.0)
                b = mat.addmm(mat1, mat2, alpha=int(alpha), beta=int(beta))

                return a + b

            orig = str(addmm.graph)
            self.run_pass('decompose_ops', addmm.graph)
            self.assertTrue(orig == str(addmm.graph))

        does_decompose()
        doesnt_decompose()

    def test_fuse_addmm(self):
        class AddmmModel(torch.nn.Module):
            def forward(self, x):
                return torch.mm(x, x) + x

        x = torch.ones(3, 3)
        f = io.BytesIO()
        torch.onnx._export(AddmmModel(), x, f, verbose=False)

    def test_index_put(self):
        ten = torch.zeros(3, 3)
        mask = torch.tensor([[True, True, True],
                             [True, False, False],
                             [True, True, False]])

        def test_fn(ten, mask):
            ten[mask] = torch.ones(6)
            return ten

        traced_test_fn = torch.jit.trace(test_fn, (ten, mask))

        ten = torch.rand(3, 3)
        self.assertEqual(test_fn(ten, mask), traced_test_fn(ten, mask))

    @suppress_warnings
    def test_sparse_tensors(self):
        @torch.jit.ignore
        def get_sparse():
            return torch.sparse.FloatTensor(2, 3)

        @torch.jit.script
        def test_is_sparse(input):
            # type: (Tensor) -> bool
            return input.is_sparse

        script_out_is_sparse = test_is_sparse(get_sparse())
        script_out_is_dense = test_is_sparse(torch.randn(2, 3))
        self.assertEqual(script_out_is_sparse, True)
        self.assertEqual(script_out_is_dense, False)

        def test_basic_sparse(input):
            output = get_sparse()
            return output, input

        self.checkScript(test_basic_sparse, (get_sparse(),))
        self.checkScript(test_basic_sparse, (torch.tensor([1]),))

        def test_sparse_sum(input):
            return torch.sparse.sum(input)

        self.checkScript(test_sparse_sum, (get_sparse(),))

        def test_sparse_mm(input1, input2):
            return torch.sparse.mm(input1, input2)

        self.checkScript(test_sparse_mm, (get_sparse(), torch.randn(3, 4)))

        def test_sparse_addmm(input, input1, input2):
            return torch.sparse.addmm(input, input1, input2)

        def test_sparse_addmm_alpha_beta(input, input1, input2):
            return torch.sparse.addmm(input, input1, input2, 1.3, 1.5)

        self.checkScript(test_sparse_addmm, (torch.randn(2, 4), get_sparse(), torch.randn(3, 4)))
        self.checkScript(test_sparse_addmm_alpha_beta, (torch.randn(2, 4), get_sparse(), torch.randn(3, 4)))

    def test_tuple_specialization(self):
        @torch.jit.script
        def f(t, s):
            # type: (Tuple[Tensor, Tuple[int, Tensor]], str) -> Tensor
            x, t2 = t
            _, y = t2
            return x + y

        t = torch.randn(2, 2), (1, torch.randn(2, 2)),
        f(t, "hi")
        graph = f.graph_for(t, "hi")
        input_types = list(next(graph.inputs()).type().elements())
        w = input_types[0]
        self.assertEqual(input_types[0].kind(), 'TensorType')
        self.assertEqual(input_types[1].elements()[1].kind(), 'TensorType')

    def test_constant_prop_simple(self):
        @torch.jit.script
        def constant_prop(input_int):
            # type: (int) -> int
            a = 2 * 3
            b = a + 2
            return b - input_int

        out_ref = constant_prop(2)
        self.run_pass('constant_propagation', constant_prop.graph)
        out_test = constant_prop(2)
        self.assertEqual(out_ref, out_test)
        graph_str = str(constant_prop.graph)
        self.assertTrue("aten::add" not in graph_str and "aten::mul" not in graph_str)
        const = constant_prop.graph.findNode("prim::Constant").output().toIValue()
        self.assertEqual(const, 8)

    def test_constant_prop_nested(self):
        @torch.jit.script
        def constant_prop(a):
            b = 2 + 1
            if bool(a < 2):
                c = b + 2
            else:
                c = b - 2
            return c
        out_ref = constant_prop(torch.tensor(2))
        self.run_pass('constant_propagation', constant_prop.graph)
        out_test = constant_prop(torch.tensor(2))
        self.assertEqual(out_ref, out_test)
        if_node = constant_prop.graph.findNode("prim::If")
        for block in if_node.blocks():
            for node in block.nodes():
                self.assertTrue(node.kind() == "prim::Constant")

    def test_constant_prop_print(self):
        @torch.jit.script
        def constant_prop(input_tensor):
            a = 2 * 3
            print(a)
            b = a + 2
            return b + input_tensor

        self.run_pass('constant_propagation', constant_prop.graph)
        graph = constant_prop.graph
        print_node = graph.findNode("prim::Print")
        self.assertTrue(print_node.input().toIValue() == 6)

    def test_constant_prop_rand(self):
        @torch.jit.script
        def constant_prop():
            a = torch.randn([3])
            b = a + 2
            return b

        self.run_pass('constant_propagation', constant_prop.graph)
        self.assertTrue("aten::randn" in str(constant_prop.graph))

    def test_constant_prop_none(self):
        @torch.jit.script
        def typed_none():
            # type: () -> Optional[int]
            return None

        @torch.jit.script
        def constant_prop():
            a = typed_none()
            b = typed_none()
            if (a is None and b is None):
                a = 2
            else:
                a = 1
            return a

        self.run_pass('constant_propagation', constant_prop.graph)
        FileCheck().check("prim::Constant").run(constant_prop.graph)

    def test_constant_prop_if_inline(self):
        @torch.jit.script
        def constant_prop():
            cond = True
            a = 1
            if cond:
                a = 1 * 2
            else:
                a = 1 // 0
            return a

        # testing that 1 // 0 error is not thrownn
        self.run_pass('constant_propagation', constant_prop.graph)

    def test_constant_prop_exception(self):
        # checking y = a[4] does not error in constant propagation
        def bad_index(x):
            # type: (bool)
            y = 0
            if x:
                a = [1, 2, 3]
                y = a[4]
            return y

        self.checkScript(bad_index, (False,))

    def test_constant_prop_aliasing_type(self):
        @torch.jit.script
        def foo():
            return len([1]), len(torch.tensor([2]))

        FileCheck().check_dag("aten::tensor").check_dag("aten::len").run(foo.graph)

        @torch.jit.script
        def fn():
            if True:
                return 1
            else:
                return 2

        FileCheck().check_not("prim::If").run(fn.graph)

    def test_short_circuit_optimization(self):
        @torch.jit.script
        def const_expressions(x):
            # type: (int) -> Tuple[bool, bool]
            return x == 1 and False, x == 1 or True
        self.run_pass('constant_propagation', const_expressions.graph)
        FileCheck().check_not("prim::If").check_not("aten::eq").run(const_expressions.graph)
        self.assertEqual(const_expressions(1), (False, True))

        @torch.jit.script
        def redundant_expressions(x):
            # type: (int) -> Tuple[bool, bool]
            return x == 1 and True, x == 1 or False

        self.run_pass('peephole', redundant_expressions.graph)
        self.assertEqual(redundant_expressions(1), (True, True))
        self.assertEqual(redundant_expressions(0), (False, False))
        # and True / or False are removed from graph
        FileCheck().check("aten::eq").check_not("prim::If").run(redundant_expressions.graph)

    @_inline_everything
    def test_peephole_type_refinements(self):
        def refine(x):
            # type: (Optional[Tensor]) -> Tensor
            return x if x is not None else torch.tensor(3)

        @torch.jit.script
        def test():
            return refine(torch.tensor(4))

        FileCheck().check("prim::unchecked_cast").run(test.graph)
        self.run_pass('peephole', test.graph)
        FileCheck().check_not("prim::unchecked_cast").run(test.graph)

        # refinement not optimzied out
        def is_int_tensor(x):
            scalar = x.item()
            if isinstance(scalar, int):
                return scalar + 3
            else:
                return 8

        self.checkScript(is_int_tensor, (torch.tensor(2),))
        self.checkScript(is_int_tensor, (torch.tensor(2.5),))
        graph = torch.jit.script(is_int_tensor).graph
        self.run_pass('peephole', graph)
        FileCheck().check("prim::unchecked_cast").run(graph)

    def test_unchecked_cast(self):
        def test(cond):
            # type: (bool)
            a = torch.tensor([10])
            if cond:
                b = None
            else:
                b = a
            if b is not None:
                b[0] = 5
            return a.int()

        self.checkScript(test, (True,))
        self.checkScript(test, (False,))

    def test_trace_records_names(self):
        def foo(bar, baz):
            baz = bar + 3
            quick_brown_fox = torch.neg(baz)
            for _ in range(20):
                yeet = quick_brown_fox - 3.14
            return yeet

        traced = torch.jit.trace(foo, (torch.rand(3, 3), torch.rand(3, 3)))
        graph_str = str(traced.graph)
        assert 'bar' in graph_str
        assert 'baz' in graph_str
        assert 'quick_brown_fox' in graph_str

    def test_constant_prop_if_constant(self):
        @torch.jit.script
        def constant_prop(a, b):
            c0 = 1
            c1 = 1
            c2 = 1
            if bool(a):  # -> c0, c1
                if bool(b):  # -> c0
                    if True:  # -> c0
                        c0 = c0 + 1
                        if False:
                            c1 = c1 + 1
                            c2 = c2 + 1
            else:  # -> c0, c1
                c1 = c1 + 1

            if True:  # inlined
                c0 = c0 + 1  # dynamic
                c2 = c2 + 4  # set to 5
            return a + c0 + c1 + c2

        graph = constant_prop.graph
        self.run_pass('constant_propagation', graph)
        ifs = graph.findAllNodes("prim::If", recurse=False)
        snd_if_inlined = len(ifs) == 1
        self.assertTrue(snd_if_inlined)
        first_if = ifs[0]
        self.assertTrue(first_if.outputsSize() == 2)
        second_if = first_if.findNode("prim::If", recurse=False)
        self.assertTrue(second_if.outputsSize() == 1)
        self.assertTrue(second_if.findNode("prim::If") is None)

    def test_constant_prop_loop_constant(self):
        @torch.jit.script
        def constant_prop(cond, iter):
            # type: (bool, int) -> int
            b = 0
            while True:
                print("stays")
            for _ in range(2):
                print("stays")
            for _ in range(iter):
                print("stays")
            while cond:
                print("stays")
            while False:
                print("removed")
            for _i in range(0):
                print("removed")
            for _i in range(-4):
                print("removed")
            return b

        self.run_pass('constant_propagation', constant_prop.graph)
        graph = canonical(constant_prop.graph)
        self.assertTrue(graph.count("removed") == 0)
        self.assertTrue(graph.count("stays") == 1)  # constant gets pooled
        self.assertTrue(graph.count("prim::Print") == 4)

    def test_constant_prop_remove_output(self):
        @torch.jit.script
        def constant_prop(iter):
            # type: (int) -> None
            a = 1
            b = 1
            c = 1
            for i in range(iter):
                if False:
                    a = 10
                if i == 5:
                    b = 2
                    c = 3
            print(a, b, c)

        graph = constant_prop.graph
        self.run_pass('constant_propagation', graph)
        self.assertTrue(graph.findNode("prim::Loop").outputsSize() == 2)

    def test_trace_detach(self):
        def foo(x, w):
            return torch.matmul(x, w).detach()

        traced = torch.jit.trace(foo, (torch.rand(3, 4), torch.rand(4, 5)))

        FileCheck().check("matmul").check("detach").run(str(traced.graph))
        x, w = torch.rand(3, 4), torch.rand(4, 5, requires_grad=True)
        traced_result = traced(x, w)
        self.assertEqual(foo(x, w), traced_result)
        self.assertFalse(traced_result.requires_grad)
        self.assertIsNone(traced_result.grad_fn)

    def test_trace_detach_inplace(self):
        def foo(x, w):
            y = torch.matmul(x, w)
            y.detach_()
            return y

        traced = torch.jit.trace(foo, (torch.rand(3, 4), torch.rand(4, 5)))

        FileCheck().check("matmul").check("detach(").run(str(traced.graph))
        x, w = torch.rand(3, 4), torch.rand(4, 5)
        traced_result = traced(x, w)
        self.assertEqual(foo(x, w), traced_result)
        self.assertFalse(traced_result.requires_grad)
        self.assertIsNone(traced_result.grad_fn)

    def test_trace_detach_onnx_erase(self):
        class Mod(torch.nn.Module):
            def forward(self, x, w):
                return torch.matmul(x, w).detach()

        f = io.BytesIO()
        torch.onnx.export_to_pretty_string(
            Mod(), (torch.rand(3, 4), torch.rand(4, 5)), f)

    def test_trace_slice_full_dim(self):
        def foo(x):
            return x[0:5, 0] + 1.0

        traced = torch.jit.trace(foo, (torch.rand(5, 4),))
        test_x = torch.rand(6, 3)
        self.assertEqual(foo(test_x), traced(test_x))

    def test_export_dropout(self):
        test = torch.nn.Dropout()
        test.eval()

        traced = torch.jit.trace(test, (torch.rand(3, 4),), check_trace=False)
        imported = self.getExportImportCopy(traced)
        x = torch.randn(3, 4)
        self.assertEqual(traced(x), imported(x))

    def test_onnx_transpose_incomplete_tensor_type(self):
        # Smoke test to get us into the state where we are attempting to export
        # a transpose op, where the input is a TensorType without size information.
        # This would previously not work, since we would
        # take the size of the input and use the length of its sizes as the
        # number of dimensions in the permutation.
        class Foo(torch.jit.ScriptModule):
            @torch.jit.script_method
            def forward(self, x):
                return x.contiguous().transpose(0, 1).sum()

        class TraceMe(torch.nn.Module):
            def __init__(self):
                super(TraceMe, self).__init__()
                self.foo = Foo()

            def forward(self, x):
                return self.foo(x)

        tm = TraceMe()
        tm = torch.jit.trace(tm, torch.rand(3, 4))
        example_outputs = (tm(torch.rand(3, 4)),)
        f = io.BytesIO()
        torch.onnx._export(tm, (torch.rand(3, 4),), f, example_outputs=example_outputs)

    @unittest.skipIf(not RUN_CUDA, "requires CUDA")
    def test_cuda_export_restore(self):
        class Sub(torch.jit.ScriptModule):
            def __init__(self):
                super(Sub, self).__init__()
                self.weight = nn.Parameter(torch.randn(3, 4))

            @torch.jit.script_method
            def forward(self, thing):
                return self.weight + thing

        class M(torch.jit.ScriptModule):
            def __init__(self):
                super(M, self).__init__()
                self.mod = Sub()

            @torch.jit.script_method
            def forward(self, v):
                return self.mod(v)
        m = M()
        m.cuda()
        m2 = self.getExportImportCopy(m)
        m2.cuda()
        input = torch.rand(3, 4).cuda()
        self.assertEqual(m(input), m2(input))

    @slowTest
    def test_export_batchnorm(self):
        for mode in ['eval', 'train']:
            for clazz in [
                    torch.nn.BatchNorm1d(100),
                    torch.nn.BatchNorm1d(100, affine=False),
                    torch.nn.BatchNorm2d(100),
                    torch.nn.BatchNorm2d(100, affine=False)]:
                getattr(clazz, mode)()
                input = torch.randn(20, 100) if isinstance(clazz, torch.nn.BatchNorm1d) else \
                    torch.randn(20, 100, 35, 45)
                traced = torch.jit.trace(clazz, (input,))
                imported = self.getExportImportCopy(traced)
                x = torch.randn(20, 100) if isinstance(clazz, torch.nn.BatchNorm1d) else \
                    torch.randn(20, 100, 35, 45)
                self.assertEqual(traced(x), imported(x))

    def test_export_rnn(self):
        for clazz in [nn.RNN(10, 20, 2), nn.GRU(10, 20, 2)]:
            class RNNTest(torch.nn.Module):
                def __init__(self):
                    super(RNNTest, self).__init__()
                    self.rnn = clazz

                def forward(self, x, lengths, h0):
                    packed = torch.nn.utils.rnn.pack_padded_sequence(x, lengths)
                    out, h = self.rnn(packed, h0)
                    padded_outs, _ = torch.nn.utils.rnn.pad_packed_sequence(out)
                    return padded_outs

            test = RNNTest()

            traced = torch.jit.trace(test, (torch.randn(5, 3, 10), torch.LongTensor([3, 2, 1]), torch.randn(2, 3, 20)))
            imported = self.getExportImportCopy(traced)
            # NB: We make sure to pass in a batch with a different max sequence
            # length to ensure that the argument stashing for pad_packed works
            # properly.
            x, lengths, h0 = torch.randn(7, 4, 10), torch.LongTensor([7, 3, 2, 1]), torch.randn(2, 4, 20)
            self.assertEqual(traced(x, lengths, h0), imported(x, lengths, h0))

    def test_export_lstm(self):
        class LSTMTest(torch.nn.Module):
            def __init__(self):
                super(LSTMTest, self).__init__()
                self.rnn = nn.LSTM(10, 20, 2)

            def forward(self, x, lengths, hiddens):
                h0, c0 = hiddens
                packed = torch.nn.utils.rnn.pack_padded_sequence(x, lengths)
                out, (h, c) = self.rnn(packed, (h0, c0))
                padded_outs, _ = torch.nn.utils.rnn.pad_packed_sequence(out)
                return padded_outs

        test = LSTMTest()

        traced = torch.jit.trace(test, (torch.randn(5, 3, 10),
                                        torch.LongTensor([3, 2, 1]),
                                        (torch.randn(2, 3, 20), torch.randn(2, 3, 20))))
        imported = self.getExportImportCopy(traced)
        x, lengths, h0, c0 = \
            torch.randn(7, 3, 10), torch.LongTensor([7, 5, 2]), torch.randn(2, 3, 20), torch.randn(2, 3, 20)
        self.assertEqual(traced(x, lengths, (h0, c0)), imported(x, lengths, (h0, c0)))

    def test_unique_state_dict(self):
        class MyModule(torch.nn.Module):
            def __init__(self):
                super(MyModule, self).__init__()
                shared_param = torch.nn.Parameter(torch.ones(1))
                self.register_parameter('w1', shared_param)
                self.register_parameter('w2', shared_param)

            def forward(self, input):
                return input + self.w1 + self.w2

        model = MyModule()
        unittest.TestCase.assertEqual(
            self, len(torch.jit._unique_state_dict(model, keep_vars=False)), 1)
        unittest.TestCase.assertEqual(
            self, len(torch.jit._unique_state_dict(model, keep_vars=True)), 1)

    def test_trace_dict_input(self):
        class Bar(torch.nn.Module):
            def __init__(self):
                super(Bar, self).__init__()
                self.foo = Foo()

            def forward(self, a, b):
                return self.foo({'a': a, 'b': b})['a']

        class Foo(torch.nn.Module):
            def forward(self, x):
                return {'a': x['a'] * x['b']}

        x = (torch.rand(3), torch.rand(3))
        model = Bar()
        self.checkTrace(model, x)

    def test_trace_variable_instantiation(self):
        def random_foo(x):
            return Variable(Variable(x) + 1.0)

        random_foo_traced = torch.jit.trace(random_foo, (torch.rand(3, 4),))

        x = torch.rand(5, 6)
        self.assertEqual(random_foo(x), random_foo_traced(x))

    def test_trace_slice_expr_complete_type(self):
        def random_foo(x):
            return x + 1.0

        random_foo_traced = torch.jit.trace(random_foo, (torch.rand(3, 4),))

        @torch.jit.script
        def random_bar(x):
            return random_foo_traced(x)[0:1]

        x = torch.rand(3, 4)
        self.assertEqual(random_bar(x), (x + 1)[0:1])

    def test_export_tensoroption_to(self):
        def foo(x):
            return x[0].clone().detach().cpu() + x

        traced = torch.jit.trace(foo, (torch.rand([2])))
        example_outputs = traced(torch.rand([2]))

        f = io.BytesIO()
        torch.onnx._export_to_pretty_string(traced, (torch.rand([2]),), f,
                                            example_outputs=example_outputs)

    def test_pretty_printer(self):
        @torch.jit.script
        def if_test(a, b):
            # FIXME: use 0 instead of a.
            # c = 0
            c = a
            if bool(a < b):
                c = b
            else:
                c = a
            return c

        @torch.jit.script
        def if_one(a, b):
            c = b
            if bool(a < b):
                c = a
            return c

        @torch.jit.script
        def while_test(a, i):
            while bool(i < 3):
                a *= a
                i += 1
            return a

        @torch.jit.script
        def while_if_test(a, b):
            c = 0
            while bool(a < 10):
                a = a + 1
                b = b + 1
                if bool(a > b):
                    c = 2
                else:
                    c = 3
            return a + 1 + c

        @torch.jit.script
        def loop_use_test(y):
            x = y + 1
            z = x + 5
            while bool(y < 8):
                y += 1
                z = x
            return x, z

        @torch.jit.ignore
        def python_fn(x):
            return x + 10

        @torch.jit.script
        def python_op_name_test(y):
            return python_fn(y)

        @torch.jit.script
        def empty_int_list_test(y):
            x = torch.jit.annotate(List[int], [])
            return x[0]

        @torch.jit.script
        def empty_float_list_test(y):
            return [1.0, 2.0, 3.0]

        @torch.jit.script
        def print_weird_test(y):
            print("hi\016")

        self.assertExpected(if_test.code, "if_test")
        self.assertExpected(if_one.code, "if_one")
        self.assertExpected(while_test.code, "while_test")
        self.assertExpected(while_if_test.code, "while_if_test")
        self.assertExpected(loop_use_test.code, "loop_use_test")
        self.assertExpected(python_op_name_test.code, "python_op_name_test")
        self.assertExpected(empty_int_list_test.code, "empty_int_list_test")
        self.assertExpected(empty_float_list_test.code, "empty_float_list_test")
        self.assertExpected(print_weird_test.code, "print_weird_test")

    def test_cu_escaped_number(self):
        cu = torch.jit.CompilationUnit('''
            def foo(a):
                print("hi\016")
        ''')
        self.assertExpected(cu.foo.code)

    def test_import_method(self):
        with torch.jit._disable_emit_hooks():
            class Foo(torch.jit.ScriptModule):
                def __init__(self):
                    super(Foo, self).__init__()

                @torch.jit.script_method
                def forward(self, x, y):
                    return 2 * x + y

            foo = Foo()
            buffer = io.BytesIO()
            torch.jit.save(foo, buffer)

            buffer.seek(0)
            foo_loaded = torch.jit.load(buffer)
            self.assertExpected(foo_loaded.forward.code)

    def test_function_default_values(self):
        outer_var = torch.tensor(20)
        outer_var2 = torch.tensor(30)
        a = torch.tensor(0.5)
        b = torch.tensor(10)

        @torch.jit.script
        def simple_fn(x, a=a, b=b, c=outer_var + outer_var2):
            return x + a + b + c

        self.assertEqual(
            simple_fn(torch.ones(1)),
            torch.ones(1) + 0.5 + 10 + (20 + 30))
        self.assertEqual(
            simple_fn(torch.ones(1), torch.tensor(1), torch.tensor(3), torch.tensor(4)),
            torch.ones(1) + 1 + 3 + 4)

        outer_c = torch.tensor(9)
        outer_flag = torch.tensor(False)

        @torch.jit.script
        def bool_fn(x, a=outer_c, flag=outer_flag):
            if bool(flag):
                result = x
            else:
                result = x + a
            return result

        self.assertEqual(bool_fn(torch.ones(1)), torch.ones(1) + 9)
        self.assertEqual(
            bool_fn(torch.ones(1), torch.tensor(1), torch.tensor(True)),
            torch.ones(1))

        @torch.jit.script
        def none_fn(x=None):
            # type: (Optional[int]) -> Optional[int]
            return x

        self.assertEqual(none_fn(), None)
        self.assertEqual(none_fn(1), 1)

        @torch.jit.script
        def hints(x, a=0.5, b=10):
            # type: (Tensor, float, int) -> Tensor
            return x + a + b

        self.assertEqual(hints(torch.ones(1)), torch.ones(1) + 0.5 + 10)

        with self.assertRaisesRegex(RuntimeError, "Expected a default value"):

            @torch.jit.script
            def hints_bad_types(x, a=10, b=0.5):  # noqa: T484
                # type: (Tensor, float, int) -> Tensor
                return x + a + b
        with self.assertRaisesRegex(RuntimeError, "Expected a default value"):
            @torch.jit.script
            def bad_no_optional(x=None):
                # type: (Dict[str, int]) -> Dict[str, int]
                return x


    def test_module_default_values(self):
        four = torch.tensor(4)

        class Test(torch.jit.ScriptModule):
            def __init__(self):
                super(Test, self).__init__()

            @torch.jit.script_method
            def forward(self, input, other=four):
                return input + other

        t = Test()
        self.assertEqual(t(torch.ones(1)), torch.ones(1) + 4)

    def test_mutable_default_values(self):
        with self.assertRaisesRegex(Exception, "Mutable default parameters"):
            @torch.jit.script
            def foo(x=(1, [])):
                # type: (Tuple[int, List[Tensor]])
                return x

        class Test(torch.nn.Module):
            def forward(self, input=[]):  # noqa: B006
                return input

        with self.assertRaisesRegex(Exception, "Mutable default parameters"):
            torch.jit.script(Test())

    def test_warnings(self):
        import warnings

        def fn(x):
            if bool(x < 2):
                warnings.warn("x is less than 2")
            return x

        scripted_fn = torch.jit.script(fn)

        with warnings.catch_warnings(record=True) as warns:
            fn(torch.ones(1))

        with warnings.catch_warnings(record=True) as script_warns:
            scripted_fn(torch.ones(1))

        self.assertEqual(str(warns[0]), str(script_warns[0]))

    def test_no_erroneous_warnings(self):
        import warnings

        def fn(x):
            if bool(x > 0):
                warnings.warn('This should NOT be printed')
                x += 1
            return x

        with warnings.catch_warnings(record=True) as warns:
            fn_script = torch.jit.script(fn)
            fn_script(torch.tensor(0))
        warns = [str(w.message) for w in warns]
        self.assertEqual(len(warns), 0)

    @unittest.skipIf(IS_WINDOWS, "temp file name on windows")
    def test_trace_save(self):
        def fn(x):
            return x + 2

        def check(func):
            with tempfile.NamedTemporaryFile() as f:
                func.save(f.name)
                loaded = torch.jit.load(f.name)
                input = torch.randn(2, 2)
                self.assertEqual(func(input), loaded(input))

        out = torch.jit.trace(fn, (torch.ones(2, 2),))
        check(out)

    @unittest.skipIf(IS_WINDOWS or True, "TODO: need to fix this test case for "
                                         "Windows, re-enable with https://github.com/pytorch/pytorch/pull/29339")
    def test_torch_load_error(self):
        class J(torch.jit.ScriptModule):
            def __init__(self):
                super(J, self).__init__()

            @torch.jit.script_method
            def forward(self, input):
                return input + 100

        j = J()
        with tempfile.NamedTemporaryFile() as f:
            j.save(f.name)
            with self.assertRaisesRegex(RuntimeError, "is a zip"):
                torch.load(f.name)

    @unittest.skipIf(IS_WINDOWS, "TODO: need to fix this test case for Windows")
    def test_torch_load_zipfile_check(self):
        @torch.jit.script
        def fn(x):
            return x + 10

        with tempfile.NamedTemporaryFile() as f:
            fn.save(f.name)
            self.assertTrue(torch.serialization._is_zipfile(f))

    def test_python_bindings(self):
        lstm_cell = torch.jit.script(LSTMCellS)

        def lstm(x, hx, cx, w_ih, w_hh, b_ih, b_hh):
            for i in range(x.size(0)):
                hx, cx = lstm_cell(x[i], hx, cx, w_ih, w_hh, b_ih, b_hh)
            return hx

        slstm = torch.jit.script(lstm)

        inputs = get_lstm_inputs('cpu', training=True, seq_length=10)
        slstm(*inputs).sum().backward()
        global fw_graph
        fw_graph = slstm.graph_for(*inputs)
        nodes = [n for n in fw_graph.nodes()]
        tested_blocks = False
        for node in nodes:
            for output in [o for o in node.outputs()]:
                self.assertTrue(hasattr(output, 'type'))
                self.assertTrue(output.type() is not None)
            for input in [i for i in node.inputs()]:
                self.assertTrue(hasattr(input, 'type'))
                self.assertTrue(input.type() is not None)
            for block in [b for b in node.blocks()]:
                tested_blocks = True
                self.assertTrue(hasattr(block, 'inputs'))
                self.assertTrue(hasattr(block, 'outputs'))
                for output in [o for o in block.outputs()]:
                    self.assertTrue(hasattr(output, 'type'))
                    self.assertTrue(output.type() is not None)
                for input in [i for i in block.inputs()]:
                    self.assertTrue(hasattr(input, 'type'))
                    self.assertTrue(input.type() is not None)
                self.assertTrue(hasattr(block, 'returnNode'))
                self.assertTrue(type(block.returnNode()) == torch._C.Node)
                self.assertTrue(hasattr(block, 'paramNode'))
                self.assertTrue(type(block.paramNode()) == torch._C.Node)
        self.assertTrue(tested_blocks)

    def test_export_opnames(self):
        class Foo(torch.jit.ScriptModule):
            def __init__(self):
                super(Foo, self).__init__()

            def one(self, x, y):
                # type: (Tensor, Tensor) -> Tensor
                return x + y

            def two(self, x):
                # type: (Tensor) -> Tensor
                return 2 * x

            @torch.jit.script_method
            def forward(self, x):
                # type: (Tensor) -> Tensor
                return self.one(self.two(x), x)

        class Bar(torch.jit.ScriptModule):
            def __init__(self):
                super(Bar, self).__init__()
                self.sub = Foo()

            def forward(self, x):
                # type: (Tensor) -> Tensor
                return self.sub.forward(x)

        bar = Bar()
        ops = torch.jit.export_opnames(bar)
        expected = ['aten::add.Tensor', 'aten::mul.Scalar', 'prim::Constant']
        self.assertEqual(ops, expected)

    def test_pytorch_jit_env_off(self):
        import subprocess
        env = os.environ.copy()
        env['PYTORCH_JIT'] = '0'
        try:
            subprocess.check_output([sys.executable, '-c', 'import torch'], env=env)
        except subprocess.CalledProcessError as e:
            raise RuntimeError("Could not 'import torch' with PYTORCH_JIT=0")

    def test_print_op_module(self):
        # Issue #19351: python2 and python3 go through different paths.
        # python2 returns '<module 'torch.ops' (built-in)>'
        # python3 uses __file__ and return
        # '<module 'torch.ops' from '/scratch/ailzhang/pytorch/torch/_ops.py'>'
        s = str(torch.ops)
        self.assertRegex(s, r'ops')

    def test_serialize_qtensor(self):
        class SimpleQTensor(torch.jit.ScriptModule):
            def __init__(self, per_channel):
                super(SimpleQTensor, self).__init__()
                x = torch.rand(5, 5).float()
                if not per_channel:
                    x_q = torch.quantize_per_tensor(x, 0.2, 10, torch.quint8)
                else:
                    s = torch.rand(5, dtype=torch.float64) + 0.1
                    zp = torch.randint(5, 15, (5,))
                    x_q = torch.quantize_per_channel(x, s, zp, 1, torch.quint8)
                self.register_buffer('x', x_q)

            @torch.jit.script_method
            def forward(self):
                return self.x

        for per_channel in [False, True]:
            model = SimpleQTensor(per_channel)
            buffer = io.BytesIO()
            torch.jit.save(model, buffer)
            buffer.seek(0)
            model_loaded = torch.jit.load(buffer)
            self.assertEqual(model_loaded(), model())

class TestFrontend(JitTestCase):

    def test_instancing_error(self):
        @torch.jit.ignore
        class MyScriptClass(object):
            def unscriptable(self):
                return "a" + 200


        class TestModule(torch.nn.Module):
            def __init__(self):
                super(TestModule, self).__init__()

            def forward(self, x):
                return MyScriptClass()

        with self.assertRaises(torch.jit.frontend.FrontendError) as cm:
            torch.jit.script(TestModule())

        checker = FileCheck()
        checker.check("Cannot instantiate class")
        checker.check("def forward")
        checker.run(str(cm.exception))


class TestScript(JitTestCase):
    def test_nested_bailouts(self):
        @torch.jit.script
        def fct_loop(x):
            for i in range(3):
                x = torch.cat((x, x), 0)
            return x

        x = torch.ones(2, 3, 4, dtype=torch.float32)
        out = fct_loop(x)
        jit_trace = torch.jit.trace(fct_loop, x)
        out_trace = jit_trace(x)

    def test_loop_liveness(self):
        with enable_profiling_mode():
            @torch.jit.script
            def f(i):
                # type: (int) -> Tensor
                l = []
                for n in [2, 1]:
                    l.append(torch.zeros(n, i))

                return l[0]

            f(2)
            f(1)

    def test_bailout_loop_carried_deps_name_clash(self):
        with enable_profiling_mode():
            NUM_ITERATIONS = 10
            @torch.jit.script
            def fct_loop(z, size):
                # type: (int, int) -> Tuple[Tensor, List[int]]
                counters = torch.jit.annotate(List[int], [])
                j = 0
                y = torch.ones(2)
                for i in range(size):
                    counters.append(i + j)
                    y = torch.cat((y, torch.ones(z)), 0)
                    j = j + 1
                return y, counters

            inputs = [1, 2, 3, 4]
            expected = [x * 2 for x in range(NUM_ITERATIONS)]
            for inp in inputs:
                results = fct_loop(inp, NUM_ITERATIONS)
                self.assertEqual(results[1], expected)

    def test_bailout_loop_counter_transition(self):
        with enable_profiling_mode():
            NUM_ITERATIONS = 10
            @torch.jit.script
            def fct_loop(z, size):
                # type: (int, int) -> Tuple[Tensor, List[int]]
                counters = torch.jit.annotate(List[int], [])
                y = torch.ones(2)
                for i in range(size):
                    counters.append(i)
                    y = torch.cat((y, torch.ones(z)), 0)
                return y, counters

            inputs = [1, 2, 3, 4]
            expected = list(range(NUM_ITERATIONS))
            for inp in inputs:
                results = fct_loop(inp, NUM_ITERATIONS)
                self.assertEqual(results[1], expected)

    def test_set_attribute_through_optional(self):
        class A(torch.nn.Module):
            __annotations__ = {"x": Optional[torch.Tensor]}

            def __init__(self):
                super(A, self).__init__()
                self.x = None

            @torch.jit.ignore
            def foo(self):
                if self.x is None:
                    self.x = torch.tensor([3])
                return self.x

            def forward(self, x):
                a = self.foo()
                return x + 1

        m = torch.jit.script(A())
        self.assertEqual(m.x, None)
        m(torch.rand(1))
        self.assertEqual(m.x, torch.tensor([3]))

    def test_mutate_constant(self):
        class M(torch.jit.ScriptModule):
            __constants__ = ["foo"]

            def __init__(self, foo):
                super(M, self).__init__()
                self.foo = foo

        m = M(5)
        # m has a constant attribute, but we can't
        # assign to it
        with self.assertRaises(RuntimeError):
            m.foo = 6


    def test_class_attribute(self):
        class M(torch.jit.ScriptModule):
            FOO = 0

            def __init__(self):
                super(M, self).__init__()
                self.foo = self.FOO
        m = M()
        self.assertEqual(m.foo, M.FOO)

    def test_class_attribute_in_script(self):
        class M(torch.jit.ScriptModule):
            FOO = 0

            def __init__(self):
                super(M, self).__init__()

            @torch.jit.script_method
            def forward(self):
                return self.FOO
        with self.assertRaises(RuntimeError):
            M()

    def test_not_initialized_err(self):
        class M(torch.jit.ScriptModule):
            def __init__(self):
                self.foo = torch.rand(2, 3)
        with self.assertRaises(RuntimeError):
            M()

    def test_attribute_in_init(self):
        class M(torch.jit.ScriptModule):
            def __init__(self):
                super(M, self).__init__()
                self.foo = torch.jit.Attribute(0.1, float)
                # we should be able to use self.foo as a float here
                assert 0.0 < self.foo
        M()

    def test_scriptable_fn_as_attr(self):
        class M(torch.nn.Module):
            def __init__(self, fn):
                super(M, self).__init__()
                self.fn = fn

            def forward(self, x):
                return self.fn(x)

        m = M(F.sigmoid)
        inp = torch.rand(2, 3)
        self.checkModule(m, (inp, ))

    def test_sequence_parsing(self):
        tests = [
            ("return [x, x,]", True),
            ("return [x x]", "expected ]"),
            ("return x, x,", True),
            ("return bar(x, x,)", True),
            ("return bar()", "Argument x not provided"),
            ("for a, b, in x, x,:\n        pass", "List of iterables"),
            ("a, b, = x, x,\n    return a + b", True)
        ]
        for exp, result in tests:
            cu = torch.jit.CompilationUnit()
            full = """
def bar(x, y):
    return x + y
def foo(x):
    {}
            """.format(exp)
            if isinstance(result, str):
                with self.assertRaisesRegex(RuntimeError, result):
                    cu.define(full)
            else:
                cu.define(full)

    def test_namedtuple_python(self):
        global MyTuple, MyMod  # see [local resolution in python]
        MyTuple = namedtuple('MyTuple', ['a'])

        @torch.jit.unused
        def fn():
            # type: () -> MyTuple
            return MyTuple(1)

        # Only check compilation
        @torch.jit.script
        def fn2():
            # type: () -> MyTuple
            return fn()

        FileCheck().check("NamedTuple").run(fn2.graph)

        class MyMod(torch.nn.Module):
            def __init__(self):
                super(MyMod, self).__init__()

            @torch.jit.unused
            def fn(self):
                # type: () -> MyTuple
                return MyTuple(1)

            def forward(self, x):
                if True:
                    return MyTuple(torch.rand(2, 3))
                else:
                    return self.fn()

        # shouldn't throw a type error
        torch.jit.script(MyMod())

    def test_big_int_literals(self):
        def ok():
            # signed 64 bit max
            a = 9223372036854775807
            return a

        def toobig():
            a = 9223372036854775808
            return a

        def waytoobig():
            a = 99999999999999999999
            return a

        self.checkScript(ok, [])

        with self.assertRaisesRegex(RuntimeError, "out of range"):
            torch.jit.script(toobig)

        with self.assertRaisesRegex(RuntimeError, "out of range"):
            torch.jit.script(waytoobig)

    def test_hex_literals(self):
        def test1():
            return 0xaaaaaa

        def test2():
            return 0xaaaaaa

        def test3():
            return -0xaaaaaa

        self.checkScript(test1, [])
        self.checkScript(test2, [])
        self.checkScript(test3, [])

        def ok():
            a = 0x7FFFFFFFFFFFFFFF
            return a

        def toobig():
            a = 0xFFFFFFFFFFFFFFFF
            return a

        def waytoobig():
            a = 0xFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
            return a

        self.checkScript(ok, [])

        with self.assertRaisesRegex(RuntimeError, "out of range"):
            torch.jit.script(toobig)

        with self.assertRaisesRegex(RuntimeError, "out of range"):
            torch.jit.script(waytoobig)

    def test_big_float_literals(self):
        def ok():
            # Python interprets this as inf
            a = 1.2E400
            return a

        def check(fn):
            self.assertTrue(fn() == ok())

        # checkScript doesn't work since assertEqual doesn't consider
        # `inf` == `inf`
        check(torch.jit.script(ok))

        cu = torch.jit.CompilationUnit()
        cu.define(dedent(inspect.getsource(ok)))
        check(cu.ok)

    def test_eval_python(self):
        def _test(m):
            self.assertTrue(m(torch.ones(2, 2)))
            self.assertTrue(m.training)
            self.assertTrue(m._c.getattr('training'))

            m.eval()

            self.assertFalse(m.training)
            self.assertFalse(m._c.getattr('training'))
            self.assertFalse(m(torch.ones(2, 2)))

            if not PY2:
                buffer = io.BytesIO()
                torch.jit.save(m, buffer)
                buffer.seek(0)

                loaded = torch.jit.load(buffer)

                self.assertFalse(loaded.training)
                self.assertFalse(loaded._c.getattr('training'))

        class M(nn.Module):
            def __init__(self):
                super(M, self).__init__()

            def forward(self, x):
                return self.training

        class OldM(torch.jit.ScriptModule):
            def __init__(self):
                super(OldM, self).__init__()

            @torch.jit.script_method
            def forward(self, x):
                return self.training

        _test(torch.jit.script(M()))
        _test(OldM())

    def test_inherit_method(self):
        class A(torch.jit.ScriptModule):
            def __init__(self):
                super(A, self).__init__()

            @torch.jit.script_method
            def forward(self, x):
                return x + self.bar(x)

        class B(A):
            def __init__(self):
                super(B, self).__init__()

            @torch.jit.script_method
            def bar(self, x):
                return x * x

        with self.assertRaisesRegex(RuntimeError, 'attribute'):
            A()  # cannot use because bar is not defined

        v = torch.rand(3, 4)
        b = B()
        self.assertEqual(b(v), v + v * v)

        class C(torch.jit.ScriptModule):
            def __init__(self):
                super(C, self).__init__()

            @torch.jit.script_method
            def bar(self, x):
                return x

        class D(C, B):
            def __init__(self):
                super(D, self).__init__()

        self.assertEqual(D()(v), v + v)

    def test_first_class_module(self):
        class Foo(torch.jit.ScriptModule):
            def __init__(self):
                super(Foo, self).__init__()
                self.foo = nn.Parameter(torch.rand(3, 4))

            @torch.jit.script_method
            def forward(self, input):
                self.foo = input
                return self.foo
        foo = Foo()
        input = torch.rand(3, 4)
        foo.forward(input)
        self.assertEqual(input, foo.foo)

    @_tmp_donotuse_dont_inline_everything
    def test_first_class_calls(self):
        @torch.jit.script
        class Foo(object):
            def __init__(self, x):
                self.bar = x

            def stuff(self, x):
                return self.bar + x

        @torch.jit.script
        def foo(x):
            return x * x + Foo(x).stuff(2 * x)

        @torch.jit.script
        def bar(x):
            return foo(x) * foo(x)

        x = torch.rand(3, 4)
        self.assertEqual(bar(x), (x * x + 3 * x) * (x * x + 3 * x))

    def test_static_methods(self):
        class M(nn.Module):
            def __init__(self):
                super(M, self).__init__()

            @staticmethod
            def my_method(x):
                return x + 100

            def forward(self, x):
                return x + M.my_method(x)

        class N(nn.Module):
            def __init__(self):
                super(N, self).__init__()

            @staticmethod
            def my_method(x):
                return x * 100

            def forward(self, x):
                return x - M.my_method(x) + N.my_method(x)

        self.checkModule(M(), (torch.ones(2, 2),))

        self.checkModule(N(), (torch.ones(2, 2),))

    def test_invalid_prefix_annotation(self):
        with self.assertRaisesRegex(RuntimeError, "annotation prefix in line"):
            with self.capture_stdout() as captured:
                @torch.jit.script
                def invalid_prefix_annotation1(a):
                    #type: (Int) -> Int # noqa
                    return a + 2

        with self.assertRaisesRegex(RuntimeError, "annotation prefix in line"):
            with self.capture_stdout() as captured:
                @torch.jit.script
                def invalid_prefix_annotation2(a):
                    #type   : (Int) -> Int # noqa
                    return a + 2

        with self.assertRaisesRegex(RuntimeError, "annotation prefix in line"):
            with self.capture_stdout() as captured:
                @torch.jit.script
                def invalid_prefix_annotation3(a):
                    #     type: (Int) -> Int
                    return a + 2

    def test_builtin_function_attributes(self):
        class Add(nn.Module):
            def __init__(self):
                super(Add, self).__init__()
                self.add = torch.add

            def forward(self, input):
                return self.add(input, input)

        self.checkModule(Add(), [torch.randn(2, 2)])

    def test_unmatched_type_annotation(self):
        message1 = re.escape("Number of type annotations (2) did not match the number of function parameters (1):")
        message2 = re.escape("""
            def invalid2(a):
            ~~~~~~~~~~~~~~ <--- HERE
                # type: (Int, Int) -> Int
                return a + 2
        """.strip())
        message3 = re.escape("""
            def invalid4(a):
            ~~~~~~~~~~~~~~ <--- HERE
                # type: (Int, Int) -> Int
                return a + 2
        """.strip())
        with self.assertRaisesRegex(RuntimeError, message1):
            @torch.jit.script
            def invalid1(a):
                # type: (Int, Int) -> Int
                return a + 2

        with self.assertRaisesRegex(RuntimeError, message2):
            @torch.jit.script
            def invalid2(a):
                # type: (Int, Int) -> Int
                return a + 2

        with self.assertRaisesRegex(RuntimeError, message1):
            def invalid3(a):
                # type: (Int, Int) -> Int
                return a + 2
            torch.jit.script(invalid3)

        with self.assertRaisesRegex(RuntimeError, message3):
            def invalid4(a):
                # type: (Int, Int) -> Int
                return a + 2
            torch.jit.script(invalid4)

    def test_is_optional(self):
        ann = Union[List[int], List[float]]
        torch._jit_internal.is_optional(ann)

    def test_interpreter_fuzz(self):
        # This test generates random tree-like programs to fuzz test
        # that the interpreter does not have a bug in its stack manipulation
        # code. An assert in that code ensures individual operators are
        # not reordered.
        templates = [
            "torch.rand(3, 4)",
            "({} + {})",
            "-{}",
            "({} * {})",
            "torch.tanh({})",
            "VAR {}",
        ]

        def gen_code():
            src_lines = ['def f():']
            exprs = []
            n_variables = 0

            def get_expr(idx):
                elem = exprs[idx]
                exprs[idx] = exprs[-1]
                exprs.pop()
                return elem

            def select_expr_or_var():
                idx = random.randrange(0, len(exprs) + n_variables)
                if idx < len(exprs):
                    return get_expr(idx)
                else:
                    return 'v{}'.format(idx - len(exprs))

            for i in range(50):
                n = None
                while n is None or n > len(exprs) + n_variables:
                    template = random.choice(templates)
                    n = template.count('{}')

                if 'VAR' in template:
                    src_lines.append('  v{} = {}'.format(n_variables, select_expr_or_var()))
                    n_variables += 1
                else:
                    exprs.append(template.format(*(select_expr_or_var() for _ in range(n))))

            src_lines.append('  return ({})\n'.format(''.join('v{},'.format(i) for i in range(n_variables))))
            return '\n'.join(src_lines)

        for i in range(100):
            g = {'torch': torch}
            code = gen_code()
            torch._six.exec_(code, g, None)
            cu = torch.jit.CompilationUnit(code)
            with freeze_rng_state():
                o1 = g['f']()
            with freeze_rng_state():
                o2 = cu.f()
            self.assertEqual(o1, o2)

    def test_cpp_module_iterator(self):
        a = nn.Module()
        a.name = 'a'
        a.p = nn.Parameter(torch.rand(3, 4))
        a.foo = nn.Module()
        a.foo.name = 'foo'
        a.foo.register_buffer('b', torch.rand(1, 1))
        a.foo.bar = nn.Module()
        a.foo.bar.name = 'bar'
        a.foo.bar.an_int = 4
        a.another = nn.Module()
        a.another.name = 'another'
        sa = torch.jit.script(a)
        result = torch._C._jit_debug_module_iterators(sa._c)

        def replace(e):
            if e is a.p:
                return 'P'
            elif e is a.foo.b:
                return 'B'
            elif isinstance(e, torch._C.ScriptModule):
                return e.getattr('name')

            return e
        for k, v in result.items():
            for i in range(len(v)):
                if isinstance(v[i], tuple):
                    n, v2 = v[i]
                    v[i] = (n, replace(v2))
                else:
                    v[i] = replace(v[i])
            # module type creation is not deterministic, so we have to sort
            # the result
            v.sort()
        expected = {'buffers': [],
                    'buffers_r': ['B'],
                    'children': ['another', 'foo'],
                    'modules': ['a', 'another', 'bar', 'foo'],
                    'named_attributes': [('another', 'another'),
                                         ('foo', 'foo'),
                                         ('name', 'a'),
                                         ('p', 'P'),
                                         ('training', True)],
                    'named_attributes_r': [('another', 'another'),
                                           ('another.name', 'another'),
                                           ('another.training', True),
                                           ('foo', 'foo'),
                                           ('foo.b', 'B'),
                                           ('foo.bar', 'bar'),
                                           ('foo.bar.an_int', 4),
                                           ('foo.bar.name', 'bar'),
                                           ('foo.bar.training', True),
                                           ('foo.name', 'foo'),
                                           ('foo.training', True),
                                           ('name', 'a'),
                                           ('p', 'P'),
                                           ('training', True)],
                    'named_buffers': [],
                    'named_buffers_r': [('foo.b', 'B')],
                    'named_children': [('another', 'another'), ('foo', 'foo')],
                    'named_modules': [('', 'a'),
                                      ('another', 'another'),
                                      ('foo', 'foo'),
                                      ('foo.bar', 'bar')],
                    'named_parameters': [('p', 'P')],
                    'named_parameters_r': [('p', 'P')],
                    'parameters': ['P'],
                    'parameters_r': ['P']}
        self.assertEqual(expected, result)

    def test_tracing_hooks(self):
        class Net(nn.Module):
            def __init__(self):
                super(Net, self).__init__()

            def forward(self, x):
                return x + x

        def test_hook(is_post_hook, hook, fc):
            n = Net()
            if is_post_hook:
                n.register_forward_hook(hook)
            else:
                n.register_forward_pre_hook(hook)

            module = torch.jit.trace(n, (torch.tensor(1.0),))

            eager_input = torch.tensor(1.0)
            eager_out = n(eager_input)

            fc.run(module.forward.graph)
            input = torch.tensor(1.0)
            output = module(input)

            self.assertEqual(input, eager_input)
            self.assertEqual(output, eager_out)

        def hook_no_return(mod, input, output):
            input[0].add_(1)
            output.sub_(1)

        fc = FileCheck().check("add(").check("add_(").check("sub_(")
        test_hook(True, hook_no_return, fc)

        def hook_return(mod, input, output):
            input[0].add_(1)
            return output - 3

        fc = FileCheck().check("add(").check("add_(").check("sub(")
        test_hook(True, hook_return, fc)

        b = torch.tensor(3.0)

        def captured_hook(mod, input, output):
            return output - b

        fc = FileCheck().check("add(").check("sub(")
        test_hook(True, captured_hook, fc)

        def pre_hook_no_ret(mod, input):
            input[0].add_(3)

        fc = FileCheck().check("add_(").check("add(")
        test_hook(False, pre_hook_no_ret, fc)

        def pre_hook_ret(mod, input):
            return input[0] - 4

        fc = FileCheck().check("sub(").check("add(")
        test_hook(False, pre_hook_ret, fc)

    def test_tracing_backward_hook_error(self):
        class Net(nn.Module):
            def __init__(self):
                super(Net, self).__init__()

            def forward(self, x):
                return x + x

        n = Net()

        def backward_hook(module, grad_input, grad_output):
            pass

        n.register_backward_hook(backward_hook)
        with self.assertRaisesRegex(Exception, "backward hooks assigned"):
            torch.jit.trace(n, (torch.tensor(1.0),))

    def test_python_op_builtins(self):
        @torch.jit.unused
        def fn(x):
            # type: (List[int]) -> int
            return sum(x)

        @torch.jit.script
        def script_fn(x):
            # type: (List[int]) -> int
            return fn(x)

    def test_tracing_multiple_methods(self):
        class Net(nn.Module):
            def __init__(self):
                super(Net, self).__init__()
                self.conv = nn.Conv2d(1, 1, 3)

            def forward(self, x):
                return self.conv(x)

            def weighted_kernel_sum(self, weight):
                return weight * self.conv.weight

        example_weight = torch.rand(1, 1, 3, 3)
        example_forward_input = torch.rand(1, 1, 3, 3)
        inputs = {'forward' : example_forward_input, 'weighted_kernel_sum' : example_weight}
        n = Net()
        module = torch.jit.trace_module(n, inputs)

        check_inputs = []
        for i in range(2):
            check_weight = torch.rand(1, 1, 3, 3)
            check_forward_input = torch.rand(1, 1, 3, 3)
            check_inputs.append({'forward' : check_forward_input, 'weighted_kernel_sum' : check_weight})
        module = torch.jit.trace_module(n, inputs, True, True, check_inputs)
        self.assertTrue(module._c._has_method("forward"))
        self.assertTrue(module._c._has_method("weighted_kernel_sum"))

        module = torch.jit.trace(n.forward, example_forward_input)
        module = torch.jit.trace(n.forward, example_forward_input, True, [example_forward_input])
        with self.assertRaisesRegex(AttributeError, "trace doesn't support compiling individual module's functions"):
            module = torch.jit.trace(n.weighted_kernel_sum, inputs)

    def test_submodule_twice(self):
        @torch.jit.script
        def foo(x):
            return x * x

        class What(torch.jit.ScriptModule):
            def __init__(self, x):
                super(What, self).__init__()
                self.foo = x
        a = What(foo)
        c = What(foo)

    def test_training_param(self):
        class What(torch.jit.ScriptModule):
            def __init__(self):
                super(What, self).__init__()

            @torch.jit.script_method
            def forward(self, x):
                # type: (int) -> int
                if self.training:
                    r = x
                else:
                    r = x + 4
                # check double use of training
                if self.training:
                    r = r + 1
                return r

        w = What()
        self.assertEqual(4, w(3))
        w.train(False)
        self.assertEqual(7, w(3))
        self.assertFalse("training" in w.state_dict())

    @skipIfRocm
    @unittest.skipIf(IS_WINDOWS, "TODO: Fix this test case")
    def test_torchbind(self):
        def test_equality(f, cmp_key):
            obj1 = f()
            obj2 = torch.jit.script(f)()
            return (cmp_key(obj1), cmp_key(obj2))

        def f():
            val = torch.classes._TorchScriptTesting_Foo(5, 3)
            val.increment(1)
            return val
        test_equality(f, lambda x: x)

        with self.assertRaisesRegex(RuntimeError, "Expected a value of type 'int'"):
            val = torch.classes._TorchScriptTesting_Foo(5, 3)
            val.increment('foo')

        def f():
            ss = torch.classes._TorchScriptTesting_StackString(["asdf", "bruh"])
            return ss.pop()
        test_equality(f, lambda x: x)

        def f():
            ss1 = torch.classes._TorchScriptTesting_StackString(["asdf", "bruh"])
            ss2 = torch.classes._TorchScriptTesting_StackString(["111", "222"])
            ss1.push(ss2.pop())
            return ss1.pop() + ss2.pop()
        test_equality(f, lambda x: x)

    def test_jitter_bug(self):
        @torch.jit.script
        def fn2(input, kernel_size):
            # type: (Tensor, List[int]) -> Tensor
            if kernel_size[0] > 1:
                _stride = [2]
            else:
                _stride = kernel_size
            print(_stride, kernel_size)
            return input

        @torch.jit.script
        def fn(input):
            # type: (Tensor) -> Tensor
            return fn2(input, [1])

    def test_parser_kwargonly(self):
        cu = torch.jit.CompilationUnit('''
            def foo(x, *, y) -> Tuple[Tensor, Tensor]:
                return x, x
            def bar(x):
                return foo(x, y=x)
        ''')
        self.assertTrue('*' in str(cu.foo.schema))
        with self.assertRaisesRegex(RuntimeError, "not provided"):
            torch.jit.CompilationUnit('''
                def foo(x, *, y) -> Tuple[Tensor, Tensor]:
                    return x, x
                def bar(x):
                    return foo(x, x)
            ''')

    def test_annoying_doubles(self):
        mod = types.ModuleType("temp")
        mod.inf = float("inf")
        mod.ninf = float("-inf")
        mod.nan = float("nan")

        with torch.jit._disable_emit_hooks():
            class Foo(torch.jit.ScriptModule):
                def __init__(self):
                    super(Foo, self).__init__()

                @torch.jit.script_method
                def forward(self):
                    return math.pi, 0.1, mod.inf, mod.ninf, 2.225073858507201e-308, mod.nan

            foo = Foo()
            buffer = io.BytesIO()
            torch.jit.save(foo, buffer)

            buffer.seek(0)
            foo_loaded = torch.jit.load(buffer)

            r = foo()
            r2 = foo_loaded()
            # use precise assert, we are checking floating point details
            self.assertTrue(r[:-1] == r2[:-1])
            self.assertTrue(math.isnan(r[-1]) and math.isnan(r2[-1]))

    def test_type_annotate(self):

        def foo(a):
            return torch.jit.annotate(torch.Tensor, a)

        self.checkScript(foo, (torch.rand(3),))

        def bar():
            a = torch.jit.annotate(List[int], [])
            for _ in range(10):
                a.append(4)
            return a

        self.checkScript(bar, ())

        def baz(a):
            return torch.jit.annotate(float, a)
        self.checkScript(baz, (torch.rand(()),))

        # test annotate none types
        def annotate_none():
            return torch.jit.annotate(Optional[torch.Tensor], None)

        self.checkScript(annotate_none, ())

    def test_list_unification(self):
        def fn():
            return [1, None, 2]

        def fn2(x):
            return [torch.ones(2, 2), None, x]

        self.checkScript(fn, [])
        self.checkScript(fn2, (torch.ones(2, 2),))

        with self.assertRaisesRegex(RuntimeError, "Could not unify"):
            @torch.jit.script
            def fn():
                return [1, 1.2]

        with self.assertRaisesRegex(RuntimeError, "Could not unify"):
            @torch.jit.script
            def fn():
                return [1, torch.ones(1, 2)]

    def test_robust_op_resolution(self):
        neg = torch.add  # misleading name to make sure we resolve by function

        def stuff(x):
            return neg(x, x)

        a = (torch.rand(3),)
        self.checkScript(stuff, a)

    def test_tuple_io(self):
        def stuff(x):
            # type: (Tuple[Tensor, Tensor]) -> Tuple[Tensor, Tensor]
            a, b = x
            return b, a

        a = (torch.rand(3), torch.rand(3))
        self.checkScript(stuff, (a,))

    def test_tuple_keyword(self):
        def bar():
            f = tuple((1, 2))  # noqa: C409
            return f

        self.checkScript(bar, ())

        def foo():
            return tuple(1, 2)

        self.checkScriptRaisesRegex(foo, (), Exception,
                                    "1 argument")

        def cant_infer_size():
            return tuple([1, 2, 3])  # noqa: C409

        with self.assertRaisesRegex(Exception, "cannot statically infer the expected"):
            torch.jit.script(cant_infer_size)

    def test_tuple_create_return(self):
        def stuff2(x):
            # type: (int) -> Tuple[Tensor, Tensor]
            a = (torch.ones(x), torch.zeros(x))
            return a
        self.checkScript(stuff2, (3,))

    def test_list_io(self):
        def stuff3(x):
            # type: (List[int]) -> Tuple[Tensor, List[int]]
            return torch.ones(x), x
        self.checkScript(stuff3, ([3, 2],))

    def test_bool_list_io(self):
        @torch.jit.script
        def stuff4(x):
            # type: (List[bool]) -> Tuple[List[bool], List[bool], List[List[bool]]]
            return x, [True, False], [[True]]

        li_1, li_2, li_3 = stuff4([True])
        li_3 = li_3[0]
        for li in [li_1, li_2, li_3]:
            self.assertTrue(type(li[0]) == type(True))

    def test_nested_list(self):
        def foo(z):
            # type: (Tuple[int, List[List[int]]]) -> int
            x, y = z
            return y[0][1]
        self.checkScript(foo, ((1, [[1, 2], [3, 4]]),))

    def test_nested_list_construct(self):
        def foo():
            return [[4]] + [[4, 5]]
        self.checkScript(foo, ())

    def test_file_line_error(self):
        def foobar(xyz):
            return torch.blargh(xyz)

        _, lineno = inspect.getsourcelines(foobar)
        with self.assertRaisesRegex(RuntimeError, "test_jit.py\", line {}".format(lineno + 1)):
            scripted = torch.jit.script(foobar)

    def test_file_line_error_class_defn(self):
        class FooBar(object):
            def baz(self, xyz):
                return torch.blargh(xyz)

        _, lineno = inspect.getsourcelines(FooBar)
        with self.assertRaisesRegex(RuntimeError, "test_jit.py\", line {}".format(lineno + 2)):
            torch.jit.script(FooBar)

    def test_file_line_graph(self):
        def foobar(xyz):
            return torch.neg(xyz)

        scripted = torch.jit.script(foobar)

        _, lineno = inspect.getsourcelines(foobar)
        fc = FileCheck().check('test_jit.py:{}:19'.format(lineno + 1))
        fc.run(scripted.graph)
        fc.run(str(scripted.graph))

    def test_file_line_save_load(self):
        class Scripted(torch.jit.ScriptModule):
            @torch.jit.script_method
            def forward(self, xyz):
                return torch.neg(xyz)

        scripted = Scripted()

        # NB: not using getExportImportCopy because that takes a different
        # code path that calls CompilationUnit._import rather than
        # going through the full save/load pathway
        buffer = scripted.save_to_buffer()
        bytesio = io.BytesIO(buffer)
        scripted = torch.jit.load(bytesio)

        _, lineno = inspect.getsourcelines(Scripted)
        fc = FileCheck().check(':{}'.format(lineno + 3))
        fc.run(scripted.graph)
        fc.run(str(scripted.graph))

    def test_file_line_string(self):
        scripted = torch.jit.CompilationUnit('''
def foo(xyz):
    return torch.neg(xyz)
        ''')

        fc = FileCheck().check('<string>:3:11')
        fc.run(scripted.foo.graph)
        fc.run(str(scripted.foo.graph))

    def test_file_line_trace(self):
        def foobar(xyz):
            return torch.neg(xyz)

        scripted = torch.jit.trace(foobar, (torch.rand(3, 4)))

        _, lineno = inspect.getsourcelines(foobar)
        fc = FileCheck().check('test_jit.py:{}:0'.format(lineno + 1))
        fc.run(scripted.graph)
        fc.run(str(scripted.graph))

    def test_serialized_source_ranges(self):

        class FooTest(torch.jit.ScriptModule):
            @torch.jit.script_method
            def forward(self, x, w):
                return torch.mm(x, w.t())

        ft = FooTest()
        loaded = self.getExportImportCopy(ft)
        _, lineno = inspect.getsourcelines(FooTest)

        with self.assertRaisesRegex(RuntimeError, 'test_jit.py\", line {}'.format(lineno + 3)):
            loaded(torch.rand(3, 4), torch.rand(30, 40))

    def test_serialized_source_ranges_graph(self):

        class FooTest3(torch.jit.ScriptModule):
            @torch.jit.script_method
            def forward(self, x, w):
                return torch.mm(x, w.t())

        ft = FooTest3()
        loaded = self.getExportImportCopy(ft)
        _, lineno = inspect.getsourcelines(FooTest3)

        fc = FileCheck().check('test_jit.py:{}'.format(lineno + 3))
        fc.run(loaded.graph)

    def test_serialized_source_ranges2(self):

        class FooTest2(torch.jit.ScriptModule):
            @torch.jit.script_method
            def forward(self):
                raise RuntimeError('foo')

        _, lineno = inspect.getsourcelines(FooTest2)

        with self.assertRaisesRegex(torch.jit.Error, 'test_jit.py\", line {}'.format(lineno + 3)):
            ft = FooTest2()
            loaded = self.getExportImportCopy(ft)
            loaded()

    def test_serialized_source_ranges_dont_jitter(self):
        class FooTest3(torch.jit.ScriptModule):
            @torch.jit.script_method
            def forward(self, lim):
                first = 1
                second = 1
                i = 1
                somenum = 5
                dontmutateme = 3
                third = 0
                while bool(i < lim):
                    third = first + second
                    first = second
                    second = third
                    j = 0
                    while j < 10:
                        somenum = somenum * 2
                        j = j + 1
                    i = i + j
                    i = i + dontmutateme

                st = second + third
                fs = first + second
                return third, st, fs

        ft3 = FooTest3()

        def debug_records_from_mod(self, mod):
            buffer = io.BytesIO()
            torch.jit.save(ft3, buffer)
            buffer.seek(0)
            archive = zipfile.ZipFile(buffer)
            files = filter(lambda x: x.startswith('archive/code/'), archive.namelist())
            debug_files = list(filter(lambda f: f.endswith('.debug_pkl'), files))
            self.assertEqual(len(debug_files), 1)
            debug_file = archive.open(debug_files[0])
            return pickle.load(debug_file), buffer

        records1, buffer = debug_records_from_mod(self, ft3)

        buffer.seek(0)
        loaded = torch.jit.load(buffer)
        records2, buffer = debug_records_from_mod(self, loaded)

        buffer.seek(0)
        loaded2 = torch.jit.load(buffer)
        records3, _ = debug_records_from_mod(self, loaded2)

        self.assertEqual(records1, records2)
        self.assertEqual(records2, records3)

    def test_serialized_source_ranges_no_dups(self):
        class FooTest3(torch.jit.ScriptModule):
            @torch.jit.script_method
            def forward(self, lim):
                first = 1
                second = 1
                i = 1
                somenum = 5
                dontmutateme = 3
                third = 0
                while bool(i < lim):
                    third = first + second
                    first = second
                    second = third
                    j = 0
                    while j < 10:
                        somenum = somenum * 2
                        j = j + 1
                    i = i + j
                    i = i + dontmutateme

                st = second + third
                fs = first + second
                return third, st, fs

        ft3 = FooTest3()

        def debug_records_from_mod(mod):
            buffer = io.BytesIO()
            torch.jit.save(ft3, buffer)
            buffer.seek(0)
            archive = zipfile.ZipFile(buffer)
            files = list(filter(lambda x: x.startswith('archive/code/'), archive.namelist()))
            debug_files = filter(lambda f: f.endswith('.debug_pkl'), files)
            debug_files = map(lambda f: archive.open(f), debug_files)
            debug_files = map(lambda f: pickle.load(f), debug_files)
            return list(debug_files)

        debug_files = debug_records_from_mod(ft3)
        for debug_file in debug_files:
            for i in range(len(debug_file) - 1):
                offset, source_range = debug_file[i]
                offset2, source_range2 = debug_file[i + 1]
                self.assertNotEqual(source_range, source_range2)

    def test_circular_dependency(self):
        """
        https://github.com/pytorch/pytorch/issues/25871
        """
        class A(torch.jit.ScriptModule):
            def __init__(self):
                super(A, self).__init__()

            @torch.jit.script_method
            def forward(self, x):
                return x

        class B(torch.jit.ScriptModule):
            def __init__(self):
                super(B, self).__init__()
                self.foo = torch.nn.ModuleList([A()])

            @torch.jit.script_method
            def forward(self, x):
                for f in self.foo:
                    x = f(x)
                return x

        class C(torch.jit.ScriptModule):
            def __init__(self):
                super(C, self).__init__()
                self.foo = torch.nn.Sequential(B())

            @torch.jit.script_method
            def forward(self, x):
                for f in self.foo:
                    x = f(x)
                return x
        self.getExportImportCopy(C())


    def test_tensor_shape(self):
        x = torch.empty(34, 56, 78)

        def f(x):
            return x.shape

        self.checkScript(f, (x,))

    def test_tensor_grad(self):
        x = torch.randn(3, 4, requires_grad=True)
        y = torch.randn(3, 4, requires_grad=False)

        def f_requires_grad(x):
            return x.requires_grad

        self.checkScript(f_requires_grad, (x,))
        self.checkScript(f_requires_grad, (y,))

        def f_grad(x):
            return x.grad

        x.sum().backward()
        self.checkScript(f_grad, (x,))
        self.checkScript(f_grad, (y,))

    def test_tensor_data(self):
        x = torch.randn(3, 4, requires_grad=True)
        y = torch.randn(4, 5)

        def f_data(x):
            return x.data

        scripted_f_data = torch.jit.script(f_data)

        scripted_x = scripted_f_data(x)
        self.assertEqual(scripted_x, f_data(x))
        self.assertEqual(scripted_x.requires_grad, False)

        scripted_y = scripted_f_data(y)
        self.assertEqual(scripted_y, f_data(y))
        self.assertEqual(scripted_x.requires_grad, False)


    def test_tensor_dtype(self):
        x_byte = torch.empty(34, 56, 78, dtype=torch.uint8)
        x_long = torch.empty(34, 56, 78, dtype=torch.long)
        x_float32 = torch.empty(34, 56, 78, dtype=torch.float32)

        @torch.jit.script
        def byte(x):
            return x.dtype == torch.uint8

        @torch.jit.script
        def long(x):
            return x.dtype == torch.long

        @torch.jit.script
        def float32(x):
            return x.dtype == torch.float32

        self.assertTrue(byte(x_byte))
        self.assertFalse(byte(x_long))
        self.assertFalse(byte(x_float32))
        self.assertFalse(long(x_byte))
        self.assertTrue(long(x_long))
        self.assertFalse(long(x_float32))
        self.assertFalse(float32(x_byte))
        self.assertFalse(float32(x_long))
        self.assertTrue(float32(x_float32))

    @unittest.skipIf(not RUN_CUDA, "device tests require CUDA")
    def test_tensor_device(self):
        cpu = torch.empty(34, 56, 78, device='cpu')
        gpu = torch.empty(34, 56, 78, device='cuda')

        @torch.jit.script
        def same_device(x, y):
            return x.device == y.device

        self.assertTrue(same_device(cpu, cpu))
        self.assertTrue(same_device(gpu, gpu))
        self.assertFalse(same_device(cpu, gpu))

    @unittest.skipIf(not RUN_CUDA, "device tests require CUDA")
    def test_tensor_to_device(self):
        def to_device(x):
            return x.to(device="cuda").to(device=torch.device("cpu"))

        self.checkScript(to_device, (torch.ones(3, 4),))

    def test_tensor_to_cpu(self):
        def to_cpu(x):
            return x.cpu()

        x = torch.ones(3, 4)
        script_fn = torch.jit.script(to_cpu)
        self.assertEqual(to_cpu(x).device, script_fn(x).device)
        self.checkScript(to_cpu, (x,))

    @unittest.skipIf(not RUN_CUDA, "device tests require CUDA")
    def test_tensor_to_cuda(self):
        def to_cuda(x):
            return x.cuda()

        x = torch.ones(3, 4)
        script_fn = torch.jit.script(to_cuda)
        self.assertEqual(to_cuda(x).device, script_fn(x).device)
        self.checkScript(to_cuda, (x,))

    def test_generic_list_errors(self):
        with self.assertRaisesRegex(RuntimeError, "previously matched to type"):
            @torch.jit.script
            def foo(x):
                return [[x]] + [[1]]

    def test_script_cu(self):
        cu = torch.jit.CompilationUnit('''
            def foo(a):
                b = a
                return b
        ''')
        a = Variable(torch.rand(1))
        self.assertEqual(a, cu.foo(a))

    # because the compilation unit ingests python strings
    # to use an escape sequence escape the backslash (\\n = \n)
    def test_string_cu(self):
        cu = torch.jit.CompilationUnit('''
            def foo(a):
                print(a, """a\\n\tb\\n""", 2, "a\
a")
                return a
        ''')
        FileCheck().check("aa").check("a\\n\\tb\\n").run(str(cu.foo.graph))

    def test_function_compilation_caching(self):
        def fun():
            return 1 + 2

        fun_compiled = torch.jit.script(fun)
        # python wrapper around the script function is a different pointer,
        # but the underlying script function graph is the same
        self.assertIs(fun_compiled.graph, torch.jit.script(fun).graph)

        def fun():
            return 3 + 4

        num_ref_counts = sys.getrefcount(fun)

        # caching doesn't get tripped up by same qualname
        fun_compiled_2 = torch.jit.script(fun)
        self.assertIsNot(fun_compiled, fun_compiled_2)
        self.assertEqual(fun_compiled_2(), 7)

        # caching doesnt increase refcounts to function (holds weak reference)
        self.assertTrue(sys.getrefcount(fun), num_ref_counts)

    def test_string_ops(self):
        def foo():
            a = "a" + "b"
            return a + a, "ab" == "b", "ab" != "b", "ab" == "ab", "ab" != "ab"

        self.checkScript(foo, ())

    def test_string_new_line(self):
        with self.assertRaisesRegex(RuntimeError, "expected a valid token*"):
            torch.jit.CompilationUnit('''
            def test_while(a):
                print("
                    a")
                return a
            ''')

    def test_string_single_escape(self):
        with self.assertRaisesRegex(RuntimeError, "expected a valid token*"):
            torch.jit.CompilationUnit('''
            def test_while(a):
                print("\\")
                return a
            ''')

    def test_script_annotation(self):
        @torch.jit.script
        def foo(a):
            return a + a + a
        s = Variable(torch.rand(2))
        self.assertEqual(s + s + s, foo(s))

    def test_inf(self):
        @torch.jit.script
        def foo(a):
            return a < float('inf')
        s = torch.rand(1)
        self.assertTrue(foo(s))

        @torch.jit.script
        def bar(a):
            return a > float('-inf')
        s = torch.rand(1)
        self.assertTrue(foo(s))

        # test re-assignment on imported source
        str = """
        def foo(x):
            # type: (bool)
            a = float("-inf")
            if not x:
                a = float(torch.tensor([5]))
            return a < 4
        """
        cu = torch.jit.CompilationUnit(str)
        self.assertTrue(cu.foo(True))
        self.assertFalse(cu.foo(False))

    def test_add(self):
        def func(a, b):
            c = a + b
            c += a
            return c

        a = torch.rand(1, requires_grad=True)
        b = torch.rand(1, requires_grad=True)
        self.checkScript(func, (a, b), optimize=True)

    def test_trace_optioanl_dtype(self):
        class Test(torch.nn.Module):
            def forward(self):
                return torch.arange(5)

        traced = torch.jit.trace(Test(), ())
        torch.allclose(traced(), Test()())

    def test_trace_save_load_copy(self):
        class Test(torch.nn.Module):
            def __init__(self):
                super(Test, self).__init__()
                self.conv = torch.nn.Conv2d(3, 3, 3)

            def forward(self, x):
                return self.conv(x)

        traced = torch.jit.trace(Test(), torch.rand(1, 3, 224, 224))
        buffer = io.BytesIO()
        torch.jit.save(traced, buffer)
        buffer.seek(0)
        loaded = torch.jit.load(buffer)
        # should work
        loaded.copy()

    def test_mul(self):
        def func(a, b):
            return a * b

        a = torch.rand(1, requires_grad=True)
        b = torch.rand(1, requires_grad=True)
        self.checkScript(func, (a, b), optimize=True)

    @unittest.skipIf(not PY35, "Python 3.5 needed")
    def test_matmul_py3(self):
        code = dedent("""
        def fn(a, b):
            return a @ b
        """)

        with tempfile.TemporaryDirectory() as tmp_dir:
            script_path = os.path.join(tmp_dir, 'script.py')
            with open(script_path, 'w') as f:
                f.write(code)
            fn = get_fn('test_matmul_py3', script_path)

            a = torch.rand(4, 3, requires_grad=True)
            b = torch.rand(3, 2, requires_grad=True)
            self.checkScript(fn, (a, b), optimize=True)

    def test_pow(self):
        def func(a, b):
            return a ** b

        def func2(a, b, c, d):
            return c + a ** b ** d

        def func3(a, b):
            # type: (int, float) -> float
            return a ** b

        def func4():
            # type: () -> float
            return 2 ** -2

        def func5(x, y):
            return x.item() ** y.item()

        a = torch.rand(1, requires_grad=True)
        b = torch.rand(1, requires_grad=True)
        c = torch.rand(1, requires_grad=True)
        d = torch.rand(1, requires_grad=True)
        self.checkScript(func, (a, b), optimize=True)
        self.checkScript(func2, (a, b, c, d), optimize=True)
        self.checkScript(func3, (4, -0.5), optimize=True)
        self.checkScript(func4, ())

        inputs = [torch.tensor(2), torch.tensor(-2), torch.tensor(.5), torch.tensor(.2)]
        for x in inputs:
            for y in inputs:
                if x < 0:
                    continue
                else:
                    self.checkScript(func5, (x, y))

    @unittest.skipIf(not RUN_CUDA, "device tests require CUDA")
    def test_pow_scalar_backward_cuda(self):
        # see that scalar exponent works with cuda base (#19253)
        with enable_profiling_mode():
            for dtype in [torch.float, torch.double]:
                @torch.jit.script
                def func(a, b):
                    # type: (Tensor, float) -> Tensor
                    return (a * 2) ** b

                a = torch.rand(1, requires_grad=True, device='cuda', dtype=dtype)
                func(a, 1, profile_and_replay=True).backward()

                @torch.jit.script
                def func(a, b):
                    # type: (float, Tensor) -> Tensor
                    return a ** (b * 2 + 1)  # noqa T484

                a = torch.rand(1, requires_grad=True, device='cuda', dtype=dtype)
                func(2, a, profile_and_replay=True).backward()

    def test_triple(self):
        def func(x):
            return 3. * x

        x = torch.rand(1, dtype=torch.float, requires_grad=True)
        self.checkScript(func, [x], optimize=True)

    def test_slice(self):
        def func(x):
            return x[:5]

        x = torch.rand(10, dtype=torch.float, requires_grad=True)
        self.checkScript(func, [x], optimize=True)

        def func2(x):
            return x[5:]

        self.checkScript(func2, [x], optimize=True)

        def func3(x):
            return x[:8:2]

        self.checkScript(func3, [x], optimize=True)

        def func4(x):
            return x[1::4]

        self.checkScript(func4, [x], optimize=True)

    def test_gather(self):
        def func(x):
            return x[0]

        x = torch.rand(10, dtype=torch.float, requires_grad=True)
        self.checkScript(func, [x], optimize=True)

    def test_random(self):
        @torch.jit.script
        def f(mean, std):
            return torch.normal(mean, std)

        mean, std = torch.zeros(5, 5), torch.ones(5, 5)
        with torch.random.fork_rng(devices=[]):
            output = torch.normal(mean, std)
        with torch.random.fork_rng(devices=[]):
            script_output = f(mean, std)
        self.assertEqual(output, script_output)

    def _check_code(self, code_str, fn_name, inputs):
        scope = {}
        exec(code_str, globals(), scope)
        cu = torch.jit.CompilationUnit(code_str)
        self.assertEqual(cu.func(*inputs), scope[fn_name](*inputs))

    @unittest.skipIf(not RUN_CUDA, 'no CUDA')
    def test_scriptmodule_releases_tensors_cuda(self):
        with enable_profiling_mode():
            @torch.jit.script
            def fn(x, y):
                return x.sigmoid() * y.tanh()

            def test(backward=False):
                x = torch.randn(3, 3, dtype=torch.double, device='cuda', requires_grad=True)
                y = torch.randn(3, 3, dtype=torch.double, device='cuda', requires_grad=True)
                out = fn(x, y, profile_and_replay=True)
                if backward:
                    out.sum().backward()

            with self.assertLeaksNoCudaTensors():
                test()
                test()
                test()

            if GRAPH_EXECUTOR != ProfilingMode.SIMPLE:
                with self.assertLeaksNoCudaTensors():
                    test(backward=True)
                    test(backward=True)
                    test(backward=True)

    def test_index(self):
        def consec(size, start=0):
            numel = torch.tensor(size).prod().item()
            return torch.arange(numel).view(size)

        def check_indexing(indexing, tensor):
            template = dedent("""
            def func(x):
                return x{}
            """)

            self._check_code(template.format(indexing), "func", [tensor])

        def check_dynamic_indexing(indexing, tensor, value1, value2):
            value1 = torch.tensor(value1)
            value2 = torch.tensor(value2)

            template = dedent("""
            def func(x, value1, value2):
                i = int(value1)
                j = int(value2)
                return x{}
            """)

            self._check_code(template.format(indexing), "func", [tensor, value1, value2])

        # basic slices
        check_indexing('[0]', consec((3, 3)))
        check_indexing('[1]', consec((3, 3), 10))
        check_indexing('[2]', consec((3, 3), 19))
        check_indexing('[2]', consec((3,)))
        check_indexing('[-1]', consec((3, 3), 19))
        check_indexing('[0:2]', consec((3, 3, 3)))
        check_indexing('[1:-1]', consec((3, 3, 3)))
        check_indexing('[-3:-1]', consec((6, 3)))
        check_indexing('[1:]', consec((3, 3)))
        check_indexing('[:1]', consec((3, 3)))
        check_indexing('[:]', consec((3, 2)))

        # multi-dim: indexes
        check_indexing('[0, 1]', consec((3, 3)))
        check_indexing('[0, 1]', consec((3, 3, 2)))
        check_indexing('[1, 0, 2]', consec((3, 3, 3)))
        check_indexing('[2, -1]', consec((3, 3)))

        # multi-dim: mixed slicing and indexing
        check_indexing('[0, 1:2]', consec((3, 3)))
        check_indexing('[0, :1]', consec((3, 3, 2)))
        check_indexing('[1, 2:]', consec((3, 3, 3)))
        check_indexing('[-1, 1:, 0]', consec((3, 3, 3, 3)))
        check_indexing('[1:, -1, 0]', consec((3, 3, 3, 3)))
        check_indexing('[-1, 2:, 1:2]', consec((3, 3, 3, 3)))
        check_indexing('[-1, 1:, 0]', consec((3, 3, 3, 3)))
        check_indexing('[-1, :, 0, 2]', consec((3, 3, 3, 3)))

        # zero-sized slices
        check_indexing('[0:0]', consec((2, 2)))
        check_indexing('[0:0, 1]', consec((3, 3)))

        # trivial expression usage
        check_indexing('[1+1]', consec((3, 3)))
        check_indexing('[1:(0 + 2)]', consec((3, 3, 3)))

        # None for new dimensions
        check_indexing('[None, 0]', consec((3, 3)))
        check_indexing('[1, None]', consec((3, 3), 10))
        check_indexing('[None, None, 2]', consec((3, 3), 19))
        check_indexing('[None, 2, None]', consec((3,)))
        check_indexing('[0:2, None]', consec((3, 3, 3)))
        check_indexing('[None, 1:-1]', consec((3, 3, 3)))
        check_indexing('[None, -3:-1, None]', consec((6, 3)))
        check_indexing('[-1, None, 2:, None, 1:2]', consec((3, 3, 3, 3)))
        check_indexing('[None, -1, None, 2:, None, 1:2, None]', consec((3, 3, 3, 3)))

        # dynamic expression usage
        check_dynamic_indexing("[i + j]", consec((3, 3)), 0, 1)
        check_dynamic_indexing("[i:j, i]", consec((3, 3, 2)), 0, 2)

    def test_index_ellipses(self):
        vals = [":", 1, None]
        for _ in range(100):
            indices = [random.choice(vals) for _ in range(4)]
            indices[random.randint(0, len(indices) - 1)] = "..."
            test_str = dedent("""
            def f():
                x = torch.ones(10, 9, 8, 7, 6)
                return x{indices}.shape
            """.format(indices=indices))
            test_str = test_str.replace(r"'", r'')
            scope = {}
            execWrapper(test_str, globals(), scope)
            cu = torch.jit.CompilationUnit(test_str)
            res1 = cu.f()
            res2 = scope['f']()
            self.assertEqual(res1, res2)


    def test_tensor_item(self):
        def test_scalar_cast(x):
            scalar = x.item()
            return int(scalar), float(scalar)

        graph = torch.jit.script(test_scalar_cast).graph
        FileCheck().check("(int, float) = prim::TupleConstruct").run(graph)
        self.checkScript(test_scalar_cast, (torch.tensor(1.0),))
        self.checkScript(test_scalar_cast, (torch.tensor(1),))

    def test_method_on_number(self):
        def func():
            c = 1
            return c.add(1)
        with self.assertRaisesRegex(RuntimeError, 'nonexistent attribute or method'):
            torch.jit.script(func)

    # testing implicit conversion of tensors to scalars to match function arguments
    def test_scalar_to_num_conversions(self):
        @torch.jit.script
        def multiple_defs(x):
            c = 1
            x = x + c
            return x

        self.assertTrue("ImplicitTensorToNum" not in str(multiple_defs.graph))

        @torch.jit.script
        def tensor_to_int_script(x, tensor):
            return x.unsqueeze(tensor)

        def tensor_to_int(x, tensor):
            return x.unsqueeze(tensor)

        @torch.jit.script
        def tensor_to_float_script(x, tensor):
            return x.addcmul(tensor, tensor, value=tensor)

        def tensor_to_float(x, tensor):
            return x.addcmul(tensor, tensor, value=tensor)

        x = torch.zeros(10)
        # float tensor, float tensor with grad, int tensor (can't set grad on int tensor)
        tensors = [torch.tensor(1.1),
                   torch.tensor(1.1, requires_grad=True),
                   torch.tensor(0),
                   torch.tensor([2])]

        script_funs = [tensor_to_int_script, tensor_to_float_script]
        funs = [tensor_to_int, tensor_to_float]

        # return the result, or whether exception was thrown
        def test_func(func, x, tensor):
            try:
                result = func(x, tensor)
            except RuntimeError as e:
                result = True
            except TypeError as e:
                result = True
            return result

        # assert result or exception equal for each (function, inputs)
        for tensor in tensors:
            for i in range(len(script_funs)):
                self.assertEqual(test_func(script_funs[i], x, tensor), test_func(funs[i], x, tensor))

    def test_module_copy_with_attributes(self):
        class Vocabulary(torch.jit.ScriptModule):
            def __init__(self, vocab_list):
                super(Vocabulary, self).__init__()
                self._vocab = torch.jit.Attribute(vocab_list, List[str])
                self.some_idx = torch.jit.Attribute(2, int)
                self.idx = torch.jit.Attribute(
                    {word: i for i, word in enumerate(vocab_list)}, Dict[str, int]
                )

            @torch.jit.script_method
            def lookup_indices_1d(self, values):
                # type: (List[str]) -> List[int]
                result = torch.jit.annotate(List[int], [])
                # Direct list iteration not supported
                for i in range(len(values)):
                    value = values[i]
                    result.append(self.idx.get(value, self.some_idx))
                return result

            @torch.jit.script_method
            def forward(self, values):
                # type: (List[List[str]]) -> List[List[int]]
                result = torch.jit.annotate(List[List[int]], [])
                # Direct list iteration not supported
                for i in range(len(values)):
                    result.append(self.lookup_indices_1d(values[i]))
                return result

        v = Vocabulary(list('uabcdefg'))
        v.copy()

    def test_tuple_to_opt_list(self):
        @torch.jit.script
        def foo(x):
            # type: (Optional[List[int]]) -> int
            return 1

        @torch.jit.script
        def tuple_call():
            return foo((1, 2))

    def test_advancedindex(self):
        def consec(size, start=0):
            numel = torch.tensor(size).prod().item()
            return torch.arange(numel).view(size)

        def check_indexing(indexing, tensor, **kwargs):
            indices_dict = kwargs

            template = dedent("""
            def func(x{formals}):
                return x{expr}
            """)

            formals = []
            values = []
            for formal, value in indices_dict.items():
                formals.append(formal)
                values.append(value)

            formals = ''.join(map(', {}'.format, formals))
            inputs = [tensor] + values
            self._check_code(template.format(formals=formals, expr=indexing),
                             "func", inputs)

        # Indexing with tensor (basic)
        check_indexing('[i]', consec((3, 3)), i=torch.tensor([0]))
        check_indexing('[i]', consec((3, 3)), i=torch.tensor(1))
        check_indexing('[i]', consec((3, 3)), i=torch.tensor([-2]))
        check_indexing('[i]', consec((3, 3), 2), i=torch.tensor([0, 0]))
        check_indexing('[i]', consec((3, 3, 2, 2)), i=torch.tensor([0, -2, 1]))

        # NB: indexing with tensors and indexing with sequences can be implemented
        # in a very similar way (sequences are converted to tensors), so only one
        # case needs to be tested extensively.
        # XXX: When we can index with sequences, replace these cases with
        # sequence indexing expressions; those are much easier to read.

        # Misc sequence advanced indexing
        inp = consec((4, 8, 5))
        to_check = [
            # [[0, 2], [1, 3]]
            ['[i, j]', {'i': [0, 2], 'j': [1, 3]}],
            # [[0, 2], [1, 3], [1, 1]]
            ['[i, j, k]', {'i': [0, 2], 'j': [1, 3], 'k': [1, 1]}],
            # [[0, 2], 1, [1, 1]]
            ['[i, j, k]', {'i': [0, 2], 'j': 1, 'k': [1, 1]}],
            # [:, :, [0, 3, 4]]
            ['[:, :, i]', {'i': [0, 3, 4]}],
            # [:, [2, 4, 5, 7], 2:4]
            ['[:, i, 2:4]', {'i': [0, 2, 3]}],
            # [[2, 3], :, :]
            ['[i, :, :]', {'i': [2, 3]}],
            # [:, [0, 2, 3], [1, 3, 4]]
            ['[:, i, j]', {'i': [0, 2, 3], 'j': [1, 3, 4]}],
            # [:, [0], [1, 2, 4]]
            ['[:, i, j]', {'i': [0], 'j': [1, 2, 4]}],
            # [:, [0, 1, 3], [4]]
            ['[:, i, j]', {'i': [0, 1, 3], 'j': [4]}],
            # [:, [[0, 1], [1, 0]], [[2, 3]]]
            ['[:, i, j]', {'i': [[0, 1], [1, 0]], 'j': [[2, 3]]}],
            # [:, [[0, 1], [2, 3]], [[0]]]
            ['[:, i, j]', {'i': [[0, 1], [2, 3]], 'j': [[0]]}],
            # [:, [[5, 6]], [[0, 3], [4, 4]]]
            ['[:, i, j]', {'i': [[5, 6]], 'j': [[0, 3], [4, 4]]}],
            # [[0, 2, 3], [1, 3, 4], :]
            ['[i, j, :]', {'i': [0, 2, 3], 'j': [1, 3, 4]}],
            # [0, [1, 2, 4], :]
            ['[i, j, :]', {'i': 0, 'j': [1, 2, 4]}],
            # [[0, 1, 3], 4, :]
            ['[i, j, :]', {'i': [0, 1, 3], 'j': 4}],
            # [[[0, 1], [1, 0]], [[2, 1], [3, 5]], :]
            ['[i, j, :]', {'i': [[0, 1], [1, 0]], 'j': [[2, 1], [3, 5]]}],
            # [[[0, 1], [1, 0]], [[2, 3]], :]
            ['[i, j, :]', {'i': [[0, 1], [1, 0]], 'j': [[2, 3]]}],
            # [[[0, 1], [2, 3]], [[0]], :]
            ['[i, j, :]', {'i': [[0, 1], [2, 3]], 'j': [[0]]}],
            # [[[2, 1]], [[0, 3], [4, 4]], :]
            ['[i, j, :]', {'i': [[2, 1]], 'j': [[0, 3], [4, 4]]}],
            # [[[2]], [[0, 3], [4, 1]], 0:2]
            ['[i, j, 0:2]', {'i': [[2]], 'j': [[0, 3], [4, 1]]}],
        ]

        for expr, argdict in to_check:
            tensordict = {k: torch.tensor(v) for (k, v) in argdict.items()}
            check_indexing(expr, inp, **tensordict)

    def test_keyword(self):
        @torch.jit.script
        def func(x):
            return torch.sum(x, dim=0)

        x = torch.rand(10, dtype=torch.float, requires_grad=True)
        y = func(x)
        y2 = torch.sum(x, dim=0)
        self.assertEqual(y, y2)

    def test_constant_pooling_none(self):
        @torch.jit.script
        def typed_nones(a=None, b=None, c=None):
            # type: (Optional[int], Optional[bool], Optional[Tensor]) -> Tuple[Optional[int], Optional[bool], Optional[Tensor]] # noqa
            return a, b, c

        @torch.jit.script
        def test(a):
            # type: (bool) -> None
            if a:
                print(typed_nones())
            else:
                print(typed_nones())

        graph_str = str(test.graph)
        self.assertTrue(graph_str.count("None = prim::Constant") == 1)

    def test_constant_pooling_same_identity(self):
        def foo():
            a = torch.tensor([4])
            b = (a,)
            index = len(a) - 1
            c = b[index]
            d = b[index]
            return c, d

        foo_script = torch.jit.script(foo)
        self.run_pass('constant_propagation', foo_script.graph)
        self.run_pass('constant_pooling', foo_script.graph)
        # even though the c & d escape scope, we are still able
        # pool them into one constant because they are the same object
        FileCheck().check_count("prim::Constant", 1, exactly=True).run(foo_script.graph)
        self.assertEqual(foo(), foo_script())

    def test_constant_pooling_introduce_aliasing(self):
        @torch.jit.script
        def foo():
            a = torch.tensor(1)
            b = torch.tensor(2)
            return a, b

        self.run_pass('constant_propagation', foo.graph)
        self.run_pass('constant_pooling', foo.graph)
        # dont pool constants bc it would introduce observable alias relationship changing
        FileCheck().check_count("prim::Constant", 2, exactly=True).run(foo.graph)

    def test_literal(self):
        def func1(a, b):
            c = a, b
            d, e = c
            return d + e

        def func2(a, b):
            c = a, (a, b)
            d, e = c
            f, g = e
            return d + f + g

        def func3(a, b):
            # type: (float, float) -> float
            c = 0., (0., 0.)
            x = True
            while x:
                x = False
                c = a, (a, b)
            d, e = c
            f, g = e
            return d + f + g

        a = torch.rand(1, requires_grad=True)
        b = torch.rand(1, requires_grad=True)
        self.checkScript(func1, (a, b), optimize=True)
        self.checkScript(func2, (a, b), optimize=True)
        self.checkScript(func3, (a.item(), b.item()), optimize=True)

    def test_expand(self):
        @torch.jit.script
        def func(x, y):
            return x + y

        x = torch.rand(2, 3, dtype=torch.float, requires_grad=True)
        y = torch.rand(3, dtype=torch.float, requires_grad=True)
        out = func(x, y)
        self.assertEqual(func(x, y), x + y)

        grad = torch.randn(2, 3, dtype=torch.float)
        out.backward(grad)
        self.assertEqual(x.grad, grad)
        self.assertEqual(y.grad, grad.sum(dim=0))

    def test_sum(self):
        @torch.jit.script
        def func(x):
            return x.sum(dim=[4])

        @torch.jit.script
        def func2(x):
            return x.sum(dim=4)

        # test that shape analysis is written correctly for sum with IntArrayRef[1] dim argument
        self.run_pass('constant_propagation', func.graph)
        self.run_pass('constant_propagation', func2.graph)
        g = _propagate_shapes(func.graph, (torch.zeros(1, 1, 1, 1, 4),), False)
        g2 = _propagate_shapes(func2.graph, (torch.zeros(1, 1, 1, 1, 4),), False)

    def test_cat(self):
        with enable_profiling_mode():
            @torch.jit.script
            def func(x):
                return torch.cat((x, x), dim=0)

            x = torch.rand(10, dtype=torch.float, requires_grad=True)
            self.assertEqual(func(x, profile_and_replay=True), torch.cat((x, x), dim=0))

            @torch.jit.script
            def func2(x, y):
                return torch.cat((x, x), y)

            with disable_autodiff_subgraph_inlining():
                x = torch.rand([2, 2]).requires_grad_()
                y = torch.tensor(1)

                output = func2(x, y, profile_and_replay=True)
                output_ref = torch.cat((x, x), y)
                self.assertEqual(output, output_ref)

                if GRAPH_EXECUTOR != ProfilingMode.SIMPLE:
                    self.assertAutodiffNode(func2.graph_for(x, y), True, ['aten::cat'], [])

                    grad = torch.autograd.grad(output.sum(), x)
                    grad_ref = torch.autograd.grad(output_ref.sum(), x)
                    self.assertEqual(grad, grad_ref)

    def test_cat_lifts(self):
        @torch.jit.script
        def foo(x):
            return torch.cat([x, x], dim=1)

        @torch.jit.script
        def foo2(x):
            return torch.cat([], dim=1)

        @torch.jit.script
        def foo3(x):
            return torch.cat([x], dim=1)

        for g in [foo.graph, foo2.graph, foo3.graph]:
            FileCheck().check("int =").check("ListConstruct").check("aten::cat").run(str(g))

    @unittest.skipIf(PY2, "Requires python 3")
    def test_stack(self):
        with enable_profiling_mode():
            @torch.jit.script
            def func(x):
                return torch.stack((x, x), dim=1)
            x = torch.rand(10, 10)
            self.assertEqual(func(x, profile_and_replay=True), torch.stack((x, x), dim=1))

            @torch.jit.script
            def func2(x, y):
                return torch.stack((x, y), dim=0)

            with disable_autodiff_subgraph_inlining():
                x = torch.randn([2, 2]).requires_grad_()
                y = torch.randn([2, 2]).requires_grad_()

                output = func2(x, y, profile_and_replay=True)
                output_ref = torch.stack((x, y), 0)
                self.assertEqual(output, output_ref)
                if GRAPH_EXECUTOR != ProfilingMode.SIMPLE:
                    self.assertAutodiffNode(func2.graph_for(x, y), True, ['aten::stack'], [])

                    grads = torch.autograd.grad(output.sum(), (x, y))
                    grads_ref = torch.autograd.grad(output_ref.sum(), (x, y))
                    self.assertEqual(grads, grads_ref)

    def test_unbind(self):
        with enable_profiling_mode():
            @torch.jit.script
            def func(x, y):
                # type: (Tensor, int) -> List[Tensor]
                return torch.unbind(x, y)  # noqa T484

            with disable_autodiff_subgraph_inlining():
                x = torch.rand([2, 2]).requires_grad_()
                y = 0
                outputs = func(x, y, profile_and_replay=True)
                outputs_ref = torch.unbind(x, dim=y)
                self.assertEqual(outputs, outputs_ref)

                if GRAPH_EXECUTOR != ProfilingMode.SIMPLE:
                    self.assertAutodiffNode(func.graph_for(x, y), True, ['aten::unbind'], [])

                    grad = torch.autograd.grad(_sum_of_list(outputs), x)
                    grad_ref = torch.autograd.grad(_sum_of_list(outputs_ref), x)
                    self.assertEqual(grad, grad_ref)


    @unittest.skipIf(GRAPH_EXECUTOR == ProfilingMode.PROFILING,
                     "Profiling executor fails to recognize that tensors in a list require gradients")
    def test_meshgrid(self):
        with enable_profiling_mode():
            @torch.jit.script
            def func(a):
                # type: (List[Tensor]) -> List[Tensor]
                return torch.meshgrid(a)  # noqa T484
            with disable_autodiff_subgraph_inlining():
                a = torch.tensor([1.0, 2, 3]).requires_grad_()
                b = torch.tensor([1.0, 2, 3, 4]).requires_grad_()
                inputs = [a, b]

                outputs_ref = torch.meshgrid(inputs)
                outputs = func(inputs, profile_and_replay=True)
                self.assertEqual(outputs, outputs_ref)

                if GRAPH_EXECUTOR != ProfilingMode.SIMPLE:
                    self.assertAutodiffNode(func.graph_for(inputs), True, ['aten::meshgrid'], [])

                    grads = torch.autograd.grad(_sum_of_list(outputs), inputs)
                    grads_ref = torch.autograd.grad(_sum_of_list(outputs_ref), inputs)
                    self.assertEqual(grads, grads_ref)

    def test_tensor_len(self):
        def func(x):
            return len(x)

        self.checkScript(func, [torch.ones(4, 5, 6)])

    def test_func_call(self):
        def add(a, b):
            return a + b

        def mul(a, x):
            return a * x

        def func(alpha, beta, x, y):
            return add(mul(alpha, x), mul(beta, y))

        alpha = torch.rand(1, dtype=torch.float, requires_grad=True)
        beta = torch.rand(1, dtype=torch.float, requires_grad=True)
        x = torch.rand(3, dtype=torch.float, requires_grad=True)
        y = torch.rand(3, dtype=torch.float, requires_grad=True)

        # NOTE: cannot optimize yet because broadcasts are not inserted before the fuser runs
        self.checkScript(func, [alpha, beta, x, y], optimize=False)

    @unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.PROFILING, "skip if profiling isn't enabled")
    def test_profiling_graph_executor(self):
        @torch.jit.script
        def def_in_one_branch(x, z):
            # type: (Tensor, bool) -> float
            y = x
            if z is False:
                y = x + 1

            return y.sum()

        a = torch.rand(2, 3)

        with enable_profiling_mode():
            # check prim::profile are inserted
            profiled_graph_str = str(def_in_one_branch.graph_for(a, True))
            FileCheck().check_count("prim::profile", 4).run(profiled_graph_str)
            # this call is optimized for
            # the given shape of (2, 3)
            def_in_one_branch(a, False)
            # change shape to (3)
            # so we go down a bailout path
            a = torch.ones(3)
            # check prim::BailOuts are inserted
            bailout_graph_str = str(def_in_one_branch.graph_for(a, True))
            FileCheck().check_count("prim::BailOut", 3).run(bailout_graph_str)
            # this triggers all 3 bailouts
            self.assertEqual(def_in_one_branch(a, False), 6.0)
            # this triggers 2 bailouts
            self.assertEqual(def_in_one_branch(a, True), 3.0)

    def test_resize_input_ops(self):
        # resize_ and resize_as resize the input tensor. because our shape analysis
        # is flow invariant, we set any Tensor that can alias a resized Tensor
        # to the base Tensor Type, without size information.

        # testing that value which is an input of a graph gets handled
        def out_op_graph_input():
            @torch.jit.script
            def test(x, y, z):
                torch.mul(x, y, out=z)
                return z

            graph = _propagate_shapes(test.graph,
                                      (torch.zeros(2, 1), torch.zeros(1, 2), torch.zeros(1, 1, 1)), False)
            self.assertTrue(next(graph.outputs()).type() == TensorType.get())
        out_op_graph_input()

        def test_resize():
            @torch.jit.script
            def test(x):
                after_resize_alias = torch.zeros([2])
                for _i in range(5):
                    b = x + 1
                    f = [1]
                    before_resize_alias = b.sub_(1)
                    # for i in range(10):
                    f.append(1)
                    b.resize_(f)
                    after_resize_alias = b.add_(1)
                return after_resize_alias

            self.run_pass('constant_propagation', test.graph)
            g = _propagate_shapes(test.graph, (torch.zeros(1, 1),), False)
            resize_node = g.findNode("aten::resize_")
            # first input and output of b.resize_ is b
            self.assertTrue(next(resize_node.inputs()).type() == TensorType.get())
            self.assertTrue(next(resize_node.outputs()).type() == TensorType.get())

            # correctly propagates to b alias set
            before_resize = g.findNode("aten::sub_")
            self.assertTrue(next(before_resize.outputs()).type() == TensorType.get())

            after_resize = g.findNode("aten::add_")
            self.assertTrue(next(after_resize.outputs()).type() == TensorType.get())

        test_resize()

        def test_resize_as():
            @torch.jit.script
            def test(x):
                b = torch.zeros([2, 2])
                b.resize_as_(x)
                return b

            g = test.graph
            self.run_pass('constant_propagation', g)
            g = _propagate_shapes(test.graph, (torch.zeros(1, 1),), False)

            # x doesn't alias a resized op so it shouldn't be set to base Tensor type
            self.assertTrue(next(g.inputs()).type() != TensorType.get())
            # return is resized
            self.assertTrue(next(g.outputs()).type() == TensorType.get())

        test_resize_as()

    def test_uninitialized(self):
        graph_str = """graph():
          %1 : int = prim::Uninitialized()
          %2 : int = prim::Constant[value=1]()
          %3 : int = aten::add(%1, %2)
          return (%3)
        """
        g = parse_ir(graph_str)
        m = self.createFunctionFromGraph(g)
        self.getExportImportCopy(m)
        with self.assertRaisesRegex(RuntimeError, "isInt"):
            m()


    @unittest.skipIf(GRAPH_EXECUTOR == ProfilingMode.SIMPLE, "Simple Executor doesn't use requires_grad information")
    def test_requires_grad_loop(self):
        @torch.jit.script
        def test(x, y, z):
            # type: (Tensor, Tensor, int) -> Tensor
            for _ in range(z):
                x = y
            return x

        # x requires grad, y does not
        # testing that requires grad analysis correctly exits, with its input
        # to the loop (x) requiring grad and its output to the loop not requiring grad
        # and the output of the node conservatively setting grad to true

        inps = (torch.tensor(1.0, requires_grad=True), torch.tensor(1), 10)
        test(*inps, profile_and_replay=True)

        graph = test.graph_for(*inps)
        loop = graph.findNode("prim::Loop")
        loop_body = next(loop.blocks())
        loop_inputs = list(loop_body.inputs())
        loop_outputs = list(loop_body.outputs())

        self.assertTrue(loop_inputs[1].requires_grad())

        if GRAPH_EXECUTOR == ProfilingMode.PROFILING:
            bailouts_in_outer_block = graph.findAllNodes("prim::BailOut", False)
            self.assertFalse(bailouts_in_outer_block[1].output().requires_grad())
        else:
            self.assertTrue(loop.output().requires_grad())
            self.assertFalse(loop_outputs[1].requires_grad())

    def test_view_shape_prop(self):
        cu = torch.jit.CompilationUnit('''
        def test_view_shape_prop(a):
            return a.view(size=[-1])
        ''')
        inputs = [torch.zeros(10, 10)]
        outputs = torch.zeros(100)

        real_outs = cu.test_view_shape_prop(*inputs)
        self.assertEqual(real_outs, outputs)

    def test_view_listconstruct_shape_prop(self):
        def fn(x):
            B = x.size(0)
            C = x.size(1)
            T = x.size(2)
            return x.view(T, B, C)

        x = torch.randn(3, 1, 5, requires_grad=True)
        fn = torch.jit.script(fn)
        graph = _propagate_shapes(fn.graph, (x,), False)
        self.assertTrue(next(graph.outputs()).type().scalarType() == 'Double')

    def test_shape_prop_promotion(self):
        @torch.jit.script
        def fn(x, y):
            return x + y

        x, y = torch.rand(3, 4, dtype=torch.float), torch.rand(3, 4, dtype=torch.double)
        graph = _propagate_shapes(fn.graph, (x, y), False)
        FileCheck().check('Double(*, *) = aten::add').run(graph)

    def test_shape_prop_promote_scalar_arg(self):
        @torch.jit.script
        def fn(x):
            return math.pi + x

        x = torch.zeros(3, 4, dtype=torch.long)
        graph = _propagate_shapes(fn.graph, (x,), False)
        default = torch.get_default_dtype()
        if(default == torch.float):
            FileCheck().check('Float(*, *) = aten::add').run(graph)
        else:
            FileCheck().check('Double(*, *) = aten::add').run(graph)

    def test_integral_shape_inference(self):
        cu = torch.jit.CompilationUnit('''
        def test_integral_shape_inference(a):
            return a / a
        ''')
        inputs = [torch.ones(10, 10).type(torch.LongTensor)]
        outputs = torch.ones(10, 10)

        self.assertEqual(cu.test_integral_shape_inference(*inputs), outputs)

    @unittest.skipIf(RUN_CUDA, 'This tests the CPU fuser')
    @unittest.skipIf(IS_SANDCASTLE, "NYI: fuser support for Sandcastle")
    @enable_cpu_fuser
    def test_batchnorm_fuser_cpu(self):
        code = '''
            graph(%3 : Tensor,
                  %7 : Tensor,
                  %12 : Float(*, *),
                  %13 : Tensor,
                  %25 : Tensor):
                %23 : int = prim::Constant[value=1]()
                %22 : float = prim::Constant[value=1e-05]()
                %26 : Tensor = aten::sqrt(%25)
                %24 : Tensor = aten::add(%26, %22, %23)
                %20 : Tensor = aten::reciprocal(%24)
                %norm_invstd : Tensor = aten::mul(%20, %23)
                %15 : Tensor = aten::sub(%12, %13, %23)
                %11 : Tensor = aten::mul(%15, %norm_invstd)
                %8 : Tensor = aten::mul(%11, %7)
                %5 : Tensor = aten::add(%8, %3, %23)
                %1 : Float(*, *) = aten::relu(%5)
                return (%1)
        '''

        graph = parse_ir(code)
        inputs = 5 * [torch.rand(26, 2048, dtype=torch.float)]
        code = torch._C._jit_fuser_get_fused_kernel_code(graph, inputs)
        FileCheck().check('sqrtf').run(code)

    @slowTest
    @unittest.skipIf(RUN_CUDA, 'This tests the CPU fuser')
    @unittest.skipIf(IS_SANDCASTLE, "NYI: fuser support for Sandcastle")
    @enable_cpu_fuser
    def test_fuser_double_float_codegen(self):
        fns = ['log', 'log10', 'log1p', 'log2', 'lgamma', 'exp', 'expm1', 'erf',
               'erfc', 'cos', 'acos', 'cosh', 'sin', 'asin', 'sinh', 'tan',
               'atan', 'tanh', 'sqrt', 'ceil', 'floor', 'round', 'trunc',
               'frac']

        def lookup_c_equivalent_fn(aten_fn):
            if aten_fn == 'min':
                return 'fmin'
            elif aten_fn == 'max':
                return 'fmax'
            else:
                return aten_fn

        def test_dispatch(op, expects, dtype, binary=False):
            if dtype == torch.double:
                dtype_str = 'Double'
            elif dtype == torch.float:
                dtype_str = 'Float'
            else:
                raise RuntimeError('Unknown dtype')

            if binary:
                code = '''
                    graph(%3 : Tensor, %4 : Tensor):
                        %2 : {dtype}(*, *) = aten::{op}(%3, %4)
                        %1 : {dtype}(*, *) = aten::relu(%2)
                        return (%1)
                '''.format(op=op, dtype=dtype_str)
            else:
                code = '''
                    graph(%3 : Tensor):
                        %2 : {dtype}(*, *) = aten::{op}(%3)
                        %1 : {dtype}(*, *) = aten::relu(%2)
                        return (%1)
                '''.format(op=op, dtype=dtype_str)

            graph = parse_ir(code)
            inputs = (2 if binary else 1) * [torch.rand(26, 2048, dtype=dtype)]
            code = torch._C._jit_fuser_get_fused_kernel_code(graph, inputs)
            FileCheck().check(expects).run(code)

        for fn in fns:
            test_dispatch(fn, lookup_c_equivalent_fn(fn) + '(', torch.double)
            test_dispatch(fn, lookup_c_equivalent_fn(fn) + 'f(', torch.float)

        binary_fns = ['min', 'max', 'pow']
        for fn in binary_fns:
            test_dispatch(fn, lookup_c_equivalent_fn(fn) + '(', torch.double, binary=True)
            test_dispatch(fn, lookup_c_equivalent_fn(fn) + 'f(', torch.float, binary=True)

    @unittest.skipIf(RUN_CUDA, 'This tests the CPU fuser')
    @unittest.skipIf(IS_SANDCASTLE, "NYI: fuser support for Sandcastle")
    @enable_cpu_fuser
    def test_fuser_double_literal_precision(self):
        code = '''
        graph(%2 : Float(*, *)):
            %4 : int = prim::Constant[value=1]()
            %3 : float = prim::Constant[value=1.282549830161864]()
            %5 : Float(*, *) = aten::add(%2, %3, %4)
            %1 : Float(*, *) = aten::relu(%5)
            return (%1)
        '''

        graph = parse_ir(code)
        code = torch._C._jit_fuser_get_fused_kernel_code(graph, [torch.rand(3, 4)])
        FileCheck().check('1.282549830161864').run(code)

    def test_fuser_multiple_blocks(self):
        cu = torch.jit.CompilationUnit('''
        def test_fuser_multiple_blocks(this, that, theother, meme):
            i = 0
            while i < 20:
                this = torch.cat([this, meme], dim=0)
                that = torch.cat([that, meme], dim=0)
                theother = torch.cat([theother, meme], dim=0)
                i = i + 1
            return this, that, theother
        ''')

        inputs = [torch.ones(0, 10, 10)] * 3
        inputs += [torch.ones(1, 10, 10)]
        outputs = [torch.ones(20, 10, 10)] * 3

        self.assertEqual(cu.test_fuser_multiple_blocks(*inputs), outputs)

    def test_dropout_script(self):

        eg = torch.zeros(1, 2, 3, requires_grad=True)

        @_trace(eg)
        def foo(x):
            x = torch.neg(x)
            return F.dropout(x)

        class MyDrop(nn.Module):
            def forward(self, x):
                return foo(x)

        f = io.BytesIO()
        with warnings.catch_warnings(record=True):
            torch.onnx.export(MyDrop(), (eg,), f, verbose=False)

    @unittest.skip("RuntimeError: VariableType::ID() not implemented")
    def test_cast(self):
        script = '''
        def to_int(x):
            return int(x)
        '''
        x = Variable(torch.FloatTensor([1.1, 2.3]), requires_grad=True)
        out = Variable(torch.IntTensor([1, 2]), requires_grad=True)
        self.checkScript(script, [x], optimize=True, outputs=[out], func='to_int')

    def test_str_cast(self):
        @torch.jit.script
        def to_str(x):
            # type: (int) -> str
            return str((x, x))

        self.assertEqual("(1, 1)", to_str(1))

    def test_python_frontend(self):
        def fn(x, y, z):
            q = None
            q = x + y - z.sigmoid()
            print(q)
            w = -z
            if not x and not y and z:
                m = x if not z else y
            while x < y > z:
                q = x
            assert 1 == 1, "hello"
            return x

        ast = torch.jit.frontend.get_jit_def(fn)
        self.assertExpected(str(ast))

    @unittest.skipIf(not PY2, "Requires python 2")
    def test_python_frontend_py2(self):
        def fn():
            raise Exception("hello")
        ast = torch.jit.frontend.get_jit_def(fn)
        self.assertExpected(str(ast))

    @unittest.skipIf(PY2, "Requires python 3")
    def test_python_frontend_py3(self):
        def fn():
            raise Exception("hello")
        ast = torch.jit.frontend.get_jit_def(fn)
        self.assertExpected(str(ast))

    def _make_scalar_vars(self, arr, dtype):
        return [torch.tensor(val, dtype=dtype) for val in arr]


    @unittest.skipIf(PY2, "tuple printing in py2 is different than torchscript")
    def test_string_print(self):
        def func(a):
            print(a, "a" 'b' '''c''' """d""", 2, 1.5)
            return a

        inputs = self._make_scalar_vars([1], torch.int64)
        self.checkScript(func, inputs, capture_output=True)

    def test_while(self):
        def func(a, b, max):
            while bool(a < max):
                a = a + 1
                b = b + 1
            c = a + b
            return c

        inputs = self._make_scalar_vars([1, 1, 10], torch.int64)
        self.checkScript(func, inputs, optimize=True)

    def test_fibb(self):
        def func(lim):
            first = 1
            second = 1
            i = 1
            somenum = 5
            dontmutateme = 3
            third = 0
            while bool(i < lim):
                third = first + second
                first = second
                second = third
                j = 0
                while j < 10:
                    somenum = somenum * 2
                    j = j + 1
                i = i + j
                i = i + dontmutateme

            st = second + third
            fs = first + second
            return third, st, fs

        inputs = self._make_scalar_vars([10], torch.int64)
        self.checkScript(func, inputs, optimize=True)

    def test_fibb_totally_better(self):
        def fib(x):
            # type: (int) -> int
            prev = 1
            v = 1
            for i in range(0, x):
                save = v
                v = v + prev
                prev = save
            return v

        self.checkScript(fib, (10,))

    def test_if(self):
        def func(a, b):
            # type: (int, int) -> int
            d = 3
            if bool(a > 10):
                a = 3 + d
            else:
                b = 3 + d
                d = 4
            c = a + b
            return c

        inputs = self._make_scalar_vars([1, -1], torch.int64)
        self.checkScript(func, inputs, optimize=True)

    def test_if_for_in_range(self):
        def func(a, b):
            # type: (int, int) -> int
            d = 3
            for _ in range(20):
                if bool(a > 10):
                    a = 3 + d
                else:
                    b = 3 + d
                    d = 4
                c = a + b
            return d
        inputs = self._make_scalar_vars([1, -1], torch.int64)
        self.checkScript(func, inputs, optimize=True)

    def test_if_noelse(self):
        def func(a, b):
            if bool(a > 10):
                a = 3 + b
            c = a + b
            return c

        inputs = self._make_scalar_vars([-1, 1], torch.int64)
        self.checkScript(func, inputs, optimize=True)

    def test_if_is_none_dispatch(self):

        @torch.jit.script
        def test_lhs_none_rhs_none():
            # LHS, RHS both alwaysNone, dispatch always_none_branch
            # only emit one prim::Constant
            if None is None:
                return 1
            elif None is not None:
                return 2
            else:
                return 3

        self.assertTrue(str(test_lhs_none_rhs_none.graph).count(': int = prim::Constant') == 1)

        @torch.jit.script
        def test_lhs_opt_rhs_none(lhs=None):
            # type: (Optional[Tensor]) -> int
            # LHS maybeNone: emit normal if stmt that contains 3 constants
            if lhs is not None:
                return 2
            elif lhs is None:
                return 1
            else:
                return 3

        self.assertTrue(str(test_lhs_opt_rhs_none.graph).count(': int = prim::Constant') == 3)

        @torch.jit.script
        def test_lhs_none_rhs_opt(rhs=None):
            # type: (Optional[Tensor]) -> int
            # RHS maybeNone, emit normal if stmt that contains 3 constants
            if None is rhs:
                return 1
            elif None is not rhs:
                return 2
            else:
                return 3

        self.assertTrue(str(test_lhs_opt_rhs_none.graph).count(': int = prim::Constant') == 3)

        @torch.jit.script
        def test_lhs_never_rhs_none(lhs):
            # LHS neverNone, RHS alwaysNone dispatch never_none_branch
            # only emit one prim::Constant
            if lhs is None:
                return 1
            elif lhs is not None:
                return 2
            else:
                return 3

        self.assertTrue(str(test_lhs_never_rhs_none.graph).count(': int = prim::Constant') == 1)

        @torch.jit.script
        def test_lhs_none_rhs_never(rhs):
            # LHS alwaysNone, RHS neverNone dispatch never_none_branch
            # only emit one prim::Constant
            if None is rhs:
                return 1
            elif None is not rhs:
                return 2
            else:
                return 3

        self.assertTrue(str(test_lhs_none_rhs_never.graph).count(': int = prim::Constant') == 1)

        @torch.jit.script
        def test_bool_arith_and(lhs):
            if lhs is None and lhs is not None:
                return 1
            else:
                return 2
        self.assertEqual(test_bool_arith_and(torch.zeros(3)), 2)
        self.assertTrue(str(test_bool_arith_and.graph).count('if') == 0)

        @torch.jit.script
        def test_bool_arith_or(lhs):
            if lhs is None or lhs is not None:
                return 1
            else:
                return 2
        self.assertEqual(test_bool_arith_or(torch.zeros(3)), 1)
        self.assertTrue(str(test_bool_arith_or.graph).count('if') == 0)


        @torch.jit.script
        def test_bool_arith_not(lhs):
            if not (lhs is None):
                return 1
            else:
                return 2
        self.assertEqual(test_bool_arith_not(torch.zeros(3)), 1)
        self.assertTrue(str(test_bool_arith_not.graph).count('if') == 0)


    def test_conditional_casting(self):
        def test_bool_cast_tensor(x):
            if x:
                return 1
            else:
                return 0

        for make_one_dim in [True, False]:
            for inp_val in [0.1, 0.0, -0.0, -0.1, -1, 0, 1]:
                inp_val = [inp_val] if make_one_dim else inp_val
                self.checkScript(test_bool_cast_tensor, (torch.tensor(inp_val),))

        self.checkScriptRaisesRegex(test_bool_cast_tensor, (torch.tensor([1, 1]),), Exception,
                                    "bool value of Tensor with more than one value")

        def test_not_cast(x):
            if not x:
                return 1
            else:
                return 0

        self.checkScript(test_not_cast, (torch.tensor(1),))
        self.checkScript(test_not_cast, (torch.tensor(0),))

        with self.assertRaisesRegex(RuntimeError, r"Could not cast value of type Tuple\[Tensor, Tensor\]"):  # noqa: W605
            @torch.jit.script
            def test_mult(x, y):
                return not(x, y)

        def test_cast_int(x):
            # type: (int) -> int
            if x:
                return 1
            else:
                return 0
        self.checkScript(test_cast_int, (1,))
        self.checkScript(test_cast_int, (0,))
        self.checkScript(test_cast_int, (-1,))

        def test_cast_float(x):
            # type: (float) -> int
            if x:
                return 1
            else:
                return 0
        self.checkScript(test_cast_float, (1.,))
        self.checkScript(test_cast_float, (0.,))
        self.checkScript(test_cast_float, (-1.,))

        with self.assertRaisesRegex(RuntimeError, r"Could not cast value of type Tuple\[int, int\] to bool"):  # noqa: W605
            @torch.jit.script
            def test_bad_conditional(x):
                if (1, 2):
                    return
                else:
                    return 0

    def test_while_nonexistent_value(self):
        with self.assertRaisesRegex(RuntimeError, "undefined value x"):
            torch.jit.CompilationUnit('''
            def test_while(a, b):
                while bool(a < 10):
                    a = a + x
                    b = b + 1
                return a + b
            ''')

    def test_while_nonexistent_cond_value(self):
        with self.assertRaisesRegex(RuntimeError, "undefined value x"):
            torch.jit.CompilationUnit('''
            def test_while(a, b):
                while a < x:
                    a = a + 1
                    b = b + 1
                return a + b
            ''')

    def test_opt_opt_refinement(self):
        @torch.jit.script
        def test_unify(weight, bias):
            # type: (Optional[int], Optional[int]) -> Optional[int]
            if weight is not None:
                opt = None
            else:
                if bias is not None:
                    opt = 1
                else:
                    opt = None

            return opt

    def test_optional_refinement(self):
        @torch.jit.script
        def test_if_none_assignment(x):
            # type: (Optional[int]) -> int
            if x is None:
                x = 1
            return x + 1

        self.assertEqual(test_if_none_assignment(1), 2)

        @torch.jit.script
        def test_ternary(x):
            # type: (Optional[int]) -> int
            x = x if x is not None else 2
            return x

        @torch.jit.script
        def test_not_none(x):
            # type: (Optional[int]) -> None
            if x is not None:
                print(x + 1)

        @torch.jit.script
        def test_and(x, y):
            # type: (Optional[int], Optional[int]) -> None
            if x is not None and y is not None:
                print(x + y)

        @torch.jit.script
        def test_not(x, y):
            # type: (Optional[int], Optional[int]) -> None
            if not (x is not None and y is not None):
                pass
            else:
                print(x + y)

        @torch.jit.script
        def test_bool_expression(x):
            # type: (Optional[int]) -> None
            if x is not None and x < 2:
                print(x + 1)

        @torch.jit.script
        def test_nested_bool_expression(x, y):
            # type: (Optional[int], Optional[int]) -> int
            if x is not None and x < 2 and y is not None:
                x = x + y
            else:
                x = 5
            return x + 2

        @torch.jit.script
        def test_or(x, y):
            # type: (Optional[int], Optional[int]) -> None
            if y is None or x is None:
                pass
            else:
                print(x + y)

        # backwards compatibility
        @torch.jit.script
        def test_manual_unwrap_opt(x):
            # type: (Optional[int]) -> int
            if x is None:
                x = 1
            else:
                x = torch.jit._unwrap_optional(x)
            return x  # noqa: T484

        with self.assertRaisesRegex(RuntimeError, "Arguments for call are not valid"):
            @torch.jit.script
            def or_error(x, y):
                # type: (Optional[int], Optional[int]) -> None
                if x is None or y is None:
                    print(x + y)  # noqa: T484

        with self.assertRaisesRegex(RuntimeError, "Arguments for call are not valid"):
            @torch.jit.script
            def and_error(x, y):
                # type: (Optional[int], Optional[int]) -> None
                if x is None and y is None:
                    pass
                else:
                    print(x + y)  # noqa: T484

        with self.assertRaisesRegex(RuntimeError, "Arguments for call are not valid"):
            @torch.jit.script
            def named_var(x):
                # type: (Optional[int]) -> None
                x_none = x is not None
                if x_none:
                    print(x + 1)  # noqa: T484

        with self.assertRaisesRegex(RuntimeError, "Arguments for call are not valid"):
            @torch.jit.script
            def named_var_and(x, y):
                # type: (Optional[int], Optional[int]) -> None
                x_none = x is not None
                if y is not None and x_none:
                    print(x + y)  # noqa: T484

    def test_assertion_optional_refinement(self):
        @torch.jit.script
        def test(x, y):
            # type: (Optional[int], Optional[int]) -> int
            assert x is not None and y is not None
            return x + y

        self.assertEqual(test(2, 2), 4)
        with self.assertRaisesRegex(Exception, ""):
            test(1, None)

    @unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.LEGACY, "the current version of Profiler doesn't profile/specialize Optionals")
    def test_optional_tensor(self):
        @torch.jit.script
        def fn(x, y):
            # type: (Optional[Tensor], int) -> int
            if x is None:
                return y
            else:
                return 0

        res = fn(None, 1)
        self.assertEqual(res, 1)
        g = torch.jit.last_executed_optimized_graph()
        first_input = next(g.inputs())
        # check if input is disconnected
        self.assertEqual(first_input.type().kind(), 'OptionalType')
        self.assertEqual(first_input.uses(), [])
        t = torch.ones(1)
        res = fn(t, 1)
        self.assertEqual(res, 0)
        g = torch.jit.last_executed_optimized_graph()
        self.assertEqual(next(g.inputs()).type().kind(), 'TensorType')

        @torch.jit.script
        def fn(x, y, b):
            # type: (Optional[Tensor], Tensor, bool) -> Tensor
            if b:
                res = y
            else:
                res = torch.jit._unwrap_optional(x)
            return res

        t2 = torch.zeros(1)
        res = fn(t, t2, True)
        self.assertEqual(res, t2)
        with self.assertRaisesRegex(RuntimeError, "Unwrapping null optional"):
            res = fn(None, t2, False)
        res = fn(None, t2, True)
        g = torch.jit.last_executed_optimized_graph()
        self.assertEqual(next(g.outputs()).type().str(), "Tensor")

    @unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.LEGACY, "the current version of Profiler doesn't profile/specialize Optionals")
    def test_optional_list(self):
        @torch.jit.script
        def fn(x, y):
            # type: (Optional[List[int]], int) -> int
            if x is None:
                return y
            else:
                res = 0
                for d in x:
                    res += d
                return res

        res = fn(None, 1)
        self.assertEqual(res, 1)
        g = torch.jit.last_executed_optimized_graph()
        first_input = next(g.inputs())
        # check if input is disconnected
        self.assertEqual(first_input.type().kind(), 'OptionalType')
        self.assertEqual(first_input.uses(), [])
        l = [2, 3]
        res = fn(l, 1)
        self.assertEqual(res, 5)
        g = torch.jit.last_executed_optimized_graph()
        self.assertEqual(next(g.inputs()).type().kind(), 'ListType')

        @torch.jit.script
        def fn(x, y, b):
            # type: (Optional[List[int]], List[int], bool) -> List[int]
            if b:
                l = torch.jit._unwrap_optional(x)
            else:
                l = y
            return l

        l2 = [0, 1]
        res = fn(l, l2, True)
        self.assertEqual(res, l)
        with self.assertRaisesRegex(RuntimeError, "Unwrapping null optional"):
            res = fn(None, l2, True)
        res = fn(None, l2, False)
        g = torch.jit.last_executed_optimized_graph()
        self.assertEqual(next(g.outputs()).type().str(), "int[]")

    def test_while_write_outer_then_read(self):
        def func(a, b):
            while bool(a < 10):
                a = a + 1
                b = a + 1
            return a + b

        inputs = self._make_scalar_vars([42, 1337], torch.int64)
        self.checkScript(func, inputs, optimize=True)

    def test_while_nest_if(self):
        def func(a, b):
            # type: (int, int) -> int
            c = 0
            while a < 10:
                a = a + 1
                b = b + 1
                if a > b:
                    c = -a
                else:
                    c = -b
            return c + 1

        inputs = self._make_scalar_vars([-1234, 4321], torch.int64)
        self.checkScript(func, inputs, optimize=True)

    def test_divmod(self):
        def func_int(a, b):
            # type: (int, int) -> Tuple[int, int]
            return divmod(a, b)

        def func_float(a, b):
            # type: (float, float) -> Tuple[float, float]
            return divmod(a, b)

        def func_int_float(a, b):
            # type: (int, float) -> Tuple[float, float]
            return divmod(a, b)

        def func_float_int(a, b):
            # type: (float, int) -> Tuple[float, float]
            return divmod(a, b)

        def divmod_test_iterator(func, num, den):
            for i in num:
                for j in den:
                    self.checkScript(func, (i, j), frames_up=2)

        num_int = [1024, -1024]
        den_int = [10, -10]
        num_float = [5.3, -5.3]
        den_float = [2.0, -2.0]
        divmod_test_iterator(func_int, num_int, den_int)
        divmod_test_iterator(func_float, num_float, den_float)
        divmod_test_iterator(func_int_float, num_int, den_float)
        divmod_test_iterator(func_float_int, num_float, den_int)

        with self.assertRaisesRegex(RuntimeError, "ZeroDivisionError: integer division or modulo by zero"):
            cu = torch.jit.CompilationUnit(dedent(inspect.getsource(func_int)))
            cu.func_int(1024, 0)
        with self.assertRaisesRegex(RuntimeError, "ZeroDivisionError: float divmod()"):
            cu = torch.jit.CompilationUnit(dedent(inspect.getsource(func_float)))
            cu.func_float(5.3, 0.0)
        with self.assertRaisesRegex(RuntimeError, "ZeroDivisionError: float divmod()"):
            cu = torch.jit.CompilationUnit(dedent(inspect.getsource(func_int_float)))
            cu.func_int_float(1024, 0.0)
        with self.assertRaisesRegex(RuntimeError, "ZeroDivisionError: float divmod()"):
            cu = torch.jit.CompilationUnit(dedent(inspect.getsource(func_float_int)))
            cu.func_float_int(5.3, 0)

    def test_math_ops(self):
        def checkMathWrap(func_name, num_args=1, is_float=True, **args):
            if is_float:
                checkMath(func_name, num_args, True, **args)
                checkMath(func_name, num_args, False, **args)
            else:
                checkMath(func_name, num_args, is_float, **args)

        inf = float("inf")
        NaN = float("nan")
        mx_int = 2**31 - 1
        mn_int = -2**31
        float_vals = ([inf, NaN, 0.0, 1.0, 2.2, -1.0, -0.0, -2.2, -inf, 1, 0, 2] +
                      [10.0 ** i for i in range(5)] + [-(10.0 ** i) for i in range(5)])
        int_vals = list(range(-5, 5, 1)) + [mx_int + 5, mx_int * 2, mn_int - 5, mn_int * 2]

        def checkMath(func_name, num_args, is_float=True, ret_type="float", debug=False, vals=None, args_type=None):
            funcs_template = dedent('''
            def func(a, b):
                # type: {args_type} -> {ret_type}
                return math.{func}({args})
            ''')
            if num_args == 1:
                args = "a"
            elif num_args == 2:
                args = "a, b"
            else:
                raise RuntimeError("Test doesn't support more than 2 arguments")
            if args_type is None:
                args_type = "(float, float)" if is_float else "(int, int)"
            funcs_str = funcs_template.format(func=func_name, args=args, args_type=args_type, ret_type=ret_type)
            scope = {}
            execWrapper(funcs_str, globals(), scope)
            cu = torch.jit.CompilationUnit(funcs_str)
            f_script = cu.func
            f = scope['func']

            if vals is None:
                vals = float_vals if is_float else int_vals
                vals = [(i, j) for i in vals for j in vals]

            for a, b in vals:
                res_python = None
                res_script = None
                try:
                    res_python = f(a, b)
                except Exception as e:
                    res_python = e
                try:
                    res_script = f_script(a, b)
                except Exception as e:
                    res_script = e
                if debug:
                    print("in: ", a, b)
                    print("out: ", res_python, res_script)
                # We can't use assertEqual because of a couple of differences:
                # 1. nan == nan should return true
                # 2. When python functions throw an exception, we usually want to silently ignore them.
                # (ie: We want to return `nan` for math.sqrt(-5))
                if res_python != res_script:
                    if isinstance(res_python, Exception):
                        continue

                    if type(res_python) == type(res_script):
                        if isinstance(res_python, tuple) and (math.isnan(res_python[0]) == math.isnan(res_script[0])):
                            continue
                        if isinstance(res_python, float) and math.isnan(res_python) and math.isnan(res_script):
                            continue
                    msg = ("Failed on {func_name} with inputs {a} {b}. Python: {res_python}, Script: {res_script}"
                           .format(func_name=func_name, a=a, b=b, res_python=res_python, res_script=res_script))
                    self.assertEqual(res_python, res_script, message=msg, prec=(1e-4) * max(abs(res_python), res_script))

        unary_float_ops = ["log", "log1p", "log10", "exp", "sqrt", "gamma", "lgamma", "erf",
                           "erfc", "expm1", "fabs", "acos", "asin", "atan", "cos", "sin", "tan",
                           "asinh", "atanh", "acosh", "sinh", "cosh", "tanh", "degrees", "radians"]
        binary_float_ops = ["atan2", "fmod", "copysign"]
        for op in unary_float_ops:
            checkMathWrap(op, 1)
        for op in binary_float_ops:
            checkMathWrap(op, 2)

        checkMath("modf", 1, ret_type="Tuple[float, float]")
        checkMath("frexp", 1, ret_type="Tuple[float, int]")
        checkMath("isnan", 1, ret_type="bool")
        checkMath("isinf", 1, ret_type="bool")
        checkMath("ldexp", 2, is_float=False, ret_type="float", args_type="(float, int)",
                  vals=[(i, j) for i in float_vals for j in range(-10, 10)])
        checkMath("pow", 2, is_float=False, ret_type="float")
        checkMath("pow", 2, is_float=True, ret_type="float")
        if not PY2:
            checkMathWrap("floor", ret_type="int")
            checkMathWrap("ceil", ret_type="int")
            checkMathWrap("gcd", 2, is_float=False, ret_type="int")
            checkMath("isfinite", 1, ret_type="bool")
        if PY37:
            checkMathWrap("remainder", 2)
        checkMathWrap("factorial", 1, is_float=False, ret_type="int", vals=[(i, 0) for i in range(-2, 10)])

    def test_if_nest_while(self):
        def func(a, b):
            # type: (int, int) -> int
            c = 0
            if a > b:
                while a > b:
                    b = b + 1
                    c = -b
            return c

        inputs = self._make_scalar_vars([4321, 1234], torch.int64)
        self.checkScript(func, inputs)

    def test_script_optional_none(self):
        def none_stmt(x):
            output = None
            output = x
            return output

        def none_args(x):
            # type: (Optional[Tensor]) -> Optional[Tensor]
            return None

        self.checkScript(none_stmt, [torch.arange(0, 2)], optimize=True)
        self.checkScript(none_args, [None], optimize=True)

        # test undefined tensor None as default param
        def test_script_optional_tensor_none(x=None):
            # type: (Optional[Tensor]) -> Tensor
            res = torch.zeros(1, dtype=torch.int8)
            if x is None:
                res = res + 1
            else:
                res = x
            return res

        fn = test_script_optional_tensor_none
        scripted_fn = torch.jit.script(fn)
        self.assertEqual(fn(), scripted_fn())
        self.assertEqual(fn(torch.zeros(1)), scripted_fn(torch.zeros(1)))

        # test typical None as default param
        def test_script_optional_other_none(x=None):
            # type: (Optional[float]) -> float
            res = 2.0
            if x is None:
                res = res + 1.0
            else:
                res = x
            return res

        fn = test_script_optional_other_none
        scripted_fn = torch.jit.script(fn)
        self.assertEqual(fn(), scripted_fn())
        self.assertEqual(fn(1.0), scripted_fn(1.0))

    def test_script_clamp_none(self):
        def test_script_clamp_max_none(x):
            return torch.clamp(x, min=2, max=None)

        def test_script_clamp_max(x):
            return torch.clamp(x, max=2)

        def test_script_clamp_min_none(x):
            return torch.clamp(x, min=None, max=2)

        def test_script_clamp_min(x):
            return torch.clamp(x, min=2)

        input = [torch.arange(0, 3)]
        self.checkScript(test_script_clamp_max_none, input, optimize=True)
        self.checkScript(test_script_clamp_max, input, optimize=True)
        self.checkScript(test_script_clamp_min_none, input, optimize=True)
        self.checkScript(test_script_clamp_min, input, optimize=True)

    def test_script_bool_constant(self):
        def test_script_bool_constant():
            a = True
            return a
        self.checkScript(test_script_bool_constant, [])

    def test_ternary(self):
        def func(a, b):
            c = 3
            c = a + b if bool(a > 3) else b
            return c

        inputs_true = self._make_scalar_vars([5, 2], torch.int64)
        inputs_false = self._make_scalar_vars([1, 0], torch.int64)
        self.checkScript(func, inputs_true, optimize=True)
        self.checkScript(func, inputs_false, optimize=True)

    @unittest.skipIf(PY2, "tuple printing in py2 is different than torchscript")
    def test_print(self):
        def func(x, y):
            q = (x + y).sigmoid()
            print(q, 1, 2, [1, 2], [1.0, 2.0])
            w = -q
            return w * w

        x = torch.arange(4., requires_grad=True)
        y = torch.arange(0., 8, 2, requires_grad=True)
        self.checkScript(func, [x, y], optimize=True, capture_output=True)

    def test_format(self):
        def func(x):
            print("{}, I'm a {}".format("Hello", "test"))
            print("format blank".format())
            print("stuff before {}".format("hi"))
            print("{} stuff after".format("hi"))
            return x + 1

        x = torch.arange(4., requires_grad=True)
        self.checkScript(func, [x], optimize=True, capture_output=True)

    def test_logical_short_circuit(self):
        @torch.jit.script
        def testNoThrows(t):
            c1 = 1
            if (False and bool(t[1])) or (True or bool(t[1])):
                c1 = 0
            return c1

        FileCheck().check_not("prim::If").run(testNoThrows.graph)
        self.assertEqual(0, testNoThrows(torch.randn(0)))
        self.assertEqual(0, testNoThrows(torch.randn([2, 3])))

        @torch.jit.script
        def throwsOr(t):
            c0 = False or bool(t[1])
            print(c0)

        @torch.jit.script
        def throwsAnd(t):
            c0 = True and bool(t[1])
            print(c0)

        t = torch.randn(0)
        with self.assertRaisesRegex(RuntimeError, "index 1 out of range for tensor of size"):
            throwsOr(t)
        with self.assertRaisesRegex(RuntimeError, "index 1 out of range for tensor of size"):
            throwsAnd(t)

    def test_type_cast(self):
        template = dedent('''
        def func(v):
            # type: ({from_type}) -> {to_type}
            return {to_type}(v)
        ''')

        def check_cast(from_type, to_type, value, raises=False):
            code = template.format(from_type=from_type, to_type=to_type)
            self.checkScript(code, (value,))

        check_cast('int', 'float', 1)
        check_cast('int', 'bool', 1)
        check_cast('int', 'bool', 0)

        check_cast('float', 'int', 1.)
        check_cast('float', 'bool', 1.)
        check_cast('float', 'bool', 0.)

        check_cast('bool', 'int', True)
        check_cast('bool', 'float', True)

    def test_multiple_assignment(self):
        def outer_func(x):
            return x * 2, x + 2

        @torch.jit.script
        def func(x):
            y, z = outer_func(x)
            return y + z

        x = torch.arange(4)
        self.assertEqual(func(x), x * 2 + x + 2)

    def test_literals(self):
        def func(a):
            return a.view(size=[1, 2, 3])

        a = torch.randn(6)
        self.checkScript(func, [a], optimize=True)

    def test_return(self):
        def no_return(a):
            a + 1

        def void_return(a):
            return

        def one_return(a):
            return a + 1.

        def multiple_returns(a):
            return a * 1., a * 2., a * 3.

        a = torch.randn(1, dtype=torch.float)
        self.checkScript(no_return, [a], optimize=True)
        self.checkScript(void_return, [a], optimize=True)
        self.checkScript(one_return, [a], optimize=True)
        self.checkScript(multiple_returns, [a], optimize=True)

        with self.assertRaisesRegex(RuntimeError, "does not return along all paths"):  # noqa
            torch.jit.CompilationUnit('''
            def no_return_bad_annotation(a):
                # type: (Tensor) -> Tensor
                a + 1
            ''')

    def test_error(self):
        @torch.jit.script
        def foo(a):
            return a.t()
        s = Variable(torch.rand(5, 5, 5))
        # XXX: this should stay quiet in stay propagation and only fail in the interpreter
        with self.assertRaisesRegex(RuntimeError, "failed in interpreter"):
            foo(s)

        @torch.jit.script
        def bar(c, b):
            return c + b

        with self.assertRaisesRegex(RuntimeError, "failed in interpreter"):
            bar(Variable(torch.rand(10), requires_grad=True), Variable(torch.rand(9), requires_grad=True))

    def test_error_stacktrace(self):
        @torch.jit.script
        def baz(c, b):
            return c + b

        @torch.jit.script
        def foo(c, b):
            return baz(c, b)

        @torch.jit.script
        def bar(c, b):
            return foo(c, b)

        with self.assertRaises(RuntimeError) as cm:
            bar(torch.rand(10), torch.rand(9))
        FileCheck().check("The above operation failed in interpreter") \
                   .check("Traceback (most recent call last)") \
                   .check("in foo").check("in baz").run(str(cm.exception))

    def test_error_stacktrace_interface(self):
        global IFace
        @torch.jit.script
        def baz(c, b):
            return c + b

        @torch.jit.script
        def foo(c, b):
            return baz(c, b)

        @torch.jit.script
        def bar(c, b):
            return foo(c, b)

        @torch.jit.script
        class Bar(object):
            def one(self, x, y):
                return bar(x, y)

        @torch.jit.interface
        class IFace(object):
            def one(self, x, y):
                # type: (Tensor, Tensor) -> Tensor
                pass

        @torch.jit.script
        def as_interface(x):
            # type: (IFace) -> IFace
            return x

        f = as_interface(Bar())

        with self.assertRaises(RuntimeError) as cm:
            x = f.one(torch.rand(10), torch.rand(9))
            bar(torch.rand(10), torch.rand(9))
        FileCheck().check("The above operation failed in interpreter") \
                   .check("Traceback (most recent call last)") \
                   .check("in foo").check("in baz").run(str(cm.exception))

    def test_binop_unsupported_error(self):
        with self.assertRaisesRegex(NotSupportedError, "unsupported binary operator:"):
            @torch.jit.script
            def binop(x, y):
                # Replace this with another unsupported op when/if it gets supported
                return x << y

    def test_bitwise_ops(self):

        def int_test():
            return 2 & 3, 2 ^ 3, 2 | 3

        self.checkScript(int_test, ())

        def bool_test(x, y):
            # type: (bool, bool) -> Tuple[bool, bool, bool]
            return x & y, x ^ y, x | y

        self.checkScript(bool_test, (True, False))
        self.checkScript(bool_test, (True, True))

        def tensor_test(x, y):
            return x & y, x ^ y, x | y

        x = torch.tensor(2)
        y = torch.tensor(3)

        self.checkScript(tensor_test, (x, y))

        def not_test(x):
            return ~x

        self.checkScript(not_test, (torch.tensor([2, 4]), ))

    def test_number_all(self):
        def int1():
            return all(torch.tensor([1, 2, 3], dtype=torch.uint8))

        def int2():
            return all(torch.tensor([1, 0, 3], dtype=torch.uint8))

        self.checkScript(int1, ())
        self.checkScript(int2, ())

    def test_number_math(self):
        ops_template = dedent('''
        def func():
            return {scalar1} {op} {scalar2}
        ''')
        ops = ['+', '-', '*', '%', '<', '<=', '>', '>=', '==', '!=', '//']
        funcs_template = dedent('''
        def func():
            return {func}({scalar1}, {scalar2})
        ''')
        funcs = ['min', 'max']
        scalars = ['7', '2', '3', '-3', '3.14', '0.125', '-0.5', '2.0', '-2.0']
        scalar_pairs = [(scalar1, scalar2) for scalar1 in scalars for scalar2 in scalars]

        def run_test(code):
            scope = {}
            execWrapper(code, globals(), scope)
            cu = torch.jit.CompilationUnit(code)

            self.assertEqual(cu.func(), scope['func']())

        for scalar1, scalar2 in scalar_pairs:
            for op in ops:
                code = ops_template.format(op=op, scalar1=scalar1, scalar2=scalar2)
                run_test(code)
            for func in funcs:
                code = funcs_template.format(func=func, scalar1=scalar1, scalar2=scalar2)
                run_test(code)

        # test Scalar overloads
        for scalar1, scalar2 in scalar_pairs:
            item1 = 'torch.tensor(' + scalar1 + ').item()'
            item2 = 'torch.tensor(' + scalar2 + ').item()'
            for op in ops:
                code = ops_template.format(op=op, scalar1=item1, scalar2=scalar2)
                run_test(code)
                code = ops_template.format(op=op, scalar1=scalar1, scalar2=item2)
                run_test(code)
                code = ops_template.format(op=op, scalar1=item1, scalar2=item2)
                run_test(code)
            for func in funcs:
                code = funcs_template.format(func=func, scalar1=item1, scalar2=scalar2)
                run_test(code)
                code = funcs_template.format(func=func, scalar1=scalar1, scalar2=item2)
                run_test(code)
                code = funcs_template.format(func=func, scalar1=item1, scalar2=item2)
                run_test(code)

    def test_number_abs(self):
        def func1(x):
            # type: (float) -> float
            return abs(x)

        def func2(x):
            # type: (int) -> int
            return abs(x)

        def func3(x):
            return abs(x)

        self.checkScript(func1, (-3.14,))
        self.checkScript(func1, (3.14,))
        self.checkScript(func2, (-10,))
        self.checkScript(func2, (10,))
        self.checkScript(func3, (torch.tensor([-5, -10, -20]),))
        self.checkScript(func3, (torch.tensor([5, 10, 20]),))
        self.checkScript(func3, (torch.tensor([-5, 10, -20]),))

    def test_number_div(self):
        self.assertEqual(div_int_future(), torch.jit.script(div_int_future)())
        self.checkScript(div_float_future, ())

        if PY2:
            with self.assertRaisesRegex(torch.jit.frontend.FrontendError, 'from __future__ import division') as cm:
                torch.jit.script(div_int_nofuture)
            FileCheck().check("div_int_nofuture").run(str(cm.exception))
            with self.assertRaisesRegex(torch.jit.frontend.FrontendError, 'from __future__ import division') as cm:
                torch.jit.script(div_float_nofuture)
            FileCheck().check("div_float_nofuture").run(str(cm.exception))
        else:
            self.checkScript(div_int_nofuture, ())
            self.checkScript(div_float_nofuture, ())

    def test_floor_div(self):
        @torch.jit.script
        def foo(a, b):
            # type: (int, int) -> int
            return a // b
        for i in range(-8, 8):
            for j in range(-8, 8):
                if j != 0:
                    self.assertEqual(foo(i, j), i // j)
                else:
                    with self.assertRaisesRegex(RuntimeError, 'division by 0'):
                        foo(i, j)

    def test_number_augassign(self):
        def func():
            z = 1
            z += 2
            return z

        self.checkScript(func, (), optimize=True)

    def test_number_neg(self):
        # int -> int
        def func1():
            return -8

        # float -> float
        def func2():
            return -3.14

        self.checkScript(func1, (), optimize=True)
        self.checkScript(func2, (), optimize=True)

    def _test_tensor_number_math(self, device='cpu'):
        template = dedent('''
        def func(t):
            return {lhs} {op} {rhs}
        ''')

        def test(op, tensor, const, swap_args, template=template):
            args = ('t', const)
            if swap_args:
                args = (const, 't')

            code = template.format(lhs=args[0], rhs=args[1], op=op)
            scope = {}
            execWrapper(code, globals(), scope)
            cu = torch.jit.CompilationUnit(code)
            message = 'with code `{} {} {}` and t={}'.format(args[0], op, args[1], tensor)
            res1 = cu.func(tensor)
            res2 = scope['func'](tensor)
            self.assertEqual(res1, res2, message + "\nres1=" + str(res1) + "\nres2=" + str(res2))
            self.assertEqual(res1.dtype, res2.dtype, message + "\nres1=" + str(res1) + "\nres2=" + str(res2))

        var_int = [2, -2]
        var_float = [1.4321, -1.2]

        ops = ['+', '-', '*', '%', '<', '<=', '>', '>=', '==', '!=', '/']

        float_tensor = torch.randn(5, 5, device=device)
        double_tensor = torch.randn(5, 5, dtype=torch.double, device=device)
        long_tensor = torch.randint(-5, 5, (5, 5), dtype=torch.long, device=device)
        long_tensor[long_tensor == 0] = 2

        tensors = [float_tensor, double_tensor, long_tensor]
        consts = var_int + var_float

        for op, tensor, const, swap_args in product(ops, tensors, consts, [True, False]):
            # FIXME: things like 2 / long_tensor are not implemented correctly
            # Look in torch/tensor.py to see how pytorch implements it.
            if op == '/' and tensor.data_ptr() == long_tensor.data_ptr():
                continue

            # % operator does not take: const % tensor
            if op == '%' and swap_args is True:
                continue

            test(op, tensor, const, swap_args)

    def test_tensor_number_math(self):
        self._test_tensor_number_math()

    def test_torch_tensor_bad_input(self):
        with self.assertRaisesRegex(RuntimeError, "Input list to torch.tensor must be of ints, floats, "
                                    "or bools, got None"):
            @torch.jit.script
            def test():
                return torch.tensor([None])

        with self.assertRaisesRegex(RuntimeError, r"Empty lists default to List\[Tensor\]"):
            @torch.jit.script
            def tmp():
                return torch.tensor([])

        @torch.jit.script
        def foo():
            return torch.tensor([[2, 2], [1]])
        with self.assertRaisesRegex(RuntimeError, "Expected sequence of length"):
            foo()

    @suppress_warnings
    def test_torch_tensor_as_tensor_empty_list(self):
        tensor_template = dedent('''
        def func():
            empty_list = torch.jit.annotate(List[int], [])
            ten1 = torch.{tensor_op}({input})
            return ten1
        ''')
        ops = ['tensor', 'as_tensor']
        inputs = ['empty_list', '[empty_list, empty_list]', '[[[empty_list]]]']

        for op in ops:
            for inp in inputs:
                code = tensor_template.format(tensor_op=op, input=inp)
                scope = {}
                exec(code, globals(), scope)
                cu = torch.jit.CompilationUnit(code)
                t1 = cu.func()
                t2 = scope['func']()
                if inp == 'empty_list':
                    # torchscript returns int tensor, python returns float tensor
                    self.assertNotEqual(t1.dtype, t2.dtype)

                self.assertEqual(t1, t2)
                self.assertEqual(t1.device, t2.device)


    @unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.LEGACY, "Simple Executor doesn't have any shapes to propagate")
    def test_tensor_as_tensor_shape_prop(self):
        tensor_template = dedent('''
        def func():
            return torch.{tensor_op}({input})
        ''')
        ops = ['tensor', 'as_tensor']
        inputs = ['[1]', '[False]', '[2.5]', '0.5', '1', 'False', '[[1]]']
        expected_shape = ["Long(*)", ("Bool(*)"), "Double(*)", "Double()", "Long()", "Bool()", "Long(*, *)"]

        for op in ops:
            for inp, expect in zip(inputs, expected_shape):
                code = tensor_template.format(tensor_op=op, input=inp)
                scope = {}
                exec(code, globals(), scope)
                cu = torch.jit.CompilationUnit(code)
                torch._C._jit_pass_complete_shape_analysis(cu.func.graph, (), False)
                FileCheck().check(expect).check("aten::{tensor_op}".format(tensor_op=op)).run(cu.func.graph)

        @torch.jit.script
        def test_dtype(inp_dtype):
            # type: (int) -> Tuple[Tensor, Tensor]
            a = torch.tensor(1.0, dtype=torch.float, requires_grad=True)
            return a, torch.tensor(1.0, dtype=inp_dtype)  # noqa T484

        if GRAPH_EXECUTOR == ProfilingMode.PROFILING:
            g = test_dtype.graph_for(5, profile_and_replay=True)
            # both should have completed shapes
            FileCheck().check("Tensor = aten::tensor").check("Float() = prim::BailOut") \
                       .check("Tensor = aten::tensor").check("Half() = prim::BailOut").run(g)
        else:
            g = test_dtype.graph_for(5)
            # first should have type set second should not
            FileCheck().check("Float() = aten::tensor").check("Tensor = aten::tensor").run(g)

        @torch.jit.script
        def test_as_tensor_tensor_input(input):
            a = torch.as_tensor(input, dtype=input.dtype)
            return a, torch.as_tensor(input, dtype=torch.float)

        if GRAPH_EXECUTOR == ProfilingMode.PROFILING:
            g = test_as_tensor_tensor_input.graph_for(torch.ones(3, 4), profile_and_replay=True)
            FileCheck().check("Tensor = aten::as_tensor").check("Float(3, 4) = prim::BailOut") \
                       .check("Tensor = aten::as_tensor").check("Float(3, 4) = prim::BailOut").run(g)
        else:
            g = test_as_tensor_tensor_input.graph_for(torch.ones(3, 4))
            FileCheck().check("Tensor = aten::as_tensor").check("Float(*, *) = aten::as_tensor").run(g)


    def test_tensor_requires_grad(self):
        @torch.jit.script
        def test(b):
            # type: (bool) -> Tuple[Tensor, Tensor, Tensor]
            a = torch.tensor(1., requires_grad=b)
            b = torch.tensor(1., requires_grad=True)  # noqa T484
            c = torch.tensor(1., requires_grad=False)
            return a, b, c  # noqa T484

        g = test.graph_for(True)
        out = next(g.outputs())
        out_inp = list(out.node().inputs())

        self.assertTrue(out_inp[0].requires_grad())
        self.assertTrue(out_inp[1].requires_grad())
        self.assertFalse(out_inp[2].requires_grad())

    def test_grad_from_script(self):
        def test():
            a = torch.tensor(2.5, requires_grad=True)
            b = a * 2
            return a, b

        a, b = test()
        b.backward()

        a_script, b_script = torch.jit.script(test)()
        b_script.backward()
        self.assertEqual(a.grad, a_script.grad)

    def test_torch_tensor_as_tensor(self):
        tensor_template = dedent('''
        def func():
            li = {list_create}
            ten1 = torch.{tensor_op}(li {options})
            return ten1
        ''')

        lists = ["2.5", "4", "True", "False", "[2]", "[-.5]", "[False, True, False]", "[2, 2]", "(1, 1)",
                 "torch.jit.annotate(List[int], [])", "[2.5, 2.5]", "[[2], [2]]", "[[-.5], [2.2]]", "[[False], [True]]"]

        dtypes = ["", ", dtype=torch.float", ", dtype=torch.double", ", dtype=torch.half",
                  ", dtype=torch.uint8", ", dtype=torch.int8", ", dtype=torch.short",
                  ", dtype=torch.int", ", dtype=torch.long"]

        ops = ['tensor', 'as_tensor']
        devices = ['', ", device='cpu'"]
        if RUN_CUDA:
            devices.append(", device='cuda'")

        option_pairs = [dtype + device for dtype in dtypes for device in devices]
        for op in ops:
            for li in lists:
                for option in option_pairs:
                    # tensor from empty list is type float in python and annotated type in torchscript
                    if "annotate" in li and "dtype" not in option:
                        continue
                    code = tensor_template.format(list_create=li, tensor_op=op, options=option)
                    scope = {}
                    exec(code, globals(), scope)
                    cu = torch.jit.CompilationUnit(code)
                    t1 = cu.func()
                    t2 = scope['func']()
                    if t1.dtype == torch.float16:  # equality NYI for half tensor
                        self.assertTrue(str(t1) == str(t2))
                    else:
                        self.assertEqual(t1, t2)
                    self.assertEqual(t1.dtype, t2.dtype)
                    self.assertEqual(t1.device, t2.device)

        def test_as_tensor_tensor_input(input):
            # type: (Tensor) -> Tuple[Tensor, Tensor]
            return torch.as_tensor(input, dtype=torch.float), torch.as_tensor(input, dtype=torch.int32)

        inp = torch.randn(3, 4)
        self.checkScript(test_as_tensor_tensor_input, (inp,))

    # adapted from test in test_torch
    def test_tensor_to(self):
        template = dedent('''
        def func(t):
            cuda = "{cuda}"
            device = "{device}"
            non_blocking = {non_blocking}
            return {to_str}
        ''')

        def s(t, to_str, non_blocking=None, device=None, cuda=None):
            device = device if device is not None else str(t.device)
            non_blocking = non_blocking if non_blocking is not None else False
            cuda = "cuda" if cuda is None else cuda
            code = template.format(to_str=to_str, device=device, non_blocking=non_blocking, cuda=cuda)
            scope = {}
            cu = torch.jit.CompilationUnit(code)
            return cu.func(t, profile_and_replay=True)

        def test_copy_behavior(t, non_blocking=False):
            self.assertIs(t, s(t, 't.to(t, non_blocking=non_blocking)', non_blocking))
            self.assertIs(t, s(t, 't.to(t.dtype, non_blocking=non_blocking)', non_blocking))
            self.assertIs(t, s(t, 't.to(torch.empty_like(t), non_blocking=non_blocking)', non_blocking))
            self.assertIsNot(t, s(t, 't.to(t, non_blocking=non_blocking, copy=True)', non_blocking))
            self.assertIsNot(t, s(t, 't.to(t.dtype, non_blocking=non_blocking, copy=True)', non_blocking))
            self.assertIsNot(t, s(t, 't.to(torch.empty_like(t), non_blocking=non_blocking, copy=True)', non_blocking))

            devices = [t.device]
            if t.device.type == 'cuda':
                if t.device.index == -1:
                    devices.append('cuda:{}'.format(torch.cuda.current_device()))
                elif t.device.index == torch.cuda.current_device():
                    devices.append('cuda')
            for device in devices:
                self.assertIs(t, s(t, 't.to(device, non_blocking=non_blocking)', non_blocking, device))
                self.assertIs(t, s(t, 't.to(device, t.dtype, non_blocking=non_blocking)', non_blocking, device))
                self.assertIsNot(t, s(t, 't.to(device, non_blocking=non_blocking, copy=True)', non_blocking, device))
                self.assertIsNot(t, s(t, 't.to(device, t.dtype, non_blocking=non_blocking, copy=True)',
                                      non_blocking, device))

        t = torch.tensor(5)
        test_copy_behavior(t)

        self.assertEqual(t.device, s(t, "t.to('cpu')").device)
        self.assertEqual(t.device, s(t, "t.to('cpu', dtype=torch.float32)").device)
        self.assertIs(torch.float32, s(t, "t.to('cpu', dtype=torch.float32)").dtype)
        self.assertEqual(t.device, s(t, "t.to(torch.float32)").device)
        self.assertIs(torch.float32, s(t, "t.to(dtype=torch.float32)").dtype)
        self.assertEqual(t.data_ptr(), s(t, "t.to('cpu')").data_ptr())
        self.assertEqual(t.data_ptr(), s(t, "t.to(dtype=t.dtype, device=t.device, copy=False)").data_ptr())
        self.assertEqual(t.data_ptr(), s(t, "t.to('cpu', copy=False)").data_ptr())
        self.assertNotEqual(t.data_ptr(), s(t, "t.to('cpu', copy=True)").data_ptr())

        a = torch.tensor(5)
        if torch.cuda.is_available():
            for non_blocking in [True, False]:
                for cuda in ['cuda', 'cuda:0' if torch.cuda.device_count() == 1 else 'cuda:1']:
                    b = torch.tensor(5., device=cuda)
                    test_copy_behavior(b, non_blocking)
                    self.assertEqual(b.device, s(b, "t.to(cuda, non_blocking=non_blocking).device", cuda=cuda))
                    self.assertEqual(a.device, s(b, "t.to('cpu', non_blocking=non_blocking).device"))
                    self.assertEqual(b.device, s(b, "t.to(cuda, non_blocking=non_blocking).device", cuda=cuda))
                    self.assertIs(torch.int32, s(b, "t.to('cpu', dtype=torch.int32, non_blocking=non_blocking)").dtype)
                    self.assertEqual(a.device, s(b, "t.to('cpu', dtype=torch.int32, non_blocking=non_blocking)").device)
                    self.assertIs(torch.int32, s(b, "t.to(dtype=torch.int32)").dtype)
                    self.assertEqual(b.device, s(b, "t.to(dtype=torch.int32)").device)

        # Test AD: aten::to(Tensor self, int dtype, bool non_blocking, bool copy) -> Tensor
        t = torch.tensor(5).float().requires_grad_()
        out_ref = t.to(torch.float32)
        out = s(t, "t.to(torch.float32)")
        self.assertEqual(out_ref, out)

        grad_ref = torch.autograd.grad(out_ref.sum(), t)
        grad = torch.autograd.grad(out.sum(), t)
        self.assertEqual(grad_ref, grad)

        # Test AD: aten::to(Tensor self, Device? device, int? dtype, bool non_blocking, bool copy) -> Tensor
        out_ref = t.to('cpu')
        out = s(t, "t.to('cpu')")
        self.assertEqual(out_ref, out)

        grad_ref = torch.autograd.grad(out_ref.sum(), t)
        grad = torch.autograd.grad(out.sum(), t)
        self.assertEqual(grad_ref, grad)

        # Test AD: aten::to(Tensor self, Tensor other, bool non_blocking, bool copy) -> Tensor
        @torch.jit.script
        def func2(t, t_ref):
            return t.to(t_ref)

        with disable_autodiff_subgraph_inlining():
            t_ref = torch.tensor(4).double()
            out_ref = t.to(t_ref)
            out = func2(t, t_ref)
            grad_ref = torch.autograd.grad(out_ref.sum(), t)
            grad = torch.autograd.grad(out.sum(), t)
            self.assertEqual(grad_ref, grad)

    @unittest.skipIf(not RUN_CUDA, "No CUDA")
    def test_tensor_number_math_cuda(self):
        self._test_tensor_number_math(device='cuda')

    def test_not(self):
        # test not operator in python
        # TODO: add more tests when bool conversions ready
        def test_not_op(a):
            return not bool(a > 1)

        self.checkScript(test_not_op, (torch.tensor(2), ), optimize=True)

    def test_is_isnot(self):
        # test is and is not operator in python
        template = dedent('''
        def func():
            # type: () -> bool
            return {lhs} {op} {rhs}
        ''')

        def test(op, args):
            code = template.format(lhs=args[0], rhs=args[1], op=op)
            scope = {}
            execWrapper(code, globals(), scope)
            cu = torch.jit.CompilationUnit(code)
            self.assertEqual(
                cu.func(),
                scope['func'](),
                "Failed with op: {}, lhs: {}, rhs: {}"
                .format(op, args[0], args[1])
            )

        ops = ['is', 'is not']
        type_literals = [True, False, None, [1, 1], 1, 2, .5, 1.5]

        # do literals product to try any types combinations
        for op, lhs, rhs in product(ops, type_literals, type_literals):
            test(op, [lhs, rhs])

    def test_isinstance_refinement(self):
        @torch.jit.script
        def foo(a):
            # type: (Optional[int]) -> int
            if isinstance(a, int):
                return a + 3
            else:
                return 4
        self.assertEqual(foo(4), 7)
        self.assertEqual(foo(None), 4)
        @torch.jit.script
        def foo2(a, b):
            # type: (Optional[int], Optional[int]) -> int
            if not isinstance(a, int) or not isinstance(b, int):
                return 0
            else:
                return a + b
        self.assertEqual(foo2(3, 4), 7)
        self.assertEqual(foo2(None, 4), 0)
        self.assertEqual(foo2(4, None), 0)

        @torch.jit.script
        def any_refinement(a, b):
            # type: (Any, Any) -> int
            if isinstance(a, int) and isinstance(b, int):
                return a + b
            return 0

        self.assertEqual(any_refinement(3, 4), 7)
        self.assertEqual(any_refinement(3, "hi"), 0)

    def test_any_in_class_fails(self):
        with self.assertRaisesRegex(RuntimeError, "contains an Any"):
            @torch.jit.script
            class Foo(object):
                def __init__(self, a):
                    # type: (Tuple[int,Any]) -> None
                    self.a = a

                def hi(self):
                    pass

    def test_isinstance(self):
        # test isinstance operator for static type checking
        template = dedent('''
        def func(x):
            # type: ({type_hint}) -> bool
            return isinstance(x, {typ})
        ''')

        def test(inp, typ, type_hint):
            code = template.format(typ=typ, type_hint=type_hint)
            scope = {}
            execWrapper(code, globals(), scope)
            cu = torch.jit.CompilationUnit(code)
            self.assertEqual(
                cu.func(inp),
                scope['func'](inp),
                "Failed with typ: {}"
                .format(typ)
            )

        inputs = [True, 1, 1.0, torch.tensor(1), [1, 2], (1.0,), [1, 2], 1]
        type_literals = ['bool', 'int', 'float', 'torch.Tensor', 'list', 'tuple',
                         '(list, tuple)', '(int, float, bool)']
        type_annotations = ['bool', 'int', 'float', 'Tensor', 'List[int]', 'Tuple[float]',
                            'List[int]', 'int']

        # do zipping to try different types
        for inp, typ, type_hint in zip(inputs, type_literals, type_annotations):
            test(inp, typ, type_hint)

        # test optional isinstance check
        @torch.jit.script
        def opt_func(x):
            # type: (Optional[int]) -> bool
            return isinstance(x, int)
        self.assertTrue(opt_func(3))
        self.assertFalse(opt_func(None))

    def test_dropout_eval(self):
        class ScriptedConv2d(torch.jit.ScriptModule):
            def __init__(self, in_channels, out_channels, **kwargs):
                super(ScriptedConv2d, self).__init__()
                self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
                self.bn = nn.BatchNorm2d(out_channels, eps=0.001)

            @torch.jit.script_method
            def forward(self, x):
                x = self.conv(x)
                x = self.bn(x)
                return F.relu(x, inplace=True)

        class ScriptMod(torch.jit.ScriptModule):
            def __init__(self):
                super(ScriptMod, self).__init__()
                self.Conv2d_1a_3x3 = ScriptedConv2d(3, 32, kernel_size=3, stride=2)

            @torch.jit.script_method
            def forward(self, x):
                x = self.Conv2d_1a_3x3(x)
                return F.dropout(x, training=self.training)

        class EagerConv2d(torch.nn.Module):
            def __init__(self, in_channels, out_channels, **kwargs):
                super(EagerConv2d, self).__init__()
                self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
                self.bn = nn.BatchNorm2d(out_channels, eps=0.001)

            def forward(self, x):
                x = self.conv(x)
                x = self.bn(x)
                return F.relu(x, inplace=True)

        class EagerMod(torch.nn.Module):
            def __init__(self):
                super(EagerMod, self).__init__()
                self.Conv2d_1a_3x3 = EagerConv2d(3, 32, kernel_size=3, stride=2)

            def forward(self, x):
                x = self.Conv2d_1a_3x3(x)
                return F.dropout(x, training=self.training)

        script_input = torch.rand(4, 3, 299, 299)
        eager_input = script_input.clone()

        with freeze_rng_state():
            script_mod = ScriptMod()
            script_mod.eval()
            script_output = script_mod(script_input)

        with freeze_rng_state():
            eager_mod = EagerMod()
            eager_mod.eval()
            eager_output = eager_mod(eager_input)

        self.assertEqual(script_output, eager_output)

        with freeze_rng_state():
            script_mod = ScriptMod()
            script_mod.train()
            script_output = script_mod(script_input)

        with freeze_rng_state():
            eager_mod = EagerMod()
            eager_mod.train()
            eager_output = eager_mod(eager_input)

        self.assertEqual(script_output, eager_output)

    def test_nested_breaks(self):
        def no_bool_loop_outputs(g):
            # testing that the "did exit" transform values are not loop block
            # outputs (and thus not affecting one loop from another)
            loops = g.findAllNodes("prim::Loop")
            for loop in loops:
                for out in loop.outputs():
                    self.assertTrue(out.type() != BoolType.get())

        def test(y):
            # type: (int)
            ret = 0
            tensor = torch.tensor(0)
            while int(tensor.add_(1)) < 4:
                if y == 1:
                    continue
                for i in range(y):
                    continue
                    ret += 1
                ret += 1
            return ret, int(tensor)

        self.assertEqual(torch.jit.script(test)(1), test(1))
        self.assertEqual(torch.jit.script(test)(2), test(2))
        no_bool_loop_outputs(torch.jit.script(test).graph)

        def foo():
            y = torch.tensor(0)
            z = 0
            while int(y.add_(1)) < 20:
                if int(y) < 10:
                    for i in range(6):
                        if i == 3:
                            continue
                        else:
                            if i > 3:
                                break
                        z += 2
                if int(y) == 18:
                    break
                if int(y) == 15:
                    continue
                z += 1
            return int(y), z

        no_bool_loop_outputs(torch.jit.script(foo).graph)
        self.checkScript(foo, ())

        def test_nested_two():
            i = 0
            k = 0
            while i < 5:
                for j in range(5):
                    k += 1
                    if j == 3:
                        continue
                i += 1
                k += 1
                if i == 4:
                    break
            return i, k

        self.checkScript(test_nested_two, ())
        no_bool_loop_outputs(torch.jit.script(test_nested_two).graph)

    def test_breaks_continues(self):
        def foo_continue(cond):
            # type: (int)
            j = 1
            for i in range(5):
                if i == cond:
                    continue
                j += 1
            return j

        def foo_break(cond):
            # type: (int)
            j = 1
            for i in range(5):
                if i == cond:
                    break
                j += 1
            return j

        for i in range(1, 4):
            self.checkScript(foo_continue, (i,))
            self.checkScript(foo_break, (i,))

        def test_refine_outside_loop():
            if True:
                x = None
            else:
                x = 1
            i = 0
            j = 0
            while (x is None or torch.jit._unwrap_optional(x) > 3):
                if i < 3:
                    if i < 3:
                        x = torch.jit.annotate(Optional[int], None)
                        i += 1
                        continue
                    x = 1
                else:
                    x = 1 if x is None else x
                x = x + 1
                j = x + x

            return x, j

        self.checkScript(test_refine_outside_loop, ())

        def assign_after_break(y):
            # type: (int)
            x = 0
            for i in range(y):
                x = y * 2 + i
                break
                x = 4
            return x

        self.checkScript(assign_after_break, (1,))
        self.checkScript(assign_after_break, (2,))
        self.checkScript(assign_after_break, (3,))

        def assign_after_break_nested(y):
            # type: (int)
            x = 0
            for i in range(y):
                if y == 1:
                    x = 5
                    break
                    assert 1 == 2
                else:
                    x = x + 1
                    break
                    assert 1 == 2
                x = -30
                assert 1 == 2
            return x

        self.checkScript(assign_after_break_nested, (1,))
        self.checkScript(assign_after_break_nested, (2,))
        self.checkScript(assign_after_break_nested, (3,))

        def may_break(y):
            # type: (int)
            x = 0
            for i in range(y):
                if y == 1:
                    x = 5
                else:
                    x = x + 1
                    break
                x = -30
            return x

        self.checkScript(may_break, (1,))
        self.checkScript(may_break, (2,))
        self.checkScript(may_break, (3,))

        def test(x, y):
            # type: (int, int)
            a = 1
            while (x > 0):
                if y == 3:
                    for i in range(y):
                        a += (1 % (i + 1))
                        x -= 1
                if x == 3:
                    a = x * 3
                    break
                if x < 3:
                    if x == 1:
                        a -= 2
                        x -= 1
                        break
                a -= 1
                x -= 3
            return a, x

        self.checkScript(test, (10, 3))
        self.checkScript(test, (10, 2))
        self.checkScript(test, (3, 2))
        self.checkScript(test, (5, 3))
        self.checkScript(test, (2, 3))

        def test_delete_after_break(x):
            # type: (int)
            a = 1
            b = 1
            for i in range(x):
                a = i * 3
                break
                b = i * 5
            return a, b

        self.checkScript(test_delete_after_break, (0,))
        self.checkScript(test_delete_after_break, (1,))

        def test_will_break_after_guard(x):
            # type: (int)
            a = 1
            for i in range(x):
                if i == 4:
                    a = 3
                    break
                a -= 1
                break
                assert 1 == 2
                a -= -100
            return a

        self.checkScript(test_will_break_after_guard, (0,))
        self.checkScript(test_will_break_after_guard, (2,))
        self.checkScript(test_will_break_after_guard, (4,))

        def test_varexit(cond):
            # type: (int)
            m = 0
            for i in range(3):
                if cond == 2:
                    if cond == 2:
                        m = 2
                        break
                    k = 1
                else:
                    k = 2
                m += k
            return m

        # use of k tests the pathway where we have to insert unitialized
        self.checkScript(test_varexit, (3,))
        self.checkScript(test_varexit, (2,))

        def test_break_true():
            i = 0
            while True:
                i += 1
                if i == 3:
                    break
            while False:
                i += 1
            return i

        self.checkScript(test_break_true, ())

    def test_break_continue_error(self):
        with self.assertRaisesRegex(RuntimeError, "Syntax"):
            cu = torch.jit.CompilationUnit('''
            def other_func(a):
                break
                ''')

        with self.assertRaisesRegex(RuntimeError, "Syntax"):
            cu = torch.jit.CompilationUnit('''
            def other_func(a):
                for i in range(5):
                    def foo():
                        break
                ''')

        with self.assertRaisesRegex(RuntimeError, "do not support break or continue inside"):
            @torch.jit.script
            def foo(x):
                i = 0
                for a in (1, "2", 1.5):
                    b = a
                    if x:
                        break
                return b

    def test_python_call(self):
        def pyfunc(a):
            return a * 3.0

        cu = torch.jit.CompilationUnit('''
        def other_func(a):
            return a + a

        def test_call_python(a):
            b = pyfunc(a)
            b = other_func(b)
            i = 0
            step = 1
            while i < 10:
                b = pyfunc(b)
                if bool(b > 3.0):
                    b = pyfunc(b)
                i = 11
            return b
        ''')
        inputs = self._make_scalar_vars([1], torch.float)
        outputs = self._make_scalar_vars([54], torch.float)

        self.assertEqual(cu.test_call_python(*inputs), outputs[0])

    def test_python_call_failure(self):
        with self.assertRaisesRegex(RuntimeError, "undefined value pyfunc2"):
            def pyfunc(a):
                return a * 3.0

            cu = torch.jit.CompilationUnit('''
            def other_func(a):
                return a + a

            def test_call_python(a):
                b = pyfunc(a)
                b = other_func(b)
                i = 0
                step = 1
                while i < 10:
                    b = pyfunc2(b)
                    if b > 3.0:
                        b = pyfunc(b)
                    i = 11
                return b
            ''')
            inputs = self._make_scalar_vars([1], torch.float)
            outputs = self._make_scalar_vars([54], torch.float)

            self.assertEqual(cu.test_call_python(*inputs), outputs)

    def test_type_call_in_script(self):
        @torch.jit.script
        def fn(x):
            return type(x)

        with self.assertRaisesRegex(RuntimeError, "value of type type"):
            fn(torch.tensor(.5))

    def test_python_call_annotation(self):
        def pyfunc(a):
            return a * 3.0

        @torch.jit.script
        def foo(a):
            return pyfunc(a) + pyfunc(a)

        inputs = self._make_scalar_vars([1], torch.float)
        outputs = self._make_scalar_vars([6], torch.float)
        self.assertEqual(foo(*inputs), outputs[0])

    def test_python_call_annoytation_failure(self):
        with self.assertRaisesRegex(RuntimeError, "undefined value pyfunc2"):
            def pyfunc(a):
                return a * 3.0

            @torch.jit.script
            def foo(a):
                return pyfunc2(a) + pyfunc(a)

            inputs = self._make_scalar_vars([1], torch.float)
            outputs = self._make_scalar_vars([6], torch.float)

            self.assertEqual(foo(*inputs), outputs[0])

    def test_desugar_module(self):
        import torch.nn.functional as F

        def fn(x, slope):
            a = torch.abs(x)
            b = torch.nn.functional.prelu(x, slope)
            c = F.prelu(x, slope)
            return a, b, c

        x = torch.arange(-3., 4)
        slope = torch.tensor([0.5])
        self.checkScript(fn, [x, slope], optimize=True)

    def test_script_docstring(self):
        @torch.jit.script
        def with_docstring(x):
            """test str"""
            y = x
            """y is the same as x"""
            return y
        self.assertEqual(with_docstring.__doc__, 'test str')

    def test_script_method_docstring(self):
        class A(torch.jit.ScriptModule):
            @torch.jit.script_method
            def with_docstring(self, x):
                """test str"""
                y = x
                """y is the same as x"""
                return y
        a = A()
        self.assertEqual(a.with_docstring.__doc__, 'test str')

    @unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines,
                         'Quantized RNN requires FBGEMM. FBGEMM is only optimized for CPUs'
                         ' with instruction set support avx2 or newer.')
    def test_rnn_cell_quantized(self):
        d_in, d_hid = 2, 2

        for cell in [
            torch.nn.LSTMCell(d_in, d_hid).float(),
            torch.nn.GRUCell(d_in, d_hid).float(),
            torch.nn.RNNCell(d_in, d_hid).float(),
        ]:
            if isinstance(cell, torch.nn.LSTMCell):
                num_chunks = 4
            elif isinstance(cell, torch.nn.GRUCell):
                num_chunks = 3
            elif isinstance(cell, torch.nn.RNNCell):
                num_chunks = 1

            # Replace parameter values s.t. the range of values is exactly
            # 255, thus we will have 0 quantization error in the quantized
            # GEMM call. This i s for testing purposes.
            #
            # Note that the current implementation does not support
            # accumulation values outside of the range representable by a
            # 16 bit integer, instead resulting in a saturated value. We
            # must take care that in our test we do not end up with a dot
            # product that overflows the int16 range, e.g.
            # (255*127+255*127) = 64770. So, we hardcode the test values
            # here and ensure a mix of signedness.
            vals = [[100, -155],
                    [100, -155],
                    [-155, 100],
                    [-155, 100],
                    [100, -155],
                    [-155, 100],
                    [-155, 100],
                    [100, -155]]
            vals = vals[:d_hid * num_chunks]
            cell.weight_ih = torch.nn.Parameter(
                torch.tensor(vals, dtype=torch.float),
                requires_grad=False)
            cell.weight_hh = torch.nn.Parameter(
                torch.tensor(vals, dtype=torch.float),
                requires_grad=False)

            ref = copy.deepcopy(cell)

            cell = torch.jit.quantized.quantize_rnn_cell_modules(cell)
            x = torch.tensor([[100, -155],
                              [-155, 100],
                              [100, -155]], dtype=torch.float)
            h0_vals = [[-155, 100],
                       [-155, 155],
                       [100, -155]]
            hx = torch.tensor(h0_vals, dtype=torch.float)
            if isinstance(cell, torch.jit.quantized.QuantizedLSTMCell):
                cx = torch.tensor(h0_vals, dtype=torch.float)
                hiddens = (hx, cx)
            else:
                hiddens = hx

            if isinstance(cell, torch.jit.quantized.QuantizedLSTMCell):
                class ScriptWrapper(torch.jit.ScriptModule):
                    def __init__(self, cell):
                        super(ScriptWrapper, self).__init__()
                        self.cell = cell

                    @torch.jit.script_method
                    def forward(self, x, hiddens):
                        # type: (torch.Tensor, Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]
                        return self.cell(x, hiddens)
            else:

                class ScriptWrapper(torch.jit.ScriptModule):
                    def __init__(self, cell):
                        super(ScriptWrapper, self).__init__()
                        self.cell = cell

                    @torch.jit.script_method
                    def forward(self, x, hiddens):
                        # type: (torch.Tensor, torch.Tensor) -> torch.Tensor
                        return self.cell(x, hiddens)

            cell = ScriptWrapper(cell)
            outs = cell(x, hiddens)
            cell = self.getExportImportCopyWithPacking(cell)

            outs = cell(x, hiddens)
            ref_outs = ref(x, hiddens)

            self.assertEqual(len(outs), len(ref_outs))
            for out, ref_out in zip(outs, ref_outs):
                torch.testing.assert_allclose(out, ref_out)

    @unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines,
                         'Quantized RNN requires FBGEMM. FBGEMM is only optimized for CPUs'
                         ' with instruction set support avx2 or newer.')
    def test_rnn_quantized(self):
        d_in, d_hid = 2, 2

        for cell in [
            torch.nn.LSTM(d_in, d_hid).float(),
            torch.nn.GRU(d_in, d_hid).float(),
        ]:

            # Replace parameter values s.t. the range of values is exactly
            # 255, thus we will have 0 quantization error in the quantized
            # GEMM call. This i s for testing purposes.
            #
            # Note that the current implementation does not support
            # accumulation values outside of the range representable by a
            # 16 bit integer, instead resulting in a saturated value. We
            # must take care that in our test we do not end up with a dot
            # product that overflows the int16 range, e.g.
            # (255*127+255*127) = 64770. So, we hardcode the test values
            # here and ensure a mix of signedness.
            vals = [[100, -155],
                    [100, -155],
                    [-155, 100],
                    [-155, 100],
                    [100, -155],
                    [-155, 100],
                    [-155, 100],
                    [100, -155]]
            if isinstance(cell, torch.nn.LSTM):
                num_chunks = 4
            elif isinstance(cell, torch.nn.GRU):
                num_chunks = 3
            vals = vals[:d_hid * num_chunks]
            cell.weight_ih_l0 = torch.nn.Parameter(
                torch.tensor(vals, dtype=torch.float),
                requires_grad=False)
            cell.weight_hh_l0 = torch.nn.Parameter(
                torch.tensor(vals, dtype=torch.float),
                requires_grad=False)

            ref = copy.deepcopy(cell)
            cell_int8 = torch.jit.quantized.quantize_rnn_modules(cell, dtype=torch.int8)
            cell_fp16 = torch.jit.quantized.quantize_rnn_modules(cell, dtype=torch.float16)

            niter = 10
            x = torch.tensor([[100, -155],
                              [-155, 100],
                              [100, -155]], dtype=torch.float).unsqueeze(0).repeat(niter, 1, 1)
            h0_vals = [[-155, 100],
                       [-155, 155],
                       [100, -155]]
            hx = torch.tensor(h0_vals, dtype=torch.float).unsqueeze(0)
            cx = torch.tensor(h0_vals, dtype=torch.float).unsqueeze(0)

            if isinstance(ref, torch.nn.LSTM):
                hiddens = (hx, cx)
            elif isinstance(ref, torch.nn.GRU):
                hiddens = hx

            ref_out, ref_hid = ref(x, hiddens)

            # Compare int8 quantized to unquantized
            output_int8, final_hiddens_int8 = cell_int8(x, hiddens)

            torch.testing.assert_allclose(output_int8, ref_out)
            for out, ref in zip(final_hiddens_int8, ref_hid):
                torch.testing.assert_allclose(out, ref)

            # Compare fp16 quantized to unquantized
            output_fp16, final_hiddens_fp16 = cell_fp16(x, hiddens)

            torch.testing.assert_allclose(output_fp16, ref_out)
            for out, ref in zip(final_hiddens_fp16, ref_hid):
                torch.testing.assert_allclose(out, ref)

            def compare_quantized_unquantized(ScriptWrapper, cell):
                wrapper = ScriptWrapper(cell)

                # Compare quantize scripted module to unquantized
                script_out, script_hid = wrapper(x, hiddens)
                torch.testing.assert_allclose(script_out, ref_out)
                for out, ref in zip(script_hid, ref_hid):
                    torch.testing.assert_allclose(out, ref)

                # Compare export/import to unquantized
                export_import_wrapper = self.getExportImportCopyWithPacking(wrapper)
                ei_out, ei_hid = export_import_wrapper(x, hiddens)
                torch.testing.assert_allclose(ei_out, ref_out)
                for out, ref in zip(ei_hid, ref_hid):
                    torch.testing.assert_allclose(out, ref)

            if isinstance(cell, torch.jit.quantized.QuantizedGRU):
                class ScriptWrapper(torch.jit.ScriptModule):
                    def __init__(self, cell):
                        super(ScriptWrapper, self).__init__()
                        self.cell = cell

                    @torch.jit.script_method
                    def forward(self, x, hiddens):
                        # type: (torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]
                        return self.cell(x, hiddens)

                compare_quantized_unquantized(ScriptWrapper, cell)
            elif isinstance(cell, torch.jit.quantized.QuantizedLSTM):
                for cell in [cell_int8, cell_fp16]:
                    class ScriptWrapper(torch.jit.ScriptModule):
                        def __init__(self, cell):
                            super(ScriptWrapper, self).__init__()
                            self.cell = cell

                        @torch.jit.script_method
                        def forward(self, x, hiddens):
                            # type: (torch.Tensor, Tuple[torch.Tensor, torch.Tensor])
                            #        -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]
                            return self.cell(x, hiddens)
                    compare_quantized_unquantized(ScriptWrapper, cell)

    def test_script_module(self):
        class M1(torch.jit.ScriptModule):
            def __init__(self):
                super(M1, self).__init__()
                self.weight = nn.Parameter(torch.randn(2))

            @torch.jit.script_method
            def forward(self, thing):
                return self.weight + thing

        class PModule(nn.Module):
            def __init__(self):
                super(PModule, self).__init__()
                self.a = nn.Parameter(torch.randn(2, 3))

            def forward(self, a):
                return self.a.mm(a)

        class M2(torch.jit.ScriptModule):
            def __init__(self):
                super(M2, self).__init__()
                # test submodule
                self.sub = M1()
                self.sub2 = PModule()
                # test parameters
                self.weight = nn.Parameter(torch.randn(2, 3))
                self.bias = nn.Parameter(torch.randn(2))
                # test defining a method from a string
                self.define("""
                    def hi(self, a):
                        return self.weight.mm(a)
                """)
            # test script methods

            @torch.jit.script_method
            def doit(self, input):
                # test use of parameter
                return self.weight.mm(input)

            @torch.jit.script_method
            def doit2(self, input):
                return self.weight.mm(input)

            @torch.jit.script_method
            def forward(self, input):
                a = self.doit(input)
                b = self.doit2(input)
                c = self.hi(input)
                d = self.sub2(input)
                return a + b + self.bias + self.sub(a) + c + d
        with torch.jit.optimized_execution(False):
            m2 = M2()
            input = torch.randn(3, 2)
            a = m2.weight.mm(input)
            b = m2.weight.mm(input)
            c = m2.weight.mm(input)
            d = m2.sub2.a.mm(input)
            ref = a + b + m2.bias + m2.sub.weight + a + c + d
            self.assertEqual(ref, m2.forward(input))
            m2.weight = nn.Parameter(torch.zeros_like(m2.weight))
            m2.bias = nn.Parameter(torch.zeros_like(m2.bias))
            m2.sub.weight = nn.Parameter(torch.zeros_like(m2.sub.weight))
            m2.sub2.a.data.zero_()
            self.assertEqual(torch.zeros(2, 2), m2.forward(torch.randn(3, 2)))

    def test_irparser(self):
        graph_str = """graph(%0 : Double(5, 5)):
          # CHECK: aten::relu
          %1 : Double(5, 5) = aten::relu(%0)
          return (%1)
        """
        FileCheck().run(graph_str, parse_ir(graph_str))

    def test_canonicalize_control_outputs(self):
        def test_all_outputs(g):
            ifs = g.findAllNodes("prim::If")
            loops = g.findAllNodes("prim::Loop")

            def contained_blocks(node):
                return len(node.findAllNodes("prim::If")) * 2 + len(node.findAllNodes("prim::Loop"))
            for node in ifs + loops:
                outs = list(node.outputs())
                out_name = list(map(lambda x: x.debugName(), outs))
                if len(out_name) == 0:
                    continue
                fc = FileCheck()
                # find the last output, then all subsequent uses
                fc.check(out_name[-1] + " : ")
                # skip past node body
                for i in range(contained_blocks(node)):
                    fc.check("->")
                if (node.kind() == "prim::If"):
                    fc.check("->").check("->").check("\n")
                else:
                    fc.check("->").check("\n")
                # the canonical order is the same order as the first use
                # appears in text
                for name in out_name:
                    fc.check(name)
                fc.run(g)

        @torch.jit.script
        def test(x):
            # type: (bool) -> Tuple[int, int]
            b = 2
            a = 1
            if x:
                a = 1
                b = 2
                x = False
            if x:
                b = a
            else:
                a = b

            return a, b
        test_all_outputs(test.graph)

        @torch.jit.script
        def test2(x):
            # type: (bool) -> Tuple[int, int]
            b = 2
            a = 1
            if x:
                a = 1
                b = 2
                x = False
            if x:
                print(a)
            else:
                if x:
                    print(b)

            return a, b
        test_all_outputs(test2.graph)

        @torch.jit.script
        def test_loop(x, iter):
            # type: (bool, int) -> (None)
            a = 1
            b = 2
            c = 3
            for i in range(iter):
                a = 4
                b = 5
                c = 6
                x = True
            print(c)
            if x:
                print(a, b)
        test_all_outputs(test_loop.graph)

        @torch.jit.script
        def loop_unused(iter):
            # type: (int) -> (None)
            a = 1
            b = 2
            c = 3
            for i in range(iter):
                c = c + 1
                b = b + 1
                a = a + 1
                print(a, b)
            print(c)

        # c is used, then unused should be ordered by alphabetical
        FileCheck().check(r"%c : int, %a : int, %b : int").run(loop_unused.graph)

    def test_filecheck(self):
        def test_check():
            file = "232"
            FileCheck().check("2").check("3").check("2").run(file)
            FileCheck().check("232").run(file)

            with self.assertRaisesRegex(RuntimeError, 'Expected to find "22"'):
                FileCheck().check("22").run(file)
            with self.assertRaisesRegex(RuntimeError, "CHECK: 3"):
                FileCheck().check("3").check("3").run(file)

        test_check()

        def test_check_count():
            file = "22222"
            FileCheck().check_count("2", 5).run(file)
            FileCheck().check_count("22", 2).run(file)
            FileCheck().check_count("222", 1).run(file)

            with self.assertRaisesRegex(RuntimeError, 'Expected to not find'):
                FileCheck().check_count("2", 4, exactly=True).run(file)

            with self.assertRaisesRegex(RuntimeError, 'Expected to find "22"'):
                FileCheck().check_count("22", 3).run(file)

            with self.assertRaisesRegex(RuntimeError, "CHECK-COUNT-6: 2"):
                FileCheck().check_count("2", 6).run(file)

        test_check_count()

        def test_check_same():
            file = "22\n33"
            FileCheck().check_same("22").run(file)

            with self.assertRaisesRegex(RuntimeError, "Expected to not find"):
                FileCheck().check_same("33").run(file)

            file = "22  1  3"

            FileCheck().check("2").check_same("3").run(file)
            FileCheck().check_count("2", 2).check_same("3").run(file)

        test_check_same()

        def test_check_next():
            file = "\n1\n2\n3"
            FileCheck().check("1").check_next("2").check_next("3").run(file)
            FileCheck().check_next("1").check_next("2").check_next("3").run(file)

            with self.assertRaisesRegex(RuntimeError, "Expected to find"):
                FileCheck().check("1").check_next("2").run("12")

            with self.assertRaisesRegex(RuntimeError, "Expected to not find"):
                FileCheck().check("1").check_next("2").run("1\n\n2")

        test_check_next()

        def test_check_dag():
            fc = FileCheck().check_dag("1").check_dag("2").check_not("2")
            fc.run("12")
            fc.run("21")

            fc = FileCheck()
            fc.check_not("3").check_dag("1").check_dag("2").check_not("3")
            fc.run("1 3 2")
            fc.run("2 3 1")

            fc = FileCheck().check_dag("1").check_dag("2").check("3")
            with self.assertRaisesRegex(RuntimeError, 'Expected to find "3" but did not find it'):
                fc.run("1 3 2")

        test_check_dag()

        def test_check_not():
            FileCheck().check_not("2").check("1").run("12")
            FileCheck().check("2").check_not("2").run("12")

            with self.assertRaisesRegex(RuntimeError, 'Expected to not find "2"'):
                FileCheck().check_not("2").check("1").run("21")

            with self.assertRaisesRegex(RuntimeError, 'Expected to not find "1"'):
                FileCheck().check("2").check_not("1").run("21")

            # checks with distinct range matchings
            fb = FileCheck().check_count("2", 2).check_count("2", 2).check_not("2")
            with self.assertRaisesRegex(RuntimeError, 'Expected to not find "2"'):
                fb.run("22 2 22")

            fb = FileCheck().check_count("2", 2).check_not("1").check_count("2", 2)
            with self.assertRaisesRegex(RuntimeError, 'Expected to not find "1"'):
                fb.run("22 1 22")

    def _dtype_to_jit_name(self, dtype):
        if(dtype == torch.float32):
            return "Float"
        if(dtype == torch.float64):
            return "Double"
        if(dtype == torch.int64):
            return "Long"
        if(dtype == torch.int32):
            return "Int"
        if(dtype == torch.bool):
            return "Bool"
        raise RuntimeError('dtype not handled')

    def _dtype_to_expect(self, dtype, dim=0):
        param = ', '.join(['*'] * dim)
        param = '(' + param + ')'
        jit_type = self._dtype_to_jit_name(dtype)
        if dim >= 0:
            return jit_type + param
        # special case representing wrapped number
        else:
            return jit_type.lower()


    def _test_dtype_op_shape(self, ops, args, input_dims=1):
        if input_dims < 1:
            raise 'input dims must be at least 1'
        dtypes = [torch.float32, torch.float64, torch.int64, torch.int32]
        str_args = ', '.join([str(arg) for arg in args]) + (', ' if len(args) else '')
        tensor_data = ('[' * input_dims) + '1, 2, 3' + (input_dims * ']')
        template = dedent('''
        def func():
            return {return_line}
        ''')

        for op in ops:
            for dtype in (dtypes + [None]):
                for tensor_type in dtypes:
                    # a couple of ops aren't implemented for non-floating types
                    if(not tensor_type.is_floating_point or (dtype is not None and not dtype.is_floating_point)):
                        if op in ['mean', 'softmax', 'log_softmax']:
                            continue
                    return_line = "torch.tensor({}, dtype={}).{}({}dtype={})".format(tensor_data, tensor_type, op, str_args, dtype)
                    # uncomment for debugging a failed test:
                    # print("testing {}".format(return_line))
                    code = template.format(return_line=return_line)
                    scope = {}
                    exec(code, globals(), scope)
                    cu = torch.jit.CompilationUnit(code)
                    graph = cu.func.graph
                    torch._C._jit_pass_complete_shape_analysis(graph, (), False)
                    input_array = [1, 2, 3]
                    for _ in range(1, input_dims):
                        input_array = [input_array]
                    t = torch.tensor(input_array, dtype=tensor_type)
                    attr = getattr(t, op)
                    kwargs = {'dtype': dtype}
                    result = attr(*args, **kwargs)
                    expect = self._dtype_to_expect(result.dtype, result.dim())
                    FileCheck().check("aten::tensor").check(expect).run(graph)

    def test_dtype_op_shape(self):
        ops = ['prod']
        self._test_dtype_op_shape(ops, args=[])
        self._test_dtype_op_shape(ops, args=[0, False])
        self._test_dtype_op_shape(ops, args=[0, False])
        self._test_dtype_op_shape(ops, args=[0, True])

    def test_dtype_op_shape2(self):
        ops = ['cumprod', 'cumsum', 'softmax', 'log_softmax']
        self._test_dtype_op_shape(ops, args=[0])

        self._test_dtype_op_shape(ops, args=[1], input_dims=4)


    def _test_binary_op_shape(self, ops, input_dims=1):

        dtypes = [torch.float32, torch.float64, torch.int64, torch.int32, torch.bool]

        if input_dims == 0:
            shape = '1'
        else:
            shape = '[' + ('1,' * 4) + ']'
            for _ in range(1, input_dims):
                shape = '[' + ",".join([shape] * 4) + ']'

        template = dedent('''
        def func():
            arg1 = {}
            arg2 = {}
            return torch.{}(arg1, arg2)
        ''')

        args = []
        for dtype in dtypes:
            args = args + ["torch.tensor({}, dtype={})".format(shape, dtype)]
        args = args + [1, 1.5]

        def isBool(arg):
            return type(arg) == bool or (type(arg) == str and "torch.bool" in arg)

        for op in ops:
            for first_arg in args:
                for second_arg in args:
                    # subtract not supported for bool
                    if (op == 'sub' or op == 'div') and (isBool(first_arg) or isBool(second_arg)):
                        continue
                    # div not implemneted correctly for mixed-type or in params
                    if (op == 'div' and (type(first_arg) != type(second_arg) or type(first_arg) == int)):
                        continue
                    return_line = "torch.{}({}, {})".format(op, first_arg, second_arg)
                    # uncomment for debugging a failed test:
                    # print("testing {}".format(return_line))
                    code = template.format(first_arg, second_arg, op)
                    scope = {}
                    exec(code, globals(), scope)
                    non_jit_result = scope['func']()

                    cu = torch.jit.CompilationUnit(code)
                    graph = cu.func.graph
                    torch._C._jit_pass_complete_shape_analysis(graph, (), False)
                    # use dim=-1 to represent a python/jit scalar.
                    dim = -1 if type(first_arg) != str and type(second_arg) != str else non_jit_result.dim()
                    dtype = non_jit_result.dtype
                    # jit only supports int/float scalars.
                    if dim < 0:
                        if dtype == torch.int64:
                            dtype = torch.int32
                        if dtype == torch.float64:
                            dtype = torch.float32
                    expect = self._dtype_to_expect(dtype, dim)
                    jit_output = next(graph.outputs())

                    check = FileCheck()
                    check.check(expect).run(str(jit_output))

    def test_binary_op_shape(self):
        self._test_binary_op_shape(['mul', 'div', 'add', 'sub'], 0)
        self._test_binary_op_shape(['mul', 'div', 'add', 'sub'], 3)

    def test_wrapped_number(self):
        # Scalar's get converted to 'wrapped' tensors of default tensor type.
        # Wrapped tensors behave differently in certain promotion operations:
        # float_tensor * double -> float but wrapped_float * double -> double.
        # This can cause issues in check-trace if not handled correctly in
        # `aten::isclose()`.

        def foobar():
            x = -10000.0
            result = x * torch.ones(1, dtype=torch.float)
            return result

        scripted = torch.jit.trace(foobar, (), check_trace=True)

    def test_no_dtype_shape(self):

        @torch.jit.script
        def foo(x):
            scalar_number = x.item()
            return x.add(scalar_number)

        @torch.jit.script
        def foo2(x):
            scalar_number = x.item()
            return torch.tensor(1).add(scalar_number)

        t = torch.tensor(5)
        g = foo.graph_for(t)
        type = next(g.outputs())
        self.assertTrue(type.type() == torch._C.TensorType.get())
        g2 = foo2.graph_for(t)
        type = next(g.outputs())
        self.assertTrue(type.type() == torch._C.TensorType.get())


    def test_filecheck_parse(self):
        def test_check():
            file = """
                # CHECK: 2
                # CHECK: 3
                # CHECK: 2
                232
                """
            FileCheck().run(checks_file=file, test_file=file)
            file = """
                # CHECK: 232
                232
                """
            FileCheck().run(file, "232")
            with self.assertRaisesRegex(RuntimeError, 'Expected to find "232"'):
                FileCheck().run(file, "22")
            with self.assertRaisesRegex(RuntimeError, 'Expected to find "22"'):
                FileCheck().run("# CHECK: 22", "23")
        test_check()

        def test_check_count():
            file = "22222"
            FileCheck().run("# CHECK-COUNT-5: 2", file)
            FileCheck().run("# CHECK-COUNT-EXACTLY-5: 2", file)
            FileCheck().run("# CHECK-COUNT-2: 22", file)
            FileCheck().run("# CHECK-COUNT-1: 222", file)

            with self.assertRaisesRegex(RuntimeError, 'Expected to not find'):
                FileCheck().run("# CHECK-COUNT-EXACTLY-2: 2", file)
        test_check_count()

        def test_check_same():
            file = "22\n33"
            FileCheck().run("# CHECK-SAME: 22", file)

            with self.assertRaisesRegex(RuntimeError, "Expected to not find"):
                FileCheck().run("# CHECK-SAME: 33", file)

            file = "22  1  3"

            FileCheck().run("# CHECK: 2\n # CHECK-SAME: 3", file)
            FileCheck().run("# CHECK-COUNT-2: 2\n # CHECK-SAME: 3", file)
        test_check_same()

        def test_bad_input():
            with self.assertRaisesRegex(RuntimeError, "Check for bad input"):
                FileCheck().run("", "1")

            with self.assertRaisesRegex(RuntimeError, "Could not parse check"):
                FileCheck().run("# CHECK1", "")

        test_bad_input()

    def test_script_module_call_noscript(self):
        class M(torch.jit.ScriptModule):
            def __init__(self):
                super(M, self).__init__()
                self.value = 1

            @torch.jit.ignore
            def foo(self):
                return torch.ones(2, 2) + self.value

            @torch.jit.script_method
            def forward(self, input):
                return input + self.foo()

        with torch.jit.optimized_execution(False):
            m = M()
            input = torch.randn(2, 2)
            o = m(input)
            self.assertEqual(o, input + torch.ones(2, 2) + 1)
            # check that we can change python attributes
            # and that those changes are picked up in script methods
            m.value = 2
            o = m(input)
            self.assertEqual(o, input + torch.ones(2, 2) + 2)

    def test_script_module_nochange_submodule(self):
        class M(torch.jit.ScriptModule):
            def __init__(self):
                super(M, self).__init__()
                self.sub = nn.Linear(5, 5)

            @torch.jit.script_method
            def forward(self, input):
                return self.sub(input)
        with torch.jit.optimized_execution(False):
            m = M()
            input = torch.randn(1, 5, 5)
            o = m(input)
            self.assertEqual(o, m.sub(input))
            with self.assertRaisesRegex(RuntimeError, "Cannot re-assign"):
                m.sub = nn.Linear(5, 5)

    def test_script_inline_trace_multiple_args(self):
        class M(torch.nn.Module):
            def __init__(self):
                super(M, self).__init__()

            def forward(self, input, input2):
                return input + input2

        class M2(torch.jit.ScriptModule):
            def __init__(self):
                super(M2, self).__init__()
                self.m = torch.jit.trace(M(), (torch.zeros(4, 3), torch.zeros(4, 3)))

            @torch.jit.script_method
            def forward(self, inp):
                return self.m(inp, inp)

        with torch.jit.optimized_execution(False):
            m2 = M2()
            m2(torch.zeros(4, 3))

    def test_script_module_const(self):
        class M(torch.jit.ScriptModule):

            __constants__ = ['b', 'i', 'c', 's']

            def __init__(self):
                super(M, self).__init__()
                self.b = False
                self.i = 1
                self.c = 3.5
                self.s = ["hello"]

            @torch.jit.script_method
            def forward(self):
                return self.b, self.i, self.c

        with torch.jit.optimized_execution(False):
            m = M()
            o0, o1, o2 = m()
        self.assertEqual(o0, 0)
        self.assertEqual(o1, 1)
        self.assertEqual(o2, 3.5)

    def test_script_module_fail_exist(self):
        class M(torch.jit.ScriptModule):
            def __init__(self):
                super(M, self).__init__()

            @torch.jit.script_method
            def forward(self, x):
                return x + self.whatisgoingon
        with self.assertRaisesRegex(RuntimeError, "Module 'M' has no attribute"):
            M()

    @unittest.skip("[module dedupe] currently NoneType refinement on optional attributes doesn't work.")
    def test_script_module_none_exist_fail(self):
        class M(torch.jit.ScriptModule):
            def __init__(self, my_optional):
                super(M, self).__init__()
                self.my_optional = my_optional

            @torch.jit.script_method
            def forward(self, x):
                if self.my_optional is not None:
                    return torch.neg(x) + self.my_optional
                return torch.neg(x)
        with self.assertRaisesRegex(RuntimeError, "has no attribute 'my_optional'"):
            x = torch.rand(3, 4)
            fb = M(None)
            fb(x)

    def test_script_module_invalid_consts(self):
        class Foo(torch.jit.ScriptModule):
            __constants__ = ['invalid']

            def __init__(self):
                super(Foo, self).__init__()
                self.invalid = [nn.Linear(3, 4)]

        with self.assertRaisesRegex(
                TypeError,
                "'Linear' object for attribute 'invalid' is not a valid constant"):
            Foo()

        class Foo2(torch.jit.ScriptModule):
            __constants__ = ['invalid']

            def __init__(self):
                super(Foo2, self).__init__()
                self.invalid = type(1)

        with self.assertRaisesRegex(TypeError, "not a valid constant"):
            Foo2()

        class Foo3(torch.jit.ScriptModule):
            __constants__ = ['invalid']

            def __init__(self):
                super(Foo3, self).__init__()
                self.invalid = (3, 4, {})

        with self.assertRaisesRegex(TypeError, "not a valid constant"):
            Foo3()

    def test_script_module_param_buffer_mutation(self):
        # TODO: add param mutation test case after JIT support it
        class ModuleBufferMutate(torch.jit.ScriptModule):
            def __init__(self):
                super(ModuleBufferMutate, self).__init__()
                self.register_buffer('running_var', torch.tensor(0, dtype=torch.long))

            @torch.jit.script_method
            def forward(self):
                if self.training:
                    self.running_var += 1
                return self.running_var

        with torch.jit.optimized_execution(False):
            m = ModuleBufferMutate()
            self.assertEqual(m(), 1)
            m.eval()
            self.assertEqual(m(), 1)

    def test_script_module_for(self):
        class M(torch.jit.ScriptModule):
            __constants__ = ['b']

            def __init__(self):
                super(M, self).__init__()
                self.b = [1, 2, 3, 4]

            @torch.jit.script_method
            def forward(self):
                sum = 0
                for i in self.b:
                    sum += i
                return sum

        with torch.jit.optimized_execution(False):
            m = M()
            self.assertEqual(m(), 10)

    def test_moduledict(self):
        class Inner(torch.nn.Module):
            def forward(self, x):
                return x + 10

        class Inner2(torch.nn.Module):
            def forward(self, x):
                return x * 2

        class Inner3(torch.nn.Module):
            def forward(self, x):
                return (x - 4) * 3

        class M(torch.nn.Module):
            __constants__ = ['moduledict']

            def __init__(self):
                super(M, self).__init__()
                modules = OrderedDict([
                    ('one', Inner()),
                    ('two', Inner2()),
                    ('three', Inner3()),
                ])
                self.moduledict = nn.ModuleDict(modules)

            def forward(self, x, skip_name):
                # type: (Tensor, str)
                names = torch.jit.annotate(List[str], [])
                values = []
                for name in self.moduledict:
                    names.append(name)

                for name, mod in self.moduledict.items():
                    if name != skip_name:
                        names.append(name)
                        x = mod(x)
                        values.append(x)

                for mod in self.moduledict.values():
                    x = mod(x)
                    values.append(x)

                for key in self.moduledict.keys():
                    names.append(key)

                return x, names

        class M2(M):
            def __init__(self):
                super(M2, self).__init__()

            def forward(self, x, skip_name):
                # type: (Tensor, str)
                names = torch.jit.annotate(List[str], [])
                values = []
                x2 = x
                iter = 0
                for name in self.moduledict:
                    names.append(name)

                for i, (name, mod) in enumerate(self.moduledict.items()):
                    iter += i
                    if name != skip_name:
                        names.append(name)
                        x = mod(x)
                        values.append(x)

                for i, mod in enumerate(self.moduledict.values()):
                    iter += i
                    x = mod(x)
                    values.append(x)

                for i, key in enumerate(self.moduledict.keys()):
                    iter += i
                    names.append(key)

                for mod, mod in zip(self.moduledict.values(), self.moduledict.values()):
                    iter += i
                    x2 = mod(mod(x2))

                return x, x2, names, iter


        for name in ["", "one", "two", "three"]:
            inp = torch.tensor(1)
            self.checkModule(M(), (inp, name))
            self.checkModule(M2(), (inp, name))

    def test_custom_container_forward(self):
        class Inner(torch.nn.Module):
            def forward(self, x):
                return x + 10

        class CustomSequential(nn.Sequential):
            def __init__(self):
                super(CustomSequential, self).__init__(
                    nn.ReLU(), Inner())

            def forward(self, x):
                x = x + 3
                for mod in self:
                    x = mod(x)
                return x - 5

        self.checkModule(CustomSequential(), (torch.tensor(.5),))

        class CustomModuleList(nn.ModuleList):
            def __init__(self):
                super(CustomModuleList, self).__init__(
                    [nn.ReLU(), Inner()])

            def forward(self, x):
                x = x + 3
                for mod in self:
                    x = mod(x)
                return x - 5

        self.checkModule(CustomModuleList(), (torch.tensor(.5),))

        class CustomModuleDict(nn.ModuleDict):
            def __init__(self):
                super(CustomModuleDict, self).__init__(
                    OrderedDict([
                        ('one', Inner()),
                        ('two', nn.ReLU()),
                        ('three', Inner()),
                    ]))

            def forward(self, x):
                x = x + 3
                names = torch.jit.annotate(List[str], [])
                for name, mod in self.items():
                    x = mod(x)
                    names.append(name)
                return names, x - 5

        self.checkModule(CustomModuleDict(), (torch.tensor(.5),))

    def test_override_magic(self):
        class OverrideMagic(nn.Module):
            def __init__(self):
                super(OverrideMagic, self).__init__()

            @torch.jit.export
            def __len__(self):
                return 10

        mod = OverrideMagic()
        self.assertEqual(len(mod), len(torch.jit.script(mod)))

        class OverrideMagicSeq(nn.Sequential):
            def __init__(self):
                super(OverrideMagicSeq, self).__init__()

            @torch.jit.export
            def __len__(self):
                return 10

        mod = OverrideMagicSeq()
        self.assertEqual(len(mod), len(torch.jit.script(mod)))
        self.assertTrue(torch.jit.script(mod))

    def test_script_module_for2(self):
        class Sub(torch.jit.ScriptModule):
            def __init__(self):
                super(Sub, self).__init__()
                self.weight = nn.Parameter(torch.randn(2))

            @torch.jit.script_method
            def forward(self, thing):
                return self.weight + thing

        class M(torch.jit.ScriptModule):
            __constants__ = ['mods']

            def __init__(self):
                super(M, self).__init__()
                self.mods = nn.ModuleList([Sub() for i in range(10)])

            @torch.jit.script_method
            def forward(self, v):
                for m in self.mods:
                    v = m(v)
                return v

        with torch.jit.optimized_execution(False):
            i = torch.Tensor(2)
            m = M()
            o = m(i)
            v = i
            for sub in m.mods:
                v = sub(v)
            self.assertEqual(o, v)

            with self.assertRaisesRegex(Exception, "object is not iterable"):
                print([val for val in m])

    def test_attr_qscheme_script(self):
        class Foo(torch.nn.Module):
            def __init__(self):
                super(Foo, self).__init__()
                self.qscheme = torch.per_tensor_affine

            def forward(self):
                if self.qscheme == torch.per_tensor_symmetric:
                    return 3
                else:
                    return 4

        f = Foo()
        scripted = torch.jit.script(f)
        self.assertEqual(f(), scripted())

    def test_script_module_const_submodule_fail(self):
        class Sub(torch.jit.ScriptModule):
            def __init__(self):
                super(Sub, self).__init__()
                self.weight = nn.Parameter(torch.randn(2))

            @torch.jit.script_method
            def forward(self, thing):
                return self.weight + thing

        class M(torch.jit.ScriptModule):
            def __init__(self):
                super(M, self).__init__()
                self.mods = [Sub() for _ in range(10)]

            @torch.jit.script_method
            def forward(self):
                for _ in self.mods:
                    print(1)
                return 4

        with self.assertRaisesRegex(RuntimeError, "has no attribute 'mods'"):
            M()

    class DerivedStateModule(torch.jit.ScriptModule):
        def __init__(self):
            super(TestScript.DerivedStateModule, self).__init__()
            self.param = torch.nn.Parameter(torch.ones(3, 4, dtype=torch.float))
            self.register_buffer('derived', torch.neg(self.param).detach().clone())

            # This is a flag so we can test that the pack method was called
            self.register_buffer('pack_called', torch.zeros(1, dtype=torch.long))
            # This is a flag so we can test that the unpack method was called
            self.register_buffer('unpack_called', torch.zeros(1, dtype=torch.long))

        @torch.jit.script_method
        def _pack(self):
            self.pack_called.set_(torch.ones(1, dtype=torch.long))
            self.derived.set_(torch.rand(1, dtype=torch.float).detach())

        @torch.jit.script_method
        def _unpack(self):
            self.unpack_called.set_(torch.ones(1, dtype=torch.long))
            self.derived.set_(torch.neg(self.param).detach())

        @torch.jit.script_method
        def forward(self, x):
            return x + self.derived

    def test_pack_unpack_state(self):
        sm = TestScript.DerivedStateModule()
        x = torch.rand(3, 4, dtype=torch.float)
        torch.testing.assert_allclose(sm(x), x + torch.neg(torch.ones(3, 4, dtype=torch.float)))

        # Test save path
        self.assertFalse(sm.pack_called.item())
        self.assertFalse(sm.unpack_called.item())
        imported = self.getExportImportCopyWithPacking(sm)
        # ensure pack was called before serialization
        self.assertTrue(sm.pack_called.item())
        # ensure unpack was called after serialization so as to leave the module in an initialized state
        self.assertTrue(sm.unpack_called.item())

        torch.testing.assert_allclose(sm.derived, torch.neg(sm.param))

        # Test load paths
        self.assertTrue(imported.unpack_called.item())
        torch.testing.assert_allclose(imported(x), x + torch.neg(torch.ones(3, 4, dtype=torch.float)))

    def test_trace_export_fns(self):
        class Foo(torch.nn.Module):
            def __init__(self):
                super(Foo, self).__init__()
                self.a = 3

            @torch.jit.export
            def __getstate__(self):
                return (3, self.training)

            @torch.jit.export
            def __setstate__(self, state):
                self.a = state[0]
                self.training = state[1]

            def forward(self, x):
                return x + self.a

        f = Foo()

        traced = torch.jit.trace(f, (torch.rand(3, 4),))
        expected_names = ['__getstate__', '__setstate__']

        def check(mod):
            self.assertTrue(all(name in mod._c._method_names() for name in expected_names))

        check(traced)

        imported = self.getExportImportCopy(traced)
        check(imported)

    def test_trace_export_fns_recursive(self):
        class Foo(torch.nn.Module):
            def __init__(self):
                super(Foo, self).__init__()
                self.a = 3

            @torch.jit.export
            def __getstate__(self):
                return (3, self.training)

            @torch.jit.export
            def __setstate__(self, state):
                self.a = state[0]
                self.training = state[1]

            def forward(self, x):
                return x + self.a

        class Wrapper(torch.nn.Module):
            def __init__(self):
                super(Wrapper, self).__init__()
                self.foo = Foo()

            def forward(self, x):
                return self.foo(x)

        f = Wrapper()

        traced = torch.jit.trace(f, (torch.rand(3, 4),))
        expected_names = ['__getstate__', '__setstate__']

        def check(mod):
            self.assertTrue(all(name in mod._c._method_names() for name in expected_names))

        check(traced.foo)

        imported = self.getExportImportCopy(traced)
        check(imported.foo)

    def test_pack_unpack_nested(self):
        class SubSubMod(torch.jit.ScriptModule):
            def __init__(self):
                super(SubSubMod, self).__init__()
                self.register_buffer('buf', torch.ones(3, 4) * 3)

            @torch.jit.script_method
            def _pack(self):
                self.buf.set_(torch.zeros(1, dtype=torch.double))

            @torch.jit.script_method
            def _unpack(self):
                self.buf.set_(torch.ones(3, 4, dtype=torch.double) * 3)

            @torch.jit.script_method
            def forward(self, x):
                return x + self.buf

        class SubMod(torch.jit.ScriptModule):
            def __init__(self):
                super(SubMod, self).__init__()
                self.register_buffer('buf', torch.ones(3, 4) * 2)
                self.ssm = SubSubMod()

            @torch.jit.script_method
            def _pack(self):
                self.buf.set_(torch.zeros(1, dtype=torch.double))

            @torch.jit.script_method
            def _unpack(self):
                self.buf.set_(torch.ones(3, 4, dtype=torch.double) * 2)

            @torch.jit.script_method
            def forward(self, x):
                return self.ssm(x + self.buf)

        class Mod(torch.jit.ScriptModule):
            def __init__(self):
                super(Mod, self).__init__()
                self.submod = SubMod()
                self.register_buffer('buf', torch.ones(3, 4) * 1)

            @torch.jit.script_method
            def _pack(self):
                self.buf.set_(torch.zeros(1, dtype=torch.double))

            @torch.jit.script_method
            def _unpack(self):
                self.buf.set_(torch.ones(3, 4, dtype=torch.double))

            @torch.jit.script_method
            def forward(self, x):
                return self.submod(x + self.buf)

        m = Mod()
        torch.testing.assert_allclose(m(torch.zeros(3, 4)), torch.ones(3, 4) * 6)
        m.apply(lambda s: s._pack())
        torch.testing.assert_allclose(m(torch.zeros(3, 4)), torch.zeros(3, 4))
        m.apply(lambda s: s._unpack())
        torch.testing.assert_allclose(m(torch.zeros(3, 4)), torch.ones(3, 4) * 6)

    def test_script_module_not_tuple(self):
        class M(torch.jit.ScriptModule):
            __constants__ = ['mods']

            def __init__(self):
                super(M, self).__init__()
                self.mods = 1

            @torch.jit.script_method
            def forward(self, v):
                for m in self.mods:
                    print(m)
                return v
        with self.assertRaisesRegex(RuntimeError, "'int' object is not iterable"):
            M()

    def test_script_module_list_sequential(self):
        class M(torch.jit.ScriptModule):
            def __init__(self, mod_list):
                super(M, self).__init__()
                self.mods = mod_list

            @torch.jit.script_method
            def forward(self, v):
                for m in self.mods:
                    v = m(v)
                return v

        with torch.jit.optimized_execution(False):
            m = M(nn.Sequential(nn.ReLU()))
            self.assertExportImportModule(m, (torch.randn(2, 2),))

    def test_attr_module_constants(self):
        class M2(torch.jit.ScriptModule):
            def __init__(self, mod_list):
                super(M2, self).__init__()
                self.mods = mod_list

            @torch.jit.script_method
            def forward(self, x):
                return self.mods.forward(x)

        with torch.jit.optimized_execution(False):
            m = M2(nn.Sequential(nn.ReLU()))
            self.assertExportImportModule(m, (torch.randn(2, 2),))

    def test_script_sequential_for(self):
        class Sub(torch.jit.ScriptModule):
            def __init__(self):
                super(Sub, self).__init__()
                self.weight = nn.Parameter(torch.randn(2))

            @torch.jit.script_method
            def forward(self, thing):
                return self.weight + thing

        class M(torch.jit.ScriptModule):
            __constants__ = ['mods']

            def __init__(self):
                super(M, self).__init__()
                self.mods = nn.Sequential(Sub(), Sub(), Sub())

            @torch.jit.script_method
            def forward(self, v):
                for m in self.mods:
                    v = m(v)
                return v

            @torch.jit.script_method
            def forward2(self, v):
                return self.mods(v)

        with torch.jit.optimized_execution(False):
            i = torch.Tensor(2)
            m = M()
            o = m(i)
            v = i
            for sub in m.mods._modules.values():
                v = sub(v)
            self.assertEqual(o, v)

            o2 = m.forward2(i)
            self.assertEqual(o2, v)

    def test_script_sequential_orderdict(self):
        class M(torch.jit.ScriptModule):
            __constants__ = ['mods']

            def __init__(self):
                super(M, self).__init__()
                self.mods = nn.Sequential(OrderedDict([
                    ("conv", nn.Conv2d(1, 20, 5)),
                    ("relu", nn.ReLU())
                ]))

            @torch.jit.script_method
            def forward(self, input):
                return self.mods(input)

        m = M()
        self.assertTrue('mods.conv.weight' in m.state_dict().keys())

    def test_script_sequential_multi_output_fail(self):
        class Sub(torch.jit.ScriptModule):
            def __init__(self):
                super(Sub, self).__init__()
                self.weight = nn.Parameter(torch.randn(2))

            @torch.jit.script_method
            def forward(self, thing):
                return self.weight + thing

        class ReturnMulti(torch.jit.ScriptModule):
            def __init__(self):
                super(ReturnMulti, self).__init__()

            @torch.jit.script_method
            def forward(self, x):
                return x, x, x

        class HaveSequential(torch.jit.ScriptModule):
            __constants__ = ['someseq']

            def __init__(self):
                super(HaveSequential, self).__init__()
                self.someseq = nn.Sequential(
                    Sub(),
                    ReturnMulti(),
                    Sub()
                )

            @torch.jit.script_method
            def forward(self, x):
                return self.someseq(x)

        with self.assertRaisesRegex(RuntimeError, "(Tensor, Tensor, Tensor)"):
            with torch.jit.optimized_execution(False):
                hs = HaveSequential()
                i = torch.Tensor(2)
                hs(i)

    def test_constant_insert_fail_lint(self):
        @torch.jit.script
        def foo(x):
            y = x + 1
            z = torch.tensor([[1.0, 2.5]])
            print(x, z)

        # check that it doesnt error
        self.run_pass('constant_propagation', foo.graph)
        self.assertTrue("aten::tensor" in str(foo.graph))  # not constant propped

    @_tmp_donotuse_dont_inline_everything
    def test_script_sequential_in_mod_list(self):
        class Sub(torch.jit.ScriptModule):
            def __init__(self):
                super(Sub, self).__init__()
                self.weight = nn.Parameter(torch.randn(2))

            @torch.jit.script_method
            def forward(self, thing):
                return self.weight + thing

        class M(torch.jit.ScriptModule):
            __constants__ = ['mods']

            def __init__(self):
                super(M, self).__init__()
                self.mods = nn.ModuleList([Sub(), nn.Sequential(Sub(), nn.Sequential(Sub(), Sub()), Sub())])

            @torch.jit.script_method
            def forward(self, v):
                for mod in self.mods:
                    v = mod(v)
                return v

        m = M()
        graph = str(m.graph)
        self.assertTrue(graph.count("prim::CallMethod") == 2)
        self.assertTrue("python" not in graph)

    @_tmp_donotuse_dont_inline_everything
    def test_script_nested_mod_list(self):
        class Sub(torch.jit.ScriptModule):
            def __init__(self):
                super(Sub, self).__init__()
                self.weight = nn.Parameter(torch.randn(2))

            @torch.jit.script_method
            def forward(self, thing):
                return self.weight + thing

        class M(torch.jit.ScriptModule):
            __constants__ = ['mods']

            def __init__(self):
                super(M, self).__init__()
                self.mods = nn.ModuleList([nn.ModuleList([Sub()]), nn.Sequential(Sub()), nn.ModuleList([Sub(), Sub()])])

            @torch.jit.script_method
            def forward(self, v):
                for mod in self.mods:
                    for m in mod:
                        v = m(v)
                return v

        m = M()
        graph = str(m.graph)
        self.assertTrue(graph.count("prim::CallMethod") == 4)
        self.assertTrue("python" not in graph)

    def test_constant_as_attr(self):
        class M(torch.jit.ScriptModule):
            __constants__ = ['dim']

            def __init__(self):
                super(M, self).__init__()
                self.dim = 1

            @torch.jit.script_method
            def forward(self, v):
                return torch.cat([v, v, v], dim=self.dim)
        v = torch.zeros(1, 1)
        with torch.jit.optimized_execution(False):
            self.assertEqual(torch.cat([v, v, v], dim=1), M()(v))

    class StarTestSumStarred(torch.nn.Module):  # noqa T484
        def __init__(self):
            super(TestScript.StarTestSumStarred, self).__init__()

        def forward(self, *inputs):
            output = inputs[0]
            for i in range(1, len(inputs)):
                output += inputs[i]
            return output

    class StarTestReturnThree(torch.nn.Module):  # noqa T484
        def __init__(self):
            super(TestScript.StarTestReturnThree, self).__init__()

        def forward(self, rep):
            return rep, rep, rep

    def test_script_star_expr(self):

        class M2(torch.jit.ScriptModule):
            def __init__(self):
                super(M2, self).__init__()
                self.m = torch.jit.trace(TestScript.StarTestSumStarred(),
                                         (torch.ones(4, 3), torch.ones(4, 3), torch.ones(4, 3)))
                self.g = torch.jit.trace(TestScript.StarTestReturnThree(), torch.ones(4, 3))

            @torch.jit.script_method
            def forward(self, rep):
                tup = self.g(rep)
                return self.m(*tup)

        m = M2()
        self.assertEqual(m(torch.zeros(4, 3)), 3 * torch.zeros(4, 3))

    def test_script_star_expr_string(self):
        class M2(torch.jit.ScriptModule):
            def __init__(self):
                super(M2, self).__init__()
                self.m = torch.jit.trace(TestScript.StarTestSumStarred(),
                                         (torch.ones(4, 3), torch.ones(4, 3), torch.ones(4, 3)))
                self.g = torch.jit.trace(TestScript.StarTestReturnThree(), torch.ones(4, 3))

                self.define('''
            def forward(self, rep):
                tup = self.g(rep)
                return self.m(*tup)
                ''')

        m = M2()
        self.assertEqual(m(torch.zeros(4, 3)), 3 * torch.zeros(4, 3))

    class StarTestSumAndReturnThree(torch.nn.Module):  # noqa T484
        def __init__(self):
            super(TestScript.StarTestSumAndReturnThree, self).__init__()

        def forward(self, *inputs):
            output = inputs[0]
            for i in range(1, len(inputs)):
                output += inputs[i]
            return output, output, output

    def test_script_star_assign(self):
        class M2(torch.jit.ScriptModule):
            def __init__(self):
                super(M2, self).__init__()
                self.g = torch.jit.trace(TestScript.StarTestSumAndReturnThree(), torch.ones(4, 3))
                self.define('''
            def forward(self, rep):
                head, *tail = self.g(rep)
                return head
                ''')

        m = M2()
        self.assertEqual(m(torch.zeros(4, 3)), 3 * torch.zeros(4, 3))

    def test_script_module_star_assign2(self):
        class M2(torch.jit.ScriptModule):
            def __init__(self):
                super(M2, self).__init__()
                self.g = torch.jit.trace(
                    TestScript.StarTestSumAndReturnThree(),
                    (torch.ones(4, 3), torch.ones(4, 3), torch.ones(4, 3)),
                    _force_outplace=True)
                self.define('''
            def forward(self, rep):
                *head, tail = self.g(rep, rep, rep)
                return tail
                ''')

        m = M2()
        self.assertEqual(m(torch.ones(4, 3)), 3 * torch.ones(4, 3))

    def test_script_module_star_assign2_inplace(self):
        class M2(torch.jit.ScriptModule):
            def __init__(self):
                super(M2, self).__init__()
                self.g = torch.jit.trace(
                    TestScript.StarTestSumAndReturnThree(),
                    (torch.ones(4, 3), torch.ones(4, 3), torch.ones(4, 3)),
                    _force_outplace=False)
                self.define('''
            def forward(self, rep):
                *head, tail = self.g(rep, rep, rep)
                return tail
                ''')

        m = M2()
        # since forward() makes three aliases to the input `rep` before passing
        # it to StarTestSumAndReturnThree(), in-place behavior will be different
        # than the above out of place.
        self.assertEqual(m(torch.ones(4, 3)), 4 * torch.ones(4, 3))

    def test_script_module_star_assign_fail_pythonop(self):

        with self.assertRaisesRegex(RuntimeError, "cannot be used as a tuple"):
            class M2(torch.jit.ScriptModule):
                def __init__(self):
                    super(M2, self).__init__()

                    @torch.jit.ignore
                    def myfunc():
                        return torch.zeros(1, 2, 3), torch.zeros(1, 2, 3)

                    self.define('''
                def forward(self, rep):
                    a, *b = myfunc()
                    return a
                    ''')

            m = M2()
            m(torch.zeros(4, 3))

    def test_script_module_star_assign_fail_builtin(self):
        with self.assertRaisesRegex(RuntimeError, "cannot be used as a tuple"):
            class M2(torch.jit.ScriptModule):
                def __init__(self):
                    super(M2, self).__init__()

                    self.define('''
                def forward(self, rep):
                    a, *b = torch.neg(rep)
                    return a
                    ''')

            m = M2()
            m(torch.zeros(4, 3))

    @skipIfCompiledWithoutNumpy
    def test_pack_padded_pad_packed_trace(self):
        from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
        T, B, C = 3, 5, 7

        class PadPackedWrapper(torch.nn.Module):
            def __init__(self):
                super(PadPackedWrapper, self).__init__()

            def forward(self, x, seq_lens):
                x = pack_padded_sequence(x, seq_lens)
                x, _ = pad_packed_sequence(x)
                return x

        x = np.ones((T, B, C))
        seq_lens = np.array([3, 3, 2, 2, 1], dtype=np.int32)
        # set padding value so we can test equivalence
        for b in range(B):
            if seq_lens[b] < T:
                x[seq_lens[b]:, b, :] = 0
        seq_lens = torch.from_numpy(seq_lens)
        x = torch.autograd.Variable(torch.from_numpy(x), requires_grad=True)

        m = PadPackedWrapper()
        m_traced = torch.jit.trace(m, (x, seq_lens,))

        y = m(x, seq_lens)
        loss = torch.sum(y)
        loss.backward()
        grad = x.grad.clone()
        x.grad.zero_()

        y_traced = m_traced(x, seq_lens)
        loss_traced = torch.sum(y_traced)
        loss_traced.backward()
        grad_traced = x.grad.clone()

        self.assertEqual(y_traced, x)
        self.assertEqual(y_traced, y)
        self.assertEqual(grad, grad_traced)

        f = io.BytesIO()
        torch.onnx._export(m, (x, seq_lens), f, verbose=False)

    def test_script_pack_padded_sequence(self):
        from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

        def pack_padded_pad_packed_script(x, seq_lens):
            x = pack_padded_sequence(x, seq_lens)
            x, lengths = pad_packed_sequence(x)
            return x, lengths

        T, B, C = 3, 5, 7
        x = torch.ones((T, B, C))
        seq_lens = torch.tensor([3, 3, 2, 2, 1])
        # set padding value so we can test equivalence
        for b in range(B):
            if seq_lens[b] < T:
                x[seq_lens[b]:, b, :] = 0

        eager_seq, eager_lengths = pack_padded_pad_packed_script(x, seq_lens)
        scripted_pack_padded_seq = torch.jit.script(pack_padded_pad_packed_script)
        script_seq, script_lengths = scripted_pack_padded_seq(x, seq_lens)
        self.assertEqual(eager_seq, script_seq)
        self.assertEqual(eager_lengths, script_lengths)

    def test_script_get_tracing_state(self):
        def test_if_tracing(x):
            if torch._C._get_tracing_state():
                return x + 1
            else:
                return x - 1

        inp = torch.randn(3, 3)

        self.checkScript(test_if_tracing, (inp,))

    def test_is_scripting(self):
        def foo():
            return torch.jit.is_scripting()

        self.assertFalse(foo())
        scripted = torch.jit.script(foo)
        self.assertTrue(scripted())

    def test_script_outputs(self):
        with self.assertRaisesRegex(RuntimeError, "cannot be used as a tuple"):
            @torch.jit.script
            def foo(a):
                c, d = a + a
                return c + d

        @torch.jit.script
        def return3():
            return 1, 2, 3

        with self.assertRaisesRegex(RuntimeError, "too many values to unpack"):
            @torch.jit.script
            def bind2():
                a, b = return3()
                print(a)
                print(b)

    @unittest.skipIf(not RUN_CUDA, "requires CUDA")
    def test_script_get_device_cuda(self):
        @torch.jit.script
        def foo(a):
            return a.get_device()

        v = torch.randn(1, device='cuda')
        self.assertEqual(foo(v), 0)

    def test_script_chunk(self):
        @torch.jit.script
        def foo(a):
            b, c = torch.chunk(a, dim=0, chunks=2)
            return b
        v = torch.rand(10, 3)
        self.assertEqual(torch.chunk(v, dim=0, chunks=2)[0], foo(v))

    def test_trace_with_tensor_list_output(self):
        def f():
            return [torch.zeros(1), torch.zeros(5)]
        traced_f = torch.jit.trace(f, [])
        self.assertEqual(traced_f(), f())

    def test_trace_with_number_list_output(self):
        def f():
            return [1, 5]
        with self.assertRaisesRegex(RuntimeError, r"Only tensors.+can be output from traced functions"):
            traced_f = torch.jit.trace(f, [])

    def test_trace_with_nested_tensor_list_output(self):
        def f():
            return [[torch.zeros(1)], [torch.zeros(5)]]
        with self.assertRaisesRegex(RuntimeError, r"Only tensors.+can be output from traced functions"):
            traced_f = torch.jit.trace(f, [])

    def test_script_copy(self):
        class M(torch.nn.Module):
            __annotations__ = {
                "val": Optional[torch.Tensor]
            }

            def __init__(self):
                super(M, self).__init__()
                self.val = None

            def some_method(self):
                return 3

            def forward(self, x):
                # type: (Tensor) -> Tensor
                self.val = x + self.some_method()
                return x

        m = torch.jit.script(M())
        # test copy
        m_c = m.copy()

    @skipIfCompiledWithoutNumpy
    def test_rnn_trace_override(self):
        from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
        num_layers = 3
        T, B, C = 11, 5, 7

        class RNNTraceWrapper(torch.nn.Module):
            def __init__(self, cell_type):
                super(RNNTraceWrapper, self).__init__()
                if cell_type == 'RNN':
                    self.rnn = torch.nn.RNN(input_size=C, hidden_size=C, num_layers=num_layers)
                elif cell_type == 'LSTM':
                    self.rnn = torch.nn.LSTM(input_size=C, hidden_size=C, num_layers=num_layers)
                elif cell_type == 'GRU':
                    self.rnn = torch.nn.GRU(input_size=C, hidden_size=C, num_layers=num_layers)

            def forward(self, x, seq_lens):
                x = pack_padded_sequence(x, seq_lens)
                x, _ = self.rnn(x)
                x, _ = pad_packed_sequence(x)
                return x

        for cell_type in ['RNN', 'LSTM', 'GRU']:
            x = torch.ones(T, B, C, requires_grad=True)
            seq_lens = torch.from_numpy(np.array([11, 3, 2, 2, 1], dtype=np.int32))

            m = RNNTraceWrapper(cell_type)
            m_traced = torch.jit.trace(m, (x, seq_lens,))

            y = m(x, seq_lens)
            loss = torch.sum(y)
            loss.backward()
            grad = x.grad.clone()
            x.grad.zero_()

            y_traced = m_traced(x, seq_lens)
            loss_traced = torch.sum(y_traced)
            loss_traced.backward()
            grad_traced = x.grad.clone()

            self.assertEqual(y_traced, y)
            self.assertEqual(grad, grad_traced)

            f = io.BytesIO()
            torch.onnx._export(m, (x, seq_lens), f, verbose=False)

    def test_python_call_non_tensor(self):
        def foo(a, b, c):
            # type: (Tensor, int, Tuple[Tensor, int]) -> Tuple[int, Tensor]
            d, e = c
            return b + e, a + d

        @torch.jit.script
        def bar():
            x = torch.ones(3, 4)
            a, b = foo(x, 3, (x, 3))
            return a, b

        self.assertEqual((6, torch.ones(3, 4) + 1), bar())

    def test_python_call_non_tensor_wrong(self):
        with self.assertRaisesRegex(RuntimeError, r"but instead got value of type tuple"):
            @torch.jit.ignore
            def foo():
                # type: () -> Tensor
                return ((3, 4),)  # noqa: T484

            @torch.jit.script
            def bar():
                return foo()

            bar()

    def test_tuples(self):
        def foo(i):
            a = (i + 4, i * 2)
            c = a
            # some nonsense with if-statements and loops to check
            # that tuple lowering doesn't fail
            if True:
                c = (i * 9, i + 1)
            t0, t1 = c
            while False:
                t0, t1 = c
                c = (t1, t0)
            x = (1,)
            y = 1,
            return t0, x, y

        v = torch.rand(10, 3)
        self.checkScript(foo, (v,))

        with self.assertRaisesRegex(RuntimeError, r"Variable 'a' previously has type Tuple"):
            @torch.jit.script
            def mixtypes(x):
                a = (x, x)
                if True:
                    a = 4

    def test_if_tuple_sizes(self):
        with self.assertRaisesRegex(RuntimeError, "Type mismatch"):
            @torch.jit.script
            def diff_tuple_sizes(x):
                if False:
                    c0 = ((x, x), (x, x, x))
                else:
                    c0 = ((x, x, x), (x, x))
                return c0

    def test_if_different_type(self):
        with self.assertRaisesRegex(RuntimeError, "Type mismatch: c0 is set to type int "
                                    "in the true branch and type float in the false branch:"):
            @torch.jit.script
            def diff_type_used():
                if False:
                    c0 = 1
                else:
                    c0 = 1.0
                return c0

        with self.assertRaisesRegex(RuntimeError, "Variable 'c0' previously has type float"):
            @torch.jit.script
            def diff_existing_type(x):
                c0 = 1.0
                if False:
                    c0 = 1
                    print(x)
                return x

        @torch.jit.script
        def diff_type_unused():
            if True:
                c0 = 1
                print(c0)
            else:
                c0 = 1.0
                print(c0)
            return 1

    def test_if_not_defined_error(self):
        with self.assertRaisesRegex(RuntimeError, "c0 is not defined in the false branch"):
            @torch.jit.script
            def test():
                if True:
                    c0 = 1
                return c0
        with self.assertRaisesRegex(RuntimeError, "c0 is not defined in the true branch"):
            @torch.jit.script
            def test2():
                if True:
                    pass
                else:
                    c0 = 1
                return c0

    def test_if_list_cat(self):
        # testing that different length lists don't throw error on cat in shape prop
        @torch.jit.script
        def test_list(x):
            if bool(x.sum() < 1):
                c = [x, x]
            else:
                c = [x, x, x]
            return torch.cat(c)

        b = torch.zeros(2, 4)
        _propagate_shapes(test_list.graph, (b,), False)

    def test_if_supertype(self):
        @torch.jit.script
        def tensor_unifying(x, y, z):
            # testing dynamic is appropriately set for y and z
            if bool(x):
                x, y, z = x + 1, y, z
            else:
                x, y, z = x + 1, x, y

            return x, y, z

        a = torch.zeros(2, 2, dtype=torch.float)
        b = torch.zeros(2, 4, dtype=torch.long)
        c = torch.zeros(2, 4, dtype=torch.float)

        graph = _propagate_shapes(tensor_unifying.graph, (a, b, c), False)
        if_outputs = list(graph.findNode("prim::If").outputs())
        self.assertTrue(if_outputs[0].type().str() == "Float(*, *)")
        self.assertTrue(if_outputs[1].type().str() == "Tensor(*, *)")
        self.assertTrue(if_outputs[2].type().str() == "Tensor(*, *)")

    def test_list_unify(self):
        # allowing a unififed int?[] would cause a runtime error b/c
        # the index operation expects int?[] to be a generic list,
        # but in the true branch the IValue will be a int list
        with self.assertRaisesRegex(RuntimeError, "int[] in the true branch and type None[]"):
            @torch.jit.script
            def list_optional_fails(x):
                # type: (bool) -> Optional[int]
                if x:
                    y = [1]
                else:
                    y = [None]  # noqa: T484
                return y[0]

        @torch.jit.script
        def list_tensors(x):
            # type: (bool) -> Tuple[Tensor, List[Tensor]]
            if x:
                a = torch.zeros([1, 1])
                y = [a]
            else:
                a = torch.zeros([1, 2])
                y = [a]
            return a, y

        self.run_pass('constant_propagation', list_tensors.graph)
        m = self.createFunctionFromGraph(list_tensors.graph)
        # testing that tensor type of lists is unified
        self.getExportImportCopy(m)

    @_inline_everything
    def test_import_constants_not_specialized(self):
        class Mod(torch.nn.Module):
            def forward(self, x):
                return torch.cat(2 * [x], dim=0)

        class ScriptMod(torch.jit.ScriptModule):
            def __init__(self, mod):
                super(ScriptMod, self).__init__()
                x = torch.zeros(1, 3)
                mod_fn = lambda : mod(x)  # noqa: E731
                self.mod = torch.jit.trace(mod_fn, tuple())

            @torch.jit.script_method
            def forward(self):
                return self.mod()

        cm = ScriptMod(Mod())
        # specialized tensor in graph
        FileCheck().check("Double(1, 3)").run(cm.forward.graph)
        buffer = io.BytesIO()
        torch.jit.save(cm, buffer)
        buffer.seek(0)
        # when tensor is loaded as constant it isnt specialized
        cm_load = torch.jit.load(buffer)
        FileCheck().check_not("Double(1, 3)").run(cm_load.forward.graph)

    def test_type_annotations_repeated_list(self):
        @torch.jit.script
        def float_fn(x, y):
            # type: (float, BroadcastingList3[float]) -> List[float]
            return y
        self.assertEqual(float_fn(2.0, 1.0), float_fn(2.0, [1.0, 1.0, 1.0]))
        self.assertEqual(float_fn(2.0, 1.0), float_fn(2.0, (1.0, 1.0, 1.0)))

        @torch.jit.script
        def float_fn_call():
            print(float_fn(1.0, 1.0))
            print(float_fn(1.0, (1.0, 1.0, 1.0)))

        @torch.jit.script
        def int_fn(x):
            # type: (BroadcastingList3[int]) -> List[int]
            return x
        self.assertEqual(int_fn(1), int_fn([1, 1, 1]))
        self.assertEqual(int_fn(1), int_fn((1, 1, 1)))

        @torch.jit.script
        def int_fn_call():
            print(int_fn(1))
            print(int_fn((1, 1, 1)))

        with self.assertRaisesRegex(RuntimeError, "must be a positive integer:"):
            @torch.jit.script  # noqa: T484
            def fn(x):
                # type: (BroadcastingListx[int]) -> List[int]  # noqa: T484
                return x

        # using CU so that flake8 error on int[2] is not raised (noqa not working)
        with self.assertRaisesRegex(RuntimeError, "Unknown type constructor"):
            cu = torch.jit.CompilationUnit('''
                def nested(x, y):
                    # type: (int, Tuple[int, int[2]]) -> List[int]
                    return x  # noqa: T484
            ''')

    def test_ntuple_builtins(self):
        from torch.nn.modules.utils import _single, _pair, _triple, _quadruple

        def test_ints():
            return _single(1), _pair(2), _triple(3), _quadruple(4)

        def test_floats():
            return _single(1), _pair(2.1), _triple(3.1), _quadruple(4.1)

        self.checkScript(test_ints, ())
        self.checkScript(test_floats, ())

    def test_embedding_renorm_grad_error(self):
        # Testing that the builtin call to embedding_renorm_ correctly throws
        # Error when .backward() is called on its input

        def embedding_norm(input, embedding_matrix, max_norm):
            F.embedding(input, embedding_matrix, max_norm=0.01)

        @torch.jit.script
        def embedding_norm_script(input, embedding_matrix, max_norm):
            # type: (Tensor, Tensor, float) -> None
            F.embedding(input, embedding_matrix, max_norm=0.01)

        for _ in [embedding_norm, embedding_norm_script]:
            input = torch.tensor([[1, 2, 4, 5], [4, 3, 2, 9]])
            embedding_matrix = torch.randn(10, 3)

            var1 = torch.randn(10, 3, requires_grad=True)
            var2 = var1.detach().requires_grad_()
            output1 = var1 * embedding_matrix
            output2 = var2 * embedding_matrix

            output1.sum().backward()

            ignore = F.embedding(input, embedding_matrix, max_norm=0.01)
            with self.assertRaisesRegex(RuntimeError, "modified"):
                output2.sum().backward()

    def test_type_annotations(self):
        def fn(x, y):
            # type: (Tensor, Tensor) -> Tuple[Tensor, Tensor, Tensor]
            return x, x * 2, x * 3

        with self.assertRaisesRegex(RuntimeError, r"need 4 values .* found only 3"):
            @torch.jit.script
            def script_fn(x):
                x, y, z, w = fn(x, x)

        with self.assertRaisesRegex(RuntimeError, r"too many values .* need 2 but found 3"):
            @torch.jit.script
            def script_fn2(x):
                x, y = fn(x, x)

        def fn_unpack(x):
            y, z, w = fn(x, x)
            return y

        def fn_index(x):
            q = fn(x, x)
            return x

        def fn_string(str, strpair):
            # type: (str, Tuple[str, str]) -> Tuple[str, int, str, str]
            str1, str2 = strpair
            return str, 2, str1, str2

        x = torch.ones(2, 2)
        self.checkScript(fn_unpack, (x,), optimize=True)
        self.checkScript(fn_index, (x,), optimize=True)
        self.checkScript(fn_string, ("1", ("3", "4")), optimize=True)

    def test_type_annotations_varargs(self):
        @torch.jit.ignore
        def fn_varargs(x, *args):
            return args[0] if args else x

        def fn1(x, y, z):
            return fn_varargs(x)

        def fn2(x, y, z):
            return fn_varargs(x, y)

        def fn3(x, y, z):
            return fn_varargs(x, y, z)

        x, y, z = [torch.randn(2, 2) for _ in range(3)]
        self.checkScript(fn1, (x, y, z), optimize=True)
        self.checkScript(fn2, (x, y, z), optimize=True)
        self.checkScript(fn3, (x, y, z), optimize=True)

    @unittest.skipIf(not PY35, "Python 3.5 needed")
    def test_type_annotation_py3(self):
        code = dedent("""
        import torch
        from torch import Tensor
        from typing import Tuple

        def fn(x : torch.Tensor, y : Tensor, z) -> Tuple[Tensor, Tensor, Tensor]:
            return (x, y + z, z)
        """)

        with tempfile.TemporaryDirectory() as tmp_dir:
            script_path = os.path.join(tmp_dir, 'script.py')
            with open(script_path, 'w') as f:
                f.write(code)
            fn = get_fn('test_type_annotation_py3', script_path)
            fn = torch.jit.ignore(fn)

            with self.assertRaisesRegex(RuntimeError, r"Expected a value of type 'Tensor' for argument"
                                                      r" 'x' but instead found type 'Tuple\[Tensor,"):
                @torch.jit.script
                def bad_fn(x):
                    x, y = fn((x, x), x, x)
                    return y

            with self.assertRaisesRegex(RuntimeError, r"too many values .* need 2 but found 3"):
                @torch.jit.script
                def bad_fn2(x):
                    x, y = fn(x, x, x)
                    return y

            with self.assertRaisesRegex(RuntimeError, r"need 4 values .* found only 3"):
                @torch.jit.script
                def bad_fn3(x):
                    x, y, z, w = fn(x, x, x)
                    return y

            def good_fn(x):
                y, z, w = fn(x, x, x)
                return y, z, w

            self.checkScript(good_fn, (torch.ones(2, 2),), optimize=True)

    def test_tensor_with_grad_as_constant(self):
        param = torch.randn(3).requires_grad_()
        x = torch.randn(3)

        def f(x):
            return x + param
        with self.assertRaisesRegex(RuntimeError, "Cannot insert a Tensor that requires grad as a constant"):
            torch.jit.trace(f, x)

    def test_non_tensor_tracing(self):
        def f(x):
            return x + param
        with self.assertRaisesRegex(RuntimeError, r"Type 'Tuple\[int\]' cannot be traced"):
            torch.jit.trace(f, (1,))

    def test_type_annotation_module(self):
        class BaseModule(torch.jit.ScriptModule):
            @torch.jit.ignore
            def foo(self, x):
                # type: (Tensor) -> Tensor
                return x + 1

            @torch.jit.ignore
            def bar(self, x, y):
                # type: (Tensor, Tensor) -> Tuple[Tensor, Tensor]
                return x + y, y

            @torch.jit.ignore
            def baz(self, x, y):
                return x

        class ModuleTooMany(BaseModule):
            @torch.jit.script_method
            def method(self, x):
                return self.foo(x, x)

        class ModuleTooFew(BaseModule):
            @torch.jit.script_method
            def method(self, x):
                return self.bar(x)

        class ModuleTooManyAssign(BaseModule):
            @torch.jit.script_method
            def method(self, x):
                y, z, w = self.bar(x, x)
                return x

        class ModuleDefault(BaseModule):
            @torch.jit.script_method
            def method(self, x):
                y = self.baz(x)
                return x

        with self.assertRaisesRegex(RuntimeError, "Expected at most 2 arguments but found 3"):
            ModuleTooMany()
        with self.assertRaisesRegex(RuntimeError, "Argument y not provided"):
            ModuleTooFew()
        with self.assertRaisesRegex(RuntimeError, "need 3 values .* found only 2"):
            ModuleTooManyAssign()
        with self.assertRaisesRegex(RuntimeError, "Argument y not provided."):
            ModuleDefault()

    def test_script_define_order(self):
        class M(torch.jit.ScriptModule):

            @torch.jit.script_method
            def call_foo(self, input):
                return self.foo(input)

            @torch.jit.script_method
            def foo(self, input):
                return input + 1
        m = M()
        self.assertEqual(2, m.call_foo(torch.ones((), dtype=torch.int64)))

    def test_script_define_order_recursive_fail(self):
        class M(torch.jit.ScriptModule):

            @torch.jit.script_method
            def call_foo(self, input):
                return self.foo(input)

            @torch.jit.script_method
            def foo(self, input):
                self.call_foo(input)

        with self.assertRaisesRegex(RuntimeError, 'called recursively'):
            M()

    def test_script_kwargs_fn_call(self):
        class M(torch.jit.ScriptModule):

            @torch.jit.script_method
            def call_foo(self, input):
                return self.foo(input=input, bar=1)

            @torch.jit.script_method
            def foo(self, bar, input):
                # type: (int, Tensor) -> Tensor
                return input + bar
        m = M()
        self.assertEqual(2, m.call_foo(torch.ones((), dtype=torch.int64)))

    def test_trace_of_script(self):
        @torch.jit.script
        def foo(a, c):
            b = 0.0
            if bool(a == 0.0):
                b = 1.0
            return b + c

        a = torch.ones(1, dtype=torch.float)

        @_trace(torch.zeros(1, dtype=torch.float))
        def use(b):
            return foo(b - 1.0, a) + 1.0

        # test we propagated shapes through the function
        self.assertTrue("Dynamic" not in str(use.graph))

        self.assertEqual(3, use(torch.ones(1, dtype=torch.float)))
        self.assertEqual(2, use(torch.zeros(1, dtype=torch.float)))

    def test_if_define(self):
        @torch.jit.script
        def foo(a):
            if bool(a == 0):
                b = 1
            else:
                b = 0
            return b + 1

        @torch.jit.script
        def foo2(a):
            b = 0
            if bool(a == 0):
                b = 1
            return b + 1

        @torch.jit.script
        def foo3(a):
            b = 1
            if bool(a == 0):
                c = 4
            else:
                b = 0
            return b + 1

        a = torch.ones(1, dtype=torch.long)
        b = torch.zeros(1, dtype=torch.long)
        self.assertEqual(1, foo(a))
        self.assertEqual(2, foo(b))
        self.assertEqual(1, foo2(a))
        self.assertEqual(2, foo2(b))
        self.assertEqual(1, foo3(a))
        self.assertEqual(2, foo3(b))

    def test_script_module_export_submodule(self):
        class M1(torch.jit.ScriptModule):
            def __init__(self):
                super(M1, self).__init__()
                self.weight = nn.Parameter(torch.randn(2))

            @torch.jit.script_method
            def forward(self, thing):
                return self.weight + thing

        class M2(torch.jit.ScriptModule):
            def __init__(self):
                super(M2, self).__init__()
                # test submodule
                self.sub = M1()
                self.weight = nn.Parameter(torch.randn(2, 3))
                self.bias = nn.Parameter(torch.randn(2))
                self.define("""
                    def hi(self, a):
                        return self.weight.mm(a)
                """)

            @torch.jit.script_method
            def doit(self, input):
                return self.weight.mm(input)

            @torch.jit.script_method
            def doit2(self, input):
                return self.weight.mm(input)

            @torch.jit.script_method
            def doit3(self, input):
                return input + torch.ones([1], dtype=torch.double)

            @torch.jit.script_method
            def forward(self, input):
                a = self.doit(input)
                b = self.doit2(input)
                c = self.hi(input)
                return a + b + self.bias + c

        with torch.jit.optimized_execution(False):
            m_orig = M2()
            m_import = self.getExportImportCopy(m_orig)

            input = torch.randn(3, 2)
            self.assertEqual(m_orig.doit(input), m_import.doit(input))
            self.assertEqual(m_orig.hi(input), m_import.hi(input))
            self.assertEqual(m_orig.doit3(input), m_import.doit3(input))
            self.assertEqual(m_orig.forward(input), m_import.forward(input))

    @slowTest
    def test_compile_module_with_constant(self):
        class Double(nn.Module):
            def __init__(self, downsample=None):
                super(Double, self).__init__()

            def forward(self, input):
                return input * 2

        class Mod(nn.Module):
            __constants__ = ['downsample']

            def __init__(self, downsample=None):
                super(Mod, self).__init__()
                self.downsample = downsample

            def forward(self, input):
                if self.downsample is not None:
                    return self.downsample(input)
                return input

        none_mod = torch.jit.script(Mod(None))
        double_mod = torch.jit.script(Mod(Double()))
        self.assertEqual(none_mod(torch.tensor(1)), torch.tensor(1))
        self.assertEqual(double_mod(torch.tensor(1)), torch.tensor(1) * 2)

    def test_script_module_export_tensor_type(self):
        class M(torch.jit.ScriptModule):
            def __init__(self, type):
                super(M, self).__init__()
                self.param = torch.nn.Parameter(torch.zeros((5, 5), dtype=type).random_())

            @torch.jit.script_method
            def foo(self):
                return self.param

        with torch.jit.optimized_execution(False):
            for type in [torch.float, torch.double]:
                m_orig = M(type)
                m_import = self.getExportImportCopy(m_orig)
                # check to make sure the storage wasn't resized
                self.assertTrue(m_orig.param.storage().size() == 25)
                self.assertEqual(m_orig.foo(), m_import.foo())
                self.assertTrue(m_orig.foo().dtype == m_import.foo().dtype)

    @unittest.skipIf(not RUN_CUDA, "testing cuda tensors require CUDA")
    def test_script_module_export_tensor_cuda(self):
        class M(torch.jit.ScriptModule):

            def __init__(self):
                super(M, self).__init__()
                self.param = torch.nn.Parameter(torch.zeros((5, 5), device='cuda:0').random_())

            @torch.jit.script_method
            def foo(self):
                return self.param

        m_orig = M()
        m_import = self.getExportImportCopy(m_orig)
        # check to make sure the storage wasn't resized
        self.assertTrue(m_orig.param.storage().size() == 25)
        self.assertTrue(m_import.foo().device == torch.device('cuda:0'))
        self.assertEqual(m_orig.foo(), m_import.foo())
        self.assertTrue(m_orig.foo().dtype == m_import.foo().dtype)

    def test_script_module_export_blocks(self):
        class M(torch.jit.ScriptModule):
            def __init__(self, n, m):
                super(M, self).__init__()
                self.weight = torch.nn.Parameter(torch.rand(n, m))

            @torch.jit.script_method
            def forward(self, input):
                if bool(input.sum() > 0):
                    output = self.weight.mv(input)
                else:
                    output = self.weight + input
                return output

        m_orig = M(200, 200)
        m_import = self.getExportImportCopy(m_orig)

        t = torch.rand(200)
        self.assertEqual(m_orig(t), m_import(t))

    def test_script_module_export_shared_storage(self):
        class M(torch.jit.ScriptModule):

            def __init__(self):
                super(M, self).__init__()
                self.param1 = torch.nn.Parameter(torch.rand(5, 5))
                self.param2 = torch.nn.Parameter(self.param1[3])
                self.param3 = torch.nn.Parameter(torch.rand(5, 5))
                self.param4 = torch.nn.Parameter(torch.rand(11, 5)[1:6])

            @torch.jit.script_method
            def foo(self):
                return self.param1 + self.param2 + self.param3 + self.param4

        with torch.jit.optimized_execution(False):
            m_orig = M()
            m_import = self.getExportImportCopy(m_orig)

            self.assertEqual(m_orig.foo(), m_import.foo())

            self.assertTrue(m_import.param1.storage().data_ptr() == m_import.param2.storage().data_ptr())
            self.assertTrue(m_import.param1.storage().data_ptr() != m_import.param3.storage().data_ptr())

    def test_sequential_intermediary_types(self):
        class A(torch.nn.Module):
            def __init__(self):
                super(A, self).__init__()

            def forward(self, x):
                return x + 3

        class B(torch.nn.Module):
            def __init__(self):
                super(B, self).__init__()

            def forward(self, x):
                return {"1": x}

        class C(torch.nn.Module):
            __constants__ = ['foo']

            def __init__(self):
                super(C, self).__init__()
                self.foo = torch.nn.Sequential(A(), B())

            def forward(self, x):
                return self.foo(x)

        self.checkModule(C(), (torch.tensor(1),))

    def test_onnx_export_script_module(self):
        class ModuleToExport(torch.jit.ScriptModule):
            def __init__(self):
                super(ModuleToExport, self).__init__()

            @torch.jit.script_method
            def forward(self, x):
                y = x - x
                return x + x

        mte = ModuleToExport()
        outputs = mte(torch.zeros(1, 2, 3))
        torch.onnx.export_to_pretty_string(
            mte, (torch.zeros(1, 2, 3),), None, verbose=False,
            example_outputs=outputs)

    def test_trace_autograd_function(self):
        class TestFunc(torch.autograd.Function):
            @staticmethod
            def forward(ctx, input):
                return torch.neg(input)

            @staticmethod
            def backward(ctx, grad_output):
                return torch.neg(grad_output)



        class TracedModule(torch.nn.Module):
            def forward(self, x):
                return torch.relu(TestFunc.apply(x))


        class Wrapper(torch.nn.Module):
            def __init__(self):
                super(Wrapper, self).__init__()
                self.tm = TracedModule()

            def forward(self, x):
                return self.tm(x)

        traced = torch.jit.trace(Wrapper(), (torch.rand(3, 4),))

    def test_interpolate_trace(self):
        class test(nn.Module):
            def __init__(self):
                super(test, self).__init__()
                self.conv = nn.Conv2d(1, 32, kernel_size=3, padding=1)

            def forward(self, x):
                y = self.conv(x)
                w = nn.functional.interpolate(y, mode='bilinear', align_corners=False, scale_factor=3)
                return w

        f = test()
        # no failure
        g = torch.jit.trace(f, (torch.zeros(1, 1, 28, 28),))
        x = torch.zeros(1, 1, 14, 14)
        # constants not baked in
        self.assertEqual(g(x), f(x))

    @_tmp_donotuse_dont_inline_everything
    def test_trace_optional(self):
        @torch.jit.script
        def test(x):
            # type: (Optional[Tensor])
            if x is None:
                return torch.zeros(1)
            else:
                return x

        def test_none():
            return test(None)

        def test_tensor():
            return test(torch.zeros(2))

        f_none = torch.jit.trace(test_none, ())
        self.assertEqual(f_none(), torch.zeros(1))

        f_tensor = torch.jit.trace(test_tensor, ())
        self.assertEqual(f_tensor(), torch.zeros(2))

        graph = f_tensor.graph
        FileCheck().check('name="test"').check_next("prim::CallFunction").run(graph)

    def test_trace_nested_datatypes(self):
        @torch.jit.script
        def foo(x):
            return [[x + 1, x - 1], [x + 2, x - 2]]

        def bar(x):
            list_stuff = foo(x)
            return list_stuff[0][0], list_stuff[1][1]

        traced = torch.jit.trace(bar, torch.rand(3, 4))
        x = torch.rand(5, 6)
        self.assertEqual(bar(x), traced(x))

    @suppress_warnings
    def test_onnx_export_func_with_warnings(self):
        @torch.jit.script
        def func_with_warning(inp):
            return torch.nn.functional.sigmoid(inp)  # triggers a deprecation warning

        class WarningTest(torch.nn.Module):
            def __init__(self):
                super(WarningTest, self).__init__()

            def forward(self, x):
                return func_with_warning(x)

        outputs = WarningTest()(torch.randn(42))
        # no exception
        torch.onnx.export_to_pretty_string(
            WarningTest(), torch.randn(42), None, verbose=False,
            example_outputs=outputs)

    def test_onnx_export_script_python_fail(self):
        class PythonModule(torch.jit.ScriptModule):
            def __init__(self):
                super(PythonModule, self).__init__()

            @torch.jit.ignore
            def forward(self, x):
                return torch.neg(x)

        class ModuleToExport(torch.jit.ScriptModule):
            def __init__(self):
                super(ModuleToExport, self).__init__()
                self.mod = PythonModule()

            @torch.jit.script_method
            def forward(self, x):
                y = self.mod(x)
                return y + y

        mte = ModuleToExport()
        outputs = mte(torch.zeros(1, 2, 3))
        f = io.BytesIO()
        with self.assertRaisesRegex(RuntimeError, "Couldn't export Python"):
            torch.onnx._export(mte, (torch.zeros(1, 2, 3),), f, verbose=False,
                               example_outputs=outputs)

    def test_onnx_export_script_inline_trace(self):
        class ModuleToInline(torch.nn.Module):
            def __init__(self):
                super(ModuleToInline, self).__init__()

            def forward(self, x):
                return torch.neg(x)

        class ModuleToExport(torch.jit.ScriptModule):
            def __init__(self):
                super(ModuleToExport, self).__init__()
                self.mod = torch.jit.trace(ModuleToInline(), torch.zeros(1, 2, 3))

            @torch.jit.script_method
            def forward(self, x):
                y = self.mod(x)
                return y + y

        mte = ModuleToExport()
        outputs = mte(torch.zeros(1, 2, 3))
        torch.onnx.export_to_pretty_string(
            mte, (torch.zeros(1, 2, 3),), None, verbose=False,
            example_outputs=outputs)

    def test_onnx_export_script_inline_script(self):
        class ModuleToInline(torch.jit.ScriptModule):
            def __init__(self):
                super(ModuleToInline, self).__init__()

            @torch.jit.script_method
            def forward(self, x):
                return torch.neg(x)

        class ModuleToExport(torch.jit.ScriptModule):
            def __init__(self):
                super(ModuleToExport, self).__init__()
                self.mod = ModuleToInline()

            @torch.jit.script_method
            def forward(self, x):
                y = self.mod(x)
                return y + y

        mte = ModuleToExport()
        outputs = mte(torch.zeros(1, 2, 3))
        torch.onnx.export_to_pretty_string(
            mte, (torch.zeros(1, 2, 3),), None, verbose=False,
            example_outputs=outputs)

    def test_onnx_export_script_module_loop(self):
        class ModuleToExport(torch.jit.ScriptModule):
            def __init__(self):
                super(ModuleToExport, self).__init__()

            @torch.jit.script_method
            def forward(self, x):
                # test if we support end to end onnx export on loop and
                # nested loops with and without loop index
                for _ in range(5):
                    for i in range(3):
                        x = x + i
                return x

        mte = ModuleToExport()
        outputs = mte(torch.zeros(1, 2, 3))
        torch.onnx.export_to_pretty_string(
            mte, (torch.zeros(1, 2, 3),), None, verbose=False,
            example_outputs=outputs)

    def test_onnx_export_script_truediv(self):
        class ModuleToExport(torch.jit.ScriptModule):
            def __init__(self):
                super(ModuleToExport, self).__init__()

            @torch.jit.script_method
            def forward(self, x):
                z = x.size(0) / 2
                return x + z

        mte = ModuleToExport()
        outputs = mte(torch.zeros(1, 2, 3))
        torch.onnx.export_to_pretty_string(
            mte, (torch.zeros(1, 2, 3),), None, verbose=False,
            example_outputs=outputs)

    def test_onnx_raw_export_script_truediv(self):
        class ModuleToExport(torch.jit.ScriptModule):
            def __init__(self):
                super(ModuleToExport, self).__init__()

            @torch.jit.script_method
            def forward(self, x):
                z = x.size(0) / 2
                return x + z

        mte = ModuleToExport()
        outputs = mte(torch.zeros(1, 2, 3))
        torch.onnx.export_to_pretty_string(
            mte, (torch.zeros(1, 2, 3),), None, verbose=False,
            example_outputs=outputs, export_raw_ir=True)

    def test_onnx_export_script_non_alpha_add_sub(self):
        class ModuleToExport(torch.jit.ScriptModule):
            def __init__(self):
                super(ModuleToExport, self).__init__()

            @torch.jit.script_method
            def forward(self, x):
                bs = x.size(0) + 1
                return bs - 1

        mte = ModuleToExport()
        outputs = torch.LongTensor([mte(torch.rand(3, 4))])
        torch.onnx.export_to_pretty_string(
            mte, (torch.rand(3, 4),), None, verbose=False,
            example_outputs=outputs)

    def test_onnx_export_script_module_if(self):
        class ModuleToExport(torch.jit.ScriptModule):
            def __init__(self):
                super(ModuleToExport, self).__init__()

            @torch.jit.script_method
            def forward(self, x):
                if bool(torch.sum(x) > 0):
                    x = torch.neg(x)
                return x

        mte = ModuleToExport()
        outputs = mte(torch.zeros(1, 2, 3, dtype=torch.long))
        torch.onnx.export_to_pretty_string(
            mte, (torch.zeros(1, 2, 3),), None, verbose=False,
            example_outputs=outputs)

    def test_onnx_export_script_inline_params(self):
        class ModuleToInline(torch.jit.ScriptModule):
            def __init__(self):
                super(ModuleToInline, self).__init__()
                self.m = torch.nn.Parameter(torch.ones(3, 3))
                self.unused = torch.nn.Parameter(torch.ones(1, 2, 3))

            @torch.jit.script_method
            def forward(self, x):
                return torch.mm(x, self.m)

        class ModuleToExport(torch.jit.ScriptModule):
            def __init__(self):
                super(ModuleToExport, self).__init__()
                self.mod = ModuleToInline()
                self.param = torch.nn.Parameter(torch.ones(3, 4))

            @torch.jit.script_method
            def forward(self, x):
                y = self.mod(x)
                return torch.mm(y, self.param)

        mte = ModuleToExport()
        result = mte(torch.zeros(2, 3))
        reference = torch.mm(torch.mm(torch.zeros(2, 3), torch.ones(3, 3)), torch.ones(3, 4))
        self.assertEqual(result, reference)
        torch.onnx.export_to_pretty_string(
            mte, (torch.ones(2, 3),), None, verbose=False,
            example_outputs=result, propagate=True)

    def test_trace_with_size(self):
        @_trace(torch.zeros(1, 1))
        def foo(x):
            return x + 1

        @torch.jit.script
        def bar(x):
            y = int(foo(x))
            if True:
                y = 7
            return y + 1

        self.assertEqual(8, bar(torch.ones(1, 1)))

    def test_ellipsis_mid(self):
        def ellipsize(x):
            # type: (Tensor) -> List[int]
            return x[2, ..., 0:4, 4:8].size()  # noqa T484

        dummy = torch.zeros(8, 8, 8, 8, 8)
        self.checkScript(ellipsize, (dummy,), optimize=True)

    def test_ellipsis_mid_select(self):
        def ellipsize(x):
            # type: (Tensor) -> List[int]
            return x[2, ..., 4, 4, 4:8, 2].size()  # noqa T484

        dummy = torch.zeros(8, 8, 8, 8, 8, 8, 8)
        self.checkScript(ellipsize, (dummy,), optimize=True)

    def test_ellipsis_start(self):
        def ellipsize(x):
            # type: (Tensor) -> List[int]
            return x[..., 0:4, 4:8].size()  # noqa T484
        dummy = torch.zeros(8, 8, 8, 8, 8)
        self.checkScript(ellipsize, (dummy,), optimize=True)

    def test_ellipsis_end(self):
        def ellipsize(x):
            # type: (Tensor) -> List[int]
            return x[0:4, 2, ...].size()  # noqa T484
        dummy = torch.zeros(8, 8, 8, 8, 8)
        self.checkScript(ellipsize, (dummy,), optimize=True)

    def test_tracing_slicing(self):
        @_trace(torch.zeros(10))
        def foo_trace(x):
            return x[-5:-3]

        @torch.jit.script
        def foo_script(x):
            return x[-5:-3]

        def foo(x):
            return x[-5:-3]

        a = torch.arange(0, 8)
        b = torch.arange(0, 20)
        self.assertEqual(foo_trace(a), foo_script(a))
        self.assertEqual(foo_trace(a), foo(a))
        self.assertNotEqual(foo_trace(a), foo_trace(b))

    def test_torch_manual_seed(self):
        with freeze_rng_state():
            def test():
                torch.manual_seed(2)
                return torch.rand(1)

            script = torch.jit.script(test)
            self.assertEqual(test(), script())
            graph = script.graph_for()
            FileCheck().check("aten::manual_seed").run(graph)

    def test_tracing_indexing(self):
        @_trace(torch.zeros(10))
        def foo_trace(x):
            return x[-2]

        @torch.jit.script
        def foo_script(x):
            return x[-2]

        def foo(x):
            return x[-2]

        a = torch.arange(0, 8)
        b = torch.arange(0, 20)
        self.assertEqual(foo_script(a), foo_trace(a))
        self.assertEqual(foo_trace(a), foo(a))
        self.assertNotEqual(foo_trace(a), foo_trace(b))

    def test_index_select_shape_prop(self):

        @torch.jit.script
        def foo(x, y):
            return torch.index_select(x, index=y, dim=1)

        a = torch.zeros(2, 2)
        b = torch.zeros(4, dtype=torch.long)
        torch._C._jit_pass_complete_shape_analysis(foo.graph, (a, b), False)
        FileCheck().check("Double(2, 4)").run(str(foo.graph))

    def test_onnx_export_speculate(self):

        class Foo(torch.jit.ScriptModule):
            def __init__(self, m):
                super(Foo, self).__init__()
                self.m = m

            @torch.jit.script_method
            def forward(self, x):
                x += x
                # because we are testing if we emit `if` statement correctly
                # we cannot use `True` as the condition. Constant prop
                # would remove the `if` statements.
                c = torch.sum(x) > 4
                if bool(c):
                    if bool(c):
                        y = self.m(x)
                    else:
                        y = self.m(x)
                else:
                    y = self.m(x)
                return y

        linear = torch.jit.trace(nn.Linear(10, 20).float(), torch.zeros(1, 10, dtype=torch.float))

        @torch.jit.script
        def transpose(x):
            return x.t()

        f1 = Foo(transpose)
        outputs_f1 = f1(torch.ones(1, 10, dtype=torch.float))
        f2 = Foo(linear)
        outputs_f2 = f2(torch.ones(1, 10, dtype=torch.float))

        torch.onnx.export_to_pretty_string(
            f1,
            (torch.ones(1, 10, dtype=torch.float), ),
            None, verbose=False, example_outputs=outputs_f1)
        torch.onnx.export_to_pretty_string(
            f2,
            (torch.ones(1, 10, dtype=torch.float), ),
            None, verbose=False, example_outputs=outputs_f2)

    def test_onnx_export_shape_reshape(self):
        class Foo(torch.nn.Module):
            def forward(self, x):
                import torch.onnx.operators
                x = x.repeat(5, 1, 1)
                shape = torch.onnx.operators.shape_as_tensor(x)
                reshaped = torch.onnx.operators.reshape_from_tensor_shape(x, shape)
                return reshaped

        foo = torch.jit.trace(Foo(), torch.zeros(1, 2, 3))
        outputs = foo(torch.zeros(1, 2, 3))
        f = io.BytesIO()
        torch.onnx.export_to_pretty_string(foo, (torch.zeros(1, 2, 3)), f,
                                           example_outputs=outputs)

    def test_shape_analysis_loop(self):
        def foo(a, b, x):
            c = a
            # on the first iteration of the loop it appears that
            # c should have a expand to the size of b
            # but on the second+ iterations, there is no broadcast and the
            # sizes are different.
            # previously this would cause the compiler to (1) enter an infinite
            # loop trying to compute the shape, and (2) insert invalid
            # broadcasts.
            # this test ensure we don't regress on these issues
            for _ in range(2):
                a = c + b
                c = x
                b = x
            return a

        self.checkScript(foo, (torch.zeros(1), torch.zeros(4), torch.zeros(5)), optimize=False)

    def test_intlist_args(self):
        def func_1(x):
            return torch.nn.functional.adaptive_avg_pool1d(x, 1)

        def func_2(x):
            return torch.nn.functional.adaptive_avg_pool1d(x, output_size=1)

        def func_3(x):
            return torch.nn.functional.adaptive_avg_pool1d(x, output_size=[1])

        x = torch.randn(8, 8, 8)
        self.checkScript(func_1, [x], optimize=True)
        self.checkScript(func_2, [x], optimize=True)
        self.checkScript(func_3, [x], optimize=True)

    def test_wrong_implicit_expand(self):

        @_trace(torch.zeros(3), torch.zeros(1))
        def foo(a, b):
            return a + b

        a = torch.rand(4)
        b = torch.rand(4)
        self.assertEqual(a + b, foo(a, b))

    def test_builtin_args_fails(self):

        with self.assertRaisesRegex(RuntimeError, 'xpected at most'):
            @torch.jit.script
            def f0(a):
                torch.sum(a, a, a, a)

        with self.assertRaisesRegex(RuntimeError, 'Argument self not provided'):
            @torch.jit.script
            def f1(a):
                torch.sum(foo=4)

        with self.assertRaisesRegex(RuntimeError, 'specified twice'):
            @torch.jit.script
            def f2(a):
                torch.sum(a, self=a)

        with self.assertRaisesRegex(RuntimeError, 'not provided'):
            @torch.jit.script
            def f3(a):
                torch.sum(dim=4)

        with self.assertRaisesRegex(RuntimeError, 'for argument \'tensors\' but instead found type \'Tensor'):
            @torch.jit.script
            def f4(a):
                torch.cat(a)

        with self.assertRaisesRegex(RuntimeError, r'argument \'tensors\' but instead found type \'List\[int\]'):
            @torch.jit.script
            def f5(a):
                torch.cat([3])

        with self.assertRaisesRegex(RuntimeError, 'Lists must contain only a single type'):
            @torch.jit.script
            def f6(a):
                a.expand(size=[3, [4]])

        with self.assertRaisesRegex(RuntimeError, 'xpected a value of type \'Tensor\' for argument \'self\''):
            @torch.jit.script
            def f7(a):
                torch.sum([4])

    def test_builtin_args(self):

        def t0(a):
            # default arg dim
            return torch.cat([a, a])

        self.checkScript(t0, (torch.zeros(1, 1),))

        def t1(a):
            # keywords out of order
            return torch.cat(dim=1, tensors=[a, a])

        self.checkScript(t1, (torch.zeros(1, 1, 2),))

        def t2(a):
            # mix const/non-const attributes
            if True:
                b = 1
            else:
                b = 0
            return torch.sum(a, dim=b, keepdim=False)

        self.checkScript(t2, (torch.zeros(1, 1, 2),))

    def test_parser_type_annotations(self):
        cu = torch.jit.CompilationUnit('''
            def foo(x : Tensor, y : Tuple[Tuple[Tensor, Tensor], Tensor]) -> Tuple[Tensor, Tensor]:
                return x, x
        ''')

        self.assertExpected(str(cu.foo.schema))

    def test_parser_type_annotations_comment(self):
        cu = torch.jit.CompilationUnit('''
            def foo(x, y):
                # type: (Tensor, Tuple[Tuple[Tensor, Tensor], Tensor]) -> Tuple[Tensor, Tensor]
                return x, x
        ''')

        self.assertExpected(str(cu.foo.schema))

    def test_parser_type_annotations_unknown_type(self):
        with self.assertRaisesRegex(RuntimeError, "Unknown type name 'Foo'"):
            cu = torch.jit.CompilationUnit('''
                def foo(x : Tensor, y : Tuple[Tuple[Foo, Tensor], Tensor]) -> Tuple[Tensor, Tensor]:
                    return x, x
            ''')

    def test_parser_type_annotations_subscript_non_ident(self):
        with self.assertRaisesRegex(RuntimeError, r'Subscripted type must be a type identifier'):
            cu = torch.jit.CompilationUnit('''
                def foo(x : Tensor, y : Tuple[Tensor, Tensor][Tensor]) -> Tuple[Tensor, Tensor]:
                    return x, x
            ''')

    def test_parser_type_annotations_subscript_tensor(self):
        with self.assertRaisesRegex(RuntimeError, r'Unknown type constructor Tensor'):
            cu = torch.jit.CompilationUnit('''
                def foo(x : Tensor, y : Tensor[Tensor, Tensor]) -> Tuple[Tensor, Tensor]:
                    return x, x
            ''')

    def test_parser_type_annotations_incompatible_expression(self):
        with self.assertRaisesRegex(RuntimeError, r'Expression of type \+ cannot be used in a type expression'):
            cu = torch.jit.CompilationUnit('''
                def foo(x : Tensor, y : Tuple[3 + 4, Tensor]) -> Tuple[Tensor, Tensor]:
                    return x, x
            ''')

    def test_gather_dynamic_index(self):
        def t(x):
            gather1 = x[0]
            idx = 0 + 1
            gather2 = x[idx]
            return gather1 + gather2

        self.checkScript(t, (torch.zeros(3, 2, 3),))

    def test_slice_dynamic_index(self):
        def t(x):
            slice1 = x[0:1]
            zero = 0
            one = zero + 1
            slice2 = x[zero:one]
            return slice1 + slice2

        self.checkScript(t, (torch.zeros(3, 2, 3),))

    def test_addmm_grad(self):
        """ This test checks several things:
            1. An expand node was inserted before the addmm operating on the
               bias term.
            2. The fused form of addmm appears in the ultimate graph that's
               executed.
            3. A sum op was emitted for accumulating gradients along the 0th
               (expanded) dimension of the bias term.
            4. The correct symbolic representation for the backward pass of the
               mm operator was emitted (x.t() -> mm)

            TODO: we should actually check these conditions once we have a way
            to dump the GraphExecutor state. Namely the processed forward graph
            and the backward graph.
        """
        @torch.jit.script
        def addmm_grad_test(b, x, w):
            return torch.addmm(b, x, w)

        # Initialize param and input values
        w_init = torch.rand(2, 5)
        b_init = torch.rand(5)
        x = torch.rand(3, 2)

        # Clone trainable params
        b = b_init.clone()
        b.requires_grad_()
        w = w_init.clone()
        w.requires_grad_()

        # Test symbolic differentiation
        y = addmm_grad_test(b, x, w)
        y.sum().backward()

        # clone params for autograd reference
        b_ref = b_init.clone()
        b_ref.requires_grad_()
        w_ref = w_init.clone()
        w_ref.requires_grad_()
        y_ref = torch.addmm(b_ref, x, w_ref)
        y_ref.sum().backward()

        self.assertEqual(w.grad, w_ref.grad)
        self.assertEqual(b.grad, b_ref.grad)

    def test_zeros(self):
        class M(torch.jit.ScriptModule):
            __constants__ = ['d']

            def __init__(self):
                super(M, self).__init__()
                self.d = torch.device('cpu')

            @torch.jit.script_method
            def create(self):
                return torch.zeros([1, 1, 2], dtype=torch.float, device=self.d, layout=torch.strided)

        r = M().create()
        self.assertEqual(r.dtype, torch.float)
        self.assertEqual(torch.zeros([1, 1, 2], dtype=torch.float), r)

        def fn():
            return torch.zeros((1, 2, 3))

        self.checkScript(fn, ())

    def test_vararg_zeros(self):
        def foo():
            return torch.zeros(3, 4, 5, dtype=torch.int)

        self.checkScript(foo, ())

    @unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.LEGACY, "the original version of test_rand")
    def test_rand(self):
        def test_rand():
            a = torch.rand([3, 4])
            return a + 1.0 - a

        self.checkScript(test_rand, ())
        fn = torch.jit.script(test_rand)
        out = fn()
        self.assertEqual(out.dtype, torch.double)
        g = fn.graph_for()
        # Testing shape analysis correctly setting type
        if GRAPH_EXECUTOR != ProfilingMode.SIMPLE:
            FileCheck().check("Double(*, *)").check_not("Float(*, *)").run(g)

        @torch.jit.script
        def randint():
            return torch.randint(0, 5, [1, 2])
        out = randint()
        self.assertEqual(out.dtype, torch.double)
        # although the type should be int here, testing that the runtime dtype
        # and shape analysis dtype is the same.
        if GRAPH_EXECUTOR != ProfilingMode.SIMPLE:
            FileCheck().check("Double(*, *)").check_not("Float(*, *)").run(randint.graph_for())

    @unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.PROFILING, "the original version of test_rand")
    def test_rand_profiling(self):
        def test_rand():
            a = torch.rand([3, 4])
            return a + 1.0 - a

        fn = self.checkScript(test_rand, ())
        out = fn()
        self.assertEqual(out.dtype, torch.double)
        # Testing shape analysis correctly setting type
        FileCheck().check("Double(3, 4)").check_not("Float(3, 4)").run(fn.graph_for())

        @torch.jit.script
        def randint():
            return torch.randint(0, 5, [1, 2])

        out = randint(profile_and_replay=True)
        self.assertEqual(out.dtype, torch.double)
        # although the type should be int here, testing that the runtime dtype
        # and shape analysis dtype is the same.
        FileCheck().check("Double(1, 2)").check_not("Float(1, 2)").run(randint.graph_for())

    def test_erase_number_types(self):
        def func(a):
            b = 7 + 1 + 3
            c = a + b
            c += b
            return c

        graph = torch.jit.script(func).graph
        FileCheck().check("int = prim::Constant").check("aten::add_").run(str(graph))
        self.run_pass('remove_inplace_ops', graph)
        self.run_pass('erase_number_types', graph)
        FileCheck().check_not("int = prim::Constant").check_not("aten::add_").run(str(graph))

    def test_mm_batching(self):

        with enable_profiling_mode():
            lstm_cell = torch.jit.script(LSTMCellS)

            def lstm(x, hx, cx, w_ih, w_hh, b_ih, b_hh):
                for i in range(x.size(0)):
                    hx, cx = lstm_cell(x[i], hx, cx, w_ih, w_hh, b_ih, b_hh)
                return hx

            slstm = torch.jit.script(lstm)

            inputs = get_lstm_inputs('cpu', training=True, seq_length=10)
            slstm(*inputs, profile_and_replay=True).sum().backward(retain_graph=True)
            if GRAPH_EXECUTOR == ProfilingMode.PROFILING:
                slstm(*inputs, profile_and_replay=True).sum().backward()

            fw_graph = slstm.graph_for(*inputs)
            if GRAPH_EXECUTOR == ProfilingMode.LEGACY:
                bw_graph = backward_graph(slstm, diff_graph_idx=0)
                self.assertTrue('prim::MMBatchSide' in str(fw_graph))
                self.assertTrue('prim::MMTreeReduce' in str(bw_graph))

            sout = slstm(*inputs)
            out = lstm(*inputs)
            self.assertEqual(slstm(*inputs), lstm(*inputs))
            self.assertEqual(torch.autograd.grad(slstm(*inputs).sum(), inputs),
                             torch.autograd.grad(lstm(*inputs).sum(), inputs))

    def test_loop_unrolling(self):
        def fn(x):
            y = 0
            for i in range(int(x)):
                y -= i
            return y

        graph = torch.jit.script(fn).graph
        self.run_pass('loop_unrolling', graph)
        unroll_factor = 8
        FileCheck().check("prim::Loop").check_count("aten::sub", unroll_factor) \
            .check("prim::Loop").check("aten::sub").run(str(graph))
        self.checkScript(fn, (torch.tensor(10),))

    def test_loop_unrolling_const(self):
        def fn():
            y = 0
            for _ in range(10):
                y -= 1
            return y

        def fn2():
            y = 0
            for i in range(10):
                y -= i
            return y

        def check(fn, name):
            graph = torch.jit.script(fn).graph
            self.run_pass('loop_unrolling', graph)
            # entirely unrolled
            FileCheck().check_not("prim::Loop'").run(str(graph))
            self.checkScript(fn, ())

        check(fn, 'add_const')
        check(fn2, 'add_iter')

    def test_loop_unrolling_nested(self):
        def fn(x):
            y = 0
            for _ in range(10):
                for j in range(int(x)):
                    y -= j
            return y

        graph = torch.jit.script(fn).graph
        self.run_pass('loop_unrolling', graph)
        # inner loop with 8 subs followed by loop epilogue
        unroll_factor = 8
        FileCheck().check("prim::Loop").check("prim::Loop").check_count('aten::sub', unroll_factor) \
            .check("prim::Loop").check("aten::sub").run(str(graph))
        self.checkScript(fn, (torch.tensor(10),))

    def test_loop_unroll_unused_counter(self):
        def fn(x):
            y = 0
            for _ in range(int(x)):
                y -= 1
            return y

        graph = torch.jit.script(fn).graph
        self.run_pass('loop_unrolling', graph)
        FileCheck().check("prim::Loop").check_not("aten::add").check("return") \
            .run(str(graph))

    def test_loop_unroll_negative(self):
        def fn(x):
            y = 0
            for _ in range(int(x)):
                y += 1
            return y

        self.checkScript(fn, (torch.tensor(-20),))
        self.checkScript(fn, (torch.tensor(-2),))
        self.checkScript(fn, (torch.tensor(-1),))
        self.checkScript(fn, (torch.tensor(0),))
        self.checkScript(fn, (torch.tensor(1),))
        self.checkScript(fn, (torch.tensor(2),))

    def test_where(self):
        def fn(x, y):
            return torch.where(x > 0.0, x, y)

        self.checkScript(fn, (torch.randn(3, 2, dtype=torch.float), torch.ones(3, 2, dtype=torch.float)))

    def test_where_method(self):
        def fn(x, y):
            return x.where(x > 0.0, y)

        self.checkScript(fn, (torch.randn(3, 2, dtype=torch.float), torch.ones(3, 2, dtype=torch.float)))

    def test_reassign_module_lhs(self):
        with self.assertRaisesRegex(RuntimeError, 'Cannot re-assign \'self\''):
            class ReassignSelfLHS(torch.jit.ScriptModule):
                @torch.jit.script_method
                def forward(self, x):
                    for _ in range(20):
                        self = x
                    return self

            ReassignSelfLHS()

    def test_reassign_module_rhs(self):
        with self.assertRaisesRegex(RuntimeError, 'Cannot re-assign \'x\' to a value of type module'):
            class ReassignSelfRHS(torch.jit.ScriptModule):
                @torch.jit.script_method
                def forward(self, x):
                    for _ in range(20):
                        x = self
                    return self

            ReassignSelfRHS()

    def test_unknown_builtin(self):
        with self.assertRaisesRegex(RuntimeError, 'nonexistent attribute or method'):
            @torch.jit.script
            def unknown_builtin(x):
                return x.splork(3)

    def test_return_tuple(self):
        def return_tuple(x):
            a = (x, x)
            return a, x
        self.checkScript(return_tuple, (torch.rand(4),))

    def test_method_no_self(self):
        with self.assertRaisesRegex(RuntimeError, 'methods must have a self argument'):
            class MethodNoSelf(torch.jit.ScriptModule):
                @torch.jit.script_method  # noqa: B902
                def forward():  # noqa: B902
                    return torch.zeros(3, 4)

            MethodNoSelf()

    def test_return_stmt_not_at_end(self):
        def return_stmt(x):
            if bool(x > 3):
                return x + 3
            else:
                return x
        self.checkScript(return_stmt, (torch.rand(1),))

    def test_for_in_range(self):
        def fn():
            c = 0
            for i in range(100):
                c += i
            return c
        self.checkScript(fn, ())

    def test_for_in_range_dynamic(self):
        def fn():
            c = 0
            for i in range(100):
                acc = 0
                for j in range(i):
                    acc += j
                c += acc
            return c
        self.checkScript(fn, (), optimize=False)

    def test_for_in_range_ast(self):
        def test_script_for_in_range_ast():
            c = 0
            for i in range(100):
                acc = 0
                for j in range(i):
                    acc += j
                c += acc
            return c

        self.checkScript(test_script_for_in_range_ast, ())

    def test_for_in_range_if_ast(self):
        @torch.jit.script
        def test_script_for_in_range_if_ast(x):
            output = x
            for i in range(20):
                if i == 0:
                    output = x.unsqueeze(0)
                else:
                    output = torch.cat((output, x.unsqueeze(0)), dim=0)
            return output
        inputs = self._make_scalar_vars([0], torch.int64)

        self.assertEqual(test_script_for_in_range_if_ast(*inputs).shape[0], 20)

    def test_for_in_range_start_end(self):
        def fn():
            x = 0
            for i in range(7, 100):
                x += i
            return x
        self.checkScript(fn, ())

    def test_for_in_range_start_end_step(self):
        def fn(start, end, step):
            # type: (int, int, int) -> int
            x = 0
            for i in range(start, end, step):
                x += i
            return x

        self.checkScript(fn, (7, 100, 7))
        self.checkScript(fn, (7, 100, -7))
        self.checkScript(fn, (2, -11, -3))
        self.checkScript(fn, (2, -11, 3))
        self.checkScript(fn, (2, 10, 3))
        self.checkScript(fn, (-2, -10, -10))

    def test_for_in_range_zero_step(self):
        @torch.jit.script
        def fn():
            x = 0
            for i in range(2, -11, 0):
                x += i
            return x

        with self.assertRaisesRegex(RuntimeError, "must not be zero"):
            fn()

    def test_range_args(self):
        with self.assertRaisesRegex(RuntimeError, r'range expected at least 1 arguments, got 0'):
            @torch.jit.script
            def range_no_arg(x):
                for _ in range():
                    x += 1
                return x
        with self.assertRaisesRegex(RuntimeError, r'found float'):
            @torch.jit.script
            def range_non_float():
                for i in range(.5):
                    print(i)

    def test_zip_enumerate_modulelist(self):
        class Sub(torch.nn.Module):
            def __init__(self):
                super(Sub, self).__init__()

            def forward(self, thing):
                return thing - 2

        class Double(torch.nn.Module):
            def __init__(self):
                super(Double, self).__init__()

            def forward(self, thing):
                return thing * 2

        # zipping over two
        class ZipModLists(torch.nn.Module):
            __constants__ = ['mods', 'mods2']

            def __init__(self, mods, mods2):
                super(ZipModLists, self).__init__()
                self.mods = mods
                self.mods2 = mods2

            def forward(self, x):
                iter = 0
                for mod1, mod2 in zip(self.mods, self.mods2):
                    x = mod2(mod1(x))
                    iter += 1
                return x, iter

        class ZipWithValues(torch.nn.Module):
            __constants__ = ['mods', 'mods2', 'tup_larger', 'tup_smaller']

            def __init__(self, mods, mods2):
                super(ZipWithValues, self).__init__()
                self.mods = mods
                self.mods2 = mods2
                self.tup_larger = list(range(len(mods2) + 1))
                self.tup_smaller = list(range(max(len(mods2) + 1, 1)))

            def forward(self, x):
                iter = 0
                x2 = x
                for val, mod1, mod2 in zip(self.tup_larger, self.mods, self.mods2):
                    x = mod2(mod1(x)) + val
                    iter += 1
                for val, mod1, mod2 in zip(self.tup_smaller, self.mods, self.mods2):
                    x2 = mod2(mod1(x2)) + val
                    iter += 1
                return x, iter

        mods = nn.ModuleList([Double()]), nn.ModuleList([Double(), Sub(), Sub()]), nn.ModuleList([Sub(), Double()])
        for i in range(len(mods)):
            for j in range(len(mods)):
                mod = ZipModLists(mods[i], mods[j])
                self.checkModule(mod, (torch.tensor(.5),))
                mod2 = ZipWithValues(mods[i], mods[j])
                self.checkModule(mod2, (torch.tensor(.5),))


    def test_enumerate_modlist_range(self):
        class Double(torch.nn.Module):
            def forward(self, thing):
                return thing * 2

        class Mod(torch.nn.Module):
            __constants__ = ['mods']

            def __init__(self):
                super(Mod, self).__init__()
                self.mods = nn.ModuleList([Double(), Double()])

            def forward(self, x):
                x2 = x
                iter = 0
                for val, mod in enumerate(self.mods):
                    x2 = mod(x2) * val
                    iter += 1
                return iter, x, x2

        self.checkModule(Mod(), (torch.tensor(.5),))

        # variable lenghth, modulelist
        class Mod2(Mod):
            def forward(self, x):
                for val, mod in zip(range(int(x)), self.mods):
                    x = mod(x) * val
                return x

        with self.assertRaisesRegex(Exception, "that does not have a statically determinable length"):
            torch.jit.script(Mod2())

        # modulelist, variable length
        class Mod3(Mod):
            def forward(self, x):
                for val, mod in zip(self.mods, range(int(x))):
                    x = mod(x) * val
                return x

        with self.assertRaisesRegex(Exception, "that does not have a statically determinable length"):
            torch.jit.script(Mod3())

    def test_for_in_enumerate(self):
        def fn(x):
            # type: (List[int]) -> int
            sum = 0
            for (i, v) in enumerate(x):
                sum += i * v

            return sum

        self.checkScript(fn, ([1, 2, 3, 4, 5],))

        def fn_enumerate_start_index(x):
            # type: (List[int]) -> int
            sum = 0
            for (i, v) in enumerate(x, start=1):
                sum += i * v

            return sum

        self.checkScript(fn, ([1, 2, 3, 4, 5],))

        def fn_nested_enumerate(x):
            # type: (List[int]) -> int
            sum = 0
            for (i, (j, v)) in enumerate(enumerate(x)):
                sum += i * j * v

            return sum

        self.checkScript(fn, ([1, 2, 3, 4, 5],))

        with self.assertRaisesRegex(RuntimeError, r'enumerate expected at least 1 arguments, got 0'):
            @torch.jit.script
            def enumerate_no_arg(x):
                # type: (List[int]) -> int
                sum = 0
                for _ in enumerate():
                    sum += 1

                return sum

        with self.assertRaisesRegex(RuntimeError, r'enumerate expected at most 2 arguments, got 3'):
            @torch.jit.script
            def enumerate_too_many_args(x):
                # type: (List[int]) -> int
                sum = 0
                for _ in enumerate(x, x, x):
                    sum += 1

                return sum

    def test_list_comprehension_modulelist(self):
        class Inner(torch.nn.Module):
            def forward(self, x):
                return x + 10

        class M(torch.nn.Module):
            __constants__ = ["module_list"]

            def __init__(self, mod_list):
                super(M, self).__init__()
                self.module_list = mod_list

            def forward(self, x):
                out = torch.jit.annotate(List[Tensor], [mod(x) for mod in self.module_list])
                return out

        mod = M(nn.ModuleList([Inner(), Inner()]))
        self.checkModule(mod, (torch.tensor(3),))

        mod = M(nn.ModuleList([]))
        torch.jit.script(mod)

        class M2(M):
            def __init__(self, mod_list):
                super(M2, self).__init__(mod_list)

            def forward(self, x):
                out = [mod(x) for mod in self.module_list]
                return out

        mod = M2(nn.ModuleList([Inner(), Inner()]))
        self.checkModule(mod, (torch.tensor(3),))

        mod = M2(nn.ModuleList([]))
        # defaults to List of Tensor for empty modulelist
        self.assertEqual(torch.jit.script(mod)(torch.tensor(.5)), [])

        def bad_type_annotation():
            out = torch.jit.annotate(int, [x for x in [1, 2, 3]])
            return out

        with self.assertRaisesRegex(Exception, "Expected list type annotation"):
            torch.jit.script(bad_type_annotation)

    def test_for_in_zip(self):
        def fn(x, y):
            # type: (List[int], List[int]) -> int
            sum = 0
            for (i, j) in zip(x, y):
                sum += i * j

            return sum

        self.checkScript(fn, ([1, 2, 3, 4, 5], [2, 3, 4, 5, 6]))

        def fn_multi_inputs(x, y, z):
            # type: (List[int], List[int], List[int]) -> int
            sum = 0
            for (i, j, k) in zip(x, y, z):
                sum += i * j * k

            return sum

        self.checkScript(fn_multi_inputs, ([1, 2, 3, 4], [2, 3, 4, 5], [3, 4, 5, 6]))

        def fn_nested_zip(x, y, z):
            # type: (List[int], List[int], List[int]) -> int
            sum = 0
            for (i, (j, k)) in zip(x, zip(y, z)):
                sum += i * j * k

            return sum

        self.checkScript(fn_multi_inputs, ([1, 2, 3, 4], [2, 3, 4, 5], [3, 4, 5, 6]))

        with self.assertRaisesRegex(RuntimeError, r'zip expected at least 1 arguments, got 0'):
            @torch.jit.script
            def zip_no_arg(x):
                # type: (List[int]) -> int
                sum = 0
                for _ in zip():
                    sum += 1

                return sum

        with self.assertRaisesRegex(RuntimeError, r'too many values to unpack: need 2 but found 3'):
            @torch.jit.script
            def fn_nested_zip_wrong_target_assign(x, y, z):
                # type: (List[int], List[int], List[int]) -> int
                sum = 0
                for (i, (j, k)) in zip(x, y, z):
                    sum += i * j * k

                return sum

    def test_for_in_zip_enumerate(self):
        def fn_zip_enumerate(x, y):
            # type: (List[int], List[int]) -> int
            sum = 0
            for (i, (j, v), k) in zip(x, enumerate(y), range(0, 100)):
                sum += i * j * v * k

            return sum

        self.checkScript(fn_zip_enumerate, ([1, 2, 3, 4], [2, 3, 4, 5]))

        def fn_enumerate_zip(x, y):
            # type: (List[int], List[int]) -> int
            sum = 0
            for (i, (j, v)) in enumerate(zip(x, y)):
                sum += i * j * v

            return sum

        self.checkScript(fn_enumerate_zip, ([1, 2, 3, 4], [2, 3, 4, 5]))

    def test_for_in_tensors(self):
        def test_sizes(x):
            sumz = 0
            for s in x:
                sumz += 1
            return sumz
        self.checkScript(test_sizes, (torch.rand(5, 4, 3, 2, 1),))
        self.checkScript(test_sizes, (torch.rand(777),))
        self.checkScript(test_sizes, (torch.rand(0),))

    def test_for_in_tensors_rank0(self):
        with self.assertRaisesRegex(RuntimeError, "of a 0-d tensor"):
            @torch.jit.script
            def test_sizes(x):
                sumz = 0
                for s in x:
                    sumz += 1
                return sumz

            test_sizes(torch.tensor(1))

    def test_for_in_tensors_fail_scalar(self):
        with self.assertRaisesRegex(RuntimeError, "'float' object is not iterable"):
            @torch.jit.script
            def test_sizes(x):
                # type: (float) -> int
                sumz = 0
                for s in x: # noqa
                    sumz += 1
                return sumz

            test_sizes(0.0)

    def test_for_in_tensors_nested(self):
        def test_sizes(x):
            sumz = 0
            for n in x:
                for t in n:
                    sumz += 1
            return sumz

        self.checkScript(test_sizes, (torch.rand(5, 4, 3, 2, 1),))

    # to avoid defining sum_list in multiple tests
    def get_sum_list_fn(self):
        def sum_list(a):
            # type: (List[int]) -> int
            sum = 0
            for i in a:
                sum += i

            return sum

        return sum_list

    def test_sum_list_diff_elms(self):
        self.checkScript(self.get_sum_list_fn(), ([1, 2, 3, 4, 5],))

    def test_sum_list_empty(self):
        self.checkScript(self.get_sum_list_fn(), ([],))

    def test_sum_list_one(self):
        self.checkScript(self.get_sum_list_fn(), ([1],))

    def test_sum_list_literal(self):

        def sum_list():
            # type: () -> int
            sum = 0
            for i in [1, 2, 3, 4, 5]:
                sum += i

            return sum

        self.checkScript(sum_list, ())

    def test_sum_list_wrong_type(self):

        with self.assertRaisesRegex(RuntimeError, "'int' object is not iterable"):
            @torch.jit.script
            def sum_list(a):
                # type: (int) -> int
                sum = 0
                for i in a:  # noqa: T484
                    sum += i

                return sum

            sum_list(1)

    def test_list_iterables(self):
        with self.assertRaisesRegex(RuntimeError, 'List of iterables is not supported currently'):
            cu = torch.jit.CompilationUnit('''
            def list_iterables(x):
                for i, j in [2, 3, 4], [5, 6, 7]:
                    x += i
                    x += j
                return x
            ''')

    def test_for_in_string(self):
        def test_strings(x):
            # type: (str) -> str
            reverse = ""
            for c in x:
                reverse = c + reverse
            return reverse

        self.checkScript(test_strings, ("hello",))
        self.checkScript(test_strings, ("",))

        def test_list_strings(x):
            # type: (List[str]) -> str
            result = ""
            for sub_str in x:
                result += sub_str
            return result

        self.checkScript(test_list_strings, (["hello", "world"],))
        self.checkScript(test_list_strings, (["hello", " ", "world", ""],))

    def test_for_in_dict(self):
        def test_dicts(x):
            # type: (Dict[str, int]) -> int
            sum = 0
            for key in x:
                sum += x[key]
            return sum

        self.checkScript(test_dicts, ({"a": 1, "b": 2, "c": 3},))

        def test_dict_keys_values(x):
            # type: (Dict[str, int]) -> Tuple[str, int]
            key_str = ""
            sum = 0
            for key in x.keys():
                key_str += key
            for val in x.values():
                sum += val
            return key_str, sum

        self.checkScript(test_dicts, ({"a": 1, "b": 2, "c": 3},))

    def test_for_tuple_unpack(self):
        def for_tuple_unpack(x, y):
            for i, j in [[3, 4], [5, 6], [7, 8]]:
                x += i
                y += j
            return x, y

        self.checkScript(for_tuple_unpack, (torch.tensor(3), torch.tensor(5)))

        def nested_tuple_unpack(x, y):
            # type: (List[int], List[int]) -> int
            sum = 0
            for i, (j, k), v in zip(x, enumerate(x), y):
                sum += i + j + k + v
            return sum

        self.checkScript(nested_tuple_unpack, ([1, 3, 5], [2, 4, 6]))

    def test_for_tuple_assign(self):
        def test_simple_assign(x):
            # type: (Tuple[int, float]) -> float
            sum = 0.0
            for a in x:
                sum += float(a)
            return sum

        self.checkScript(test_simple_assign, ((1, 2.5),))

        def test_tuple_assign(x):
            # type: (Tuple[Tuple[int, int], Tuple[int, int]]) -> int
            sum = 0
            for a in x:
                sum += a[0]
                sum += a[1]
            return sum

        self.checkScript(test_tuple_assign, (((1, 2), (4, 7)), ))

    def test_single_starred_lhs(self):
        with self.assertRaisesRegex(RuntimeError, 'A Starred expression may only appear on the lhs within the presence'
                                                  ' of another non-starred expression'):
            cu = torch.jit.CompilationUnit('''
            def single_starred_lhs(x):
                a = (x, x, x)
                *b, = a
                return b
            ''')

    def test_singleton_tuple_unpack(self):
        def foo(a):
            b, = (a,)
            return b + 1
        self.checkScript(foo, (torch.rand(3),))

    def test_tuple_assignments(self):
        def var_tuple_assign(x, y):
            # type: (Tuple[Tensor, Tensor], Tensor) -> Tensor
            (a, b), c = x, y
            return a + b + c

        tuple_inputs = (torch.randn(1, 4), torch.randn(3, 4))
        self.checkScript(var_tuple_assign, (tuple_inputs, torch.randn(3, 4)))

        def nested_tuple_assign(x, y, z):
            # type: (int, Tuple[int, Tuple[int, int]], Tuple[int, int]) -> int
            a, (b, (c, d)), (e, f) = x, y, z
            return a + b + c + d + e + f

        self.checkScript(nested_tuple_assign, ((1, (2, (3, 4)), (5, 6))))

        def subscript_tuple_assign(a, x, i):
            # type: (List[int], Tensor, int) -> Tuple[int, Tensor, int]
            a[i], (x[i], b) = 1, (2, 3)
            return a[i] + 1, x + 5, b

        self.checkScript(subscript_tuple_assign, ([12, 7, 9, 11], torch.tensor((3, 13, 17)), 0))

        # python 2 does not support star assignments so we use compilation unit to test instead
        if not PY2:
            star_code = dedent('''
            def star_tuple_assign():
                # type: () -> Tuple[int, int, Tuple[int, int], Tuple[int, int]]
                a, (b, *c), *d = 1, (2, 3, 4), 5, 6
                return a, b, c, d
            ''')

            self.checkScript(star_code, (), name='star_tuple_assign')

        def subscript_tuple_augmented_assign(a):
            # type: (Tuple[int, int]) -> Tuple[int, int]
            a[0] += 1
            return a

        with self.assertRaisesRegex(RuntimeError, 'does not support augmented assign'):
            scripted_aug_assign = torch.jit.script(subscript_tuple_augmented_assign)

    def test_multiple_assign(self):
        def test():
            a = b, c = d, f = (1, 1)

            # side effect
            ten = torch.tensor(1)
            ten1 = ten2 = ten.add_(1)

            # ordering
            x = 1
            y = 3
            x, y = y, x + y

            return a, b, c, d, f, ten, ten1, ten2, x, y

        self.checkScript(test, ())

    def test_multi_reduction(self):
        with self.assertRaisesRegex(
                RuntimeError,
                'augmented assignment can only have one LHS expression'):
            cu = torch.jit.CompilationUnit('''
            def multi_reduction(x):
                a, b += x
                return a, b
            ''')

    def test_invalid_call_arguments(self):
        with self.assertRaisesRegex(RuntimeError, 'but instead found type '):
            @torch.jit.script
            def invalid_call_arguments(x):
                return torch.unsqueeze(3, 4, 5, 6, 7, 8)

    def test_invalid_lhs_assignment(self):
        with self.assertRaisesRegex(RuntimeError, 'unexpected expression'):
            cu = torch.jit.CompilationUnit('''
            def invalid_lhs_assignment(x):
                x + 1 = x
                return x
            ''')

    def test_multi_starred_expr_lhs(self):
        with self.assertRaisesRegex(RuntimeError, 'Only one starred expression is allowed on the lhs'):
            cu = torch.jit.CompilationUnit('''
            def multi_starred_expr_lhs():
                a, *b, *c = [1, 2, 3, 4, 5, 6]
                return a
            ''')

    def test_pack_tuple_into_non_var(self):
        with self.assertRaisesRegex(RuntimeError, 'Cannot pack a tuple into a non-variable'):
            cu = torch.jit.CompilationUnit('''
            def pack_tuple_into_non_var(x):
                a, *1 = (3, 4, 5)
                return x
            ''')

    def test_print_kwargs(self):
        with self.assertRaisesRegex(RuntimeError, 'print doesn\'t accept any keyword arguments'):
            cu = torch.jit.CompilationUnit('''
            def print_kwargs(x):
                print(x, flush=True)
                return x
            ''')

    def test_builtin_use_as_value(self):
        with self.assertRaisesRegex(RuntimeError, 'builtin cannot be used as a value'):
            @torch.jit.script
            def builtin_use_as_value(x):
                return x.unsqueeze

    def test_wrong_use_as_tuple(self):
        with self.assertRaisesRegex(RuntimeError, 'cannot be used as a tuple'):
            def test_fn():
                return 3

            @torch.jit.script
            def wrong_use_as_tuple(self):
                a, b = test_fn
                return a

    def test_wrong_attr_lookup(self):
        with self.assertRaisesRegex(RuntimeError, 'attribute lookup is not defined on builtin'):
            @torch.jit.script
            def wrong_attr_lookup(self, x):
                a = x.unsqueeze.myattr
                return a

    def test_wrong_use_as_callable(self):
        with self.assertRaisesRegex(RuntimeError, 'cannot call a value'):
            @torch.jit.script
            def wrong_use_as_callable(x):
                return x(3, 4, 5)

    def test_python_val_doesnt_have_attr(self):
        with self.assertRaisesRegex(RuntimeError, 'object has no attribute abcd'):

            @torch.jit.script
            def python_val_doesnt_have_attr():
                # this has to be a module otherwise attr lookup would not be
                # allowed in the first place
                return shutil.abcd

    def test_wrong_module_attr_lookup(self):
        with self.assertRaisesRegex(RuntimeError, 'python value of type \'type\' cannot be used as a value:'):
            import io

            @torch.jit.script
            def wrong_module_attr_lookup():
                return io.BytesIO

    def test_wrong_method_call_inputs(self):
        with self.assertRaisesRegex(RuntimeError, 'Argument y not provided'):
            class SomeModule(torch.jit.ScriptModule):

                @torch.jit.script_method
                def foo(self, x, y):
                    return x

                @torch.jit.script_method
                def forward(self, x, y):
                    return self.foo(x)
            SomeModule()

    def test_single_starred_expr_for_loop(self):
        with self.assertRaisesRegex(RuntimeError, 'A Starred expression may only appear'):
            cu = torch.jit.CompilationUnit('''
            def test():
                x = 0
                for *a in [1, 2, 3]:
                    x = x + 1
                return x
            ''')

    def test_call_ge(self):
        with self.assertRaisesRegex(RuntimeError, 'Expected at most 1 arguments but found 3'):
            @_trace(torch.zeros(1, 2, 3))
            def foo(x):
                return x

            @torch.jit.script
            def test_fn():
                return foo(torch.full([1], 1), torch.full([1], 2), torch.full([1], 3))

    def test_wrong_return_type(self):
        with self.assertRaisesRegex(RuntimeError, 'but instead got value of type tuple'):
            @torch.jit.ignore
            def somefunc():
                # type: () -> Tuple[Tuple[Tensor, Tensor]]
                return torch.zeros(3, 4), torch.zeros(4, 5)  # noqa: T484

            @torch.jit.script
            def wrong_return_type():
                return somefunc()
            wrong_return_type()

    # Tests for calling between different front-end modes
    def test_call_python_fn_from_tracing_fn(self):
        def python_fn(x):
            return torch.neg(x)

        @_trace(torch.rand(3, 4))
        def traced_fn(x):
            return python_fn(x) + 1

        # The neg op in the python function should be properly inlined to the
        # graph
        FileCheck().check("aten::neg").run(str(traced_fn.graph))

    def test_call_python_mod_from_tracing_fn(self):
        class PythonMod(torch.nn.Module):
            def __init__(self):
                super(PythonMod, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(4, 3), requires_grad=False)

            def forward(self, x):
                return torch.mm(x, self.param)

        pm = PythonMod()

        @_trace(torch.rand(3, 4))
        def traced_fn(x):
            return pm(x) + 1.0

        # Note: the parameter self.param from the Python module is inlined
        # into the graph
        self.assertTrue(len(list(traced_fn.graph.inputs())) == 1)
        FileCheck().check("aten::mm").check("aten::add").run(str(traced_fn.graph))

    @_tmp_donotuse_dont_inline_everything
    def test_call_traced_fn_from_tracing_fn(self):
        @_trace(torch.rand(3, 4))
        def traced_fn1(x):
            return torch.neg(x)

        @_trace(torch.rand(3, 4))
        def traced_fn(x):
            return traced_fn1(x) + 1

        FileCheck().check("traced_fn").check("prim::CallFunction").check("aten::add") \
            .run(str(traced_fn.graph))

    @unittest.skip("error in first class mode")
    def test_call_traced_mod_from_tracing_fn(self):
        class TracedModule(torch.nn.Module):
            def __init__(self):
                super(TracedModule, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(4, 3), requires_grad=False)

            def forward(self, x):
                return torch.mm(x, self.param)

        tm = torch.jit.trace(TracedModule(), torch.rand(3, 4))

        with self.assertRaisesRegex(RuntimeError, "must be registered as submodules"):
            @_trace(torch.rand(3, 4))
            def traced_fn(x):
                return tm(x) + 1.0

    @_tmp_donotuse_dont_inline_everything
    def test_call_script_fn_from_tracing_fn(self):
        @torch.jit.script
        def script_fn(x):
            return torch.neg(x)

        @_trace(torch.rand(3, 4))
        def traced_fn(x):
            return script_fn(x) + 1

        FileCheck().check("prim::CallFunction").check("aten::add").run(str(traced_fn.graph))

    @unittest.skip("error in first class mode")
    def test_call_script_mod_from_tracing_fn(self):
        with self.assertRaisesRegex(RuntimeError, "must be registered as submodules"):
            class ScriptMod(torch.jit.ScriptModule):
                def __init__(self):
                    super(ScriptMod, self).__init__()
                    self.param = torch.nn.Parameter(torch.rand(3, 4), requires_grad=False)

                @torch.jit.script_method
                def forward(self, x):
                    for _i in range(4):
                        x += self.param
                    return x

            sm = ScriptMod()

            @_trace(torch.rand(3, 4))
            def traced_fn(x):
                return sm(x) + 1.0


    def test_call_python_fn_from_traced_module(self):
        def python_fn(x):
            return torch.neg(x)

        class TracedModule(torch.nn.Module):
            def __init__(self):
                super(TracedModule, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(4, 3))

            def forward(self, x):
                return torch.mm(python_fn(x), self.param)

        tm = torch.jit.trace(TracedModule(), torch.rand(3, 4))

        # Note: parameter self.param from the traced module should appear as
        # an input to the graph and the neg op from the Python function should
        # be properly inlined
        self.assertTrue(len(list(tm.graph.inputs())) == 2)
        FileCheck().check("aten::neg").check("aten::mm").run(str(tm.graph))

    def test_call_python_mod_from_traced_module(self):
        class PythonModule(torch.nn.Module):
            def __init__(self):
                super(PythonModule, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(5, 7))

            def forward(self, x):
                return torch.mm(x, self.param)

        class TracedModule(torch.nn.Module):
            def __init__(self):
                super(TracedModule, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(4, 5))
                self.mod = PythonModule()

            def forward(self, x):
                return self.mod(torch.mm(x, self.param)) + 1.0

        tm = torch.jit.trace(TracedModule(), torch.rand(3, 4))

        FileCheck().check_not("value=<Tensor>").check("aten::mm")\
            .check("prim::CallMethod[name=\"forward\"]").check("aten::add") \
            .run(str(tm.graph))
        FileCheck().check("aten::mm").run(str(tm.mod.graph))

    def test_op_dtype(self):

        def check_equal_and_dtype(a, b):
            self.assertEqual(a, b)
            self.assertEqual(a.dtype, b.dtype)

        def fn():
            a = torch.arange(10)
            b = torch.arange(10, dtype=torch.float)
            c = torch.arange(1, 10, 2)
            d = torch.arange(1, 10, 2, dtype=torch.float)
            e = torch.arange(1, 10., 2)
            f = torch.arange(1, 10., 2, dtype=torch.float)
            return a, b, c, d, e, f

        scripted_fn = torch.jit.script(fn)
        eager_out = fn()
        script_out = scripted_fn()
        for a, b in zip(eager_out, script_out):
            check_equal_and_dtype(a, b)

    def test_floordiv(self):
        funcs_template = dedent('''
        def fn():
            ten = {a_construct}
            ten_or_scalar = {b_construct}
            return ten // ten_or_scalar, torch.floor_divide(ten, ten_or_scalar)
        ''')

        lhs = ["torch.tensor([5.5, 3.2])", "torch.tensor([2, 2])", "torch.tensor([3, 2])"]
        rhs = ["1.5", "2", "4", "1.1"] + lhs
        for tensor in lhs:
            for tensor_or_scalar in rhs:
                funcs_str = funcs_template.format(a_construct=tensor, b_construct=tensor_or_scalar)
                scope = {}
                execWrapper(funcs_str, globals(), scope)
                cu = torch.jit.CompilationUnit(funcs_str)
                f_script = cu.fn
                f = scope['fn']
                self.assertEqual(f_script(), f())

    @_tmp_donotuse_dont_inline_everything
    def test_call_traced_fn_from_traced_module(self):
        @_trace(torch.rand(3, 4))
        def traced_fn(x):
            return torch.neg(x)

        class TracedModule(torch.nn.Module):
            def __init__(self):
                super(TracedModule, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(4, 5))

            def forward(self, x):
                return traced_fn(torch.mm(x, self.param))

        tm = torch.jit.trace(TracedModule(), torch.rand(3, 4))

        # Note: neg op from the traced function should be properly inlined
        FileCheck().check("aten::mm") \
            .check('name="traced_fn"') \
            .check_next("prim::CallFunction") \
            .run(str(tm.graph))

    def test_trace_hierarchy(self):
        # Test that we preserve the module hierarchy for a ScriptModule
        # submodule during tracing

        class AnotherScriptMod(torch.jit.ScriptModule):
            def __init__(self):
                super(AnotherScriptMod, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(1, 2, 3))

            @torch.jit.script_method
            def bar(self):
                return torch.zeros(4, 5)

        class SomeScriptMod(torch.jit.ScriptModule):
            def __init__(self):
                super(SomeScriptMod, self).__init__()
                self.asm = AnotherScriptMod()

            @torch.jit.script_method
            def foo(self):
                return torch.zeros(3, 4)

            @torch.jit.script_method
            def bar(self):
                return torch.zeros(4, 3)

        class TraceMe(torch.nn.Module):
            def __init__(self):
                super(TraceMe, self).__init__()
                self.ssm = SomeScriptMod()

            def forward(self, x):
                return self.ssm.bar() + x

        orig = TraceMe()
        traced = torch.jit.trace(orig, (torch.rand(4, 3),))
        # for each of these checks, check that *BOTH* the underlying
        # _C.ScriptModule object has the expected method/param, as well as the
        # Python object that wraps it.
        self.assertTrue(traced.ssm._c._has_method('foo'))
        self.assertTrue(hasattr(traced.ssm, 'foo'))

        imported = self.getExportImportCopy(traced)

        self.assertTrue(imported.ssm._c._has_method('foo'))
        self.assertTrue(hasattr(imported.ssm, 'foo'))

        self.assertTrue(imported.ssm.asm._c._has_method('bar'))
        self.assertTrue(hasattr(imported.ssm.asm, 'bar'))

        self.assertTrue(hasattr(imported.ssm.asm, 'param'))

    def test_trace_parameter(self):
        class Param(nn.Module):
            def __init__(self):
                super(Param, self).__init__()
                self.register_parameter("bias", nn.Parameter(torch.Tensor(4, 4)))

            def forward(self, x):
                return x

        class M3(torch.jit.ScriptModule):
            def __init__(self, model):
                super(M3, self).__init__()
                self.traced = torch.jit.trace(model, (torch.rand(3, 3)))

            @torch.jit.script_method
            def forward(self, x):
                return self.traced(x)

        class M2(nn.Module):
            def __init__(self, model):
                super(M2, self).__init__()
                self.module = M3(model)

            def forward(self, x):
                return self.module(x)

        class M1(torch.jit.ScriptModule):
            def __init__(self, model):
                super(M1, self).__init__()
                self.traced = torch.jit.trace(M2(model), (torch.rand(3, 3)))

            @torch.jit.script_method
            def forward(self, x):
                return self.traced(x)

        with torch.jit.optimized_execution(False):
            module = M1(Param())
            f = io.BytesIO()
            torch.jit.save(module, f)

    @_tmp_donotuse_dont_inline_everything
    def test_call_traced_module_from_traced_module(self):
        class TracedModule1(torch.nn.Module):
            def __init__(self):
                super(TracedModule1, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(5, 7))

            def forward(self, x):
                return torch.mm(x, self.param)

        class TracedModule(torch.nn.Module):
            def __init__(self):
                super(TracedModule, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(4, 5))
                self.mod = torch.jit.trace(TracedModule1(), torch.rand(3, 5))

            def forward(self, x):
                return self.mod(torch.mm(x, self.param)) + 1.0

        tm = torch.jit.trace(TracedModule(), torch.rand(3, 4))

        FileCheck().check("aten::mm").check("prim::CallMethod").check_same("forward").check("aten::add").run(str(tm.graph))

    @_tmp_donotuse_dont_inline_everything
    def test_call_script_fn_from_traced_module(self):
        @torch.jit.script
        def scripted_fn(x):
            return torch.neg(x)

        class TracedModule(torch.nn.Module):
            def __init__(self):
                super(TracedModule, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(4, 5))

            def forward(self, x):
                return scripted_fn(torch.mm(x, self.param))

        tm = torch.jit.trace(TracedModule(), torch.rand(3, 4))
        FileCheck().check("aten::mm").check("name=\"scripted_fn\"").check("prim::CallFunction").run(str(tm.graph))

    @_tmp_donotuse_dont_inline_everything
    def test_call_script_module_from_traced_module(self):
        class ScriptMod(torch.jit.ScriptModule):
            def __init__(self):
                super(ScriptMod, self).__init__()
                self.param_foo = torch.nn.Parameter(torch.rand(5, 7))

            @torch.jit.script_method
            def forward(self, x):
                return torch.mm(x, self.param_foo)

        class TracedModule(torch.nn.Module):
            def __init__(self):
                super(TracedModule, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(4, 5))
                self.mod = ScriptMod()

            def forward(self, x):
                return self.mod(torch.mm(x, self.param)) + 1.0

        tm = torch.jit.trace(TracedModule(), torch.rand(3, 4))

        FileCheck().check("aten::mm").check("prim::CallMethod").check_same("forward").check("aten::add").run(str(tm.graph))

    def test_call_python_fn_from_script_fn(self):
        @torch.jit.ignore
        def python_fn(x):
            return torch.neg(x)

        @torch.jit.script
        def script_fn(x):
            return python_fn(x) + 1

        # Note: the call to python_fn appears as `^python_fn()` and is called
        # as a PythonOp in the interpreter
        a = torch.tensor(1)
        self.assertEqual(script_fn(a), torch.tensor(0))
        FileCheck().check("python_fn").run(str(script_fn.graph))

    def test_call_python_mod_from_script_fn(self):
        class PythonModule(torch.nn.Module):
            def __init__(self):
                super(PythonModule, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(5, 7))

            def forward(self, x):
                return torch.mm(x, self.param)

        pm = PythonModule()

        @torch.jit.script
        def script_fn(x):
            return pm(x) + 1

        # Note: call to pm(x) appears as ^<python_value>() in the trace.
        # Parameters are NOT inlined.
        FileCheck().check("python_value").check("aten::add").run(str(script_fn.graph))

    @_tmp_donotuse_dont_inline_everything
    def test_call_traced_fn_from_script_fn(self):
        @_trace(torch.rand(3, 4))
        def traced_fn(x):
            return torch.neg(x)

        @torch.jit.script
        def script_fn(x):
            return traced_fn(x) + 1

        FileCheck().check("prim::CallFunction").check("aten::add").run(str(script_fn.graph))

    def test_call_traced_mod_from_script_fn(self):
        with self.assertRaisesRegex(RuntimeError, "Cannot call a ScriptModule that is not a submodule of the caller"):
            class TracedModule(torch.nn.Module):
                def __init__(self):
                    super(TracedModule, self).__init__()

                def forward(self, x):
                    return torch.mm(x, torch.zeros(4, 3))

            tm = torch.jit.trace(TracedModule(), torch.rand(3, 4))

            @torch.jit.script
            def script_fn(x):
                return tm(x) + 1

    @_tmp_donotuse_dont_inline_everything
    def test_call_script_fn_from_script_fn(self):
        @torch.jit.script
        def script_fn1(x):
            return torch.neg(x)

        @torch.jit.script
        def script_fn(x):
            return script_fn1(x) + 1

        FileCheck().check("prim::CallFunction").run(str(script_fn.graph))

    def test_call_script_mod_from_script_fn(self):
        with self.assertRaisesRegex(RuntimeError, "Cannot call a ScriptModule that is not a submodule of the caller"):
            class ScriptMod(torch.jit.ScriptModule):
                def __init__(self):
                    super(ScriptMod, self).__init__()

                @torch.jit.script_method
                def forward(self, x):
                    return torch.mm(x, torch.zeros([4, 3]))

            sm = ScriptMod()

            @torch.jit.script
            def script_fn(x):
                return sm(x) + 1

    def test_call_python_fn_from_script_module(self):
        @torch.jit.ignore
        def python_fn(x):
            return torch.neg(x)

        class ScriptMod(torch.jit.ScriptModule):
            def __init__(self):
                super(ScriptMod, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(4, 3))

            @torch.jit.script_method
            def forward(self, x):
                return python_fn(torch.mm(x, self.param))

        sm = ScriptMod()
        FileCheck().check("aten::mm").check("python_fn") \
            .run(str(sm.forward.graph))

    def test_call_python_mod_from_script_module(self):
        class PythonMod(torch.nn.Module):
            def __init__(self):
                super(PythonMod, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(3, 5))

            @torch.jit.ignore
            def forward(self, x):
                return torch.mm(x, self.param)

        class ScriptMod(torch.jit.ScriptModule):
            def __init__(self):
                super(ScriptMod, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(4, 3))
                self.pm = PythonMod()

            @torch.jit.script_method
            def forward(self, x):
                return self.pm(torch.mm(x, self.param))

        sm = ScriptMod()
        # Note: the call into PythonMod appears as ^forward(). Parameters
        # are NOT inlined
        FileCheck().check("aten::mm").check("forward").run(str(sm.graph))

    @_tmp_donotuse_dont_inline_everything
    def test_call_tracing_fn_from_script_module(self):
        @_trace(torch.rand(3, 3))
        def traced_fn(x):
            return torch.neg(x)

        class ScriptMod(torch.jit.ScriptModule):
            def __init__(self):
                super(ScriptMod, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(4, 3))

            @torch.jit.script_method
            def forward(self, x):
                return traced_fn(torch.mm(x, self.param))

        sm = ScriptMod()
        FileCheck().check("aten::mm").check("prim::CallFunction").run(str(sm.forward.graph))

    @_tmp_donotuse_dont_inline_everything
    def test_call_tracing_mod_from_script_module(self):
        class TracedMod(torch.nn.Module):
            def __init__(self):
                super(TracedMod, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(3, 5))

            def forward(self, x):
                return torch.mm(x, self.param)

        class ScriptMod(torch.jit.ScriptModule):
            def __init__(self):
                super(ScriptMod, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(4, 3))
                self.tm = torch.jit.trace(TracedMod(), torch.rand(3, 3))

            @torch.jit.script_method
            def forward(self, x):
                return self.tm(torch.mm(x, self.param))

        sm = ScriptMod()
        FileCheck().check("aten::mm").check("prim::CallMethod").run(str(sm.graph))

    @_tmp_donotuse_dont_inline_everything
    def test_call_script_fn_from_script_module(self):
        @torch.jit.script
        def script_fn(x):
            return torch.neg(x)

        class ScriptMod(torch.jit.ScriptModule):
            def __init__(self):
                super(ScriptMod, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(4, 3))

            @torch.jit.script_method
            def forward(self, x):
                return script_fn(torch.mm(x, self.param))

        sm = ScriptMod()
        graph = (sm.forward.graph)
        FileCheck().check("aten::mm").check("prim::CallFunction").run(str(graph))

    @_tmp_donotuse_dont_inline_everything
    def test_call_script_mod_from_script_module(self):
        class ScriptMod1(torch.jit.ScriptModule):
            def __init__(self):
                super(ScriptMod1, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(3, 5))

            @torch.jit.script_method
            def forward(self, x):
                return torch.mm(x, self.param)

        class ScriptMod(torch.jit.ScriptModule):
            def __init__(self):
                super(ScriptMod, self).__init__()
                self.param = torch.nn.Parameter(torch.rand(4, 3))
                self.tm = ScriptMod1()

            @torch.jit.script_method
            def forward(self, x):
                return self.tm(torch.mm(x, self.param))

        sm = ScriptMod()
        # Note: the parameters from both modules should appear in the flattened
        # input list to the graph. The mm op from ScriptMod1 should be properly
        # inlined
        # 3 % values in graph input lists, two mms in body
        FileCheck().check_count('%', 3).check(":").check_count("mm", 1).check("prim::CallMethod").run(str(sm.graph))

    def test_module_with_params_called_fails(self):
        with self.assertRaisesRegex(RuntimeError, "Cannot call a ScriptModule that is not a submodule of the caller"):
            class ScriptMod(torch.jit.ScriptModule):
                def __init__(self):
                    super(ScriptMod, self).__init__()
                    self.param = torch.nn.Parameter(torch.rand(3, 3))

                @torch.jit.script_method
                def forward(self, x):
                    return torch.mm(x, self.param)

            sm = ScriptMod()

            @torch.jit.script
            def some_func(x):
                return sm(x)

    def test_index_put_trace_with_view(self):
        @_trace(torch.rand(100), torch.tensor([1, 2, 3, 4]), torch.rand(1, 1, 1, 4))
        def test_index_put(target, indices, rhs):
            target[indices] = rhs
            return target

        FileCheck().check("aten::view").check("index_put_").run(str(test_index_put.graph))

    def test_index_put_trace_without_view(self):
        @_trace(torch.rand(100), torch.tensor([1, 2, 3, 4]), torch.rand(4))
        def test_index_put(target, indices, rhs):
            target[indices] = rhs
            return target

        FileCheck().check_not("aten::view").check("index_put_").run(str(test_index_put.graph))

    def test_tuple_index_to_list(self):
        def test_non_constant_input(a):
            # type: (bool) -> int
            if a:
                b = 1
            else:
                b = 0
            c = (0, 1)
            return c[b]

        self.checkScript(test_non_constant_input, (True,))
        self.checkScript(test_non_constant_input, (False,))

        with self.assertRaisesRegex(RuntimeError, "because we cannot resolve the output type"):
            @torch.jit.script
            def test_non_constant_input(a):
                # type: (bool) -> None
                if a:
                    b = 1
                else:
                    b = 0
                c = (0, 1.1)
                print(c[b])

    def test_tuple_indexing(self):
        def tuple_index(a):
            if bool(a):
                b = (1, 2)
            else:
                b = (0, 2)
            return b[-2], b[1]

        self.checkScript(tuple_index, (torch.tensor([0]),))
        self.checkScript(tuple_index, (torch.tensor([1]),))
        self.checkScript(tuple_index, (torch.tensor([1]),), optimize=True)
        tuple_comp = torch.jit.script(tuple_index)
        FileCheck().check_count("TupleIndex", 2, exactly=True).run(str(tuple_comp.graph))

        with self.assertRaisesRegex(RuntimeError, "index must be an integer"):
            @torch.jit.script
            def test_indexing_float():
                c = (1, 2)
                return c[0.1]

        def test_indexing_out_of_bounds_pos():
            c = (1, 2)
            return c[2]

        self.checkScriptRaisesRegex(test_indexing_out_of_bounds_pos, (), Exception,
                                    "out of range")

        def test_indexing_out_of_bounds_neg():
            c = (1, 2)
            return c[-3]

        self.checkScriptRaisesRegex(test_indexing_out_of_bounds_pos, (), Exception,
                                    "out of range")

        def negative_index():
            tup = (1, 2, 3, 4)
            return tup[-1]

        self.checkScript(negative_index, [])

        def really_negative_index():
            tup = (1, 2, 3, 4)
            return tup[-100]

        self.checkScriptRaisesRegex(really_negative_index, [], Exception, "index out of range")

        def negative_slice():
            tup = (1, 2, 3, 4)
            return tup[-3:4]

        self.checkScript(negative_slice, [])

        def really_slice_out_of_bounds():
            tup = (1, 2, 3, 4)
            return tup[-300:4000]

        self.checkScript(really_slice_out_of_bounds, [])

    def test_namedtuple_attr(self):
        def f(x):
            return x.max(dim=1).indices + torch.max(x, dim=1).indices

        self.checkScript(f, (torch.rand(20, 20, 20),), optimize=True)

        with self.assertRaisesRegex(RuntimeError, "nonexistent attribute"):
            @torch.jit.script
            def g1(x):
                return x.max(dim=1).unknown_symbol

        with self.assertRaisesRegex(RuntimeError, "nonexistent attribute"):
            @torch.jit.script
            def g2(x):
                print((x, x, x).__doc__)
                return x

    def test_tuple_slicing(self):
        def tuple_slice(a):
            if bool(a):
                b = (1, 2, 3, 4)
            else:
                b = (4, 3, 2, 1)
            c = b[-4:4]
            e = c[1:-1]
            return e

        self.checkScript(tuple_slice, (torch.tensor([1]),), optimize=True)
        scripted_fn = torch.jit.script(tuple_slice)
        self.assertEqual(scripted_fn(torch.tensor(1)), (2, 3))
        tuple_graph = scripted_fn.graph
        slices = tuple_graph.findAllNodes("prim::TupleSlice")
        num_outputs = set(map(lambda x: len(x.output().type().elements()), slices))
        # one tuple slice should have an output with 2 elements, other 4
        self.assertTrue(num_outputs == {2, 4})
        self.run_pass('lower_all_tuples', tuple_graph)
        self.assertTrue('Tuple' not in str(tuple_graph))

        @torch.jit.script
        def test_indexing_end_out_of_bounds():
            c = (1, 2)
            return c[2:10]

        self.assertEqual(test_indexing_end_out_of_bounds(), ())

    def test_unwrap_optional_builtin(self):
        def test(x):
            # type: (Optional[int]) -> int
            x = torch.jit._unwrap_optional(x)
            x = x + x  # noqa: T484
            return x

        self.checkScript(test, (3,))

        with self.assertRaisesRegex(AssertionError, "Unwrapping null optional"):
            test(None)

        test_script = torch.jit.script(test)
        with self.assertRaisesRegex(RuntimeError, "Unwrapping null optional"):
            test_script(None)

        @torch.jit.script
        def test_test():
            return torch.jit._unwrap_optional(1)

        with self.assertRaisesRegex(RuntimeError, r"could not be inferred from actual type None"):
            @torch.jit.script
            def test_no_type():
                # type: () -> int
                return torch.jit._unwrap_optional(None)

    def test_indexing_error(self):
        with self.assertRaisesRegex(RuntimeError, "'int' object is not subscriptable"):
            @torch.jit.script
            def test_wrong_type():
                a = 8
                return a[0]

    def test_unsupported_builtin_error(self):
        with self.assertRaisesRegex(RuntimeError,
                                    "Python builtin <built-in function hypot> is currently"):
            @torch.jit.script
            def test_unsupported(a):
                return math.hypot(a, 2.0)

    def test_annotated_script_fn(self):
        @torch.jit.script
        def foo(x, y, z):
            # type: (Tensor, Tuple[Tensor, Tensor, Tensor], Tuple[Tensor, Tuple[Tensor, Tensor]]) -> Tensor
            return x

        self.assertExpected(str(foo.schema))

    def test_annotated_script_method(self):
        class SM(torch.jit.ScriptModule):
            @torch.jit.script_method
            def forward(self, x, y):
                # type: (Tuple[Tensor, Tensor], Tensor) -> Tuple[Tensor, Tensor, Tensor]
                return y, y, y

        sm = SM()

        self.assertExpectedStripMangled(str(sm.forward.schema))

    def test_annotated_script_fn_return_mismatch(self):
        with self.assertRaisesRegex(RuntimeError, "but is actually of type"):
            @torch.jit.script
            def return_tup(x):
                # type: (Tensor) -> Tuple[Tuple[Tensor, Tensor], Tensor]
                return x, x  # noqa: T484

    def test_annotated_script_fn_arg_mismatch(self):
        with self.assertRaisesRegex(RuntimeError, r"Arguments for call are not valid"):
            @torch.jit.script
            def tuple_arg(x):
                # type: (Tuple[Tensor, Tensor]) -> Tensor
                return x + 1  # noqa: T484

    def test_script_non_tensor_args_outputs(self):
        @torch.jit.script
        def fn(x, y):
            # type: (Tensor, float) -> float
            return float((x + y).sum())

        x = torch.ones(2, 2)
        z = fn(x, 1)
        self.assertIsInstance(z, float)
        self.assertEqual(z, 8.)

    @unittest.skip('https://github.com/pytorch/pytorch/issues/9595')
    def test_inline_and_run_annotated_script_fn(self):
        @torch.jit.script
        def to_inline(x, y):
            # type: (Tuple[Tensor, Tensor], Tensor) -> Tensor
            return y

        @torch.jit.script
        def some_func(x):
            return to_inline((x, x), x)

        x = torch.rand(3, 4)
        self.assertEqual(some_func(x), x)

    def test_file_format_serialization(self):
        filename = tempfile.mktemp()
        writer = torch._C.PyTorchFileWriter(filename)
        buffers = [os.urandom(size) for size in [random.randint(1, 100) for i in range(20)]]
        offsets = []
        for i, buf in enumerate(buffers):
            writer.write_record(str(i), buf, len(buf))
            offsets.append(i)
        serialized_offsets = pickle.dumps(offsets)
        writer.write_record("meta", serialized_offsets, len(serialized_offsets))
        writer.write_end_of_file()

        reader = torch._C.PyTorchFileReader(filename)
        serialized_offsets_read = reader.get_record("meta")
        parsed_serialized_offsets = pickle.loads(serialized_offsets)

        for i, offset in enumerate(parsed_serialized_offsets):
            data = reader.get_record(str(offset))
            assert(data == buffers[i])

    # for each type, the input type annotation and corresponding return type annotation
    def type_input_return_pairs(self):
        return [
            ('Tensor', 'Tensor'),
            ('torch.Tensor', 'Tensor'),
            ('str', 'str'),
            ('int', 'int'),
            ('bool', 'bool'),
            ('BroadcastingList3[float]', 'List[float]'),
            ('BroadcastingList2[int]', 'List[int]'),
            ('List[int]', 'List[int]'),
            ('Optional[int]', 'Optional[int]'),
        ]

    # replacing code input & return type pair
    def format_code(self, code, pair):
        return code.format(input=pair[0], output=pair[1])

    # ***** Type annotation tests ****
    # Test combinations of:
    # {String frontend, Python AST Frontend}
    # {Python 3-style type annotations, MyPy-style type comments}
    # {Script method, Script function}

    #  String frontend , Python 3-style type annotations , Script function
    def test_annot_string_py3_fn(self):
        code = '''
            def foo(x : {input}, y : Tuple[Tensor, Tensor]) -> Tuple[{output}, {output}]:
                return x, x
        '''
        test_str = []
        for pair in self.type_input_return_pairs():
            cu = torch.jit.CompilationUnit(self.format_code(code, pair))
            test_str.append(str(cu.foo.schema))
        self.assertExpected("\n".join(test_str))

    #  String frontend , Python 3-style type annotations , Script method
    def test_annot_string_py3_method(self):
        class TestModule(torch.jit.ScriptModule):
            def __init__(self):
                super(TestModule, self).__init__()

        code = '''
            def foo(self, x : {input}, y : Tuple[Tensor, Tensor]) -> Tuple[{output}, {output}]:
                return x, x
        '''
        test_str = []
        for pair in self.type_input_return_pairs():
            # clear the class registry as we will be defining foo multiple times
            jit_utils.clear_class_registry()
            tm = TestModule()
            tm.define(self.format_code(code, pair))
            test_str.append(str(tm.foo.schema))
        self.assertExpectedStripMangled("\n".join(test_str))

    #  String frontend , MyPy-style type comments , Script function
    def test_annot_string_mypy_fn(self):
        code = '''
            def foo(x, y):
                # type: ({input}, Tuple[Tensor, Tensor]) -> Tuple[{output}, {output}]
                return x, x
        '''
        test_str = []
        for pair in self.type_input_return_pairs():
            cu = torch.jit.CompilationUnit(self.format_code(code, pair))
            test_str.append(str(cu.foo.schema))
        self.assertExpectedStripMangled("\n".join(test_str))

    #  String frontend , MyPy-style type comments , Script method
    def test_annot_string_mypy_method(self):
        class TestModule(torch.jit.ScriptModule):
            def __init__(self):
                super(TestModule, self).__init__()

        code = '''
        def foo(self, x, y):
            # type: ({input}, Tuple[Tensor, Tensor]) -> Tuple[{output}, {output}]
            return x, x
        '''

        test_str = []
        for pair in self.type_input_return_pairs():
            # clear the class registry as we will be defining foo multiple times
            jit_utils.clear_class_registry()
            tm = TestModule()
            tm.define(self.format_code(code, pair))
            test_str.append(str(tm.foo.schema))
        self.assertExpectedStripMangled("\n".join(test_str))

    # Helper function to eval Python3 code without causing a syntax error for
    # this file under py2
    def _get_py3_code(self, code, fn_name):
        with tempfile.TemporaryDirectory() as tmp_dir:
            script_path = os.path.join(tmp_dir, 'script.py')
            with open(script_path, 'w') as f:
                f.write(code)
            import importlib.util
            spec = importlib.util.spec_from_file_location(fn_name, script_path)
            module = importlib.util.module_from_spec(spec)
            spec.loader.exec_module(module)
            fn = getattr(module, fn_name)
            return fn

    #  Python AST Frontend , Python 3-style type annotations , Script function
    @unittest.skipIf(not PY35, "Python 3.5 needed")
    def test_annot_ast_py3_fn(self):
        code = dedent('''
            from typing import Tuple, List, Optional
            from torch import Tensor
            from torch.jit.annotations import BroadcastingList2, BroadcastingList3
            import torch
            @torch.jit.script
            def foo(x : {input}, y : Tuple[Tensor, Tensor]) -> Tuple[{output}, {output}]:
                return x, x
        ''')
        test_str = []
        for pair in self.type_input_return_pairs():
            fn = self._get_py3_code(self.format_code(code, pair), 'foo')
            test_str.append(str(fn.schema))
        self.assertExpectedStripMangled("\n".join(test_str))

    @unittest.skipIf(not PY35, "Python 3.5 needed")
    def test_multiline_annot_ast_py3_fn(self):
        code = dedent('''
            from typing import Tuple, List, Optional
            from torch import Tensor
            from torch.jit.annotations import BroadcastingList2, BroadcastingList3
            import torch
            @torch.jit.script
            def foo(x,  # type: {input}
                    y   # type: Tuple[Tensor, Tensor]
                    ):
                # type: (...) -> Tuple[{output}, {output}]
                return x, x
        ''')
        test_str = []

        for pair in self.type_input_return_pairs():
            fn = self._get_py3_code(self.format_code(code, pair), 'foo')
            args = fn.schema.arguments
            returns = fn.schema.returns
            self.assertEqual(str(args[0].type), pair[1])
            self.assertEqual(str(args[1].type), "Tuple[Tensor, Tensor]")
            self.assertEqual(str(returns[0].type), "Tuple[{}, {}]".format(pair[1], pair[1]))

    def test_bad_multiline_annotations(self):
        with self.assertRaisesRegex(RuntimeError, "Return type line"):
            @torch.jit.script
            def bad_type_line(a,  # type: Tensor
                              b,  # type: Tensor
                              c   # type: Tensor
                              ):
                # type: (int, int, int) -> Tensor
                # type: bad type line  # noqa: F723

                return a + b + c

        with self.assertRaisesRegex(RuntimeError, "Return type line"):
            @torch.jit.script
            def bad_return_line(a,  # type: Tensor
                                b,
                                c   # type: Tensor
                                ):
                # type: (int, int, int) -> Tensor
                return a + b + c

        # TODO: this should be supported but is difficult to parse
        with self.assertRaisesRegex(RuntimeError, "Number of type annotations"):
            @torch.jit.script
            def missing_type(a,  # type: Tensor
                             b,
                             c   # type: Tensor
                             ):
                # type: (...) -> Tensor
                return a + b + c

    #  Python AST Frontend , Python 3-style type annotations , Script method
    @unittest.skipIf(not PY35, "Python 3.5 needed")
    def test_annot_ast_py3_method(self):
        code = dedent('''
            from typing import Tuple, List, Optional
            from torch import Tensor
            from torch.jit.annotations import BroadcastingList2, \\
                BroadcastingList3
            import torch
            class FooModule(torch.jit.ScriptModule):
                @torch.jit.script_method
                def foo(self, x : {input}, y : Tuple[Tensor, Tensor]) -> Tuple[{output}, {output}]:
                    return x, x
            instance = FooModule()
        ''')

        test_str = []
        for pair in self.type_input_return_pairs():
            fn = self._get_py3_code(self.format_code(code, pair), 'instance')
            test_str.append(str(fn.foo.schema))
        self.assertExpectedStripMangled("\n".join(test_str))

    #  Python AST Frontend , MyPy-style type comments , Script function
    @unittest.skipIf(not PY35, "Python 3.5 needed")
    def test_annot_ast_mypy_fn(self):
        code = dedent('''
            import torch
            @torch.jit.script
            def foo(x, y):
                # type: ({input}, Tuple[Tensor, Tensor]) -> Tuple[{output}, {output}]
                return x, x
        ''')

        test_str = []
        for pair in self.type_input_return_pairs():
            fn = self._get_py3_code(self.format_code(code, pair), 'foo')
            test_str.append(str(fn.schema))
        self.assertExpected("\n".join(test_str))

    #  Python AST Frontend , MyPy-style type comments , Script method
    @unittest.skipIf(not PY35, "Python 3.5 needed")
    def test_annot_ast_mypy_method(self):
        code = dedent('''
            import torch
            class FooModule(torch.jit.ScriptModule):
                @torch.jit.script_method
                def foo(self, x, y):
                    # type: ({input}, Tuple[Tensor, Tensor]) -> Tuple[{output}, {output}]
                    return x, x
            instance = FooModule()
        ''')

        test_str = []
        for pair in self.type_input_return_pairs():
            fn = self._get_py3_code(self.format_code(code, pair), 'instance')
            test_str.append(str(fn.foo.schema))
        self.assertExpectedStripMangled("\n".join(test_str))

    def test_method_casts_script(self):
        cast_types = [
            'byte', 'char', 'double', 'float', 'int', 'long', 'short'
        ]

        for cast_type in cast_types:
            cu = torch.jit.CompilationUnit('''
            def cast_to(x):
                return x.{cast_type}()
            '''.format(cast_type=cast_type))

            x = torch.rand(3, 4, 5) * 128
            cu_result = cu.cast_to(x)
            reference = getattr(x, cast_type)()
            self.assertEqual(cu_result, reference)

    def test_listconstruct_erasure(self):
        class FooMod(torch.nn.Module):
            def forward(self, x):
                mask = x < 0.0
                return x[mask]

        import io
        f = io.BytesIO()
        torch.onnx.export_to_pretty_string(
            FooMod(), (torch.rand(3, 4),), f,
            operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK)

    @suppress_warnings
    def test_trace_checker_dot_data(self):
        with self.assertRaisesRegex(torch.jit.TracingCheckError, r'Tensor-valued Constant nodes differed in value '
                                                                 r'across invocations'):
            @_trace(torch.rand(3, 4), check_inputs=[(torch.rand(3, 4),)])
            def foo(x):
                y = x.data
                return x + y

    @suppress_warnings
    def test_trace_checker_control_flow(self):
        def foo(x):
            for _ in range(x.size(0)):
                x = torch.neg(x)
            return x

        with self.assertRaisesRegex(torch.jit.TracingCheckError, r'Graphs differed across invocations!'):
            torch.jit.trace(foo, torch.randn(3, 4), check_inputs=[torch.randn(4, 4)])

    @suppress_warnings
    def test_trace_checker_memoization(self):
        with self.assertRaisesRegex(torch.jit.TracingCheckError, r'Graphs differed across invocations!'):
            def foo(x):
                if not hasattr(foo, 'cache'):
                    foo.cache = torch.neg(x)
                return x + foo.cache

            traced = torch.jit.trace(foo, torch.rand(3, 4), check_inputs=[(torch.rand(3, 4),)])

    if 'fbgemm' in torch.backends.quantized.supported_engines:
        def test_quantization_modules(self):
            K1, N1 = 2, 2

            class FooBar(torch.nn.Module):
                def __init__(self):
                    super(FooBar, self).__init__()
                    self.linear1 = torch.nn.Linear(K1, N1).float()

                def forward(self, x):
                    x = self.linear1(x)
                    return x

            fb = FooBar()
            fb.linear1.weight = torch.nn.Parameter(
                torch.tensor([[-150, 100], [100, -150]], dtype=torch.float), requires_grad=False)
            fb.linear1.bias = torch.nn.Parameter(torch.zeros_like(fb.linear1.bias), requires_grad=False)

            x = (torch.rand(1, K1).float() - 0.5) / 10.0
            value = torch.tensor([[100, -150]], dtype=torch.float)

            y_ref = fb(value)

            fb_int8 = torch.jit.quantized.quantize_linear_modules(fb)
            traced_int8 = torch.jit.trace(fb_int8, (x,))
            fb_int8 = self.getExportImportCopyWithPacking(traced_int8)
            y_int8 = fb_int8(value)

            fb_fp16 = torch.jit.quantized.quantize_linear_modules(fb, torch.float16)
            traced_fp16 = torch.jit.trace(fb_fp16, (x,))
            fb_fp16 = self.getExportImportCopyWithPacking(traced_fp16)
            y_fp16 = fb_fp16(value)

            torch.testing.assert_allclose(y_int8, y_ref, rtol=0.0001, atol=1e-3)
            torch.testing.assert_allclose(y_fp16, y_ref, rtol=0.0001, atol=1e-3)

    def checkTracerWarning(self, *args, **kwargs):
        with warnings.catch_warnings(record=True) as warns:
            torch.jit.trace(*args, **kwargs)
        self.assertGreater(len(warns), 0)
        self.assertTrue(any(["cause the trace to be incorrect" in str(warn.message) for warn in warns]))

    def test_trace_checker_slice_lhs(self):
        def foo(x):
            for i in range(3):
                x[i, :] = torch.zeros(4)
            return x

        self.checkTrace(foo, (torch.rand(3, 4),))

    def test_trace_checker_inplace_on_view(self):
        def foo(x):
            x.view(-1).add_(-x.view(-1))
            return x

        self.assertWarnsRegex(lambda: torch.jit.trace(foo,
                                                      torch.rand(3, 4),
                                                      check_inputs=[torch.rand(5, 6)],
                                                      _force_outplace=True),
                              'Output nr 1. of the traced function does not match the '
                              'corresponding output of the Python function')

    def test_lhs_index_fails(self):
        def foo(x):
            x[0, 1] = 4
            return x
        self.checkTracerWarning(foo, torch.rand(3, 4), _force_outplace=True)

    def test_lhs_index_trivial(self):
        def foo(y, x):
            y[...] = x
            return y
        self.checkTrace(foo, (torch.rand(3, 4), torch.rand(4)), inputs_require_grads=False)

    def test_inplace_warn(self):
        def foo(x):
            x.view(-1).add_(-x.view(-1))
            return x
        self.checkTracerWarning(foo, torch.rand(3, 4), _force_outplace=True)

    @suppress_warnings
    def test_trace_checker_dropout_train(self):
        def foo(x):
            return torch.dropout(x, p=0.5, train=True)

        self.assertWarnsRegex(lambda: torch.jit.trace(foo, torch.rand(3, 4), check_inputs=[torch.rand(5, 6)]),
                              'Output nr 1. of the traced function does not match the '
                              'corresponding output of the Python function')
        self.assertWarnsRegex(lambda: torch.jit.trace(foo, torch.rand(3, 4), check_inputs=[torch.rand(5, 6)]),
                              'Trace had nondeterministic nodes')

    def test_trace_checker_dropout_notrain(self):
        input = torch.rand(3, 4)

        @_trace(input)
        def foo(x):
            return torch.dropout(x, p=0.5, train=False)

        self.assertEqual(foo(input), input)

    def test_export_dynamic_slice(self):
        class DynamicSliceExportMod(torch.jit.ScriptModule):
            @torch.jit.script_method
            def forward(self, x):
                retval = x[0]
                for i in range(x.size(1)):
                    retval += torch.sum(x[0:i], dim=0)
                return retval

        mod = DynamicSliceExportMod()

        input = torch.rand(3, 4, 5)
        example_outs = mod(input)

        f = io.BytesIO()
        torch.onnx.export_to_pretty_string(
            DynamicSliceExportMod(), (input,), f, example_outputs=example_outs, opset_version=10)

    def test_string_frontend_elif(self):
        code = '''
            def func(niter):
                # type: (int)
                rv = 0
                for i in range(niter):
                    if i % 3 == 0 and i % 5 == 0:
                        rv += 35
                    elif i % 3 == 0:
                        rv += 3
                    elif i % 5 == 0:
                        rv += 5
                    else:
                        rv += i
                return rv
        '''

        self.checkScript(dedent(code), (101,))

    def test_pyop_exception_message(self):
        class Foo(torch.jit.ScriptModule):
            def __init__(self):
                super(Foo, self).__init__()
                self.conv = nn.Conv2d(1, 10, kernel_size=5)

            @torch.jit.script_method
            def forward(self, x):
                return self.conv(x)
        foo = Foo()
        # testing that the correct error message propagates
        with self.assertRaisesRegex(RuntimeError, "Expected 4-dimensional input for 4-dimensional weight"):
            foo(torch.ones([123]))  # wrong size

    def test_builtin_error_messsage(self):
        with self.assertRaisesRegex(RuntimeError, "Arguments for call are not valid"):
            @torch.jit.script
            def close_match(x):
                return x.masked_fill(True)

        with self.assertRaisesRegex(RuntimeError, "This op may not exist or may not be currently "
                                    "supported in TorchScript"):
            @torch.jit.script
            def unknown_op(x):
                torch.set_grad_enabled(True)
                return x

    def test_exceptions(self):
        cu = torch.jit.CompilationUnit('''
            def foo(cond):
                if bool(cond):
                    raise ValueError(3)
                return 1
        ''')

        cu.foo(torch.tensor(0))
        with self.assertRaisesRegex(torch.jit.Error, "Exception"):
            cu.foo(torch.tensor(1))

        @torch.jit.script
        def foo(cond):
            a = 3
            if bool(cond):
                raise ArbitraryError(a, "hi")
                if False:
                    raise ArbitraryError
            return a

        foo(torch.tensor(0))
        # we don't currently validate the name of the exception
        with self.assertRaisesRegex(torch.jit.Error, "Exception"):
            foo(torch.tensor(1))

        @torch.jit.script
        def foo_except_used():
            a = Exception()
            print(a)
            raise a

        # a not DCEd
        with self.assertRaisesRegex(RuntimeError, "expected value of type Tensor"):
            foo_except_used()

        @torch.jit.script
        def foo_no_decl_always_throws():
            raise "Hi"

        # function that has no declared type but always throws set to None
        output_type = next(foo_no_decl_always_throws.graph.outputs()).type()
        self.assertTrue(str(output_type) == "None")

        @torch.jit.script
        def foo_decl_always_throws():
            # type: () -> Tensor
            raise Exception("Hi")

        output_type = next(foo_decl_always_throws.graph.outputs()).type()
        self.assertTrue(str(output_type) == "Tensor")

        # We don't validate the expr following raise
        @torch.jit.script
        def foo():
            raise 3 + 4

        # a escapes scope
        @torch.jit.script
        def foo():
            if True:
                a = 1
            else:
                if True:
                    raise Exception("Hi")
                else:
                    raise Exception("Hi")
            return a
        self.assertEqual(foo(), 1)

    def test_assertions(self):
        cu = torch.jit.CompilationUnit('''
            def foo(cond):
                assert bool(cond), "hi"
                return 0
        ''')

        cu.foo(torch.tensor(1))
        with self.assertRaisesRegex(torch.jit.Error, "Exception"):
            cu.foo(torch.tensor(0))

        @torch.jit.script
        def foo(cond):
            assert bool(cond), "hi"

        foo(torch.tensor(1))
        # we don't currently validate the name of the exception
        with self.assertRaisesRegex(torch.jit.Error, "Exception"):
            foo(torch.tensor(0))

    def test_python_op_exception(self):
        @torch.jit.ignore
        def python_op(x):
            raise Exception("bad!")

        @torch.jit.script
        def fn(x):
            return python_op(x)

        with self.assertRaisesRegex(RuntimeError, "operation failed in interpreter"):
            fn(torch.tensor(4))

    def test_trace_contiguous(self):
        def foo(x):
            return x[:, :, ::2].contiguous().view(12)

        x = torch.rand(2, 3, 4)
        traced = torch.jit.trace(foo, (x,))
        y = traced(x)
        self.assertNotEqual(x.storage().data_ptr(), y.storage().data_ptr())

    # This tests the logic in THPVariable_contiguous. There is short-circuiting
    # code that prevents us from even getting to VariableType::contiguous, since
    # it is an optimization that prevents us from acquiring the GIL for touching
    # the device. We needed to add the tracing logic directly into the
    # THPVariable_contiguous function only for the path where we are skipping
    # dispatch into contiguous. We should see an aten::contiguous in this trace!
    def test_trace_contiguous_short_circuit(self):
        def foo(x):
            return x.contiguous()

        x = torch.rand(2, 3, 4)
        traced = torch.jit.trace(foo, (x,))
        FileCheck().check("aten::contiguous").run(str(traced.graph))

    def test_trace_inverse(self):
        def foo(x):
            return ~x

        foo_traced = torch.jit.trace(foo, torch.zeros(3, 4, dtype=torch.uint8))
        eg = torch.zeros(3, dtype=torch.uint8)
        self.assertEqual(foo_traced(eg), foo(eg))


    def test_module_parameters_and_buffers(self):
        weights = torch.randn(10, 10)
        bias = torch.randn(10)
        weights2 = torch.randn(10, 10)
        bias2 = torch.randn(10)

        class TestLinear(torch.nn.Module):
            def __init__(self, in_features, out_features):
                super(TestLinear, self).__init__()
                self.in_features = in_features
                self.out_features = out_features
                self.weight = torch.nn.Parameter(torch.Tensor(out_features, in_features))
                self.bias = torch.nn.Parameter(torch.Tensor(out_features))
                self.register_buffer('counter', torch.ones(out_features))
                self.reset_parameters()

            def reset_parameters(self):
                torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
                if self.bias is not None:
                    fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(self.weight)
                    bound = 1 / math.sqrt(fan_in)
                    torch.nn.init.uniform_(self.bias, -bound, bound)

            def forward(self, input):
                return F.linear(input, self.weight, self.bias) + self.counter

        # Initialize a ScriptModule that uses the weak module above multiple times
        class Strong(torch.jit.ScriptModule):
            def __init__(self):
                super(Strong, self).__init__()
                self.fc1 = TestLinear(10, 10)
                self.fc1.weight = torch.nn.Parameter(weights)
                self.fc1.bias = torch.nn.Parameter(bias)
                self.fc2 = TestLinear(10, 10)
                self.fc2.weight = torch.nn.Parameter(weights2)
                self.fc2.bias = torch.nn.Parameter(bias2)

            @torch.jit.script_method
            def forward(self, x):
                return x + self.fc1(x) + self.fc1(x) + self.fc2(x)

        strong_mod = Strong()

        # Run same calculation as module
        inp = torch.ones(10)
        lin = torch.nn.Linear(10, 10)
        lin.weight = torch.nn.Parameter(weights)
        lin.bias = torch.nn.Parameter(bias)
        lin2 = torch.nn.Linear(10, 10)
        lin2.weight = torch.nn.Parameter(weights2)
        lin2.bias = torch.nn.Parameter(bias2)
        expected_result = inp + (lin(inp) + torch.ones(10)) * 2 + lin2(inp) + torch.ones(10)

        self.assertEqual(strong_mod(inp), expected_result)
        self.assertExportImportModule(strong_mod, (inp,))

    def test_module_copying(self):
        class Submodule(torch.nn.Module):
            def __init__(self):
                super(Submodule, self).__init__()

            def forward(self, x):
                return x + 100

        class Weak(torch.nn.Module):
            def __init__(self, in_features, out_features):
                super(Weak, self).__init__()
                self.weight = torch.nn.Parameter(torch.ones(out_features, in_features))
                self.bias = torch.nn.Parameter(torch.ones(out_features))
                self.register_buffer("buffer", torch.ones(out_features))
                self.submodule = Submodule()

            def forward(self, x):
                return F.linear(x, self.weight, self.bias) \
                    + self.buffer + self.submodule(x)

        class Strong(torch.jit.ScriptModule):
            def __init__(self, weak):
                super(Strong, self).__init__()
                self.weak = weak

            @torch.jit.script_method
            def forward(self, x):
                return self.weak(x)

        inp = torch.ones(5, 5) * 5
        weak_mod = Weak(5, 5)
        strong_mod = Strong(weak_mod)

        self.assertTrue(isinstance(strong_mod.weak, torch.jit.ScriptModule))
        self.assertFalse(isinstance(weak_mod, torch.jit.ScriptModule))

        self.assertIs(strong_mod.weak.weight, weak_mod.weight)
        self.assertIs(strong_mod.weak.buffer, weak_mod.buffer)
        # strong_mod.weak.submodule has been recursively scripted
        self.assertIsNot(strong_mod.weak.submodule, weak_mod.submodule)

        weak_mod.weight.data += torch.ones(5, 5) * 100
        self.assertTrue(strong_mod(inp).allclose(weak_mod(inp)))

        # Re-assignment is not tracked
        weak_mod.weight = torch.nn.Parameter(torch.ones(5, 5) * 100)
        self.assertFalse(strong_mod(inp).allclose(weak_mod(inp)))

    def test_backend_cudnn_enabled(self):
        # Only test that this compiles
        @torch.jit.script
        def fn(x):
            if torch.backends.cudnn.enabled:
                x = x + 2
            else:
                x = x + 3
            return x

    def test_inplace_add(self):

        def foo(a, b):
            c = a + b
            c.add_(b)
            return c
        self.checkScript(foo, (torch.rand(3), torch.rand(3)))

    def test_add_out(self):
        def foo(a, b):
            c = a + b
            e = 2 * a
            torch.add(c, b, out=e)
            return e
        self.checkScript(foo, (torch.rand(3), torch.rand(3)))

    def test_augmented_assign(self):
        def foo(a, b):
            a += b
            a -= b
            a /= b
            a *= b
            return a, b
        self.checkScript(foo, (torch.rand(3), torch.rand(3)))

    def test_pass(self):
        def foo(x):
            # type: (bool) -> int
            for _i in range(3):
                pass
            if x:
                pass
            else:
                pass
            return 3

        self.checkScript(foo, (True,))

    def test_optional_conversion(self):
        @torch.jit.script
        def other_fn(x=None):
            # type: (Optional[int]) -> int
            return torch.jit._unwrap_optional(x)

        @torch.jit.script
        def fn(x):
            # type: (int) -> int
            return other_fn(x)

        self.assertEqual(fn(2), 2)

        @torch.jit.script
        def unify_to_optional(x):
            # type: (bool) -> Optional[int]
            if x:
                a = None
            else:
                a = 2
            return a

        self.assertEqual(unify_to_optional(True), None)
        self.assertEqual(unify_to_optional(False), 2)

        @torch.jit.script
        def opt_list(x):
            # type: (Optional[List[float]]) -> int
            return 2

        @torch.jit.script
        def broadcast_opt_list(x):
            # type: (Optional[BroadcastingList2[float]]) -> int
            return 2

        @torch.jit.script
        def opt_list_tuple_caller(x):
            # type: (Tuple[float, float]) -> int
            return opt_list(x) + broadcast_opt_list(x)

        self.assertEqual(opt_list_tuple_caller((2., 3.)), 4)

    def test_lhs_indexing(self):
        def foo(a, b):
            a = a.clone()
            a[0] = b
            return a
        self.checkScript(foo, (torch.rand(2, 3), torch.rand(3)))

    def test_lhs_advanced_indexing_assignment(self):
        def foo(x, y):
            a = torch.exp(x)
            b = x == 1
            a[b] = y[b]
            return a
        self.checkScript(foo, (torch.ones(4, 3), torch.ones(4, 3)))

    def test_lhs_advanced_indexing_augmented_assignment(self):
        def foo(x, y):
            a = torch.exp(x)
            b = x == 1
            a[b] += y[b]
            return a
        self.checkScript(foo, (torch.ones(4, 3), torch.ones(4, 3)))

    def test_lhs_indexing_list(self):
        def foo(a, b):
            ls = [a]
            ls[0] = b
            return ls
        self.checkScript(foo, (torch.rand(2, 3), torch.rand(3)))

    def test_inplace_copy_script(self):
        def foo(x):
            a = torch.rand(3, 4)
            a.copy_(x)
            return a
        self.checkScript(foo, (torch.rand(3, 4),))

    def test_lhs_indexing_increment(self):
        def foo(a, b):
            a[0] += b
            return a
        self.checkScript(foo, (torch.rand(2, 3), torch.rand(3)))

    def test_lhs_indexing_increment_list(self):
        def foo(a, b):
            a = a.clone()
            ls = [a, b]
            ls[0] += b
            return ls
        self.checkScript(foo, (torch.rand(2, 3), torch.rand(3)))

    def test_lhs_indexing_increment_list_prim(self):
        def foo():
            ls = [1, 2, 3]
            ls[0] += 5
            return ls
        self.checkScript(foo, ())

    def test_lhs_indexing_multi(self):
        def foo(a, b):
            a = a.clone()
            foo, a[0], bar = (1, b, 3)
            return foo, a, bar
        self.checkScript(foo, (torch.rand(2, 3), torch.rand(3)))

    def test_bool_dispatch(self):
        with torch.jit._disable_emit_hooks():  # TODO: Python print broadcasting list
            def kwarg_false(x):
                # type: (Tensor) -> Tensor
                return F.max_pool1d(x, 1, 1, return_indices=False)
            self.checkScript(kwarg_false, (torch.randn(3, 3, 3),))

            def kwarg_true(x):
                # type: (Tensor) -> Tuple[Tensor, Tensor]
                return F.max_pool1d(x, 1, 1, return_indices=True)
            self.checkScript(kwarg_true, (torch.randn(3, 3, 3),))

            def full_kwarg_false(x):
                # type: (Tensor) -> Tensor
                return F.max_pool1d(x, 1, 1, ceil_mode=False, return_indices=False)
            self.checkScript(full_kwarg_false, (torch.randn(3, 3, 3),))

            def full_kwarg_true(x):
                # type: (Tensor) -> Tuple[Tensor, Tensor]
                return F.max_pool1d(x, 1, 1, ceil_mode=False, return_indices=True)
            self.checkScript(full_kwarg_true, (torch.randn(3, 3, 3),))

            def use_default(x):
                # type: (Tensor) -> Tensor
                return F.max_pool1d(x, 1, 1)
            self.checkScript(use_default, (torch.randn(3, 3, 3),))

            def arg_false(x):
                # type: (Tensor) -> Tensor
                return F.max_pool1d(x, 1, 1, 0, 1, False, False)
            self.checkScript(arg_false, (torch.randn(3, 3, 3),))

            def arg_true(x):
                # type: (Tensor) -> Tuple[Tensor, Tensor]
                return F.max_pool1d(x, 1, 1, 0, 1, False, True)
            self.checkScript(arg_true, (torch.randn(3, 3, 3),))

    def test_infer_size(self):
        from torch._C import _infer_size

        def fn(x, y):
            # type: (Tensor, Tensor) -> List[int]
            return _infer_size(x.size(), y.size())

        self.checkScript(fn, (torch.ones(2, 4, 2), torch.ones(2, 4, 2)))

    def test_hash(self):
        def tester(fn, inputs):
            for x in inputs:
                for y in inputs:
                    if x == y:
                        self.assertEqual(fn(x), fn(y))
                    else:
                        self.assertNotEqual(fn(x), fn(y))

        @torch.jit.script
        def int_hash(x):
            # type: (int) -> int
            return hash(x)

        @torch.jit.script
        def float_hash(x):
            # type: (float) -> int
            return hash(x)

        @torch.jit.script
        def str_hash(x):
            # type: (str) -> int
            return hash(x)

        tester(int_hash, (20, 21, 22))
        tester(float_hash, (20.0, 21.00001, 22.443))
        tester(str_hash, ("", "hello", "a"))

    def test_mutable_dce(self):
        @torch.jit.script
        def foo():
            a = torch.rand(2, 3)
            a += torch.rand(2, 3)
            b = torch.rand(2, 3)
            b += torch.rand(2, 3)
            # b should be cleaned up but not a
            return a

        FileCheck().check_count("aten::rand", 2, exactly=True) \
            .check_count("aten::add", 1, exactly=True).run(str(foo.graph))

    def test_mutable_dce_block(self):
        @torch.jit.script
        def foo():
            a = torch.rand(2, 3)
            a += torch.rand(2, 3)
            b = torch.rand(2, 3)
            if bool(a > torch.zeros(2, 3)):
                b += torch.rand(2, 3)
                a += torch.rand(2, 3)
            # a should be cleaned up but not b
            return b

        FileCheck().check("prim::If").check_count("aten::rand", 1, exactly=True) \
            .run(str(foo.graph))

    def test_mutable_dce_graph_input(self):
        @torch.jit.script
        def foo(a):
            a += torch.rand(2, 3)
            # shouldn't clean up `a` even though it's not used in the output

        FileCheck().check("aten::rand").check("aten::add").run(str(foo.graph))

    def test_mutable_dce_list(self):
        @torch.jit.script
        def foo(a):
            l = []
            l.append(a)
            c = l[0]
            b = torch.rand(2, 3)
            c += torch.rand(2, 3)
            return b

        # c does not get cleaned up because there is a wildcard + mutation
        FileCheck().check_count("aten::rand", 2, exactly=True).run(str(foo.graph))

    def test_mutable_dce_loop(self):
        @torch.jit.script
        def foo(a):
            l = []
            l.append(a)
            i = 0
            b = torch.rand(2, 3)
            while i < 1:
                dead = torch.rand(2, 3)
                c = l[0]
                c += torch.rand(2, 3)
                i += 1
            return b

        FileCheck().check("prim::Loop").check_not("aten::rand").check("aten::__getitem__") \
            .check_count("aten::rand", 1, exactly=True).run(str(foo.graph))

    def test_mutable_dce_indirect_wildcards(self):
        def fn():
            x = torch.ones(2, 3)
            x_1 = x.view(-1)
            l = []
            l.append(x_1)
            x_view = l[0]
            x.add_(torch.ones(2, 3))
            return x_view
        self.checkScript(fn, ())

    def test_mutable_dce_indirect_wildcard_write(self):
        def fn():
            indexes = torch.jit.annotate(List[Tensor], [])
            word_ids = torch.zeros(10, dtype=torch.int32)
            word_ids[1] = 1
            indexes.append(word_ids)

            return word_ids
        self.checkScript(fn, ())

    def test_mutable_dce_wildcards(self):
        def fn():
            x = torch.ones(2, 3)
            l = []
            l.append(x)
            x_view = l[0]
            x.add_(torch.ones(2, 3))
            return x_view

        self.checkScript(fn, (), profiling=ProfilingMode.SIMPLE)

    def test_cpp_function_tensor_str(self):
        x = torch.randn(2, 2)
        scale = torch.randn(2, 2, requires_grad=True)
        shift = torch.randn(2, 2, requires_grad=True)

        @torch.jit.script
        def fn(x, scale, shift):
            return scale * x + shift

        with self.capture_stdout() as captured:
            print(fn(x, scale, shift))

    def test_string_index(self):
        def fn(x):
            # type: (str)
            return x[2], x[-1]

        self.checkScript(fn, ("abcde",))

    def test_ord(self):
        def fn(x):
            # type: (str) -> int
            return ord(x)

        self.checkScript(fn, ("h"))
        self.checkScript(fn, ("y"))

        def index_str_to_tensor(s):
            # type: (str) -> int
            return torch.tensor(ord(s))  # noqa T484

        s = u'\u00a3'.encode('utf8')[:1]
        self.checkScript(index_str_to_tensor, (s,))

    def test_chr(self):
        def fn(x):
            # type: (int) -> str
            return chr(x)

        self.checkScript(fn, (1,))
        self.checkScript(fn, (97,))

    def test_round(self):
        def round_float(x):
            # type: (float) -> float
            return round(x)

        def round_int(x):
            # type: (int) -> float
            return round(x)

        self.checkScript(round_float, (1.5,))
        self.checkScript(round_int, (2,))

    @unittest.skipIf(PY2, "oct() format changed from PY2 to PY3")
    def test_convert_base(self):
        def test_hex(x):
            # type: (int) -> str
            return hex(x)

        def test_oct(x):
            # type: (int) -> str
            return oct(x)

        def test_bin(x):
            # type: (int) -> str
            return bin(x)

        numbers = [-1000, -10, 0, 1, 10, 2343]
        for n in numbers:
            self.checkScript(test_bin, (n,))
            self.checkScript(test_oct, (n,))
            self.checkScript(test_hex, (n,))

    @unittest.skipIf(IS_WINDOWS or IS_SANDCASTLE, "NYI: TemporaryFileName support for Windows or Sandcastle")
    def test_get_set_state(self):
        class Root(torch.jit.ScriptModule):
            __constants__ = ['number']

            def __init__(self, number):
                super(Root, self).__init__()
                self.register_buffer('buffer1', torch.ones(2, 2))
                self.register_buffer('buffer2', torch.ones(2, 2))
                self.number = number

            @torch.jit.script_method
            def __getstate__(self):
                return (self.buffer1, self.buffer2, 74, self.training)

            @torch.jit.script_method
            def __setstate__(self, state):
                self.buffer1 = state[0] + 10
                self.buffer2 = state[1] + 10
                self.training = state[3]

        class M(torch.jit.ScriptModule):
            __constants__ = ['number']

            def __init__(self, number, submodule):
                super(M, self).__init__()
                self.register_buffer('buffer1', torch.ones(2, 2))
                self.register_buffer('buffer2', torch.ones(2, 2))
                self.number = number
                self.submodule = submodule

            @torch.jit.script_method
            def __getstate__(self):
                return (self.buffer1, self.buffer2, 74, self.submodule, self.training)

            @torch.jit.script_method
            def __setstate__(self, state):
                self.buffer1 = state[0] + 10
                self.buffer2 = state[1] + 10
                self.submodule = state[3]
                self.training = state[4]

        with TemporaryFileName() as fname:
            m = M(23, submodule=Root(99))
            m.save(fname)
            loaded = torch.jit.load(fname)

        # Check original module
        self.assertEqual(m.buffer1, torch.ones(2, 2))
        self.assertEqual(m.buffer2, torch.ones(2, 2))

        # Check top level module
        self.assertEqual(loaded.buffer1, torch.ones(2, 2) + 10)
        self.assertEqual(loaded.buffer2, torch.ones(2, 2) + 10)

        # Check submodule
        self.assertEqual(loaded.submodule.buffer1, torch.ones(2, 2) + 10)
        self.assertEqual(loaded.submodule.buffer2, torch.ones(2, 2) + 10)

        # Check simpler module
        class NoArgState(torch.nn.Module):
            def __init__(self):
                super(NoArgState, self).__init__()
                self.register_buffer('buffer1', torch.ones(2, 2))
                self.register_buffer('buffer2', torch.ones(2, 2))

            def forward(self):
                pass

            @torch.jit.export
            def __getstate__(self):
                return 5, self.training

            @torch.jit.export
            def __setstate__(self, state):
                self.buffer1 = torch.ones(2, 2) + state[0]
                self.buffer2 = torch.ones(2, 2) + 10
                self.training = state[1]

        with TemporaryFileName() as fname:
            m = torch.jit.script(NoArgState())
            m.save(fname)
            loaded = torch.jit.load(fname)
            self.assertEqual(loaded.buffer1, torch.ones(2, 2) + 5)
            self.assertEqual(loaded.buffer2, torch.ones(2, 2) + 10)



    def test_string_slicing(self):
        def fn1(x):
            # type: (str) -> str
            return x[1:3]

        def fn2(x):
            # type: (str) -> str
            return x[-1:3]

        def fn3(x):
            # type: (str) -> str
            return x[3:1]

        def fn4(x):
            # type: (str) -> str
            return x[3:100]

        self.checkScript(fn1, ("abcdefghi",))
        self.checkScript(fn2, ("abcdefghi",))
        self.checkScript(fn3, ("abcdefghi",))
        self.checkScript(fn4, ("abcdefghi",))

    def test_early_return_closure(self):
        code = dedent('''
            def tanh(self):
                output = torch.tanh(self)
                def backward(grad_output):
                    pass
                return output, backward
        ''')
        cu = torch.jit.CompilationUnit(code)
        g = cu.tanh.graph
        FileCheck().check_count("prim::Function_0", 2).check("None = prim::Constant") \
                   .check_next("return").run(g)

        code = dedent('''
            def tanh(self):
                output = torch.tanh(self)
                def backward(grad_output):
                    a = 1
                    if True:
                        return 1
                    else:
                        a = 2
                    return a
                return output, backward
        ''')
        cu = torch.jit.CompilationUnit(code)
        g = cu.tanh.graph
        FileCheck().check_count("prim::Function_0", 2).check("int = prim::If") \
                   .run(g)

        code = dedent('''
            def loop_in_closure(self):
                output = torch.tanh(self)
                def backward(grad_output):
                    for i in range(3):
                        return 1
                    return 4
                return output, backward
        ''')
        cu = torch.jit.CompilationUnit(code)
        fc = FileCheck()
        fc.check("prim::Function").check("(Tensor, None) = prim::TupleConstruct")
        # Loop then two if's added in exit transform
        fc.check("prim::Function").check("prim::Loop").check_count("prim::If", 2)
        fc.run(cu.loop_in_closure.graph)

        code = dedent('''
            def tanh(self):
                output = torch.tanh(self)
                def backward(grad_output):
                    if True:
                        return 1
                    else:
                        return 1.
                return output, backward
        ''')
        with self.assertRaisesRegex(RuntimeError, "returned a value of type int but"):
            cu = torch.jit.CompilationUnit(code)

    @_inline_everything
    def test_early_return_fork_join(self):
        @torch.jit.script
        def foo(x):
            if x.dim() == 2:
                return torch.neg(x), x
            else:
                return torch.neg(x), x + 1

        x = torch.rand(3, 4)

        @torch.jit.script
        def wait_script(x):
            fut = torch.jit._fork(foo, x)
            y_hat = foo(x)
            y = torch.jit._wait(fut)
            return y, y_hat

        FileCheck().check("with prim::fork").check("prim::If").check("return")\
                   .run(wait_script.graph)

    def test_early_return_type_refinement(self):
        @torch.jit.script
        def test(x):
            # type: (Optional[int]) -> int
            if x is None:
                return 1
            else:
                return x
        self.assertEqual(test(None), 1)
        self.assertEqual(test(2), 2)

    def test_exceptions_with_control_flow(self):
        def test_num_ifs(func, num_ifs):
            g = torch.jit.script(func).graph
            FileCheck().check_count("prim::If", num_ifs, exactly=True).run(g)

        def no_guard_ifs_added(x):
            # type: (int) -> int
            if x == 1:
                return 1
            else:
                if x == 2:
                    raise RuntimeError("hi")
                else:
                    raise RuntimeError("hi")

        self.checkScript(no_guard_ifs_added, (1,))
        self.checkScriptRaisesRegex(no_guard_ifs_added, (2,), Exception, "")
        test_num_ifs(no_guard_ifs_added, 2)

        # FUNCTION LOOKS LIKE:
        # graph(%x.1 : int):
        #   %7 : str = prim::Constant[value="Exception"]()
        #   %2 : int = prim::Constant[value=1]()
        #   %5 : int = prim::Constant[value=2]()
        #   %19 : int = prim::Uninitialized()
        #   %3 : bool = aten::eq(%x.1, %2)
        #   %20 : int = prim::If(%3)
        #     block0():
        #       -> (%2)
        #     block1():
        #       %6 : bool = aten::eq(%x.1, %5)
        #        = prim::If(%6)
        #         block0():
        #            = prim::RaiseException(%7)
        #           -> ()
        #         block1():
        #            = prim::RaiseException(%7)
        #           -> ()
        #       -> (%19)
        #   return (%20)

        def no_ifs_added(x):
            # type: (int) -> int
            if x < 0:
                raise RunTimeError("hi")
            return x

        self.checkScript(no_ifs_added, (1,))
        self.checkScriptRaisesRegex(no_ifs_added, (-2,), Exception, "")
        test_num_ifs(no_ifs_added, 1)

        def test_if_might(x):
            # type: (int)
            if x > 0:
                if x == 1:
                    return 1
                else:
                    a = 2
            else:
                raise RunTimeError("hi")
            return a + 2

        self.checkScript(test_if_might, (1,))
        self.checkScript(test_if_might, (3,))
        self.checkScriptRaisesRegex(no_ifs_added, (-2,), Exception, "")
        test_num_ifs(test_if_might, 3)  # one if added to guard a + 2

        def test_loop_no_escape(x):
            # type: (int)
            if x >= 0:
                for i in range(x):
                    raise RunTimeError("hi")
            else:
                return 5
            return x + 3

        self.checkScript(test_loop_no_escape, (0,))
        self.checkScript(test_loop_no_escape, (-1,))
        self.checkScriptRaisesRegex(test_loop_no_escape, (1,), Exception, "")

        # one if added to guard x + 3, the throw in loop does not escape
        test_num_ifs(test_loop_no_escape, 2)

        def test_loop_exception_with_continue(x):
            # type: (int)
            i = 0
            for i in range(5):
                if i == x:
                    raise RunTimeError("hi")
                else:
                    continue
                print(i)
            return i + 5

        self.checkScript(test_loop_exception_with_continue, (-1,))
        self.checkScriptRaisesRegex(test_loop_exception_with_continue, (1,), Exception, "")
        test_num_ifs(test_loop_exception_with_continue, 1)  # no ifs added to guard print


    def test_exception_exits_closure(self):
        code = dedent('''
            def no_return_func(self):
                # type: (Tensor) -> Tensor
                output = torch.tanh(self)
                def backward(grad_output):
                    raise "Hi"
        ''')
        with self.assertRaisesRegex(RuntimeError, "does not return along all"):
            cu = torch.jit.CompilationUnit(code)

        code = dedent('''
            def test_exit_pair_reset(x):
                # type: (int) -> int
                if x > 0:
                    a = 0
                    def backward(grad_output):
                        raise "Hi"
                    a = a + 1
                else:
                    return x
                return a + 1
        ''')
        func = torch.jit.CompilationUnit(code).test_exit_pair_reset
        self.assertEqual(func(1,), 2)
        self.assertEqual(func(-1,), -1)
        FileCheck().check_count("prim::If", 2, exactly=True).check("aten::add")\
            .run(func.graph)  # if added to guard a + 1

    def test_non_final_return(self):
        def simple(x):
            if bool(x > 3):
                return x + 1
            else:
                return x + 2
            raise RuntimeError("nope")

        def nest(x):
            x = x + 1
            if bool(x > 3):
                if bool(x > 4):
                    x += 1
                return x + 1
            else:
                return x + 2

        def early_ret(x):
            x = x + 1
            if bool(x > 3):
                return x + 1
            x = x + 1
            return x + 2

        def nest_early_ret(x):
            x = x + 1
            if bool(x > 3):
                if bool(x > 4):
                    return x + 2
                return x + 1
            x = x + 1
            return x + 2

        def not_early_ret(x):
            s = ""
            if bool(x > 3):
                if bool(x > 4):
                    return 1, s
                s += "foo"
            else:
                s += "5"
            s += "hi"
            return 7, s

        def not_total_ret(x):
            s = ""
            if bool(x > 3):
                if bool(x > 4):
                    return 1, s
                else:
                    return 2, s
            else:
                s += "5"
            return 7, s

        for i in range(3):
            for func in [simple, nest, early_ret, nest_early_ret, not_early_ret,
                         not_total_ret]:
                self.checkScript(func, (torch.tensor(2.5 + i),))

        def vars_used_after_ret(x):
            # type: (int) -> int
            if x == 0:
                return x
            else:
                y = 2
                z = 3
            return x + y * z

        self.checkScript(vars_used_after_ret, (1,))
        self.checkScript(vars_used_after_ret, (0,))

        def complicated(x):
            # type: (int) -> int
            if x:
                if x == 2:
                    return 1
                    assert 1 == 2
                else:
                    if x == 3:
                        return 2
                        assert 1 == 2
                    else:
                        a = 2
                        b = 3
            else:
                a = 4
                b = 1
            return a + b
            assert 1 == 2

        for i in range(4):
            self.checkScript(complicated, (i,))

    def test_partial_returns_shape_prop(self):
        @torch.jit.script
        def test_shape_prop(x):
            # type: (int) -> int
            if not bool(x):
                return x
            else:
                z = torch.zeros([2, 2], dtype=torch.int64)
            return int(z[0])

        test_shape_prop(torch.tensor(0.5))
        graph = test_shape_prop.graph_for(torch.tensor(0.5))
        # Shape analysis of z should propagate through if statement
        FileCheck().check("Long(2, 2)").check("prim::If").run(graph)

    def test_partial_returns(self):
        with self.assertRaisesRegex(RuntimeError, "does not return along all"):
            @torch.jit.script
            def no_ret():
                # type: () -> int
                pass

        with self.assertRaisesRegex(RuntimeError, "does not return along all"):
            @torch.jit.script
            def partial(x):  # noqa 484
                # type: (Tensor) -> int
                if x:
                    return 1

        with self.assertRaisesRegex(RuntimeError, "does not return along all"):
            @torch.jit.script
            def typed_none():  # noqa 484
                # type: () -> Optional[int]
                pass

        @torch.jit.script
        def none_ret():
            pass

        self.assertIs(none_ret(), None)
        FileCheck().check(": None").run(none_ret.graph)

    def test_early_returns_loops(self):
        def nest_while_ret(x):
            # type: (int) -> int
            y = 4
            while x < 4:
                if x < 3:
                    return y
                else:
                    y = y + 1
                    break
                y = y + 2
            y = y + 1
            return y

        self.checkScript(nest_while_ret, (2,))
        self.checkScript(nest_while_ret, (3,))
        self.checkScript(nest_while_ret, (4,))

        def loop_ret(x, y):
            # type: (int, int) -> (int)
            i = 0
            for i in range(x):
                if x == y:
                    return x + y
                i = i + y
            i = i - 1
            return i

        self.checkScript(loop_ret, (3, 3))
        self.checkScript(loop_ret, (2, 3))
        self.checkScript(loop_ret, (3, 1))

        def test_will_ret(y):
            # type: (int) -> int
            for i in range(y):
                return 2
            return 1

        self.checkScript(test_will_ret, (0,))
        self.checkScript(test_will_ret, (1,))

        def test_loop_nest_ret(y):
            # type: (int) -> int
            for i in range(y):
                for i in range(y - 2):
                    return 10
                return 5
            return 0

        self.checkScript(test_loop_nest_ret, (0,))
        self.checkScript(test_loop_nest_ret, (1,))
        self.checkScript(test_loop_nest_ret, (2,))

    def test_nn_init(self):
        tests = (
            ('constant_', (lambda: (torch.ones(2, 2), 2.5)), "Tensor, float"),
            ('ones_', (lambda: (torch.ones(2, 2),)), "Tensor"),
            ('zeros_', (lambda: (torch.ones(2, 2),)), "Tensor"),
            ('uniform_', (lambda: (torch.ones(2, 2),)), "Tensor"),
            ('normal_', (lambda: (torch.ones(2, 2),)), "Tensor"),
            ('xavier_normal_', (lambda: (torch.ones(2, 2),)), "Tensor"),
            ('xavier_uniform_', (lambda: (torch.ones(2, 2),)), "Tensor"),
        )

        for name, args_fn, type_str in tests:
            # Build test code
            arg_str = ', '.join([chr(i + ord('a')) for i in range(len(args_fn()))])

            code = dedent('''
                def test({arg_str}):
                    # type: ({type_str})
                    return torch.nn.init.{name}({arg_str})
            ''').format(arg_str=arg_str, type_str=type_str, name=name)
            cu = torch.jit.CompilationUnit(code)

            # Compare functions
            init_fn = getattr(torch.nn.init, name)
            script_out = self.runAndSaveRNG(cu.test, args_fn())
            eager_out = self.runAndSaveRNG(init_fn, args_fn())
            self.assertEqual(script_out, eager_out)

            FileCheck().check_not("prim::PythonOp").run(cu.test.graph)

    def test_isinstance_metacompile(self):
        @torch.jit.script
        def test_primitive_type(x):
            # type: (int) -> int
            if isinstance(x, int):
                return x + 1
            else:
                return x - 1

        self.assertEqual(test_primitive_type(1), 2)
        with self.assertRaisesRegex(Exception, "Expected a value of type"):
            test_primitive_type(1.5)

        _MyNamedTuple = namedtuple('_MyNamedTuple', ['value'])

        @torch.jit.script
        def test_non_primitive_types(x):
            # type: (_MyNamedTuple) -> Tensor
            if isinstance(1, _MyNamedTuple):
                return 10

            if isinstance(x, _MyNamedTuple):
                return x.value + 1
            else:
                return 1

        out = test_non_primitive_types(_MyNamedTuple(value=torch.tensor(5.0)))
        self.assertEqual(out, torch.tensor(6.0))

    def test_isinstance_dynamic(self):
        @torch.jit.script
        def foo(a):
            # type: (Optional[List[int]]) -> int
            b = 0
            if isinstance(a, (int, (float,), list, str)):
                b += 1
            if isinstance(a, (int, str)):
                b += 1
            if isinstance(a, List[int]):
                b += 1
            return b
        self.assertEqual(foo([3, 4]), 2)
        self.assertEqual(foo(None), 0)

    def test_function_overloads(self):
        # TODO: pyflakes currently does not compose @overload annotation with other
        # decorators. This is fixed on master but not on version 2.1.1.
        # Next version update remove noqa and add @typing.overload annotation

        @torch.jit._overload  # noqa: F811
        def test_simple(x1):  # noqa: F811
            # type: (int) -> int
            pass

        @torch.jit._overload  # noqa: F811
        def test_simple(x1):  # noqa: F811
            # type: (float) -> float
            pass

        def test_simple(x1):  # noqa: F811
            return x1

        def invoke_function():
            return test_simple(1.0), test_simple(.5)

        self.checkScript(invoke_function, ())

        # testing that the functions are cached
        compiled_fns_1 = torch.jit._get_overloads(test_simple)
        compiled_fns_2 = torch.jit._get_overloads(test_simple)
        for a, b in zip(compiled_fns_1, compiled_fns_2):
            self.assertIs(a.graph, b.graph)

        old_func = test_simple

        # testing that new functions added work with caching
        @torch.jit._overload  # noqa: F811
        def test_simple(x1):  # noqa: F811
            # type: (str) -> str
            pass

        @torch.jit.script
        def my_func():
            return old_func("hi")

        # testing new function same qualified name
        @torch.jit._overload  # noqa: F811
        def test_simple(a, b):  # noqa: F811
            # type: (int, int) -> int
            pass

        def test_simple(a, b):
            return a + b

        @torch.jit.script
        def fn():
            return test_simple(3, 4)

        self.assertEqual(fn(), 7)

        # currently we take the default values have to be specified in the
        # overload as well - TODO take them from implementation and apply
        # where the type is valid.
        @torch.jit._overload  # noqa: F811
        def identity(x1):  # noqa: F811
            # type: (str) -> str
            pass
        #
        @torch.jit._overload  # noqa: F811
        def identity(x1):  # noqa: F811
            # type: (float) -> float
            pass

        def identity(x1=1.0):  # noqa: F811
            return x1

        def invoke():
            return identity(), identity(.5), identity("hi")

        self.checkScript(invoke, ())

        def schema_match_failure():
            return identity((1, 2))

        thrown = False
        try:
            torch.jit.script(schema_match_failure)
        except Exception as e:
            thrown = True
            self.assertTrue(r"of type 'str'" in str(e) and r"of type 'float" in str(e))
        self.assertTrue(thrown)

        with self.assertRaisesRegex(Exception, "cannot be directly compiled"):
            torch.jit.script(identity)

        @torch.jit._overload  # noqa: F811
        def impl_compile_failure(x, y):  # noqa: F811
            # type: (str, str) -> (str)
            pass

        @torch.jit._overload  # noqa: F811
        def impl_compile_failure(x, y):  # noqa: F811
            # type: (int, int) -> (int)
            pass

        def impl_compile_failure(x, y):  # noqa: F811
            return x - y

        def test():
            impl_compile_failure("one", "two")


        with self.assertRaisesRegex(Exception, "Arguments for call are not valid"):
            torch.jit.script(test)

        @torch.jit._overload  # noqa: F811
        def good_overload(x=1):  # noqa: F811
            # type: (int) -> (int)
            pass

        def good_overload(x=1):  # noqa: F811
            return x

        @torch.jit.script
        def foo():
            return good_overload()

        self.assertEqual(foo(), 1)


        with self.assertRaisesRegex(Exception, "must equal to the default parameter"):
            @torch.jit._overload  # noqa: F811
            def bad_default_on_overload(x, y=2):  # noqa: F811
                # type: (int, int) -> (int)
                pass

            def bad_default_on_overload(x, y=1):  # noqa: F811
                # type: (int, int) -> (int)
                pass

            @torch.jit.script
            def test():
                return bad_default_on_overload(1, 2)

        @torch.jit._overload  # noqa: F811
        def diff_default(x):  # noqa: F811
            # type: (int) -> int
            pass

        @torch.jit._overload  # noqa: F811
        def diff_default(x):  # noqa: F811
            # type: (str) -> str
            pass

        def diff_default(x="hi"):  # noqa: F811
            return x

        def test():
            return diff_default(), diff_default(2), diff_default("abc")

        self.assertEqual(test(), torch.jit.script(test)())

        @torch.jit._overload  # noqa: F811
        def diff_num_params(x):  # noqa: F811
            # type: (float) -> float
            pass

        @torch.jit._overload  # noqa: F811
        def diff_num_params(x, y):  # noqa: F811
            # type: (int, int) -> int
            pass

        def diff_num_params(x, y=2, z=3):  # noqa: F811
            # type: (Union[float, int], int, int)
            return x + y + z

        def test():
            return diff_num_params(1.0), diff_num_params(1, 2), diff_num_params(1), diff_num_params(1, 2, 3)

        self.assertEqual(test(), torch.jit.script(test)())

        @torch.jit._overload  # noqa: F811
        def diff_num_params_no_annot():
            # type: () -> int
            pass

        def diff_num_params_no_annot(x=1):    # noqa: F811
            return x

        def test():
            return diff_num_params_no_annot(1.0)

        with self.assertRaisesRegex(Exception, "Parameters not specified"):
            torch.jit.script(test)


    def test_function_overloading_isinstance(self):
        @torch.jit._overload  # noqa: F811
        def my_conv(x, y):  # noqa: F811
            # type: (float, str) -> (float)
            pass

        @torch.jit._overload  # noqa: F811
        def my_conv(x, y):  # noqa: F811
            # type: (float, float) -> (float)
            pass

        def my_conv(x, y=2.0):  # noqa: F811
            if isinstance(y, str):
                if y == "hi":
                    return 4.0 - x
                else:
                    return 5.0 - x
            else:
                return 2.0 + x

        def test_uses():
            return my_conv(1.5), my_conv(1.5, "hi"), my_conv(1.5, 5.0)

        self.checkScript(test_uses, ())

    def test_method_overloading(self):
        class Over(torch.nn.Module):
            def __init__(self):
                super(Over, self).__init__()

            @torch.jit._overload_method  # noqa: F811
            def forward(self, x):  # noqa: F811
                # type: (Tuple[Tensor, Tensor]) -> Tensor
                pass

            @torch.jit._overload_method  # noqa: F811
            def forward(self, x):  # noqa: F811
                # type: (Tensor) -> Tensor
                pass

            def forward(self, x):  # noqa: F811
                if isinstance(x, Tensor):
                    return x + 20
                else:
                    return x[0] + 5

        class S(torch.jit.ScriptModule):
            def __init__(self):
                super(S, self).__init__()
                self.weak = Over()

            @torch.jit.script_method
            def forward(self, x):
                return self.weak(x) + self.weak((x, x))

        s_mod = S()
        x = torch.ones(1)
        self.assertEqual(s_mod(x), x + 20 + 5 + x)

        over = Over()
        self.assertEqual(over((x, x)), x + 5)
        self.assertEqual(over((x)), x + 20)

        class Unannotated(torch.nn.Module):
            def __init__(self):
                super(Unannotated, self).__init__()

            @torch.jit._overload_method  # noqa: F811
            def hello(self, x):  # noqa: F811
                pass

            @torch.jit._overload_method  # noqa: F811
            def hello(self, x):  # noqa: F811
                # type: (int) -> (int)
                pass

            def hello(self, x):  # noqa: F811
                return x + 3

            def forward(self):
                return self.hello(1), self.hello(.5)

        w = Unannotated()
        with self.assertRaisesRegex(Exception, "explicitly add type annotations to overloaded functions"):
            torch.jit.script(w)

        class CompileOverloadError(torch.nn.Module):
            def __init__(self):
                super(CompileOverloadError, self).__init__()

            @torch.jit._overload_method  # noqa: F811
            def hello(self, x):  # noqa: F811
                # type: (str) -> (int)
                pass

            @torch.jit._overload_method  # noqa: F811
            def hello(self, x):  # noqa: F811
                # type: (int) -> (int)
                pass

            def hello(self, x):  # noqa: F811
                return x + 1

            def forward(self):
                return self.hello("hi"), self.hello(.5)

        w = CompileOverloadError()
        with self.assertRaisesRegex(Exception, "but instead found type \'str\'"):
            torch.jit.script(w)

        # testing overload declared first, then non-overload
        with self.assertRaisesRegex(Exception, "Overloads are not useable when a module"):
            class W3(torch.nn.Module):
                def __init__(self):
                    super(W3, self).__init__()

                @torch.jit._overload_method  # noqa: F811
                def forward(self, x):  # noqa: F811
                    # type: (int) -> int
                    pass

                @torch.jit._overload_method  # noqa: F811
                def forward(self, x):  # noqa: F811
                    # type: (Tensor) -> Tensor
                    pass

                def forward(self, x):  # noqa: F811
                    return x + 5

            a = W3()
            b = torch.jit.script(a)

            class W3(torch.nn.Module):
                def __init__(self):
                    super(W3, self).__init__()

                def forward(self, x):  # noqa: F811
                    return x + 5 + 10

            a = W3()
            b = torch.jit.script(a)

        # testing non-overload declared first, then overload
        class W2(torch.nn.Module):
            def __init__(self):
                super(W2, self).__init__()

            def hello(self, x1, x2):
                return x1 + x2

            def forward(self, x):
                return self.hello(x, x)

        a = torch.jit.script(W2())
        self.assertEqual(a(torch.tensor(1)), torch.tensor(2))

        class W2(torch.nn.Module):
            def __init__(self):
                super(W2, self).__init__()

            @torch.jit._overload_method  # noqa: F811
            def hello(self, x):  # noqa: F811
                pass

            @torch.jit._overload_method  # noqa: F811
            def hello(self, x):  # noqa: F811
                # type: (int) -> (int)
                pass

            def hello(self, x):  # noqa: F811
                return x + 5 + 10

            def forward(self, x):
                return self.hello(1), self.hello(x)

        with self.assertRaisesRegex(Exception, "Overloads are not useable when a module"):
            a = torch.jit.script(W2())

    def test_select_after_chunk(self):
        def foo(x):
            chunked = torch.chunk(x, 1)
            foo = chunked[0]
            foo.add_(5)
            return x

        self.checkScript(foo, [torch.rand(2, 3)])

    def test_nn_LSTM_with_layers(self):
        class M(torch.jit.ScriptModule):
            def __init__(self):
                super(M, self).__init__()
                self.rnn = nn.LSTM(2, 3, 2, dropout=0)

            @torch.jit.script_method
            def forward(self, x, lengths, h0, c0):
                return self.rnn(x, (h0, c0))[0]

        class Eager(torch.nn.Module):
            def __init__(self):
                super(Eager, self).__init__()
                self.rnn = nn.LSTM(2, 3, 2, dropout=0)

            def forward(self, x, lengths, h0, c0):
                return self.rnn(x, (h0, c0))[0]

        inputs = (torch.randn(1, 1, 2), torch.LongTensor([7]), torch.randn(2, 1, 3), torch.randn(2, 1, 3))
        eager_out = self.runAndSaveRNG(lambda: Eager()(*inputs), ())[0]
        script_out = self.runAndSaveRNG(lambda: M()(*inputs), ())[0]

        self.assertEqual(eager_out, script_out)

    def test_nn_LSTM(self):
        from torch.nn.utils.rnn import PackedSequence
        input = torch.nn.utils.rnn.pack_sequence([torch.randn(5, 5)])

        class S(torch.jit.ScriptModule):
            def __init__(self):
                super(S, self).__init__()
                self.x = torch.nn.LSTM(5, 5)

            @torch.jit.script_method
            def forward(self, input):
                # type: (PackedSequence) -> Tuple[PackedSequence, Tuple[Tensor, Tensor]]  # noqa
                return self.x(input)

        eager_out = self.runAndSaveRNG(lambda x: torch.nn.LSTM(5, 5)(x), (input,))[0]
        script_out = self.runAndSaveRNG(lambda x: S()(x), (input,))[0]

        self.assertEqual(eager_out, script_out)

    def test_nn_GRU(self):
        from torch.nn.utils.rnn import PackedSequence
        seq_input = torch.nn.utils.rnn.pack_sequence([torch.randn(5, 5)])
        tensor_input = torch.randn(5, 5, 5)

        class SeqLengthGRU(torch.jit.ScriptModule):
            def __init__(self):
                super(SeqLengthGRU, self).__init__()
                self.x = torch.nn.GRU(5, 5)

            @torch.jit.script_method
            def forward(self, input):
                # type: (PackedSequence) -> Tuple[PackedSequence, Tensor]
                return self.x(input)

        class TensorGRU(torch.jit.ScriptModule):
            def __init__(self):
                super(TensorGRU, self).__init__()
                self.x = torch.nn.GRU(5, 5)

            @torch.jit.script_method
            def forward(self, input):
                # type: (Tensor) -> Tuple[Tensor, Tensor]
                return self.x(input)

        seq_eager_out = self.runAndSaveRNG(lambda x: torch.nn.GRU(5, 5)(x), (seq_input,))[0]
        seq_script_out = self.runAndSaveRNG(lambda x: SeqLengthGRU()(x), (seq_input,))[0]
        tensor_eager_out = self.runAndSaveRNG(lambda x: torch.nn.GRU(5, 5)(x), (tensor_input,))[0]
        tensor_script_out = self.runAndSaveRNG(lambda x: TensorGRU()(x), (tensor_input,))[0]

        self.assertEqual(seq_eager_out, seq_script_out)
        self.assertEqual(tensor_eager_out, tensor_script_out)

    def test_torchscript_memoryformat(self):
        @torch.jit.script
        def fn(x):
            return x.contiguous(memory_format=torch.channels_last)
        x = torch.randn(4, 3, 6, 6)
        y = fn(x)
        self.assertTrue(y.is_contiguous(memory_format=torch.channels_last))

    def test_torchscript_multi_head_attn(self):
        @torch.jit.script
        def jit_multihead_attn_forward(query,                   # type: Tensor
                                       key,                     # type: Tensor
                                       value,                   # type: Tensor
                                       embed_dim_to_check,      # type: int
                                       num_heads,               # type: int
                                       in_proj_weight,          # type: Tensor
                                       in_proj_bias,            # type: Tensor
                                       bias_k,                  # type: Optional[Tensor]
                                       bias_v,                  # type: Optional[Tensor]
                                       add_zero_attn,           # type: bool
                                       dropout,                 # type: float
                                       out_proj_weight,         # type: Tensor
                                       out_proj_bias,           # type: Tensor
                                       training=True,           # type: bool
                                       key_padding_mask=None,   # type: Optional[Tensor]
                                       need_weights=True,       # type: bool
                                       attn_mask=None           # type: Optional[Tensor]
                                       ):
            # type: (...) -> Tuple[Tensor, Optional[Tensor]]
            return torch.nn.functional.multi_head_attention_forward(query, key, value,
                                                                    embed_dim_to_check, num_heads,
                                                                    in_proj_weight, in_proj_bias,
                                                                    bias_k, bias_v,
                                                                    add_zero_attn, dropout,
                                                                    out_proj_weight, out_proj_bias,
                                                                    training, key_padding_mask,
                                                                    need_weights, attn_mask)

        src_l = 3
        bsz = 5
        embed_size = 8
        nhead = 2
        multi_head_attn = torch.nn.MultiheadAttention(embed_size, nhead)
        query = torch.rand((src_l, bsz, embed_size))
        key = torch.rand((src_l, bsz, embed_size))
        value = torch.rand((src_l, bsz, embed_size))

        mask = (torch.triu(torch.ones(src_l, src_l)) == 1).transpose(0, 1)
        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0)).double()

        jit_out = jit_multihead_attn_forward(query, key, value,
                                             embed_size, nhead,
                                             multi_head_attn.in_proj_weight,
                                             multi_head_attn.in_proj_bias,
                                             multi_head_attn.bias_k, multi_head_attn.bias_v,
                                             multi_head_attn.add_zero_attn, multi_head_attn.dropout,
                                             multi_head_attn.out_proj.weight,
                                             multi_head_attn.out_proj.bias, attn_mask=mask)[0]

        py_out = torch.nn.functional.multi_head_attention_forward(query, key, value,
                                                                  embed_size, nhead,
                                                                  multi_head_attn.in_proj_weight,
                                                                  multi_head_attn.in_proj_bias,
                                                                  multi_head_attn.bias_k,
                                                                  multi_head_attn.bias_v,
                                                                  multi_head_attn.add_zero_attn,
                                                                  multi_head_attn.dropout,
                                                                  multi_head_attn.out_proj.weight,
                                                                  multi_head_attn.out_proj.bias,
                                                                  attn_mask=mask)[0]
        # print("rel. error: ")
        # print(jit_out / py_out - 1)
        self.assertTrue(torch.allclose(jit_out, py_out, atol=5e-4, rtol=1e-4))

    @unittest.skipIf(not RUN_CUDA, "no CUDA")
    def test_scriptmodule_multi_head_attn_cuda(self):

        class MyModule(torch.jit.ScriptModule):
            def __init__(self, embed_dim, num_heads):
                super(MyModule, self).__init__()
                sample_q = torch.randn(3, 2, embed_dim)
                sample_kv = torch.randn(3, 2, embed_dim)
                attention = nn.MultiheadAttention(embed_dim, num_heads)
                attention.eval()

                self.mod = torch.jit.trace(attention,
                                           (sample_q, sample_kv, sample_kv))

            @torch.jit.script_method
            def forward(self, q, k, v):
                return self.mod(q, k, v)

        embed_dim = 8
        num_heads = 2
        sl = 3
        bs = 2
        model = MyModule(embed_dim, num_heads).cuda()
        q = torch.randn(sl, bs, embed_dim, device="cuda")
        kv = torch.randn(sl, bs, embed_dim, device="cuda")

        jit_out = model(q, kv, kv)[0]
        py_out = torch.nn.functional.multi_head_attention_forward(q, kv, kv,
                                                                  embed_dim, num_heads,
                                                                  model.mod.in_proj_weight,
                                                                  model.mod.in_proj_bias,
                                                                  None, None, None, 0.0,
                                                                  model.mod.out_proj.weight,
                                                                  model.mod.out_proj.bias)[0]
        self.assertTrue(torch.allclose(jit_out, py_out, atol=5e-4, rtol=1e-4))

    def test_trace_modulelist(self):
        class MySubmod(torch.nn.Module):
            def __init__(self):
                super(MySubmod, self).__init__()
                self.relu = torch.nn.ReLU()

            def forward(self, x):
                return self.relu(x)

        class MyMod(torch.nn.Module):
            def __init__(self):
                super(MyMod, self).__init__()
                self.ml = torch.nn.ModuleList([
                    MySubmod(),
                    MySubmod()
                ])

            def forward(self, x):
                for mod in self.ml:
                    x = mod(x)
                return x

        traced = torch.jit.trace(MyMod(), (torch.rand(3, 4),))

    def test_trace_fork_join_and_module(self):
        class MySubmod(torch.nn.Module):
            def __init__(self):
                super(MySubmod, self).__init__()
                self.relu = torch.nn.ReLU()

            def forward(self, x):
                return self.relu(x), torch.neg(x)

        class Mod(torch.nn.Module):
            def __init__(self):
                super(Mod, self).__init__()
                self.ml = torch.nn.ModuleList([
                    MySubmod() for i in range(2)
                ])

            def forward(self, x):
                futs = []
                for i in range(2):
                    futs.append(torch.jit._fork(self.ml[i], x))

                results = []
                for i in range(2):
                    results.append(torch.jit._wait(futs[i])[0])

                return torch.stack(results)

        m = Mod()
        traced = torch.jit.trace(m, torch.rand(3, 4))

    def test_trace_invert_module_hierarchy(self):
        class MySubmod(torch.nn.Module):
            def __init__(self):
                super(MySubmod, self).__init__()
                self.relu = torch.nn.ReLU()

            def forward(self, x):
                return self.relu(x), torch.neg(x)

        class MyFunctionalMod(torch.nn.Module):
            def forward(self, x, submod):
                return submod(x)

        class Mod(torch.nn.Module):
            def __init__(self):
                super(Mod, self).__init__()
                self.sm = MySubmod()
                self.fm = MyFunctionalMod()

            def forward(self, x):
                return self.fm(x, self.sm)

        torch.jit.trace(Mod(), (torch.rand(3, 4),))

    @unittest.skipIf(not RUN_CUDA, "no CUDA")
    def test_scriptmodule_transformer_cuda(self):

        class MyModule(torch.jit.ScriptModule):
            def __init__(self, transformer, sample_q, sample_kv):
                super(MyModule, self).__init__()
                transformer.eval()

                self.mod = torch.jit.trace(transformer,
                                           (sample_q, sample_kv))

            @torch.jit.script_method
            def forward(self, q, k):
                return self.mod(q, k)

        d_model = 8
        nhead = 2
        num_encoder_layers = 2
        num_decoder_layers = 2
        dim_feedforward = 16
        bsz = 2
        seq_length = 5
        tgt_length = 3

        src = torch.randn(seq_length, bsz, d_model)
        tgt = torch.randn(tgt_length, bsz, d_model)
        transformer = nn.Transformer(d_model, nhead, num_encoder_layers,
                                     num_decoder_layers, dim_feedforward, dropout=0.0)
        model = MyModule(transformer, tgt, src)

        src = torch.randn(seq_length, bsz, d_model)
        tgt = torch.randn(tgt_length, bsz, d_model)
        jit_out = model(tgt, src)
        py_out = transformer(tgt, src)

        # print(jit_out/py_out-1)
        # print(torch.allclose(jit_out, py_out, atol=5e-4, rtol=1e-4))
        self.assertTrue(torch.allclose(jit_out, py_out, atol=5e-4, rtol=1e-4))

    def test_list_python_op(self):
        def python_list_op(lst):
            # type: (List[Tensor]) -> Tensor
            return lst[0]

        def fn(lst):
            # type: (List[Tensor]) -> Tensor
            return python_list_op(lst)

        self.checkScript(fn, ([torch.ones(2) + 2, torch.ones(2)],))

    @unittest.skipIf(not RUN_CUDA, "no CUDA")
    def test_weak_cuda(self):
        class M(torch.jit.ScriptModule):
            def __init__(self):
                super(M, self).__init__()
                self.lstm = torch.nn.LSTM(5, 5)
                self.lstm.cuda()

            @torch.jit.script_method
            def forward(self, x):
                return self.lstm(x)

        m = M()
        m.cuda()
        out = m(torch.ones(5, 5, 5).cuda())
        self.assertTrue(out[0].is_cuda)

    def test_ignore_decorator(self):
        with warnings.catch_warnings(record=True) as warns:
            class M(torch.jit.ScriptModule):
                def __init__(self):
                    super(M, self).__init__()
                    tensor = torch.zeros(1, requires_grad=False)
                    self.register_buffer('some_state', torch.nn.Parameter(tensor))

                @torch.jit.script_method
                def forward(self, x):
                    self.ignored_code(x)
                    return x

                @torch.jit.ignore(drop_on_export=True)
                def ignored_code(self, x):
                    self.some_state = torch.tensor((100,))

        if not PY2:
            FileCheck().check("TorchScript will now drop the function").run(str(warns[0]))

        # Assert ignored code is run
        m = M()

        m2 = self.getExportImportCopy(m)
        pp = str(m2.forward.code)
        self.assertNotIn('ignored_code', pp)

        with self.assertRaisesRegex(torch.jit.Error, "annotated to be ignored and cannot be run"):
            m2.forward(torch.ones(1))

    def test_ignored_as_value(self):
        class Model(nn.Module):
            def __init__(self):
                super(Model, self).__init__()

            @torch.jit.unused
            def tuple_ignored(self, x):
                # type: (Tensor) -> Tuple[Tensor, Tensor]
                return x, x

            @torch.jit.unused
            def single_val_ignored(self, x, y):
                # type: (Tensor, Tensor) -> Tensor
                return x

            def forward(self, x, use_ignore_path):
                # type: (Tensor, bool) -> Tuple[Tensor, Tensor]
                if False:
                    return self.tuple_ignored(x)
                if use_ignore_path:
                    return self.single_val_ignored(x, x), self.single_val_ignored(x, x)
                return x, x

        original = Model()
        scripted = torch.jit.script(original)
        self.assertEqual(scripted(torch.tensor(.5), False), (torch.tensor(.5), torch.tensor(.5)))

        buffer = io.BytesIO()
        torch.jit.save(scripted, buffer)
        buffer.seek(0)
        loaded = torch.jit.load(buffer)

        with self.assertRaisesRegex(torch.jit.Error, "annotated to be ignored and cannot be run"):
            loaded(torch.tensor(.5), True)

    def test_module_error(self):
        class MyModule(torch.nn.Module):
            def __init__(self):
                super(MyModule, self).__init__()

            def forward(self, foo):
                return foo

        with self.assertRaisesRegex(RuntimeError, "cannot be compiled since it inherits from nn.Module"):
            torch.jit.script(MyModule)

    def test_view_write(self):
        def fn(x, y):
            l = []
            l.append(x)
            x_view = l[0]
            a = x + x
            x_view.add_(y)
            b = x + x
            return a == b
        self.checkScript(fn, (torch.rand(2, 3), torch.rand(2, 3)))

    def test_module_attrs(self):
        class M(torch.jit.ScriptModule):
            def __init__(self, table):
                super(M, self).__init__()
                self.table = torch.jit.Attribute(table, Dict[str, torch.Tensor])
                self.x = torch.nn.Parameter(torch.tensor([100.0]))

            @torch.jit.script_method
            def forward(self, key):
                # type: (str) -> Tensor
                return self.table[key] + self.x

        with torch.jit._disable_emit_hooks():
            # TODO: re-enable module hook when Python printing of attributes is
            # supported
            m = M({char : torch.ones(1) + ord(char) - ord("a") for char in "abcdefg"})
            self.assertEqual(m("c"), torch.tensor([103]))

    def test_tensor_import_export(self):
        @torch.jit.script
        def foo(x):
            a = torch.tensor(1)
            b = torch.tensor([1, 2])
            c = [a, b]
            return c

        self.run_pass('constant_propagation', foo.graph)
        m = self.createFunctionFromGraph(foo.graph)
        self.getExportImportCopy(m)

    def get_pickle_values(self):
        return (('dict', {"I": "am", "a test": "test"}, Dict[str, str]),
                ('float', 2.3, float),
                ('int', 99, int),
                ('bool', False, bool),
                ('tuple', (1, 2, 3, 4), Tuple[int, int, int, int]),
                ('list', [(1, 2), (3, 4)], List[Tuple[int, int]]),
                ('tensor', torch.randn(2, 2), torch.Tensor),
                ('int_list', [1, 2, 3, 4], List[int]),
                ('tensor_list', [torch.ones(2, 2) + i for i in range(4)], List[torch.Tensor]),
                ('bool_list', [True, True, False, True], List[bool]),
                ('float_list', [1., 2., 3., 4.], List[float]),
                ('str_list', ['hello', 'bye'], List[str]),
                ('none', None, Optional[int]),
                ('a_device', torch.device('cpu'), torch.device),
                ('another_device', torch.device('cuda:1'), torch.device))

    def test_attribute_serialization(self):
        tester = self

        class M(torch.jit.ScriptModule):
            def __init__(self):
                super(M, self).__init__()
                for name, value, the_type in tester.get_pickle_values():
                    setattr(self, name, torch.jit.Attribute(value, the_type))

            @torch.jit.script_method
            def forward(self):
                return (self.dict, self.float, self.int, self.bool, self.tuple,
                        self.list, self.int_list, self.tensor_list, self.bool_list,
                        self.float_list, self.str_list, self.none)

        m = M()
        imported_m = self.getExportImportCopy(m)
        self.assertEqual(m(), imported_m())

    def test_string_len(self):
        def fn(x):
            # type: (str) -> int
            return len(x)

        self.checkScript(fn, ("",))
        self.checkScript(fn, ("h",))
        self.checkScript(fn, ("hello",))

    @unittest.skipIf(IS_WINDOWS or IS_SANDCASTLE, "NYI: TemporaryFileName support for Windows or Sandcastle")
    def test_attribute_unpickling(self):
        tensor = torch.randn(2, 2)
        tester = self

        class M(torch.jit.ScriptModule):
            def __init__(self):
                super(M, self).__init__()
                for name, value, the_type in tester.get_pickle_values():
                    setattr(self, "_" + name, torch.jit.Attribute(value, the_type))

            @torch.jit.script_method
            def forward(self):
                return (self._dict, self._float, self._int, self._bool, self._tuple,
                        self._list, self._int_list, self._tensor_list, self._bool_list,
                        self._float_list, self._str_list, self._none)

        with TemporaryFileName() as fname:
            M().save(fname)
            loaded = torch.jit.load(fname)

            def is_tensor_value(item):
                if isinstance(item, torch.Tensor):
                    return True
                if isinstance(item, list):
                    return is_tensor_value(item[0])
                return False
            for name, value, the_type in self.get_pickle_values():
                if is_tensor_value(value):
                    continue
                self.assertEqual(value, getattr(loaded, "_" + name))

    @unittest.skipIf(IS_WINDOWS or IS_SANDCASTLE, "NYI: TemporaryFileName support for Windows or Sandcastle")
    def test_old_models_bc(self):
        model = {
            'archive/version': b'1',
            'archive/code/archive.py':
                b'''
                op_version_set = 0
                def forward(self,
                    _0: Tensor) -> Tensor:
                  _1 = torch.zeros([10], dtype=6, layout=0, device=torch.device("cpu"))
                  result = torch.to(torch.fill_(_1, 5), dtype=6, layout=0, device=torch.device("cpu"),
                                    non_blocking=False, copy=False)
                  result2 = torch.rand([10], dtype=6, layout=0, device=torch.device("cpu"))
                  result3 = torch.rand_like(result2, dtype=6, layout=0, device=torch.device("cpu"))
                  _2 = torch.add(torch.add(result, result2, alpha=1), result3, alpha=1)
                  return _2
                ''',
            'archive/attributes.pkl': b'\x80\x02](e.',
            'archive/libs.py': b'op_version_set = 0\n',
            'archive/model.json':
                b'''
                {
                   "protoVersion":"2",
                   "mainModule":{
                      "torchscriptArena":{
                         "key":"code/archive.py"
                      },
                      "name":"archive",
                      "optimize":true
                   },
                   "producerName":"pytorch",
                   "producerVersion":"1.0",
                   "libs":{
                      "torchscriptArena":{
                         "key":"libs.py"
                      }
                   }
                }'''}
        with TemporaryFileName() as fname:
            archive_name = os.path.basename(os.path.normpath(fname))
            with zipfile.ZipFile(fname, 'w') as archive:
                for k, v in model.items():
                    archive.writestr(k, v)

            with open(fname, "rb") as f:
                fn = torch.jit.load(f)

        x = torch.zeros(10)
        fn(x)

    def test_submodule_attribute_serialization(self):
        class S(torch.jit.ScriptModule):
            def __init__(self, list_data):
                super(S, self).__init__()
                self.table = torch.jit.Attribute({"I": "am", "a test": "test"}, Dict[str, str])
                self.list = torch.jit.Attribute(list_data, List[Tuple[int, int]])

            @torch.jit.script_method
            def forward(self):
                return (self.table, self.list)

        class M(torch.jit.ScriptModule):
            def __init__(self):
                super(M, self).__init__()
                self.table = torch.jit.Attribute({"this": "is", "a different": "dict"}, Dict[str, str])
                self.tensor = torch.jit.Attribute(torch.randn(2, 2), torch.Tensor)
                self.s1 = S([(1, 2)])
                self.s2 = S([(4, 5)])

            @torch.jit.script_method
            def forward(self):
                return (self.table, self.tensor, self.s1.table, self.s2.list, self.s1.list)

        m = M()
        imported_m = self.getExportImportCopy(m)
        self.assertEqual(m(), imported_m())

    def test_serialization_big_ints(self):
        class M(torch.jit.ScriptModule):
            def __init__(self):
                super(M, self).__init__()
                self.int32_max = torch.jit.Attribute(2**31 - 1, int)
                self.int32_min = torch.jit.Attribute(-2**31, int)
                self.uint32_max = torch.jit.Attribute(2**32, int)

                self.int64_max = torch.jit.Attribute(2**63 - 1, int)
                self.int64_min = torch.jit.Attribute(-2**63, int)

                self.tensor = torch.nn.Parameter(torch.ones(2, 2))

            @torch.jit.script_method
            def forward(self, x):
                # type: (int) -> (int)
                return x + (self.int32_max + self.int32_min) + (self.int64_max + self.int64_min)

        m = M()
        imported = self.getExportImportCopy(m)
        self.assertEqual(m(10), imported(10))

        self.assertEqual(m.int32_max, imported.int32_max)
        self.assertEqual(m.int32_min, imported.int32_min)
        self.assertEqual(m.uint32_max, imported.uint32_max)
        self.assertEqual(m.int64_max, imported.int64_max)
        self.assertEqual(m.int64_min, imported.int64_min)

    def test_script_scope(self):
        scripted = torch.jit.script(torch.nn.functional.pad)

    @unittest.skipIf(IS_WINDOWS, "NYI: TemporaryFileName on Windows")
    def test_serialization_sharing(self):
        class M(torch.jit.ScriptModule):
            def __init__(self):
                super(M, self).__init__()
                self.list = torch.jit.Attribute([], List[str])

            @torch.jit.script_method
            def forward(self, key):
                # type: (str) -> List[str]
                self.list.append(key)
                self.list.append(key)
                self.list.append(key)
                return self.list

        # the text of the string should only appear once in the pickling
        m = M()
        s1 = "a long string"
        s2 = "a different, even longer string"
        self.assertEqual(m(s1), [s1] * 3)
        self.assertEqual(m(s2), [s1] * 3 + [s2] * 3)
        with TemporaryFileName() as fname:
            m.save(fname)
            archive_name = os.path.basename(os.path.normpath(fname))
            archive = zipfile.ZipFile(fname, 'r')
            pickled_data = archive.read(os.path.join(archive_name, 'data.pkl'))

            out = StringIO()
            pickletools.dis(pickled_data, out=out)
            disassembled = out.getvalue()

            FileCheck().check_count(s1, 1, exactly=True) \
                .check_count("BINGET", 2, exactly=True) \
                .check_count(s2, 1, exactly=True) \
                .check_count("BINGET", 2, exactly=True).run(out.getvalue())

    def test_sys_stdout_override(self):
        @torch.jit.script
        def foo():
            print('foo')

        class Redirect(object):
            def __init__(self):
                self.s = ''

            def write(self, s):
                self.s += s

        old_stdout = sys.stdout
        redirect = Redirect()
        try:
            sys.stdout = redirect
            foo()
        finally:
            sys.stdout = old_stdout

        FileCheck().check('foo').run(redirect.s)

    def test_dtype_attr(self):
        class Foo(torch.nn.Module):
            def __init__(self):
                super(Foo, self).__init__()
                self.dtype = torch.zeros([]).dtype

            def forward(self):
                return torch.zeros(3, 4, dtype=self.dtype)

        f = Foo()
        torch.jit.script(f)

    def test_optional_tuple(self):
        def fn(x=None):
            # type: (Optional[Tuple[int, int]]) -> Tuple[int, int]
            if x is None:
                new_x = (1, 2)
            else:
                new_x = x
            return new_x

        self.checkScript(fn, ((3, 4),))
        self.checkScript(fn, ())

    def test_named_tuple_redefine(self):
        global _1, _2
        _1 = namedtuple('GoogLeNetOutputs', ['logits', 'aux_logits2', 'aux_logits1'])
        _2 = namedtuple('GoogLeNetOutputs', ['different'])

        with self.assertRaisesRegex(RuntimeError, r'redefine'):
            @torch.jit.script
            def foo(x, y):
                # type: (_1, _2) -> _1
                return x

    def test_named_tuple_py2(self):
        global _GoogLeNetOutputs  # see [local resolution in python]
        _GoogLeNetOutputs = namedtuple('GoogLeNetOutputs', ['logits', 'aux_logits2', 'aux_logits1'])

        @torch.jit.script
        def foo(x):
            # type: (_GoogLeNetOutputs) -> _GoogLeNetOutputs
            return x

        vals = torch.rand(3), torch.rand(4), torch.rand(5)
        out = foo(_GoogLeNetOutputs(logits=vals[0], aux_logits2=vals[1], aux_logits1=vals[2]))
        self.assertEqual(out.logits, vals[0])
        self.assertEqual(out.aux_logits2, vals[1])
        self.assertEqual(out.aux_logits1, vals[2])

    def test_named_tuple_good_error(self):
        global _GoogLeNetOutputs  # see [local resolution in python]
        _GoogLeNetOutputs = namedtuple('GoogLeNetOutputs', ['logits', 'aux_logits2', 'aux_logits1'])

        @torch.jit.script
        def foo(x):
            # type: (_GoogLeNetOutputs) -> _GoogLeNetOutputs
            return x

        with self.assertRaisesRegex(RuntimeError,
                                    r'aka NamedTuple\(logits, aux_logits2, aux_logits1\)'):
            out = foo(_GoogLeNetOutputs(logits=3, aux_logits2=4, aux_logits1=5))

    def _test_pickle_checkpoint(self, device):
        with TemporaryFileName() as fname:
            class M(torch.jit.ScriptModule):
                __constants__ = ['fname']

                def __init__(self, tensor):
                    super(M, self).__init__()
                    self.fname = fname
                    self.tensor = torch.nn.Parameter(tensor)

                @torch.jit.script_method
                def forward(self, x):
                    y = self.tensor + x
                    torch.save(y, self.fname)
                    return y

            param = torch.randn(2, 2).to(device)
            input = torch.randn(2, 2).to(device)
            m = M(param)
            m(input)
            with open(fname, "rb") as handle:
                loaded_tensor = torch.load(fname)
                self.assertEqual(loaded_tensor, input + param)

    def _test_pickle_checkpoint_views(self, device):
        with TemporaryFileName() as fname:
            class M(torch.jit.ScriptModule):
                __constants__ = ['fname']

                def __init__(self, tensor):
                    super(M, self).__init__()
                    self.fname = fname
                    self.tensor = torch.nn.Parameter(tensor)

                @torch.jit.script_method
                def forward(self, x):
                    y = self.tensor + x
                    y_view = y.view(4)
                    torch.save((y, y_view, y), self.fname)
                    return y

            param = torch.randn(2, 2).to(device)
            input = torch.randn(2, 2).to(device)
            m = M(param)
            m(input)
            with open(fname, "rb") as handle:
                loaded_y, loaded_y_view, loaded_y_2 = torch.load(fname)
                self.assertEqual(loaded_y, input + param)
                with torch.no_grad():
                    loaded_y_view[1] += 20
                    # assert that loaded_y changed as well
                    self.assertEqual(loaded_y.view(4), loaded_y_view)
                    self.assertEqual(loaded_y_2.view(4), loaded_y_view)

    def _test_pickle_checkpoint_qtensor(self, device):
        with TemporaryFileName() as fname:
            class M(torch.jit.ScriptModule):
                __constants__ = ['fname']

                def __init__(self):
                    super(M, self).__init__()
                    self.fname = fname

                @torch.jit.script_method
                def forward(self, x, y):
                    torch.save((x, y), self.fname)
                    return y

            q = torch.quantize_per_tensor(
                torch.rand(2, 3, dtype=torch.float), scale=0.1, zero_point=10, dtype=torch.quint8).to(device)
            qc = torch.quantize_per_channel(
                torch.rand(2, 3, dtype=torch.float),
                scales=torch.tensor([0.1, 0.5, 0.01]),
                zero_points=torch.tensor([10, 0, 20]),
                axis=1, dtype=torch.quint8).to(device)
            m = M()
            m(q, qc)
            with open(fname, "rb") as handle:
                loaded_q, loaded_qc = torch.load(fname)
                self.assertEqual(loaded_q, q)
                self.assertEqual(loaded_qc, qc)

    @unittest.skipIf(not RUN_CUDA, "no CUDA")
    def test_pickle_checkpoint_cuda(self):
        self._test_pickle_checkpoint('cuda')
        self._test_pickle_checkpoint_views('cuda')

    def test_pickle_checkpoint(self):
        self._test_pickle_checkpoint('cpu')
        self._test_pickle_checkpoint_views('cpu')
        self._test_pickle_checkpoint_qtensor('cpu')

    def test_pickle_checkpoint_tup(self):
        @torch.jit.script
        def foo(fname):
            # type: (str) -> None
            torch.save((3, 4), fname)
        with TemporaryFileName() as name:
            foo(name)
            self.assertEqual(torch.load(name), (3, 4))

    def test_string_list(self):
        def fn(string):
            # type: (str) -> List[str]
            return list(string)

        self.checkScript(fn, ("abcdefgh",))

    def test_unicode_comments(self):
        @torch.jit.script
        def test(self, a):
            # 🤷🤷🤷🤷
            return torch.nn.functional.relu(a)

    def test_dict_in_not_in(self):
        def test_in_dict(x):
            # type: (Dict[str, int]) -> bool
            return 'hi' in x

        self.checkScript(test_in_dict, ({'hi': 2, 'bye': 3},))
        self.checkScript(test_in_dict, ({'bye': 3},))

        # Check evaluation order
        @torch.jit.script
        def a():
            print("a")
            return 3

        @torch.jit.script
        def b():
            print("b")
            return {3: 2, 4: 1}

        @torch.jit.script
        def fn():
            return a() in b()

        with self.capture_stdout() as captured:
            self.assertTrue(fn())
        if not IS_WINDOWS:
            # no stdout capturing on windows
            self.assertEqual(captured[0], "a\nb\n")

        def test_not_in_dict(a):
            # type: (Dict[str, int]) -> bool
            if "hello" not in a:
                return False
            else:
                return True

        self.checkScript(test_not_in_dict, ({"hello": 1, "world": 2}, ))
        self.checkScript(test_not_in_dict, ({"world": 2}, ))

        def test_dict_tensor_key(a, t):
            # type: (Dict[Tensor, int], Tensor) -> bool
            if t in a:
                return True
            else:
                return False

        inp1 = torch.tensor(3)
        inp2 = torch.tensor(5)
        dict_a = {inp1: 1, inp2: 3}
        self.checkScript(test_dict_tensor_key, (dict_a, torch.tensor(4)))
        self.checkScript(test_dict_tensor_key, (dict_a, torch.tensor(3)))
        self.checkScript(test_dict_tensor_key, (dict_a, inp1))
        self.checkScript(test_dict_tensor_key, (dict_a, inp2))

    def test_dict_types(self):
        with self.assertRaisesRegex(RuntimeError, "single type"):
            @torch.jit.script
            def foo():
                new_item = {'score': [1.0], 'ys': [1, 2, 3]}

    def test_get_set_state_with_tensors(self):
        class M(torch.nn.Module):
            def __init__(self):
                super(M, self).__init__()
                self.tensor = torch.randn(2, 2)

            @torch.jit.export
            def __getstate__(self):
                return (self.tensor, self.training)

            @torch.jit.export
            def __setstate__(self, state):
                self.tensor = state[0]
                self.training = state[1]

            def forward(self, x):
                return x + self.tensor

        with TemporaryFileName() as fname:
            m = torch.jit.script(M())
            m.save(fname)
            loaded = torch.jit.load(fname)
            self.assertEqual(loaded.tensor, m.tensor)

    def test_in_for_and_comp_expr(self):
        def fn(d):
            # type: (Dict[str, int]) -> List[int]
            out = [1]
            for i in range(d["hi"] if "hi" in d else 6):
                out.append(i)
            return out

        self.checkScript(fn, ({'hi': 2, 'bye': 3},))
        self.checkScript(fn, ({'bye': 3},))

    def test_split(self):
        def split_two(tensor):
            a, b, c = torch.split(tensor, 2, dim=1)
            return a, b, c
        x = torch.randn(3, 6)
        y = torch.randn(3, 6)
        self.checkScript(split_two, [(x + y)])

    def test_conv_error(self):
        @torch.jit.script
        def fn(x, y):
            return F.conv2d(x, y)

        try:
            fn(torch.ones(2, 2), torch.ones(4, 4))
        except RuntimeError as e:
            self.assertFalse('frame' in str(e))

    def test_python_op_name(self):
        import random

        with self.assertRaisesRegex(RuntimeError, "randint"):
            @torch.jit.script
            def fn():
                return random.randint()

    def test_dir(self):
        class M(torch.jit.ScriptModule):
            def forward(self, t):
                return t

        self.assertTrue('forward' in dir(M()))

    @unittest.skipIf(PY2, "kwarg expansion requires Python 3")
    def test_kwarg_expansion_error(self):
        @torch.jit.ignore
        def something_else(h, i):
            pass

        def fn(x):
            something_else(**x)

        with self.assertRaisesRegex(torch.jit.frontend.NotSupportedError, "keyword-arg expansion is not supported"):
            torch.jit.script(fn)

    @unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines, "requires FBGEMM")
    def test_erase_class_tensor_shapes(self):
        class Linear(torch.nn.Module):
            def __init__(self, in_features, out_features):
                super(Linear, self).__init__()
                qweight = torch._empty_affine_quantized(
                    [out_features, in_features], scale=1, zero_point=0,
                    dtype=torch.qint8)
                self.register_buffer('_packed_weight',
                                     torch.ops.quantized.linear_prepack(qweight))

            @torch.jit.export
            def __getstate__(self):
                return (torch.ops.quantized.linear_unpack(self._packed_weight)[0], self.training)

            def forward(self):
                return self._packed_weight

            @torch.jit.export
            def __setstate__(self, state):
                self._packed_weight.set_(
                    torch.ops.quantized.linear_prepack(state[0]))
                self.training = state[1]

            @property
            def weight(self):
                return torch.ops.quantized.linear_unpack(self._packed_weight)[0]

            @weight.setter
            def weight(self, w):
                self._packed_weight = torch.ops.quantized.linear_prepack(w)

        with torch.jit._disable_emit_hooks():
            x = torch.jit.script(Linear(10, 10))
            torch._C._jit_pass_erase_shape_information(x.graph)

    @unittest.skipIf(PY2, "kwarg expansion requires Python 3")
    def test_kwargs_error_msg(self):
        def other(**kwargs):
            print(kwargs)

        def fn():
            return other()

        with self.assertRaisesRegex(torch.jit.frontend.NotSupportedError, 'variable number'):
            torch.jit.script(fn)

        def another_other(*args):
            print(args)

        def another_fn():
            return another_other()

        with self.assertRaisesRegex(torch.jit.frontend.NotSupportedError, 'variable number'):
            torch.jit.script(another_fn)

    def test_inferred_error_msg(self):
        """
        Test that when we get a type mismatch on a function where we inferred
        the type to be tensor, a good error message is given.
        """
        @torch.jit.script
        def foo(a):
            return a

        with self.assertRaisesRegex(RuntimeError, "Inferred \'a\' to be of type \'Tensor"):
            foo(1)

    def test_type_comments_in_body(self):
        @torch.jit.script
        def foo(a,  # type: int
                b,  # type: int
                ):
            # type: (...) -> int
            # type: int
            return a + b

        class M(torch.nn.Module):
            def __init__(self,
                         a,  # type: int
                         b   # type: int
                         ):
                # type: (...) -> None
                super(M, self).__init__()
                self.a = a  # type: int
                self.b = b  # type: int

        torch.jit.script(M(2, 3))

# known to be failing in tracer
EXCLUDE_TRACED = {
    # The following fail due to #12024.
    # A prim::ListConstruct is involved and the indices get traced as TensorType,
    # which always require_grad. This causes a crash in autodiff.
    'test___getitem___adv_index',
    'test___getitem___adv_index_beg',
    'test___getitem___adv_index_comb',
    'test___getitem___adv_index_dup',
    'test___getitem___adv_index_sub',
    'test___getitem___adv_index_sub_2',
    'test___getitem___adv_index_sub_3',
    'test___getitem___adv_index_var',

    # jit doesn't support sparse tensors.
    'test_to_sparse',
}

EXCLUDE_TYPE_CHECK = {
    # slogdet tests use itemgetter to select its only differentiable output,
    # but this happens outside of the graph we handle, so there are fewer
    # reference outputs than graph outputs.
    'test_slogdet_1x1_neg_det',
    'test_slogdet_1x1_pos_det',
    'test_slogdet_distinct_singular_values',
    'test_slogdet_neg_det',
    'test_slogdet_pos_det',
    'test_slogdet_symmetric',
    'test_slogdet_symmetric_pd',
    'test_slogdet_batched_1x1_neg_det',
    'test_slogdet_batched_pos_det',
    'test_slogdet_batched_symmetric',
    'test_slogdet_batched_symmetric_pd',
    'test_slogdet_batched_distinct_singular_values'
}

# known to be failing in script
EXCLUDE_SCRIPT = {
    'test_norm_fro',
    'test_norm_fro_default',
    'test_norm_nuc',
    'test_norm_nuc_batched',

    # aten op has additional cudnn argument
    'test_nn_unfold',

    # flaky test - TODO fix
    'test_nn_ctc_loss',

    # unknown builtin op
    'test_nn_fold',

    # jit doesn't support sparse tensors.
    'test_to_sparse'
}

# chunk returns a list in scripting and we don't unpack the list,
# Thus it won't be replaced by ConstantChunk and run AD.
# It's explicitly checked in test_chunk_constant_script_ad
# Similary for split, it's replaced by split_with_sizes in tracing,
# but we don't have AD formula for aten::split(Tensor, int[], int),
# an op registered in JIT so AD is not triggered in scripting.
EXCLUDE_SCRIPT_AD_CHECK = {
    'test_chunk',
    'test_chunk_dim',
    'test_chunk_dim_neg0',
    'test_split_size_list',
    'test_split_size_list_dim',
    'test_split_size_list_dim_neg0',
}

EXCLUDE_PYTHON_PRINT = {
    # no support for BroadcastingList in python printer
    'test_nn_max_unpool1d',
    'test_nn_max_unpool2d',
    'test_nn_max_unpool3d',
    'test_nn_max_pool1d',
    'test_nn_max_pool2d',
    'test_nn_max_pool3d',
    'test_nn_max_pool1d_with_indices',
}

EXCLUDE_SCRIPT_MODULES = {
    'test_nn_AdaptiveAvgPool2d_tuple_none',
    'test_nn_AdaptiveAvgPool3d_tuple_none',
    'test_nn_AdaptiveMaxPool2d_tuple_none',
    'test_nn_AdaptiveMaxPool3d_tuple_none',

    # Doesn't use future division, so this is not supported
    'test_nn_CrossMapLRN2d',
}


# make a new function where all non-tensor arguments in 'args' have been partially
# applied, and all tensor arguments remain.
# used to trace functions when some arguments are not tensors
def partial_apply_nontensors(fn, args, **kwargs):
    source = ['t' if isinstance(arg, torch.Tensor) else 's' for arg in args]

    def new_fn(*tensors_):
        tensors = iter(tensors_)
        return fn(*(args[i] if s == 's' else next(tensors) for i, s in enumerate(source)), **kwargs)

    return new_fn, [arg for arg in args if isinstance(arg, torch.Tensor)]


# create a trace function from input fn
def create_traced_fn(self, fn):
    def traced_fn(*inputs, **kwargs):
        fn_tensors, inputs_tensors = partial_apply_nontensors(fn, inputs, **kwargs)
        # `check_trace` is set to False because check_trace is run with @no_grad
        # Also, `check_against_reference` already does all the checks
        # against python function
        traced = torch.jit.trace(fn_tensors, inputs_tensors, check_trace=False)
        self.assertExportImport(traced.graph, inputs_tensors)
        output = traced(*inputs_tensors)
        traced_fn.last_graph = traced.graph_for(*inputs_tensors)
        return output
    return traced_fn

script_template = '''
def the_method({}):
    return {}
'''

script_method_template = '''
def forward({}):
    return {}
'''


def get_constant(x):
    if x == inf:
        return 'float(\'inf\')' if PY2 else 'math.inf'
    if x == -inf:
        return 'float(\'-inf\')' if PY2 else '-math.inf'
    return x


def get_script_args(args):
    formals = []
    tensors = []
    actuals = []
    for arg in args:
        if isinstance(arg, torch.Tensor):
            name = 'i{}'.format(len(formals))
            formals.append(name)
            actuals.append(name)
            tensors.append(arg)
        elif isinstance(arg, str):
            actuals.append("'{}'".format(arg))
        else:
            actuals.append(str(get_constant(arg)))
    return (formals, tensors, actuals)


def get_call(method_name, func_type, args, kwargs):
    kwargs_str = ', '.join([k + '=' + str(v) for k, v in kwargs.items()])
    self_arg = args[0]
    if(func_type == 'method'):
        args = args[1:]

    argument_str = ', '.join(args)
    argument_str += ', ' if len(args) and len(kwargs) else ''
    argument_str += kwargs_str

    if func_type == 'functional':
        call = 'torch.{}({})'.format(method_name, argument_str)
    elif func_type == 'method':
        call = '{}.{}({})'.format(self_arg, method_name, argument_str)
    elif func_type == 'nn_functional':
        call = 'torch.nn.functional.{}({})'.format(method_name, argument_str)
    else:
        raise 'Unsupported function type'

    return call

# create a script function from (name, func_type, output_process_fn),
# returns a function takes in (args, kwargs) and runs the compiled function and
# then applies the post process fn to the outputs
def create_script_fn(self, method_name, func_type, output_process_fn):
    def script_fn(*args, **kwargs):
        formals, tensors, actuals = get_script_args(args)
        call = get_call(method_name, func_type, actuals, kwargs)
        script = script_template.format(', '.join(formals), call)

        CU = torch.jit.CompilationUnit(script)
        self.assertExportImport(CU.the_method.graph, tensors)
        output = output_process_fn(CU.the_method(*tensors))
        script_fn.last_graph = CU.the_method.graph_for(*tensors)
        return output
    return script_fn


def check_alias_annotation(method_name, args, kwargs):
    formals, tensors, actuals = get_script_args(args)
    call = get_call(method_name, 'method', actuals, kwargs)
    script = script_template.format(', '.join(formals), call)
    CU = torch.jit.CompilationUnit(script)
    torch._C._jit_check_alias_annotation(CU.the_method.graph, tuple(tensors), method_name)


def check_output_types(self, func, ref_outputs, args, kwargs):
    graph = getattr(func, 'last_graph', None)
    types = [o.type() for o in graph.outputs()]
    self.assertTrue(len(types) == 1)
    t = types[0]
    torch._C._jit_assert_is_instance(ref_outputs, t)


def check_against_reference(self, func, reference_func, args, kwargs=None,
                            allow_unused=True, check_types=True, no_grad=False):
    kwargs = kwargs if kwargs else {}

    def allSum(vs):
        if isinstance(vs, torch.Tensor):
            vs = (vs,)
        return sum((i + 1) * v.sum()
                   for i, v in enumerate(vs)
                   if v is not None and v.dtype.is_floating_point)

    def clone_inputs(requires_grad):
        inputs = [
            arg.detach().clone().requires_grad_(requires_grad and arg.requires_grad)
            if isinstance(arg, torch.Tensor) else arg for arg in args
        ]
        return inputs, [input for input in inputs if isinstance(input, torch.Tensor) and input.requires_grad]

    nograd_inputs, nograd_tensors = clone_inputs(False)
    recording_inputs, recording_tensors = clone_inputs(True)

    # test no gradients case
    outputs = self.runAndSaveRNG(reference_func, nograd_inputs, kwargs)
    with enable_profiling_mode():
        outputs_test = self.runAndSaveRNG(func, nograd_inputs, kwargs)
    self.assertEqual(outputs, outputs_test)

    if check_types:
        check_output_types(self, func, outputs_test, nograd_inputs, kwargs)

    if no_grad:
        # skip grad tests
        return

    with enable_profiling_mode():
        # test single grad case
        outputs = self.runAndSaveRNG(reference_func, recording_inputs, kwargs)
        grads = torch.autograd.grad(allSum(outputs), recording_tensors,
                                    allow_unused=allow_unused)
        outputs_test = self.runAndSaveRNG(func, recording_inputs, kwargs)
        grads_test = torch.autograd.grad(allSum(outputs_test), recording_tensors,
                                         allow_unused=allow_unused)
        self.assertEqual(outputs, outputs_test)
        self.assertEqual(grads, grads_test)
        # test the grad grad case
        if self._testMethodName in nn_functional_single_grad:
            return

        outputs = self.runAndSaveRNG(reference_func, recording_inputs, kwargs)
        l1 = allSum(outputs)
        grads = torch.autograd.grad(l1, recording_tensors, create_graph=True,
                                    allow_unused=allow_unused)

        l2 = (allSum(grads) * l1)
        grads2 = torch.autograd.grad(l2, recording_tensors, allow_unused=allow_unused)
        recording_inputs, recording_tensors = clone_inputs(True)
        outputs_test = self.runAndSaveRNG(func, recording_inputs, kwargs)
        l1_test = allSum(outputs_test)
        grads_test = torch.autograd.grad(
            l1_test, recording_tensors, create_graph=True, allow_unused=allow_unused)

        l2_test = (allSum(grads_test) * l1_test)
        grads2_test = torch.autograd.grad(l2_test, recording_tensors, allow_unused=allow_unused)

        self.assertEqual(outputs, outputs_test)
        self.assertEqual(grads, grads_test)
        for g2, g2_test in zip(grads2, grads2_test):
            if g2 is None and g2_test is None:
                continue
            self.assertTrue(torch.allclose(g2, g2_test, atol=5e-4, rtol=1e-4))

class TestJitGeneratedAutograd(JitTestCase):
    pass


class TestJitGeneratedModule(JitTestCase):
    pass


class TestJitGeneratedFunctional(JitTestCase):
    pass


# UBSAN per-function exclusions don't seem to work with OpenMP pragmas,
# and we have to disable the failing tests here instead.
UBSAN_BLACKLISTED_TESTS = [
    "test___rdiv___constant",
    "test___rdiv___scalar_constant",
    "test_addcdiv",
    "test_addcdiv_broadcast_all",
    "test_addcdiv_broadcast_rhs",
    "test_addcdiv_scalar",
    "test_addcdiv_scalar_broadcast_lhs",
    "test_addcdiv_scalar_broadcast_rhs",
    "test_addcdiv_scalar_scale",
    "test_addcdiv_scalar_scale_broadcast_lhs",
    "test_addcdiv_scalar_scale_broadcast_rhs",
    "test_addcdiv_scale",
    "test_addcdiv_scale_broadcast_all",
    "test_addcdiv_scale_broadcast_rhs",
    "test_add_broadcast_all",
    "test_add_broadcast_lhs",
    "test_add_broadcast_rhs",
    "test_add_constant",
    "test_add_scalar",
    "test_add_scalar_broadcast_lhs",
    "test_add_scalar_broadcast_rhs",
    "test_div",
    "test_div_broadcast_all",
    "test_div_broadcast_lhs",
    "test_div_broadcast_rhs",
    "test_div_scalar",
    "test_div_scalar_broadcast_lhs",
    "test_div_scalar_broadcast_rhs",
    "test_rsqrt",
    "test_rsqrt_scalar",
    "test_add",
    "test_reciprocal",
    "test_reciprocal_scalar",
]

L = 20
M = 10
S = 5

#  module cannot be exported /imported currently
EXCLUDE_MODULE_EXPORT_IMPORT = {
    'EmbeddingBag',
    'MaxPool1d',
    'MaxPool2d',
    'MaxPool3d',
    'AdaptiveAvgPool2d',
    'AdaptiveAvgPool3d',
    'Fold',
    'Unfold',
}

# NB: JIT script tests for all nn functional interfaces, script mode does
# not support in_place operations yet, so no inplace operation tests added.
# removed all the deprecated functions
#
# (
#   method name,
#   input size/constructing fn,
#   args (tuple represents shape of a tensor arg),
#   test variant name(will be used at test name suffix,
#       'inplace' skips grad tests),                         // optional
#   (True, nonfusible_nodes, fusible_nodes) for autodiff     // optional
#   fn to determine if test should be skipped,               // optional
#   fn mapping output to part that should be gradcheck'ed,   // optional
#   kwargs for function,                                     // optional
# )
nn_functional_tests = [
    ('conv1d', (S, S, S), ((S, S, S),)),
    ('conv2d', (S, S, S, S), ((S, S, S, S),)),
    ('conv3d', (S, S, S, S, S), ((S, S, S, S, S),)),
    ('conv_transpose1d', (S, S, S), ((S, S, S),)),
    ('conv_transpose2d', (S, S, S, S), ((S, S, S, S),)),
    ('conv_transpose3d', (S, S, S, S, S), ((S, S, S, S, S),)),
    ('conv_tbc', (S, S, S), ((S, S, S), (S,), 2)),
    ('avg_pool1d', (S, S, S), (3,)),
    ('avg_pool2d', (S, S, S, S), (3,), '', (True,)),
    ('avg_pool3d', (S, S, S, S, S), (3,)),
    ('fractional_max_pool2d', (S, S, S, S), (3, [2, 3],)),
    ('max_pool1d', (S, S, S), (2, 1)),
    ('max_pool1d', (S, S, S), (2, 1, 1, 1, False, True), 'with_indices'),
    ('max_pool2d', (S, S, S, S), (2, 1), '', (True, 'aten::max_pool2d_with_indices')),
    ('max_pool2d', (S, S, S, S), (2, 1, 1, 1, False, True), 'with_indices', (True, 'aten::max_pool2d_with_indices')),
    ('max_pool3d', (S, S, S, S, S), (2, 1)),
    ('max_unpool1d', torch.tensor([[[2., 4]]]), (torch.tensor([[[1, 3]]]), 2, 2, 0)),
    ('max_unpool2d', torch.tensor([[[[2., 4]]]]), (torch.tensor([[[[1, 3]]]]), 2, 2, 0)),
    ('max_unpool3d', torch.tensor([[[[[2., 4]]]]]), (torch.tensor([[[[[1, 3]]]]]), 2, 2, 0)),
    ('lp_pool1d', (S, S, S), (2., 3, 2,)),
    ('lp_pool2d', (S, S, S, S), (2., 3, 2,)),
    ('adaptive_max_pool1d', (S, S, S), (5,)),
    ('adaptive_max_pool2d', (S, S, S, S), ([5, 7],)),
    ('adaptive_max_pool3d', (S, S, S, S, S), ([3, 2, 2],)),
    ('adaptive_avg_pool1d', (S, S, S), (5,), '', (True,)),
    ('adaptive_avg_pool2d', (S, S, S, S), ([5, 7],), '', (True,)),
    ('adaptive_avg_pool3d', (S, S, S, S, S), ([3, 2, 2],), '', (True,)),
    ('dropout', (S, S, S), (0.5,), '', (True,
                                        ['aten::bernoulli_',
                                         'aten::empty_like', 'aten::mul', 'aten::div'])),
    ('alpha_dropout', (S, S, S), (0.5,)),
    ('dropout2d', (S, S, S), (0.5,)),
    ('dropout3d', (S, S, S), (0.5,)),
    ('feature_alpha_dropout', (S, S, S), (0.5,)),
    ('threshold', (S, S, S), (0.1, 2.), '', (True,)),
    ('threshold', (S, S, S), (0.1, 2., True), 'inplace'),
    ('relu', (S, S, S), (), '', (True,)),
    ('relu', (S, S, S), (), 'inplace'),
    ('glu', (S - 1, S - 1, S - 1), (),),
    ('hardtanh', (S, S, S), (-0.5, 0.5),),
    ('hardtanh', (S, S, S), (-0.5, 0.5, True), 'inplace'),
    ('relu6', (S, S, S), (),),
    ('relu6', (S, S, S), (True), 'inplace'),
    ('elu', (S, S, S), (0.9,),),
    ('elu', (S, S, S), (0.9, True), 'inplace'),
    ('selu', (S, S, S), (),),
    ('selu', (S, S, S), (True), 'inplace'),
    ('celu', (S, S, S), (0.9,),),
    ('celu', (S, S, S), (0.9, True), 'inplace'),
    ('leaky_relu', (S, S, S), (0.02,),),
    ('leaky_relu', (S, S, S), (0.02,), 'inplace'),
    ('rrelu', (S, S), (0.1, 0.3, False),),
    ('rrelu', (S, S), (0.1, 0.3, False, True), 'inplace'),
    ('hardshrink', (S, S, S), (0.4,),),
    ('tanhshrink', (S, S, S), (),),
    ('softsign', (S, S, S), (),),
    ('softplus', (S, S, S), (),),
    ('softmin', (S, S, S), (0,),),
    ('softmax', (S, S, S), (0,), '', (True,)),
    ('softmax', (S, S, S), (0, 3, torch.double), 'with_all_args', (True,)),
    ('tanh', (S, S, S), (), '', (True,)),
    ('sigmoid', (S, S, S), (), '', (True,)),
    ('log_softmax', (S, S, S), (0,), '', (True,)),
    ('linear', (S, S), ((M, S),), '', (True, ['aten::t', 'aten::matmul'])),
    ('linear', (S, S), ((M, S), (M,)), 'addmm', (True, ['aten::add', 'aten::mm'])),
    ('bilinear', (S, S, S), ((S, S, M), torch.zeros(M, S, M),),),
    ('embedding', torch.tensor([[1, 2, 4, 5], [4, 3, 2, 5]]), (torch.rand(6, 3), ), '', (True,)),
    ('embedding_bag', torch.tensor([1, 2, 4, 2]), (torch.rand(5, 3), torch.tensor([0, 4]),),),
    ('batch_norm', (S, S), (non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)), ),
        '', (False, 'aten::_batch_norm_impl_index')),
    ('instance_norm', (S, S, S), (non_differentiable(torch.zeros(S)), non_differentiable(torch.ones(S))),),
    ('layer_norm', (S, S, S, S), ([5],), '',
     (False, ['aten::contiguous', 'aten::_batch_norm_impl_index'])),
    ('layer_norm', (S, S, S, S), ([5], non_differentiable(torch.rand(S)),), 'with_only_weight',
     (False, ['aten::contiguous', 'aten::_batch_norm_impl_index'])),
    ('layer_norm', (S, S, S, S), ([5], None, non_differentiable(torch.rand(S)),), 'with_only_bias',
     (False, ['aten::contiguous', 'aten::_batch_norm_impl_index'])),
    ('layer_norm', (S, S, S, S), ([5], non_differentiable(torch.rand(S)),
                                  non_differentiable(torch.rand(S))), 'with_weight_and_bias',
     (False, ['aten::contiguous', 'aten::_batch_norm_impl_index', 'aten::addcmul'])),
    ('group_norm', (S, S, S), (1, torch.rand(5),),),
    ('local_response_norm', (S, S, S), (2, ),),
    ('nll_loss', F.log_softmax(torch.randn(3, 5), dim=0), (torch.tensor([1, 0, 4]),), '', (True, 'aten::nll_loss_forward')),
    ('poisson_nll_loss', torch.rand(S, 2), (torch.rand(S, 2),),),
    ('poisson_nll_loss', torch.rand(S, 2), (torch.rand(S, 2), True, True), 'full'),
    ('kl_div', F.log_softmax(torch.randn(S, 10), 1), (F.softmax(torch.randn(S, 10), 1),),),
    ('cross_entropy', (3, S), (torch.randint(S, (3,), dtype=torch.int64),),),
    ('binary_cross_entropy_with_logits', (3,), (torch.empty(3).random_(2), ),),
    ('smooth_l1_loss', (3, S), (non_differentiable(torch.rand(3, S)),),),
    ('l1_loss', (3, S), (non_differentiable(torch.rand(3, S)),),),
    ('mse_loss', (3, S), (non_differentiable(torch.rand(3, S)),),),
    ('smooth_l1_loss', (3, S), ((torch.rand(3, S)),), 'with_grad'),
    ('l1_loss', (3, S), ((torch.rand(3, S)),), 'with_grad'),
    ('mse_loss', (3, S), ((torch.rand(3, S)),), 'with_grad'),
    ('margin_ranking_loss', (3, S), ((3, S), (S,)),),
    ('hinge_embedding_loss', (3, S), (non_differentiable(torch.rand(3, S)),),),
    ('soft_margin_loss', (3, S), (non_differentiable(torch.rand(3, S)),),),
    ('multilabel_soft_margin_loss', (3, S), (non_differentiable(torch.rand(3, S)),),),
    ('cosine_embedding_loss', (S, S), ((S, S), non_differentiable(torch.rand(S,))),),
    ('pixel_shuffle', (1, 9, 4, 4), (3,),),
    ('affine_grid', (S, 2, 3), (torch.Size([S, 1, 7, 7]),),),
    ('pad', (3, 3, 4, 2), ([1, 1],),),
    ('pairwise_distance', (S, S), ((S, S),),),
    ('pdist', (S, S), (),),
    ('cosine_similarity', (S, S), ((S, S),),),
    ('triplet_margin_loss', (S, S), ((S, S), (S, S)),),
    ('normalize', (S, S, S), (),),
    ('unfold', (S, S, S, S), ([2, 3]),),
    ('fold', (1, 3 * 2 * 2, 12), ([4, 5], [2, 2]),),
    ('grid_sample', (S, S, S, S), (non_differentiable(torch.rand(S, S, S, 2)),),),
    ('gumbel_softmax', (S, S), (2.,), '', (True, ['aten::softmax', 'aten::add', 'aten::div'], ['aten::neg'])),
    ('gumbel_softmax', (S, S), (2., True,), 'hard', (True, ['aten::softmax', 'aten::add', 'aten::div'], ['aten::neg'])),
    ('multilabel_margin_loss', torch.tensor([[0.2, -0.2, 0.07]]), (torch.tensor([[0, 0, 1]]),),),
    ('multi_margin_loss', (S, S), (non_differentiable(torch.randint(S, (S, ), dtype=torch.int64)),
                                   1, 1., non_differentiable(torch.randn(S))),),
    ('binary_cross_entropy', torch.randn(3, 2).sigmoid(), (non_differentiable(torch.rand(3, 2)),
                                                           non_differentiable(torch.randn(3, 2))),),
    ('binary_cross_entropy', torch.randn(3, 2).sigmoid(),
        (non_differentiable(torch.rand(3, 2)),
         non_differentiable(torch.randn(3, 2)), None, None, 'mean'), 'size_average'),
    ('ctc_loss', torch.rand(S, S, S).log_softmax(2).detach().requires_grad_(),
     (torch.randint(1, S, (S, S), dtype=torch.long), torch.full((S,), S, dtype=torch.long),
      torch.randint(1, S, (S,), dtype=torch.long))),
    ('upsample', torch.randn(S, S, M, M), (None, 2), 'with_scale'),
    ('upsample', torch.randn(S, S, M, M), (4,), 'with_size'),
    ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2,), 'nearest_4d', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, S, M, M), (None, 2.), 'nearest_4d_with_scale', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, S, M, M), (4,), 'nearest_4d_with_size', (True, 'aten::__interpolate')),
    ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2,), 'area_4d', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, S, M, M), (None, 2.), 'area_4d_with_scale', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, S, M, M), (4,), 'area_4d_with_size', (True, 'aten::__interpolate')),
    ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2,), 'bilinear_4d', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, S, M, M), (None, 2.), 'bilinear_4d_with_scale', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, S, M, M), (4,), 'bilinear_4d_with_size', (True, 'aten::__interpolate')),
    ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2,), 'bicubic_4d', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, S, M, M), (None, 2.), 'bicubic_4d_with_scale', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, S, M, M), (4,), 'bicubic_4d_with_size', (True, 'aten::__interpolate')),
    ('interpolate', torch.zeros(3, 3).view(1, 3, 3), (2,), 'nearest_3d', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M), (None, 2.), 'nearest_3d_with_scale', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M), (4,), 'nearest_3d_with_size', (True, 'aten::__interpolate')),
    ('interpolate', torch.zeros(3, 3).view(1, 3, 3), (2,), 'area_3d', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M), (None, 2.), 'area_3d_with_scale', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M), (4,), 'area_3d_with_size', (True, 'aten::__interpolate')),
    ('interpolate', torch.zeros(3, 3).view(1, 3, 3), (2,), 'linear_3d', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M), (None, 2.), 'linear_3d_with_scale', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M), (4,), 'linear_3d_with_size', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M, M, M), (None, 2.), 'nearest_5d_with_scale', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M, M, M), (4,), 'nearest_5d_with_size', (True, 'aten::__interpolate')),
    ('interpolate', torch.zeros(3, 3, 3).view(1, 1, 3, 3, 3), (2,), 'area_5d', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M, M, M), (None, 2.), 'area_5d_with_scale', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M, M, M), (4,), 'area_5d_with_size', (True, 'aten::__interpolate')),
    ('interpolate', torch.zeros(3, 3, 3).view(1, 1, 3, 3, 3), (2,), 'trilinear_5d', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M, M, M), (None, 2.), 'trilinear_5d_with_scale', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M, M, M), (4,), 'trilinear_5d_with_size', (True, 'aten::__interpolate')),
    ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2, None, 'nearest', None, False),
     'nearest_4d_not_recompute_scale_factor', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, S, M, M), (4, None, 'nearest', None, False),
     'nearest_4d_with_size_not_recompute_scale_factor', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, S, M, M), (None, 2., 'bilinear', None, False),
     'bilinear_4d_with_scale_not_recompute_scale_factor', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, S, M, M), (4, None, 'bilinear', None, False),
     'bilinear_4d_with_size_not_recompute_scale_factor', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, S, M, M), (None, 2., 'bicubic', None, False),
     'bicubic_4d_with_scale_not_recompute_scale_factor', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, S, M, M), (4, None, 'bicubic', None, False),
     'bicubic_4d_with_size_not_recompute_scale_factor', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M), (None, 2., 'nearest', None, False),
     'nearest_3d_with_scale_not_recompute_scale_factor', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M), (4, None, 'nearest', None, False),
     'nearest_3d_with_size_not_recompute_scale_factor', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M), (None, 2., 'linear', None, False),
     'linear_3d_with_scale_not_recompute_scale_factor', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M), (4, None, 'linear', None, False),
     'linear_3d_with_size_not_recompute_scale_factor', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M, M, M), (None, 2., 'nearest', None, False),
     'nearest_5d_with_scale_not_recompute_scale_factor', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M, M, M), (4, None, 'nearest', None, False),
     'nearest_5d_with_size_not_recompute_scale_factor', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M, M, M), (None, 2., 'trilinear', None, False),
     'trilinear_5d_with_scale_not_recompute_scale_factor', (True, 'aten::__interpolate')),
    ('interpolate', torch.randn(S, M, M, M, M), (4, None, 'trilinear', None, False),
     'trilinear_5d_with_size_not_recompute_scale_factor', (True, 'aten::__interpolate')),
]


# Test names in this set are only checked for a single derivative
nn_functional_single_grad = frozenset('test_nn_' + name for name in [
    'pdist',
    'multilabel_margin_loss',
    'max_unpool3d',
    'multi_margin_loss',
    'binary_cross_entropy',
    'binary_cross_entropy_size_average',
    'ctc_loss',
    'grid_sample',
])

# additional modules test
# TODO: delete this list once we make all nn_tests work
additional_module_tests = [
    {
        'module_name': 'Bilinear',
        'constructor_args': (S, S, M),
        'input_size': (S, S),
        'extra_args': ((S, S),)
    },
    {
        'module_name': 'RNNCell',
        'constructor_args': (S, S),
        'input_size': (S, S),
    },
    {
        'module_name': 'LSTMCell',
        'constructor_args': (S, S),
        'input_size': (S, S),
    },
    {
        'module_name': 'GRUCell',
        'constructor_args': (S, S),
        'input_size': (S, S),
    },
    {
        'module_name': 'MultiheadAttention',
        'constructor_args': (128, 8),
        'input_size': (10, 8, 128),
        'extra_args': (torch.randn(10, 8, 128), torch.randn(10, 8, 128)),
        'slowTest': True
    },
    {
        'module_name': 'Transformer',
        'constructor_args': (1, 1, 1, 1, 2),
        'input_size': (3, 1, 1),
        'extra_args': (torch.randn(1, 1, 1),),
        'slowTest': True
    }
]


def add_autograd_test(
        name,
        self_size,
        args,
        variant_name='',
        check_ad=(),
        dim_args_idx=(),
        skipTestIf=(),
        output_process_fn=lambda x: x,
        kwargs=None):
    basic_test_name = 'test_' + name
    if variant_name != '':
        basic_test_name += '_' + variant_name

    for dim_perm in product([-1, 1], repeat=len(dim_args_idx)):
        test_name = basic_test_name
        new_args = [arg * dim_perm[dim_args_idx.index(i)] if i in dim_args_idx else arg for i, arg in enumerate(args)]
        test_name = basic_test_name + ''.join('_neg' + str(i) for i, idx in enumerate(dim_perm) if idx < 0)
        new_args = tuple(new_args)

        # for-loop bodies don't define scopes, so we have to save the variables
        # we want to close over in some way
        def do_test(self, device, name=name, self_size=self_size, args=new_args, test_name=test_name,
                    check_ad=check_ad, output_process_fn=output_process_fn):
            # TODO: The rest of this function does NOT respect device.  If you want to
            # enable tests for CUDA, you'll need to update everything here to
            # handle the CUDA case correctly, including how it generates inputs,
            # and assumptions about which fuser is used.
            assert torch.device(device) == torch.device('cpu')

            # We enable the CPU fuser during these checks for more consistent
            # behavior. Otherwise, we are going to have to analyze the graph to
            # see if producer values are Dimension
            @enable_cpu_fuser_if(not IS_SANDCASTLE)
            def check(name):
                set_rng_seed(2)
                is_magic_method = name[:2] == '__' and name[-2:] == '__'
                is_inplace = name[-1] == "_" and not is_magic_method
                self_variable = create_input((self_size,))[0][0]
                # FixMe: run grad checks on inplace self
                if is_inplace:
                    self_variable.requires_grad = False
                # need to record this because methods can change the size (e.g. unsqueeze)
                args_variable, kwargs_variable = create_input(args, requires_grad=not is_inplace, call_kwargs=kwargs)
                self_tensor = deepcopy(self_variable.data)
                args_tensor = deepcopy(unpack_variables(args_variable))

                def fn(*inputs, **kwargs):
                    attr = getattr(inputs[0], name)
                    output = attr(*inputs[1:], **kwargs)
                    return output_process_fn(output)

                check_types = test_name not in EXCLUDE_TYPE_CHECK
                # XXX: this test should always run with disable_autodiff_subgraph_inlining(True),
                #      so that we don't regress on autodiff support.
                with disable_autodiff_subgraph_inlining():
                    if not is_inplace and name not in EXCLUDE_GRADCHECK and not exclude_tensor_method(name, test_name):
                        # Test with disable_autodiff_subgraph_inlining, which forces the graph
                        # to contain DifferentiableGraph nodes whenever possible. This allows us
                        # to test autodiff; we assume that autograd is correct and use autodiff for backprop
                        should_autodiff_node, autodiff_nodes, fusible_nodes = normalize_check_ad(check_ad, name)

                        if test_name not in EXCLUDE_TRACED:
                            traced_fn = create_traced_fn(self, fn)

                            check_against_reference(self, traced_fn,
                                                    fn, (self_variable,) + args_variable, kwargs_variable,
                                                    check_types=check_types)
                            if IS_SANDCASTLE:
                                autodiff_nodes = autodiff_nodes + fusible_nodes
                                fusible_nodes = []

                            if (doAutodiffCheck(test_name)):
                                self.assertAutodiffNode(traced_fn.last_graph, should_autodiff_node, autodiff_nodes, fusible_nodes)

                        if not is_magic_method and test_name not in EXCLUDE_SCRIPT:
                            script_fn = create_script_fn(self, name, 'method', output_process_fn)
                            check_against_reference(self, script_fn,
                                                    fn, (self_variable,) + args_variable, kwargs_variable,
                                                    check_types=check_types)

                            if IS_SANDCASTLE:
                                autodiff_nodes = autodiff_nodes + fusible_nodes
                                fusible_nodes = []
                            if (doAutodiffCheck(test_name)):
                                self.assertAutodiffNode(script_fn.last_graph,
                                                        should_autodiff_node and test_name not in EXCLUDE_SCRIPT_AD_CHECK,
                                                        autodiff_nodes,
                                                        fusible_nodes)

                    # functional interface tests
                    if hasattr(torch, name) and name not in EXCLUDE_FUNCTIONAL:
                        def fn(*inputs, **kwargs):
                            output = getattr(torch, name)(*inputs, **kwargs)
                            return output_process_fn(output)

                        f_args_variable = (self_variable,) + args_variable
                        f_args_tensor = (self_tensor,) + args_tensor

                        if not is_inplace and test_name not in EXCLUDE_TRACED:
                            check_against_reference(self,
                                                    create_traced_fn(self, fn),
                                                    fn, f_args_variable, kwargs_variable, check_types=check_types)

                        if not is_inplace and test_name not in EXCLUDE_SCRIPT:
                            check_against_reference(self,
                                                    create_script_fn(self, name, 'functional', output_process_fn),
                                                    fn, f_args_variable, kwargs_variable,
                                                    check_types=check_types)

                # alias annotation testing
                if is_inplace and test_name not in EXCLUDE_SCRIPT:
                    check_alias_annotation(name, (self_variable,) + args_variable, kwargs_variable)

            check(name)
            inplace_name = name + '_'
            # can't broadcast inplace to left hand side
            broadcast_skip_inplace = 'broadcast_lhs' in test_name or 'broadcast_all' in test_name
            if hasattr(torch.ones(1), inplace_name) and not broadcast_skip_inplace:
                check(inplace_name)

        post_add_test(test_name, skipTestIf, do_test, TestJitGeneratedAutograd)


def suppress_warnings(fn):
    @wraps(fn)
    def wrapper(*args, **kwargs):
        with warnings.catch_warnings(record=True):
            return fn(*args, **kwargs)
    return wrapper


def add_nn_functional_test(name, self_size, args, variant_name='', check_ad=(), skipTestIf=(),
                           output_process_fn=lambda x: x, kwargs=None):
    test_name = 'test_nn_' + name

    if variant_name != '':
        test_name = test_name + '_' + variant_name

    no_grad = variant_name == 'inplace'

    @suppress_warnings
    def do_test(self, name=name, args=args, test_name=test_name, check_ad=check_ad):
        torch.manual_seed(2)

        self_variable = create_input((self_size,))[0][0]

        # need to record this because methods can change the size (e.g. unsqueeze)
        args_variable, kwargs_variable = create_input(args, call_kwargs=kwargs)

        self_tensor = deepcopy(self_variable.data)
        args_tensor = deepcopy(unpack_variables(args_variable))

        if not no_grad:
            output_variable = getattr(F, name)(self_variable, *args_variable, **kwargs_variable)

        def fn(*inputs, **kwargs):
            output = getattr(F, name)(*inputs, **kwargs)
            return output_process_fn(output)

        f_args_variable = (self_variable,) + args_variable
        f_args_tensor = (self_tensor,) + args_tensor
        should_autodiff_node, autodiff_nodes, fusible_nodes = normalize_check_ad(check_ad, name)

        if test_name not in EXCLUDE_SCRIPT:
            def run_test():
                # XXX: this test should always run with disable_autodiff_subgraph_inlining(True),
                #      so that we don't regress on autodiff support.
                with disable_autodiff_subgraph_inlining():
                    script_fn = create_script_fn(self, name, 'nn_functional', output_process_fn)
                    check_against_reference(self, script_fn, fn, f_args_variable, kwargs_variable, no_grad=no_grad)
                    # For tests we disabled AD subgraph inlining, make sure it's not falling back to autograd
                    if (doAutodiffCheck(test_name)):
                        self.assertAutodiffNode(script_fn.last_graph, should_autodiff_node, autodiff_nodes, fusible_nodes)

            if test_name in EXCLUDE_PYTHON_PRINT:
                with torch.jit._disable_emit_hooks():
                    run_test()
            else:
                run_test()

    post_add_test(test_name, skipTestIf, do_test, TestJitGeneratedFunctional)


def add_nn_module_test(*args, **kwargs):
    if 'module_name' in kwargs:
        name = kwargs['module_name']
    elif 'fullname' in kwargs:
        name = kwargs['fullname']
    elif 'constructor' in kwargs:
        name = kwargs['constructor'].__name__

    no_grad = False if 'no_grad' not in kwargs else kwargs['no_grad']

    module_name = name.split("_")[0]

    if 'desc' in kwargs and 'eval' in kwargs['desc']:
        # eval() is not supported, so skip these tests
        return

    test_name = name
    if 'desc' in kwargs:
        test_name = "{}_{}".format(test_name, kwargs['desc'])
    test_name = 'test_nn_{}'.format(test_name)

    @suppress_warnings
    def do_test(self):
        if test_name in EXCLUDE_SCRIPT_MODULES:
            return
        if 'constructor' in kwargs:
            nn_module = kwargs['constructor']
        else:
            nn_module = getattr(torch.nn, name)

        if "FunctionalModule" in str(nn_module):
            return

        if 'constructor_args_fn' in kwargs:
            constructor_args = kwargs['constructor_args_fn']()
        else:
            constructor_args = kwargs.get('constructor_args', ())

        # Construct a script module that passes arguments through
        # to self.submodule
        def create_script_module(*args, **kwargs):
            formals, tensors, actuals = get_script_args(args)

            method_args = ', '.join(['self'] + actuals)
            call_args_str = ', '.join(actuals)
            call = "self.submodule({})".format(call_args_str)
            script = script_method_template.format(method_args, call)

            submodule_constants = []
            if kwargs.get('is_constant'):
                submodule_constants = ['submodule']

            # Create module to use the script method
            class TheModule(torch.jit.ScriptModule):
                __constants__ = submodule_constants

                def __init__(self):
                    super(TheModule, self).__init__()
                    self.submodule = nn_module(*constructor_args)

            def make_module(script):
                module = TheModule()
                # check __repr__
                str(module)
                module.define(script)
                return module

            # module cannot be imported / exported
            if module_name in EXCLUDE_MODULE_EXPORT_IMPORT:
                with torch.jit._disable_emit_hooks():
                    module = make_module(script)
                    create_script_module.last_graph = module.graph
                    mod = module(*args)
            else:
                module = make_module(script)
                self.assertExportImportModule(module, tensors)
                create_script_module.last_graph = module.graph
                mod = module(*args)
            return mod

        # Construct a normal nn module to stay consistent with create_script_module
        # and make use of a single global rng_state in module initialization
        def create_nn_module(*args, **kwargs):
            module = nn_module(*constructor_args)
            return module(*args)

        # Set up inputs from tuple of sizes or constructor fn
        if 'input_fn' in kwargs:
            input = kwargs['input_fn']()
        else:
            input = (kwargs['input_size'],)

        # Extra parameters to forward()
        if 'extra_args' in kwargs:
            input = input + kwargs['extra_args']

        if 'target_size' in kwargs:
            input = input + (kwargs['target_size'],)
        elif 'target_fn' in kwargs:
            if torch.is_tensor(input):
                input = (input,)
            input = input + (kwargs['target_fn'](),)

        args_variable, kwargs_variable = create_input(input)
        f_args_variable = deepcopy(unpack_variables(args_variable))

        # Check against Python module as reference
        check_against_reference(self, create_script_module, create_nn_module, f_args_variable, no_grad=no_grad)

    if 'slowTest' in kwargs:
        do_test = slowTest(do_test)

    post_add_test(test_name, (), do_test, TestJitGeneratedModule)


def post_add_test(test_name, skipTestIf, do_test, test_class):
    assert not hasattr(test_class, test_name), 'Two tests have the same name: ' + test_name

    for skip in skipTestIf:
        do_test = skip(do_test)

    if not (TEST_WITH_UBSAN and test_name in UBSAN_BLACKLISTED_TESTS):
        setattr(test_class, test_name, do_test)


def normalize_check_ad(check_ad, name):
    # normalized check_ad is 3-element tuple: (bool, List[str], List[str])
    if len(check_ad) == 0:
        check_ad = [False, ['aten::' + name], []]
    elif len(check_ad) == 1:
        check_ad = [check_ad[0], ['aten::' + name], []]
    elif len(check_ad) == 2:
        check_ad = [check_ad[0], check_ad[1], []]
    elif len(check_ad) == 3:
        check_ad = list(check_ad)
    else:
        raise Exception('Invalid check_ad, requires (bool, str|List[str], str|List[str])')

    check_ad = [[t] if isinstance(t, str) else t for t in check_ad]

    return check_ad


class TestDocs(unittest.TestCase):
    @slowTest
    def test_docs(self):
        import subprocess
        docs_dir = '../docs'
        docs_dir = [os.path.dirname(__file__), '..', 'docs']
        docs_dir = os.path.join(*docs_dir)

        def report_error(result):
            out = result.stdout.decode('utf-8')
            err = result.stderr.decode('utf-8')
            raise RuntimeError("{}\n{}\n".format(err, out) +
                               "Docs build failed (run `cd docs && " +
                               "pip install -r requirements.txt && make doctest`)")
        result = subprocess.run(
            ['pip', 'install', '-r', 'requirements.txt'],
            stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=docs_dir)
        if result.returncode != 0:
            report_error(result)

        result = subprocess.run(
            ['make', 'doctest'],
            stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=docs_dir)
        if result.returncode != 0:
            report_error(result)

for test in autograd_method_tests():
    add_autograd_test(*test)

# NB: There isn't much utility in running these tests for CUDA, as the kernels
# are exercised in test_autograd.py, and the JIT tests intention is to test the
# JIT infrastructure around it, not the kernels themselves
instantiate_device_type_tests(TestJitGeneratedAutograd, globals(), except_for='cuda')

for test in nn_functional_tests:
    add_nn_functional_test(*test)

for test in module_tests + new_module_tests + additional_module_tests:
    add_nn_module_test(**test)

for test in criterion_tests:
    test['no_grad'] = True
    add_nn_module_test(**test)

if __name__ == '__main__':
    run_tests()
    if not PY2:
        import test_jit_py3
        suite = unittest.findTestCases(test_jit_py3)
        unittest.TextTestRunner().run(suite)