| # Copyright (c) Facebook, Inc. and its affiliates. |
| # All rights reserved. |
| # |
| # This source code is licensed under the BSD-style license found in the |
| # LICENSE file in the root directory of this source tree. |
| |
| from torch.testing._internal.common_utils import TestCase, run_tests, is_iterable_of_tensors |
| import torch |
| from torch import Tensor |
| import torch.nn.functional as F |
| import functools |
| import unittest |
| import itertools |
| from torch.testing._internal.common_device_type import instantiate_device_type_tests |
| from torch.testing._internal.common_device_type import ops |
| from torch.testing._internal.common_dtype import integral_types |
| from functorch_lagging_op_db import functorch_lagging_op_db |
| from functorch_additional_op_db import additional_op_db |
| from common_utils import ( |
| get_fallback_and_vmap_exhaustive, |
| get_exhaustive_batched_inputs, |
| xfail, |
| skip, |
| skipOps, |
| check_vmap_fallback, |
| loop, |
| IS_FBCODE, |
| ) |
| from torch.utils._pytree import tree_flatten, tree_unflatten, tree_map |
| from functorch import grad, vjp, vmap |
| import torch.autograd.forward_ad as fwAD |
| from functorch._src.eager_transforms import _as_tuple, jvp |
| from functorch.compile import decomposition_table |
| aten = torch.ops.aten |
| |
| # Version of autograd.grad that handles outputs that don't depend on inputs |
| |
| |
| def _autograd_grad(outputs, inputs, grad_outputs=None, retain_graph=False, create_graph=True): |
| inputs, inputs_spec = tree_flatten(inputs) |
| result = [torch.zeros_like(inp) for inp in inputs] |
| diff_argnums = tuple(i for i, inp in enumerate(inputs) if inp.requires_grad) |
| inputs = tuple(inputs[i] for i in diff_argnums) |
| if grad_outputs is None: |
| diff_outputs = tuple(out for out in outputs if out.requires_grad) |
| else: |
| something = [(out, go) for out, go in zip(outputs, grad_outputs) |
| if out.requires_grad] |
| if len(something) == 0: |
| diff_outputs, grad_outputs = (), () |
| else: |
| diff_outputs, grad_outputs = zip(*something) |
| if len(diff_outputs) == 0: |
| return tuple(torch.zeros_like(inp) for inp in inputs) |
| grad_inputs = torch.autograd.grad(diff_outputs, inputs, grad_outputs, |
| retain_graph=retain_graph, |
| create_graph=create_graph, |
| allow_unused=True) |
| grad_inputs = tuple(torch.zeros_like(inp) if gi is None else gi |
| for gi, inp in zip(grad_inputs, inputs)) |
| for idx, grad_inp in zip(diff_argnums, grad_inputs): |
| result[idx] = grad_inp |
| return tree_unflatten(result, inputs_spec) |
| |
| |
| def diff_arg(arg, requires_grad=True): |
| def is_differentiable_arg(arg): |
| if requires_grad: |
| return arg.requires_grad |
| else: |
| return arg.is_floating_point() or arg.is_complex() |
| if is_iterable_of_tensors(arg): |
| if all([is_differentiable_arg(a) for a in arg]): |
| return True |
| if all([not is_differentiable_arg(a) for a in arg]): |
| return False |
| raise RuntimeError("NYI: The test runner can't handle this") |
| return isinstance(arg, Tensor) and is_differentiable_arg(arg) |
| |
| |
| # Given f, returns an f' such that: |
| # - f' takes only positional arguments |
| # - All arguments to f' are floating-point Tensors |
| # - All outputs of f' are floating-point Tensors |
| def normalize_op_input_output2(f, args, kwargs, output_process_fn_grad=None, requires_grad=True): |
| flat_args, args_spec = tree_flatten(args) |
| diff_argnums = tuple(i for i, arg in enumerate(flat_args) if diff_arg(arg, requires_grad=requires_grad)) |
| assert len(diff_argnums) > 0 |
| primals = tuple(flat_args[i] for i in diff_argnums) |
| |
| @functools.wraps(f) |
| def wrapped(*primals): |
| _args = list(flat_args) |
| for num, arg in zip(diff_argnums, primals): |
| _args[num] = arg |
| _args = tree_unflatten(_args, args_spec) |
| result = f(*_args, **kwargs) |
| if output_process_fn_grad is not None: |
| result = output_process_fn_grad(result) |
| if isinstance(result, tuple): |
| # TODO: Remove the following hack for namedtuples |
| result = tuple(result) |
| result = tuple(r for r in result if torch.is_floating_point(r)) |
| assert len(result) > 0 |
| return result |
| return wrapped, primals |
| |
| |
| # TODO: consolidate with normalize_op_input_output2 |
| def normalize_op_input_output3(f, args, kwargs, sample_args, output_process_fn_grad=None): |
| flat_args, args_spec = tree_flatten(args) |
| flat_sample_args, _ = tree_flatten(sample_args) |
| diff_argnums = tuple(i for i, (arg, sample) in enumerate(zip(flat_args, flat_sample_args)) |
| if diff_arg(sample, requires_grad=True)) |
| assert len(diff_argnums) > 0 |
| primals = tuple(flat_args[i] for i in diff_argnums) |
| |
| @functools.wraps(f) |
| def wrapped(*primals): |
| _args = list(flat_args) |
| for num, arg in zip(diff_argnums, primals): |
| _args[num] = arg |
| _args = tree_unflatten(_args, args_spec) |
| result = f(*_args, **kwargs) |
| if output_process_fn_grad is not None: |
| result = output_process_fn_grad(result) |
| if isinstance(result, tuple): |
| # TODO: Remove the following hack for namedtuples |
| result = tuple(result) |
| result = tuple(r for r in result if torch.is_floating_point(r)) |
| assert len(result) > 0 |
| return result |
| return wrapped, primals |
| |
| |
| def normalize_op_input_output(f, sample, requires_grad=True): |
| args = tuple([sample.input] + list(sample.args)) |
| return normalize_op_input_output2( |
| f, args, sample.kwargs, sample.output_process_fn_grad, requires_grad=requires_grad |
| ) |
| |
| |
| def ref_vjp(f, *primals): |
| result = f(*primals) |
| |
| def wrapped(cotangents): |
| return _autograd_grad(_as_tuple(result), primals, _as_tuple(cotangents)) |
| |
| return result, wrapped |
| |
| |
| def ref_jvp(f, primals, tangents): |
| with fwAD.dual_level(): |
| duals = tuple(fwAD.make_dual(p, t) for p, t in zip(primals, tangents)) |
| result_duals = f(*duals) |
| result_duals, spec = tree_flatten(result_duals) |
| primals_out, tangents_out = zip(*(fwAD.unpack_dual(d) for d in result_duals)) |
| return tree_unflatten(primals_out, spec), tree_unflatten(tangents_out, spec) |
| |
| |
| def get_sample_cotangents(f, sample): |
| fn, primals = normalize_op_input_output(f, sample) |
| output = fn(*primals) |
| if isinstance(output, tuple): |
| # TODO: Remove the following hack for torch.return_types |
| output = tuple(output) |
| return tree_map(torch.randn_like, output) |
| |
| |
| # returns a new function g(*args, *cotangents) |
| # that computes vjps and (*args, cotangents) |
| def get_vjp_fn_and_args_with_cotangents(f, sample, cotangents): |
| args = tuple([sample.input] + list(sample.args)) |
| kwargs = sample.kwargs |
| flat_args, args_spec = tree_flatten(args) |
| flat_cotangents, cotangents_spec = tree_flatten(cotangents) |
| |
| @functools.wraps(f) |
| def wrapped(*args): |
| assert len(args) == len(flat_args) + len(flat_cotangents) |
| actual_args = args[:len(flat_args)] |
| cotangents = args[len(flat_args):] |
| actual_args = tree_unflatten(actual_args, args_spec) |
| cotangents = tree_unflatten(cotangents, cotangents_spec) |
| |
| fn, primals = normalize_op_input_output3(f, actual_args, kwargs, |
| flat_args, |
| sample.output_process_fn_grad) |
| _, vjp_fn = vjp(fn, *primals) |
| return vjp_fn(cotangents) |
| |
| return wrapped, tuple(flat_args + flat_cotangents) |
| |
| |
| # Returns a new function g(*args, *cotangents) that computes vjps and |
| # sample (*args, *cotangents) |
| def get_vjpfull_variant(f, sample): |
| fn, primals = normalize_op_input_output(f, sample) |
| result = fn(*primals) |
| cotangents = _as_tuple( |
| tree_map(lambda x: torch.randn_like(x, requires_grad=True), result)) |
| num_primals = len(primals) |
| args = (*primals, *cotangents) |
| |
| @functools.wraps(f) |
| def wrapped(*args): |
| primals = args[:num_primals] |
| cotangents = args[num_primals:] |
| result, vjp_fn = vjp(fn, *primals) |
| if isinstance(result, torch.Tensor): |
| assert len(cotangents) == 1 |
| cotangents = cotangents[0] |
| return vjp_fn(cotangents) |
| |
| return wrapped, args |
| |
| |
| def get_jvp_variant(f, sample): |
| # We want this higher-order variant of jvp, so that it can |
| # be used to wrap vmap |
| fn, primals = normalize_op_input_output(f, sample, requires_grad=False) |
| tangents = _as_tuple( |
| tree_map(lambda x: torch.randn_like(x), primals)) |
| |
| @functools.wraps(f) |
| def wrapped(*args): |
| tangents = args |
| primals_out, tangents_out = jvp(fn, primals, tangents) |
| |
| if isinstance(primals_out, torch.Tensor): |
| return (primals_out, tangents_out) |
| else: |
| flat_primals_out, _ = tree_flatten(primals_out) |
| flat_tangents_out, _ = tree_flatten(tangents_out) |
| return tuple(flat_primals_out + flat_tangents_out) |
| |
| return wrapped, tangents |
| |
| |
| def get_jvp_variant_primals_tangents(f, sample): |
| # We want this higher-order variant of jvp, so that it can |
| # be used to wrap vmap |
| fn, primals = normalize_op_input_output(f, sample, requires_grad=False) |
| tangents = _as_tuple( |
| tree_map(lambda x: torch.randn_like(x), primals)) |
| |
| @functools.wraps(f) |
| def wrapped(*args): |
| primals_in = args[:len(primals)] |
| tangents_in = args[len(primals):] |
| primals_out, tangents_out = jvp(fn, primals_in, tangents_in) |
| |
| if isinstance(primals_out, torch.Tensor): |
| return (primals_out, tangents_out) |
| else: |
| flat_primals_out, _ = tree_flatten(primals_out) |
| flat_tangents_out, _ = tree_flatten(tangents_out) |
| return tuple(flat_primals_out + flat_tangents_out) |
| |
| return wrapped, primals + tangents |
| |
| |
| def is_inplace(op, variant): |
| if hasattr(variant, "__wrapped__"): |
| return variant.__wrapped__ is op.get_inplace() |
| return variant is op.get_inplace() |
| |
| |
| vjp_fail = { |
| skip('nn.functional.dropout'), # randomness testing artifact |
| skip('nn.functional.rrelu'), # randomness testing artifact |
| xfail('tensor_split'), |
| xfail('to_sparse'), |
| xfail('nn.functional.ctc_loss'), |
| } |
| |
| |
| class TestOperators(TestCase): |
| @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,)) |
| @skipOps('TestOperators', 'test_grad', vjp_fail.union({ |
| skip('nn.functional.fractional_max_pool2d'), # fails on cuda, runs okay on cpu |
| skip('nn.functional.fractional_max_pool3d'), # fails on cuda, runs okay on cpu |
| })) |
| def test_grad(self, device, dtype, op): |
| if op.name in vjp_fail: |
| self.skipTest("Skipped; Expected failures") |
| return |
| |
| if not op.supports_autograd: |
| self.skipTest("Skipped! Autograd not supported.") |
| return |
| |
| samples = op.sample_inputs(device, dtype, requires_grad=True) |
| |
| # TODO: test in-place |
| if is_inplace(op, op.get_op()): |
| self.skipTest("Skipped! NYI: inplace-testing not supported.") |
| return |
| |
| for sample in samples: |
| args = [sample.input] + list(sample.args) |
| kwargs = sample.kwargs |
| |
| diff_argnums = tuple(i for i, arg in enumerate(args) if diff_arg(arg)) |
| assert len(diff_argnums) > 0 |
| diff_args = tuple(args[i] for i in diff_argnums) |
| |
| def wrapped_fn(*args, **kwargs): |
| result = op(*args, **kwargs) |
| if sample.output_process_fn_grad is not None: |
| result = sample.output_process_fn_grad(result) |
| |
| # Reduce into single value for grad |
| if isinstance(result, torch.Tensor): |
| return result.sum() |
| result = sum([res.sum() for res in result]) |
| return result |
| |
| result = grad(wrapped_fn, diff_argnums)(*args, **kwargs) |
| expected = _autograd_grad(_as_tuple(wrapped_fn(*args, **kwargs)), diff_args) |
| |
| self.assertEqual(result, expected) |
| |
| @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,)) |
| @skipOps('TestOperators', 'test_jvp', set({ |
| skip('nn.functional.dropout'), # randomness testing artifact; not actually a problem |
| skip('nn.functional.rrelu'), # randomness testing artifact; not actually a problem |
| skip('nn.functional.fractional_max_pool2d'), # fails on cuda, runs okay on cpu |
| skip('nn.functional.fractional_max_pool3d'), # fails on cuda, runs okay on cpu |
| skip('nn.functional.max_pool1d'), # fails on cpu, runs okay on cuda |
| xfail('nn.functional.batch_norm', device_type='cuda'), |
| xfail('nn.functional.batch_norm', 'without_cudnn', device_type='cuda'), |
| skip('nn.functional.conv_transpose3d', device_type='cuda'), |
| |
| # See https://github.com/pytorch/pytorch/issues/69034 |
| # RuntimeError: expected scalar type double but found float |
| xfail('minimum'), |
| xfail('min', 'binary'), |
| xfail('maximum'), |
| xfail('max', 'binary'), |
| |
| # The following don't have a forward-mode AD formula in PyTorch core |
| # (check derivatives.yaml). |
| xfail('var_mean'), |
| xfail('std_mean'), |
| # https://gist.github.com/zou3519/f62a167fb46cda01d7f238f61dd9ccf9 |
| xfail('linalg.eigvalsh'), |
| # https://gist.github.com/zou3519/b86616d01ca375a4bd17403277f49225 |
| xfail('nn.functional.dropout', device_type='cuda'), |
| |
| # ============================================= |
| # NB: The above failures also fail using PyTorch core's |
| # forward-mode AD and vmap. |
| # The failures below are functorch-specific issues |
| # ============================================= |
| |
| # Composite ops that do bad things. Need to be fixed in PyTorch core. |
| # RuntimeError: Cannot access data pointer of Tensor that doesn't have storage |
| xfail('tensor_split'), |
| |
| # Causing a CUDA assert, needs investigation |
| skip('div', 'floor_rounding', device_type='cuda'), |
| skip('div', 'no_rounding_mode', device_type='cuda'), |
| skip('div', 'trunc_rounding', device_type='cuda'), |
| skip('true_divide', device_type='cuda'), |
| |
| # Causing an error with calling a forward mode of a forward mode |
| xfail('nn.functional.batch_norm', device_type='cpu'), |
| |
| # Some kind of issue with unsymmetric tangent type |
| # Runtime Error: The tangent part of the matrix A should also be symmetric. |
| xfail('linalg.eigh'), |
| |
| |
| })) |
| def test_jvp(self, device, dtype, op): |
| # TODO: when we change supports_autograd to supports_backward_ad, also change in this file |
| if not op.supports_forward_ad: |
| self.skipTest("Skipped! Forward AD not supported.") |
| return |
| |
| samples = op.sample_inputs(device, dtype, requires_grad=True) |
| # TODO: test in-place |
| if is_inplace(op, op.get_op()): |
| self.skipTest("Skipped! NYI: inplace-testing not supported.") |
| return |
| |
| for sample in samples: |
| fn, primals = normalize_op_input_output(op, sample, requires_grad=False) |
| tangents = tree_map(lambda x: torch.randn_like(x), primals) |
| primal_outs, tangent_outs = jvp(fn, primals, tangents) |
| expected_primal_outs, expected_tangent_outs = ref_jvp(fn, primals, tangents) |
| self.assertEqual(primal_outs, expected_primal_outs) |
| self.assertEqual(tangent_outs, expected_tangent_outs) |
| |
| @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,)) |
| @skipOps('TestOperators', 'test_vjp', vjp_fail.union({ |
| skip('nn.functional.fractional_max_pool2d'), # fails on cpu, runs okay on cuda |
| skip('nn.functional.fractional_max_pool3d'), # fails on cpu, runs okay on cuda |
| skip('nn.functional.conv_transpose3d', device_type='cuda'), # numerical precision |
| })) |
| def test_vjp(self, device, dtype, op): |
| if not op.supports_autograd: |
| self.skipTest("Skipped! Autograd not supported.") |
| return |
| |
| samples = op.sample_inputs(device, dtype, requires_grad=True) |
| |
| # TODO: test in-place |
| if is_inplace(op, op.get_op()): |
| self.skipTest("Skipped! NYI: inplace-testing not supported.") |
| return |
| |
| def _test(_op): |
| for sample in samples: |
| fn, primals = normalize_op_input_output(_op, sample) |
| result = fn(*primals) |
| cotangents = tree_map(lambda x: torch.randn_like(x), result) |
| |
| out, vjp_fn = vjp(fn, *primals) |
| self.assertEqual(out, result) |
| result_vjps = vjp_fn(cotangents) |
| |
| _, vjp_fn = ref_vjp(fn, *primals) |
| expected_vjps = vjp_fn(cotangents) |
| |
| self.assertEqual(result_vjps, expected_vjps) |
| |
| _test(op) |
| for a_op in op.aliases: |
| _test(a_op) |
| |
| @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,)) |
| @skipOps('TestOperators', 'test_vjpvjp', vjp_fail.union({ |
| skip('nn.functional.fractional_max_pool2d'), # fails on cuda, runs okay on cpu |
| xfail('nn.functional.fractional_max_pool3d'), |
| skip('nn.functional.conv_transpose3d'), # numerical precision problem |
| xfail('nn.functional.binary_cross_entropy'), # testing problem |
| })) |
| def test_vjpvjp(self, device, dtype, op): |
| if not op.supports_autograd: |
| self.skipTest("Skipped! Autograd not supported.") |
| return |
| if not op.supports_gradgrad: |
| self.skipTest("Skipped! Operation does not support gradgrad") |
| return |
| |
| samples = op.sample_inputs(device, dtype, requires_grad=True) |
| |
| # TODO: test in-place |
| if is_inplace(op, op.get_op()): |
| self.skipTest("Skipped! NYI: inplace-testing not supported.") |
| return |
| |
| for sample in samples: |
| fn, args = get_vjpfull_variant(op, sample) |
| result = fn(*args) |
| cotangents = tree_map(lambda x: torch.randn_like(x), result) |
| |
| # Compute vjp of vjp |
| _, vjp_fn = vjp(fn, *args) |
| result_vjps = vjp_fn(cotangents) |
| |
| # Compute ref_vjp of vjp. We could have done ref_vjp of ref_vjp, |
| # but since we're confident that vjp works by itself, this is |
| # an equivalent way to test that. |
| _, vjp_fn = ref_vjp(fn, *args) |
| expected_vjps = vjp_fn(cotangents) |
| |
| self.assertEqual(result_vjps, expected_vjps) |
| |
| @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,)) |
| def test_vmapvjpvjp(self, device, dtype, op): |
| self.skipTest("Skipped; these tests take too long") |
| op_skip = set({ |
| }) |
| op_skip = op_skip.union(vjp_fail) |
| if op.name in op_skip: |
| self.skipTest("Skipped; Expected failures") |
| return |
| |
| if not op.supports_autograd: |
| self.skipTest("Skipped! Autograd not supported.") |
| return |
| if not op.supports_gradgrad: |
| self.skipTest("Skipped! Operation does not support gradgrad") |
| return |
| |
| samples = op.sample_inputs(device, dtype, requires_grad=True) |
| |
| # TODO: test in-place |
| if is_inplace(op, op.get_op()): |
| self.skipTest("Skipped! NYI: inplace-testing not supported.") |
| return |
| |
| for sample in samples: |
| fn, args = get_vjpfull_variant(op, sample) |
| result = fn(*args) |
| cotangents = tree_map(lambda x: torch.randn_like(x), result) |
| cotangents, _ = tree_flatten(cotangents) |
| num_args = len(args) |
| |
| args_and_cotangents = tuple(args) + tuple(cotangents) |
| |
| def vjp_of_vjp(*args_and_cotangents): |
| args = args_and_cotangents[:num_args] |
| cotangents = args_and_cotangents[num_args:] |
| result, vjp_fn = vjp(fn, *args) |
| result_vjps = vjp_fn(cotangents) |
| result, _ = tree_flatten(result) |
| result_vjps, _ = tree_flatten(result_vjps) |
| return (*result, *result_vjps) |
| |
| generator = get_fallback_and_vmap_exhaustive(vjp_of_vjp, args_and_cotangents, {}, opinfo=op) |
| for loop_out, batched_out in generator: |
| self.assertEqual(loop_out, batched_out, atol=1e-4, rtol=1e-4) |
| vmapvjp_fail = vjp_fail.union({ |
| # The following are not bugs and are expected behavior |
| xfail('fill_'), # Not possible, wontfix |
| xfail('masked_select'), # Not possible due to dynamic shapes |
| |
| # All of the following are bugs and need to be fixed |
| xfail('eig'), |
| xfail('view_as_complex'), |
| xfail('fft.ihfft'), |
| xfail('fft.ihfft'), |
| xfail('fft.rfft'), |
| xfail('fft.rfft'), |
| xfail('fft.rfftn'), |
| xfail('linalg.det', ''), |
| xfail('linalg.cholesky'), |
| xfail('linalg.eig'), # Uses aten::allclose |
| xfail('linalg.eigh'), |
| xfail('linalg.householder_product'), |
| xfail('linalg.inv'), |
| xfail('linalg.matrix_norm'), |
| xfail('linalg.matrix_power'), |
| xfail('linalg.norm'), |
| xfail('linalg.slogdet'), |
| # really annoying thing where it passes correctness check but not has_batch_rule |
| skip('linalg.svdvals'), |
| xfail('logdet'), |
| xfail('lu_unpack'), |
| xfail('masked_scatter'), |
| xfail('matrix_exp'), |
| xfail('nanquantile'), |
| xfail('norm', 'nuc'), |
| xfail('prod'), |
| xfail('put'), |
| xfail('quantile'), |
| xfail('symeig'), |
| xfail('take'), |
| xfail('linalg.tensorinv'), |
| xfail('block_diag'), |
| xfail('nn.functional.dropout'), |
| xfail('fft.ihfft2'), |
| xfail('fft.ihfftn'), |
| xfail('double', 'channels_last'), |
| xfail('nn.functional.gaussian_nll_loss'), |
| xfail('fft.rfft2'), |
| skip('qr'), # Nondetermistic |
| xfail('_masked.prod'), # calls aten::item |
| xfail('stft'), |
| xfail('nn.functional.glu'), |
| xfail('nn.functional.fractional_max_pool3d'), |
| xfail('as_strided'), |
| xfail('nn.functional.fractional_max_pool2d'), |
| xfail('__getitem__', ''), |
| xfail('index_put', ''), |
| xfail('lu_solve'), |
| xfail('index_copy'), |
| }) |
| |
| @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,)) |
| @skipOps('TestOperators', 'test_vmapvjp', vmapvjp_fail) |
| def test_vmapvjp(self, device, dtype, op): |
| if not op.supports_autograd: |
| self.skipTest("Skipped! Autograd not supported.") |
| return |
| |
| samples = op.sample_inputs(device, dtype, requires_grad=True) |
| |
| # TODO: test in-place |
| if is_inplace(op, op.get_op()): |
| self.skipTest("Skipped! NYI: inplace-testing not supported.") |
| return |
| |
| for sample in samples: |
| cotangents = get_sample_cotangents(op, sample) |
| fn, args = get_vjp_fn_and_args_with_cotangents(op, sample, cotangents) |
| for loop_out, batched_out in get_fallback_and_vmap_exhaustive(fn, args, {}, opinfo=op): |
| self.assertEqual(loop_out, batched_out, atol=1e-4, rtol=1e-4) |
| |
| # There are several variations we care about |
| # 1) primal batched (TODO) |
| # 2) tangent batched (batched grads) <-- |
| # 3) both batched (TODO) |
| # The below tests (2) only. |
| @ops(functorch_lagging_op_db, allowed_dtypes=(torch.float,)) |
| @skipOps('TestOperators', 'test_vmapjvp', { |
| skip('nn.functional.dropout'), # randomness |
| skip('nn.functional.rrelu'), # randomness |
| skip('nn.functional.fractional_max_pool2d'), # randomness |
| skip('nn.functional.fractional_max_pool3d'), # randomness |
| skip('nn.functional.max_pool1d'), # fails on cpu, runs on cuda |
| |
| # TODO: fails in core due to in-place batched nto non-batched |
| # but fails here for a different reason |
| xfail('linalg.householder_product'), |
| |
| # Try to in-place batched tensor into non-batched tensor |
| xfail('matrix_exp'), |
| xfail('fill_'), |
| xfail('block_diag'), # TODO: We expect this to fail in core, but it doesn't |
| |
| # https://gist.github.com/zou3519/c42d032c0111c6b65235583d391bf7a3 |
| xfail('nn.functional.linear'), |
| |
| # These are issues that should be fixed in core. See repro in core: |
| # https://github.com/pytorch/functorch/pull/232#discussion_r751405155 |
| # RuntimeError: expected scalar type double but found float |
| xfail('minimum'), |
| xfail('min', 'binary'), |
| xfail('maximum'), |
| xfail('max', 'binary'), |
| |
| # Apprently these support forward AD, but we get "Trying to use forward AD..." |
| # These are cases where OpInfo has supports_forward_ad=True, but disables |
| # the test |
| xfail('var_mean'), |
| xfail('std_mean'), |
| xfail('linalg.eigvalsh'), |
| |
| # functorch doesn't support channels_last |
| # PyTorch core's vmap doesn't have a batching rule for `double`, if it |
| # did it would also not support channels last, so I'm including this |
| # xfail "above the line". |
| xfail('double', 'channels_last'), |
| |
| # RuntimeError: expand: the number of sizes provided (1) must be greater or |
| # equal to the number of dimensions in the tensor (2) |
| xfail('nanquantile'), |
| xfail('quantile'), |
| |
| # RuntimeError: vmap: inplace arithmetic(self, *extra_args) |
| xfail('nn.functional.gelu'), |
| |
| # Not implemented |
| xfail('scatter'), |
| |
| # ============================================= |
| # NB: The above failures also fail in PyTorch core. |
| # The failures below only fail in functorch |
| # ============================================= |
| |
| # Composite ops that do bad things. Need to be fixed in PyTorch core. |
| # RuntimeError: Cannot access data pointer of Tensor that doesn't have storage |
| xfail('tensor_split'), |
| |
| # Causing a CUDA assert, needs investigation |
| skip('div', 'floor_rounding', device_type='cuda'), |
| skip('div', 'no_rounding_mode', device_type='cuda'), |
| skip('div', 'trunc_rounding', device_type='cuda'), |
| skip('true_divide', device_type='cuda'), |
| |
| # Causing multiple forward mode AD issues, needs investigation |
| xfail('nn.functional.batch_norm'), |
| xfail('nn.functional.batch_norm', 'without_cudnn', device_type='cuda'), |
| |
| # Some kind of issue with unsymmetric tangent type |
| # Runtime Error: The tangent part of the matrix A should also be symmetric. |
| xfail('linalg.eigh'), |
| }) |
| def test_vmapjvp(self, device, dtype, op): |
| if is_inplace(op, op.get_op()): |
| # TODO: test in-place |
| self.skipTest("Skipped! NYI: inplace-testing not supported.") |
| return |
| |
| samples = op.sample_inputs(device, dtype, requires_grad=False) |
| |
| if not op.supports_forward_ad: |
| self.skipTest("Skipped! Forward AD not supported.") |
| return |
| |
| for sample in samples: |
| arg_values = [sample.input] + list(sample.args) |
| kwarg_values = sample.kwargs |
| args = tuple([*arg_values, *kwarg_values]) |
| fn, args = get_jvp_variant(op, sample) |
| for loop_out, batched_out in get_fallback_and_vmap_exhaustive(fn, args, {}, opinfo=op, bdims=(0,)): |
| self.assertEqual(loop_out, batched_out, atol=1e-4, rtol=1e-4) |
| |
| @ops(functorch_lagging_op_db, allowed_dtypes=(torch.float,)) |
| @skipOps('TestOperators', 'test_vmapjvpall', { |
| skip('nn.functional.dropout'), # randomness |
| skip('nn.functional.rrelu'), # randomness |
| xfail('nn.functional.fractional_max_pool2d'), # Cannot access data pointer of Tensor that doesn't have storage |
| xfail('nn.functional.fractional_max_pool3d'), # Cannot access data pointer of Tensor that doesn't have storage |
| skip('nn.functional.max_pool1d'), # fails on cpu, runs on cuda |
| xfail('_masked.mean', device_type='cuda'), |
| xfail('_masked.prod', device_type='cuda'), |
| xfail('nn.functional.batch_norm', device_type='cuda'), |
| xfail('nn.functional.batch_norm', 'without_cudnn', device_type='cuda'), |
| xfail('nn.functional.hinge_embedding_loss', device_type='cuda'), |
| |
| # Causing a CUDA assert, needs investigation |
| skip('div', 'floor_rounding', device_type='cuda'), |
| skip('div', 'no_rounding_mode', device_type='cuda'), |
| skip('div', 'trunc_rounding', device_type='cuda'), |
| skip('true_divide', device_type='cuda'), |
| |
| # Causing issues with multiple cpu levels of forward mode AD |
| xfail('_masked.mean', device_type='cpu'), |
| xfail('_masked.prod', device_type='cpu'), |
| xfail('nn.functional.batch_norm', device_type='cpu'), |
| xfail('nn.functional.hinge_embedding_loss', device_type='cpu'), |
| |
| # xfail list |
| xfail('norm', 'nuc'), |
| xfail('linalg.inv'), |
| xfail('linalg.tensorinv'), |
| xfail('linalg.matrix_power'), |
| xfail('maximum'), |
| xfail('linalg.householder_product'), |
| xfail('tensor_split'), |
| xfail('nn.functional.gelu'), |
| xfail('quantile'), |
| xfail('var_mean'), |
| xfail('as_strided'), |
| xfail('linalg.eigvalsh'), |
| xfail('fill_'), |
| xfail('linalg.cholesky'), |
| xfail('max', 'binary'), |
| xfail('nn.functional.gaussian_nll_loss'), |
| xfail('min', 'binary'), |
| xfail('std_mean'), |
| xfail('double', 'channels_last'), |
| xfail('block_diag'), |
| xfail('minimum'), |
| xfail('scatter'), |
| xfail('matrix_exp'), |
| xfail('nanquantile'), |
| xfail('nn.functional.linear'), |
| xfail('view_as_complex'), |
| xfail('prod'), |
| |
| # Some kind of issue with unsymmetric tangent type |
| # Runtime Error: The tangent part of the matrix A should also be symmetric. |
| xfail('linalg.eigh'), |
| }) |
| # This is technically a superset of test_vmapjvp. We should either delete test_vmapjvp |
| # or figure out if we can split vmapjvpall. It's useful to keep test_vmapjvp intact |
| # because that coresponds to "batched forward-mode AD" testing in PyTorch core |
| def test_vmapjvpall(self, device, dtype, op): |
| if is_inplace(op, op.get_op()): |
| # TODO: test in-place |
| self.skipTest("Skipped! NYI: inplace-testing not supported.") |
| return |
| |
| samples = op.sample_inputs(device, dtype, requires_grad=False) |
| |
| if not op.supports_forward_ad: |
| self.skipTest("Skipped! Forward AD not supported.") |
| return |
| |
| for sample in samples: |
| arg_values = [sample.input] + list(sample.args) |
| kwarg_values = sample.kwargs |
| args = tuple([*arg_values, *kwarg_values]) |
| fn, args = get_jvp_variant_primals_tangents(op, sample) |
| for loop_out, batched_out in get_fallback_and_vmap_exhaustive(fn, args, {}, opinfo=op): |
| self.assertEqual(loop_out, batched_out, atol=1e-4, rtol=1e-4) |
| |
| @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,)) |
| @skipOps('TestOperators', 'test_vmapvjp_has_batch_rule', vmapvjp_fail.union({ |
| xfail('view_as_complex'), |
| xfail('__getitem__', ''), |
| xfail('cholesky'), |
| xfail('complex'), |
| xfail('copysign'), |
| xfail('cummax'), |
| xfail('cummin'), |
| xfail('cumprod'), |
| xfail('eig'), |
| xfail('fmin'), |
| xfail('fmax'), |
| xfail('fft.ihfft'), |
| xfail('fft.rfft'), |
| xfail('fft.rfftn'), |
| xfail('fill_'), |
| xfail('index_copy'), |
| xfail('index_fill'), |
| xfail('linalg.cholesky'), |
| xfail('linalg.cholesky_ex'), |
| xfail('linalg.det'), |
| xfail('linalg.eig'), |
| xfail('linalg.eigh'), |
| xfail('linalg.eigvals'), |
| xfail('linalg.householder_product'), |
| xfail('linalg.lstsq'), |
| xfail('linalg.inv'), |
| xfail('linalg.matrix_norm'), |
| xfail('linalg.matrix_power'), |
| xfail('linalg.norm'), |
| xfail('linalg.pinv'), |
| xfail('linalg.qr'), |
| xfail('linalg.pinv', 'hermitian'), |
| xfail('linalg.slogdet'), |
| xfail('linalg.solve'), |
| xfail('linalg.tensorinv'), |
| xfail('logdet'), |
| xfail('lu'), |
| xfail('lu_solve'), |
| xfail('lu_unpack'), |
| xfail('masked_fill'), |
| xfail('masked_scatter'), |
| xfail('masked_select'), |
| xfail('matrix_exp'), |
| xfail('nanquantile'), |
| xfail('nn.functional.gelu'), |
| xfail('norm', 'nuc'), |
| xfail('pinverse'), |
| xfail('prod'), |
| xfail('put'), |
| xfail('quantile'), |
| xfail('renorm'), |
| xfail('solve'), |
| xfail('symeig'), |
| xfail('take'), |
| xfail('tensor_split'), |
| xfail('to_sparse'), |
| xfail('trace'), |
| xfail('unfold'), |
| xfail('vdot'), |
| xfail('block_diag'), |
| xfail('nn.functional.dropout'), |
| xfail('_masked.prod'), |
| xfail('fft.ihfft2'), |
| xfail('fft.ihfftn'), |
| xfail('fft.rfft2'), |
| xfail('cross'), |
| xfail('double', 'channels_last'), |
| xfail('linalg.cross'), |
| xfail('nn.functional.gaussian_nll_loss'), |
| xfail('nn.functional.huber_loss'), |
| xfail('nn.functional.poisson_nll_loss'), |
| xfail('nn.functional.bilinear'), |
| xfail('nn.functional.prelu'), |
| xfail('nn.functional.glu'), |
| xfail('nn.functional.fractional_max_pool3d'), |
| xfail('as_strided'), |
| xfail('linalg.solve_triangular'), |
| xfail('stft'), |
| xfail('nn.functional.rrelu'), |
| xfail('nn.functional.embedding_bag'), |
| xfail('nn.functional.max_pool3d'), |
| xfail('istft'), |
| xfail('nn.functional.fractional_max_pool2d'), |
| xfail('linalg.tensorsolve'), |
| })) |
| def test_vmapvjp_has_batch_rule(self, device, dtype, op): |
| if not op.supports_autograd: |
| self.skipTest("Skipped! Autograd not supported.") |
| return |
| |
| samples = op.sample_inputs(device, dtype, requires_grad=True) |
| |
| # TODO: test in-place |
| if is_inplace(op, op.get_op()): |
| self.skipTest("Skipped! NYI: inplace-testing not supported.") |
| return |
| |
| def test(): |
| for sample in samples: |
| cotangents = get_sample_cotangents(op, sample) |
| fn, args = get_vjp_fn_and_args_with_cotangents(op, sample, cotangents) |
| for loop_out, batched_out in get_fallback_and_vmap_exhaustive( |
| fn, args, {}, opinfo=op, compute_loop_out=False): |
| pass |
| for a_op in op.aliases: |
| fn, args = get_vjp_fn_and_args_with_cotangents(a_op, sample, cotangents) |
| for loop_out, batched_out in get_fallback_and_vmap_exhaustive( |
| fn, args, {}, opinfo=op, compute_loop_out=False): |
| pass |
| |
| check_vmap_fallback(self, test, op, dry_run=False) |
| |
| @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,)) |
| @skipOps('TestOperators', 'test_vjpvmap', vjp_fail.union({ |
| # fallback path doesn't work |
| # All of the following are bugs and need to be fixed |
| xfail('__getitem__', ''), |
| xfail('clamp', ''), |
| xfail('fill_'), |
| xfail('index_put', ''), |
| xfail('lu_solve'), |
| xfail('lu_unpack'), |
| xfail('matrix_exp'), |
| xfail('view_as_complex'), |
| xfail('nn.functional.gaussian_nll_loss'), |
| xfail('double', 'channels_last'), |
| xfail('masked_select'), |
| skip('nn.functional.fractional_max_pool3d'), # generator works on cpu, fails on cuda |
| xfail('nn.functional.glu'), |
| xfail('as_strided'), |
| skip('nn.functional.fractional_max_pool2d'), # generator works on cpu, fails on cuda |
| skip('solve'), |
| xfail('linalg.cond'), |
| xfail('linalg.svdvals'), |
| })) |
| def test_vjpvmap(self, device, dtype, op): |
| # NB: there is no vjpvmap_has_batch_rule test because that is almost |
| # certainly redundant with the vmap_has_batch_rule test in test_vmap.py |
| |
| # one-off skip |
| if op.name == 'nn.functional.dropout': |
| self.skipTest("Skipped!") |
| |
| if not op.supports_autograd: |
| # If the op doesn't support autograd, vmap(op) won't either |
| self.skipTest("Skipped! Autograd not supported.") |
| return |
| |
| # TODO: test in-place |
| if is_inplace(op, op.get_op()): |
| self.skipTest("Skipped! NYI: inplace-testing not supported.") |
| return |
| |
| samples = op.sample_inputs(device, dtype, requires_grad=True) |
| batch_norm_fns = ("nn.functional.batch_norm", "nn.functional.instance_norm") # instance norm calls batch norm |
| is_batch_norm = op.name in batch_norm_fns |
| |
| for sample in samples: |
| args = [sample.input] + list(sample.args) |
| kwargs = sample.kwargs |
| generator = get_exhaustive_batched_inputs(args, kwargs, for_batch_norm=is_batch_norm) |
| |
| for batched_args, in_dims, kwargs in generator: |
| vmapped_op = vmap(op, in_dims) |
| fn, primals = normalize_op_input_output2(vmapped_op, batched_args, kwargs, |
| sample.output_process_fn_grad) |
| result = fn(*primals) |
| cotangents = tree_map(lambda x: torch.randn_like(x), result) |
| |
| _, vjp_fn = vjp(fn, *primals) |
| result_vjps = vjp_fn(cotangents) |
| |
| _, vjp_fn = ref_vjp(fn, *primals) |
| expected_vjps = vjp_fn(cotangents) |
| |
| self.assertEqual(result_vjps, expected_vjps) |
| |
| |
| class InplaceError(Exception): |
| def __repr__(self): |
| return "Decomposition Tensor with no elem was created (probably due to an in-place op)" |
| |
| |
| def ref_vjp_no_create(f, *primals): |
| result = f(*primals) |
| |
| def wrapped(cotangents): |
| return _autograd_grad(_as_tuple(result), primals, _as_tuple(cotangents), create_graph=False) |
| |
| return result, wrapped |
| |
| |
| run_decompositions = set() |
| run_ops = set() |
| |
| |
| class TestDecompositionOpInfo(TestCase): |
| |
| @unittest.skipIf(IS_FBCODE, "__torch_dispatch__ is buggy") |
| @ops( |
| functorch_lagging_op_db + additional_op_db, |
| allowed_dtypes=[torch.float32, torch.float64, torch.float16, torch.bfloat16] + [*integral_types()] |
| ) |
| # entries in here need don't work and need to be fixed. |
| # Each one of these is a bug (or needs to be investigated) |
| @skipOps('TestDecompositionOpInfo', 'test_decomposition', { |
| skip('view_as_complex'), |
| # skip('log_softmax', device_type='cuda'), |
| # skip('nn.functional.softmin', device_type='cuda'), |
| xfail('linalg.cholesky'), |
| xfail('linalg.inv'), |
| skip('linalg.det', 'singular', device_type='cuda'), # this is nasty and seems to stop the test suite |
| xfail('linalg.matrix_power'), |
| xfail('linalg.tensorinv'), |
| xfail('to_sparse'), |
| skip('tensor_split'), |
| skip('mvlgamma'), |
| skip('eig'), |
| skip('nn.functional.dropout'), |
| skip('_masked.softmin'), |
| skip('_masked.log_softmax'), |
| skip('stft'), |
| skip('_masked.softmax'), |
| skip('_masked.normalize'), |
| # Some weird matmul stuff with int64 matmuls |
| # inplace op |
| skip('resize_'), |
| }) |
| def test_decomposition(self, device, dtype, op): |
| # dtype is too confusing of a name for how we're using it |
| TEST_DTYPE = dtype |
| # copied from common_utils.py |
| dtype_precisions = { |
| torch.float16: (0.001, 1e-5), |
| torch.bfloat16: (0.016, 1e-4), |
| torch.float32: (1.3e-6, 1e-5), |
| torch.float64: (1e-7, 1e-7), |
| torch.complex32: (0.001, 1e-5), |
| torch.complex64: (1.3e-6, 1e-5), |
| torch.complex128: (1e-7, 1e-7), |
| } |
| # Returns the "default" rtol and atol for comparing scalars or |
| # tensors of the given dtypes. |
| |
| def _getDefaultRtolAndAtol(dtype0, dtype1): |
| rtol = max(dtype_precisions.get(dtype0, (0, 0))[0], |
| dtype_precisions.get(dtype1, (0, 0))[0]) |
| atol = max(dtype_precisions.get(dtype0, (0, 0))[1], |
| dtype_precisions.get(dtype1, (0, 0))[1]) |
| return rtol, atol |
| |
| def op_assert_ref(op, orig, decomp, ref): |
| if orig.numel() == 0: |
| return |
| orig_diff = (orig - ref).abs().max() |
| decomp_diff = (decomp - ref).abs().max() |
| atol = 1e-10 |
| if decomp_diff > orig_diff + atol: |
| msg = (f"Difference from float64 is larger with decomposition {op.__name__}" + |
| f" than original. Original max diff: {orig_diff}, Decomp max diff: {decomp_diff}") |
| raise RuntimeError(msg) |
| |
| def op_assert_equal(op, a, b): |
| assert a.dtype == b.dtype |
| rtol, atol = _getDefaultRtolAndAtol(a.dtype, b.dtype) |
| if not torch.allclose(a, b, rtol=rtol, atol=atol): |
| atol_diff = (a - b).abs().max() |
| rtol_diff = ((a - b).abs()/b.abs()).nan_to_num(0).max() |
| msg = f"{op.__name__} decomposition failed, max rel: {rtol_diff}, max abs: {atol_diff}" |
| raise RuntimeError(msg) |
| |
| # We check the correctness of each decomposition right after running it. |
| # So, when we encounter a decomposition, we run the function normally, and |
| # then run the decomposition, and ensure they're identical. |
| # The way this is implemented, there could .... technically be an exponential blow up, |
| # but it's probably fine for now. |
| class DecompositionTensor(torch.Tensor): |
| elem: torch.Tensor |
| |
| __slots__ = ['elem'] |
| |
| @staticmethod |
| def __new__(cls, elem): |
| r = torch.Tensor._make_wrapper_subclass( |
| cls, elem.size(), |
| strides=elem.stride(), storage_offset=elem.storage_offset(), |
| dtype=elem.dtype, layout=elem.layout, |
| device=elem.device, requires_grad=elem.requires_grad |
| ) |
| |
| r.elem = elem |
| return r |
| |
| def __repr__(self): |
| return f"DecompositionTensor(elem={self.elem})" |
| |
| @classmethod |
| def __torch_dispatch__(cls, func, types, args=(), kwargs=None): |
| global run_ops |
| run_ops.add(func) |
| |
| def unwrap_tensor(e): |
| if isinstance(e, DecompositionTensor): |
| if not hasattr(e, 'elem'): |
| raise InplaceError() |
| return e.elem |
| return e |
| |
| # We take 2 main strategies for verifying correctness/numerical stability of decompositions |
| # The first one is simply tolerance checking between decomp_out and pytorch_out |
| # However, for fp16/bf16 and reductions, this becomes very |
| # finicky, as there are not many guarantees we can make. |
| # So, for fp16/bf16, we instead compare the difference of |
| # {decomp_out, pytorch_out_64} and {pytorch_out, |
| # pytorch_out_64}. In other words, we compare how far the |
| # decomposition and pytorch are from the "ground truth" (i.e. |
| # fp64). If the decomposition results in more error, we error |
| if func in decomposition_table and func != torch.ops.aten.detach: |
| # Some functions take a dtype as argument, so we need to |
| # manually change that dtype in order to run it with a |
| # higher precision |
| dtype_arg_table = set([ |
| aten._softmax_backward_data, |
| aten._log_softmax_backward_data, |
| ]) |
| decomposition = decomposition_table[func] |
| global run_decompositions |
| run_decompositions.add(func) |
| |
| def upcast_tensor(x, dtype=torch.float32): |
| if isinstance(x, Tensor) and (x.dtype == torch.bfloat16 or x.dtype == torch.float16): |
| x = x.to(dtype=dtype) |
| FLOAT16_DTYPE = 5 |
| BFLOAT16_DTYPE = 15 |
| FLOAT64_DTYPE = 7 |
| if isinstance(x, int) and func in dtype_arg_table and x in [FLOAT16_DTYPE, BFLOAT16_DTYPE]: |
| x = FLOAT64_DTYPE |
| return x |
| |
| def call_op(func, map_fn, *args, **kwargs): |
| return tree_flatten(func(*tree_map(map_fn, args), **tree_map(map_fn, kwargs)))[0] |
| |
| # Theoretically, most PyTorch ops compute intermediates as fp32. But this breaks some ops... |
| if TEST_DTYPE in [torch.float16, torch.bfloat16]: |
| decomp_out = call_op(decomposition, upcast_tensor, *args, **kwargs) |
| else: |
| decomp_out = call_op(decomposition, lambda x: x, *args, **kwargs) |
| |
| real_out_double = call_op(func, lambda x: upcast_tensor(unwrap_tensor(x), dtype=torch.float64), |
| *args, **kwargs) |
| |
| real_out = call_op(func, unwrap_tensor, *args, **kwargs) |
| assert(len(real_out) == len(decomp_out)) |
| for orig, decomp, ref in zip(real_out, decomp_out, real_out_double): |
| orig = orig.to(dtype=TEST_DTYPE) |
| decomp = decomp.to(dtype=TEST_DTYPE) |
| if TEST_DTYPE in [torch.float16, torch.bfloat16]: |
| op_assert_ref(func, orig, decomp, ref) |
| else: |
| op_assert_equal(func, orig, decomp) |
| |
| real_out = func(*tree_map(unwrap_tensor, args), **tree_map(unwrap_tensor, kwargs)) |
| |
| def wrap_tensor(e): |
| if e is None: |
| return DecompositionTensor(torch.empty(())) |
| return DecompositionTensor(e) if type(e) == torch.Tensor else e |
| wrapped_out = tree_map(wrap_tensor, real_out) |
| return wrapped_out |
| |
| if TEST_DTYPE not in op.supported_dtypes(self.device_type): |
| self.skipTest("Dtype not in op's supported dtypes") |
| return |
| if is_inplace(op, op.get_op()): |
| self.skipTest("op is inplace") |
| return |
| _requires_grad = op.supports_autograd and TEST_DTYPE.is_floating_point |
| |
| samples = op.sample_inputs(device, TEST_DTYPE, requires_grad=_requires_grad) |
| |
| # Acquires variants to test |
| def wrap_tensor(x): |
| if type(x) == torch.Tensor: |
| return DecompositionTensor(x) |
| return x |
| |
| try: |
| func = op.get_op() |
| for sample_input in samples: |
| if _requires_grad: |
| fn, primals = normalize_op_input_output(func, sample_input) |
| primals = tree_map(lambda x: x.abs() if isinstance(x, torch.Tensor) else x, primals) |
| |
| decomp_out, decomp_vjp_fn = ref_vjp_no_create(fn, *tree_map(wrap_tensor, primals)) |
| cotangents = tree_map(lambda x: torch.randn_like(x), decomp_out) |
| |
| _ = decomp_vjp_fn(cotangents) |
| |
| else: |
| args = [sample_input.input] + list(sample_input.args) |
| kwargs = sample_input.kwargs |
| _ = func(*args, **kwargs) |
| |
| args = tree_map(wrap_tensor, args) |
| kwargs = tree_map(wrap_tensor, kwargs) |
| decomp_out = func(*args, **kwargs) |
| |
| except InplaceError: |
| self.skipTest("op is inplace") |
| return |
| except RuntimeError as e: |
| if "not implemented for" in str(e): |
| self.skipTest(str(e)) |
| return |
| if "Mismatch in shape: grad_output" in str(e): |
| self.skipTest("Some weird issue with autograd engine and tensor subclasses") |
| return |
| raise e |
| |
| @unittest.skipIf(IS_FBCODE, "__torch_dispatch__ is buggy") |
| def test_placeholder(self): |
| global run_ops, run_decompositions |
| with open('op_analysis/run_ops.txt', 'w') as f: |
| def get_names(inpt): |
| return sorted([x.__name__ for x in inpt]) |
| for op in get_names(run_ops): |
| f.write(f'{op}\n') |
| with open('op_analysis/run_decompositions.txt', 'w') as f: |
| for op in get_names(run_decompositions): |
| f.write(f'{op}\n') |
| |
| def test_group_norm_backward(self, device): |
| # group norm will hit the decomposable ``infinitely_differentiable_group_norm_backward`` when |
| # GradMode is on, which happens by default in the grad transform. This avoids that |
| def f(x, weight, bias, grad_out): |
| output = F.group_norm(x, 6, weight, bias) |
| inputs = [] |
| for input in (x, weight, bias): |
| if input.requires_grad: |
| inputs.append(input) |
| return torch.autograd.grad(outputs=output, inputs=inputs, grad_outputs=grad_out) |
| |
| B, N, C, H, W = 2, 3, 24, 5, 7 |
| for (input_grad, weight_grad, bias_grad) in itertools.product((True, False), (True, False), (True, False)): |
| if not input_grad and not weight_grad and not bias_grad: |
| continue |
| x = torch.randn(N, C, H, W, device=device, requires_grad=input_grad) |
| weight = torch.randn(C, device=device, requires_grad=weight_grad) |
| bias = torch.randn(C, device=device, requires_grad=bias_grad) |
| grad_out = torch.randn(B, N, C, H, W, device=device) |
| loop_out = loop(f, (None, None, None, 0), 0, 2, x, weight, bias, grad_out) |
| batched_out = vmap(f, (None, None, None, 0), 0)(x, weight, bias, grad_out) |
| self.assertEqual(loop_out, batched_out) |
| |
| |
| only_for = ("cpu", "cuda") |
| instantiate_device_type_tests(TestOperators, globals(), only_for=only_for) |
| instantiate_device_type_tests(TestDecompositionOpInfo, globals(), only_for=only_for) |
| |
| if __name__ == '__main__': |
| run_tests() |
