| # -*- coding: utf-8 -*- |
| # Owner(s): ["oncall: jit"] |
| |
| import unittest |
| import os |
| import sys |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from torch.testing import FileCheck |
| from unittest import skipIf |
| |
| from torch.testing._internal.common_utils import run_tests, IS_SANDCASTLE, ProfilingMode, GRAPH_EXECUTOR, \ |
| enable_profiling_mode_for_profiling_tests, IS_WINDOWS, TemporaryDirectoryName, shell |
| from torch.testing._internal.jit_utils import JitTestCase, enable_cpu_fuser, _inline_everything, \ |
| RUN_CUDA, RUN_CUDA_HALF, RUN_CUDA_MULTI_GPU, warmup_backward |
| from textwrap import dedent |
| from itertools import product, permutations |
| from torch.testing._internal.common_cuda import with_tf32_off |
| |
| from test_jit import backward_graph, all_backward_graphs, get_lstm_inputs, get_milstm_inputs, \ |
| LSTMCellC, LSTMCellF, LSTMCellS, MiLSTMCell |
| |
| if GRAPH_EXECUTOR == ProfilingMode.PROFILING: |
| torch._C._jit_set_profiling_executor(True) |
| torch._C._jit_set_profiling_mode(True) |
| |
| |
| def strip_profiling_nodes(nodes): |
| profiling_opcodes = set(['prim::BailoutTemplate', 'prim::BailOut']) |
| return [n for n in nodes if n.kind() not in profiling_opcodes] |
| |
| |
| def warmup_forward(f, *args): |
| profiling_count = 2 |
| for i in range(profiling_count): |
| results = f(*args) |
| |
| return results |
| |
| |
| @skipIf(GRAPH_EXECUTOR == ProfilingMode.LEGACY, "skip due to SIGIOT failures, #67646") |
| class TestFuser(JitTestCase): |
| def assertAllFused(self, graph, except_for=()): |
| |
| diff_graphs = [n for n in graph.nodes() if n.kind() == 'prim::DifferentiableGraph'] |
| if len(diff_graphs) > 0: |
| self.assertEqual(len(diff_graphs), 1) |
| graph = diff_graphs[0].g('Subgraph') |
| |
| allowed_nodes = {'prim::Constant', 'prim::FusionGroup', 'prim::BailoutTemplate', |
| 'prim::BailOut', 'prim::TupleConstruct'} | set(except_for) |
| self.assertTrue(all(node.kind() in allowed_nodes for node in graph.nodes()), |
| 'got {}'.format(graph)) |
| self.assertTrue([node.kind() for node in graph.nodes()].count('prim::FusionGroup') == 1) |
| |
| def _test_fused_abs(self, device='cpu'): |
| def func(x): |
| return x.abs() * 2 |
| |
| a = torch.randn(5, device=device) |
| scripted = self.checkScript(func, (a,)) |
| self.assertAllFused(scripted.graph_for(a)) |
| |
| @unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle") |
| @enable_cpu_fuser |
| def test_abs_cpu(self): |
| self._test_fused_abs() |
| |
| @unittest.skipIf(not IS_WINDOWS, "This is meant to be Windows-specific") |
| @unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle") |
| @enable_cpu_fuser |
| def test_abs_cpu_unicode_temp_dir(self): |
| with TemporaryDirectoryName(suffix='中文') as dname: |
| shell_env = os.environ.copy() |
| shell_env['TMP'] = dname |
| cmd = [sys.executable, os.path.basename(__file__), type(self).__name__ + '.test_abs_cpu'] |
| legacy_jit_flag = '--jit_executor=legacy' |
| for v in sys.argv: |
| if v == legacy_jit_flag: |
| cmd.append(legacy_jit_flag) |
| return_code = shell(cmd, cwd=os.path.dirname(__file__), env=shell_env) |
| self.assertEqual(return_code, 0) |
| |
| @unittest.skipIf(not RUN_CUDA, "requires CUDA") |
| def test_abs_cuda(self): |
| self._test_fused_abs(device="cuda") |
| |
| @unittest.skipIf(not RUN_CUDA, "requires CUDA") |
| def test_zero_element_tensors(self): |
| def decode(sin_t, cos_t): |
| theta = torch.atan2(sin_t.float(), cos_t.float()) |
| return theta |
| |
| sin = torch.zeros(0, device="cuda") |
| cos = torch.zeros(0, device="cuda") |
| inputs = [sin, cos] |
| ge = self.checkScript(decode, inputs) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_arg_configurations_smoke_cuda(self): |
| # A smoke test to make sure we won't use the same kernel for contiguous |
| # and non-contiguous arguments. |
| # TODO: add optionally enabled debug counters to the fuser to verify |
| # that we really can tell the difference between configurations |
| def f(x, y): |
| z1, z2 = (x + y).chunk(2, dim=1) |
| return z1 * z2 |
| |
| x = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| y = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| traced_f = torch.jit.trace(f, (x, y,)) |
| self.assertEqual(traced_f(x.t().contiguous(), y), traced_f(x.t(), y)) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_broadcast_cuda(self): |
| def scaleshift(x, scale, shift): |
| return x * scale + shift |
| |
| inputs = [ |
| torch.randn(4, 4, dtype=torch.float, device='cuda'), |
| torch.randn(4, dtype=torch.float, device='cuda'), |
| torch.randn(4, dtype=torch.float, device='cuda'), |
| ] |
| ge = self.checkTrace(scaleshift, inputs) |
| self.assertAllFused(ge.graph_for(*inputs)) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| @unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.LEGACY, "no bfloat support with profiling on") |
| def test_cuda_bfloat16(self): |
| def foo(x, y): |
| return (x + y).relu() |
| m = torch.jit.script(foo) |
| x = torch.randn(65536).cuda().bfloat16() |
| y = torch.randn_like(x) |
| self.assertAllFused(m.graph_for(x, y)) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| @unittest.skipIf(not RUN_CUDA_HALF, "no half support") |
| @unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.LEGACY, "no half support with profiling on") |
| def test_cuda_half(self): |
| x = torch.randn(4, 4, dtype=torch.half, device='cuda') |
| y = torch.randn(4, 4, dtype=torch.half, device='cuda') |
| |
| funcs = [ |
| self.fn_test_comparison_gt_lt, |
| self.fn_test_relu, |
| self.fn_test_exp |
| ] |
| |
| # Note: Non fused inputs must be float to prevent loss of precision |
| inputs = (x.float(), y.float()) |
| fusion_inputs = (x, y) |
| for fn in funcs: |
| local_inputs = [t.clone().requires_grad_() for t in inputs] |
| local_fusion_inputs = [t.clone().requires_grad_() for t in fusion_inputs] |
| |
| # Verifies outputs |
| fusion = torch.jit.trace(fn, local_fusion_inputs, check_trace=False) |
| outputs = fn(*local_inputs) |
| fusion_outputs = fusion(*local_fusion_inputs) |
| outputs_half = [t.half() for t in outputs] |
| self.assertEqual(outputs_half, fusion_outputs) |
| |
| # Verifies gradients |
| for output, fusion_output in zip(outputs_half, fusion_outputs): |
| grads = torch.autograd.grad( |
| output.float().sum(), local_inputs, allow_unused=True, retain_graph=True) |
| fusion_grads = torch.autograd.grad( |
| fusion_output.sum(), local_fusion_inputs, allow_unused=True, retain_graph=True) |
| grads_half = [t.half() for t in grads] |
| self.assertEqual(grads_half, fusion_grads) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_checks_cat_inputs(self): |
| # We shouldn't treat cat nodes as broadcasting. All their inputs |
| # need to be checked for having the same map size, before we can |
| # run the kernel. |
| def f(x, y): |
| return torch.cat([x + 2 * x + x ** 2, y + 4 * y + y ** 3], dim=0) |
| |
| # NOTE: y is broadcastable to x, but output of f(x, y) should have |
| # shape 3x4, and not 4x4. |
| x = torch.randn(2, 4, dtype=torch.float, device='cuda') |
| y = torch.randn(1, 4, dtype=torch.float, device='cuda') |
| |
| scripted = self.checkScript(f, (x, y)) |
| self.assertAllFused(scripted.graph_for(x, y)) |
| |
| @unittest.skipIf(not RUN_CUDA, "No CUDA") |
| def test_remainder_cuda(self): |
| def cuda_rem(x, y): |
| return 1 + torch.remainder(x, y) - 1 |
| |
| a = torch.rand([512], dtype=torch.float).cuda() |
| b = torch.rand([512], dtype=torch.float).cuda() |
| inputs = [a, b] |
| ge = self.checkScript(cuda_rem, inputs) |
| graph = ge.graph_for(*inputs) |
| self.assertAllFused(graph) |
| |
| @unittest.skipIf(not RUN_CUDA, "No CUDA") |
| def test_chunk_cuda(self): |
| def fn(x): |
| a, b, c = x.chunk(3, 1) |
| return a * b + c |
| |
| inputs = [torch.randn(10, 6, dtype=torch.float, device='cuda')] |
| |
| ge = self.checkScript(fn, inputs) |
| graph = ge.graph_for(*inputs) |
| self.assertAllFused(graph) |
| FileCheck().check("prim::ConstantChunk[chunks=3, dim=1]").run(str(graph)) |
| |
| @staticmethod |
| def _test_chunk_correctness(self, device='cpu'): |
| def chunk_4_0(x): |
| x0, x1, x2, x3 = x.chunk(4, 0) |
| return x0 + x1 + x2 + x3 |
| |
| def chunk_4_1(x): |
| x0, x1, x2, x3 = x.chunk(4, 1) |
| return x0 + x1 + x2 + x3 |
| |
| def chunk_4_last(x): |
| x0, x1, x2, x3 = x.chunk(4, 2) |
| return x0 + x1 + x2 + x3 |
| |
| fns = [chunk_4_0, chunk_4_1, chunk_4_last] |
| tensors = [ |
| # splitSize = 1 |
| torch.randn(4, 4, 4, dtype=torch.float, device=device), |
| |
| # contiguous case |
| torch.randn(12, 8, 16, dtype=torch.float, device=device), |
| |
| # non-contiguous case |
| torch.randn(12, 8, 16, dtype=torch.float, device=device).transpose(1, 2), |
| ] |
| |
| for tensor in tensors: |
| for fn in fns: |
| self.checkScript(fn, [tensor]) |
| |
| @unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle") |
| @enable_cpu_fuser |
| def test_chunk_correctness(self): |
| return self._test_chunk_correctness(self, 'cpu') |
| |
| @unittest.skipIf(not RUN_CUDA, "No CUDA") |
| def test_chunk_correctness_cuda(self): |
| return self._test_chunk_correctness(self, 'cuda') |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_chunk_distributes_cuda(self): |
| def f(x, y): |
| z1, z2 = (x + y).chunk(2, dim=1) |
| return z1 * z2 |
| |
| x = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| y = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| |
| ge = self.checkTrace(f, (x, y)) |
| graph = ge.graph_for(x, y) |
| FileCheck().check("broadcast_tensors").check('with prim::FusionGroup_') \ |
| .check_count('ConstantChunk', 2, exactly=True).run(str(graph)) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_chunk_motion_deduplicates_inputs(self): |
| def func1(x): |
| z = x * x |
| z0, z1 = z.chunk(2) |
| return z0 * z1 |
| |
| def func2(x): |
| z = x * x * x |
| z0, z1 = z.chunk(2) |
| return z0 * z1 |
| |
| inputs = [ |
| torch.tensor([1.1, 1.2], device='cuda', dtype=torch.float), |
| ] |
| for func in [func1, func2]: |
| module = self.checkScript(func, inputs) |
| forward_graph = module.graph_for(*inputs) |
| self.assertGraphContainsExactly(forward_graph, 'prim::FusionGroup', 1) |
| fusion_group = list(forward_graph.nodes())[-1] |
| self.assertEqual(len(list(fusion_group.inputs())), 1) |
| |
| @unittest.skipIf(not RUN_CUDA, "No CUDA") |
| def test_chunk_multiple_cuda(self): |
| # The arguments are intentionally used out of order as a test to see |
| # if the fusion compiler adds extra args in the correct order |
| def fn(s, x, y, z): |
| z1, z2 = z.chunk(2, 2) |
| x1, x2, x3 = x.chunk(3, 1) |
| y1, y2 = y.chunk(2, 0) |
| return s + x1 + x2 + x3 + y1 + y2 + z1 + z2 |
| |
| inputs = [ |
| torch.randn(5, 2, 3, dtype=torch.float, device='cuda'), |
| torch.randn(5, 6, 3, dtype=torch.float, device='cuda'), |
| torch.randn(10, 2, 3, dtype=torch.float, device='cuda'), |
| torch.randn(5, 2, 6, dtype=torch.float, device='cuda'), |
| ] |
| |
| ge = self.checkScript(fn, inputs) |
| self.assertAllFused(ge.graph_for(*inputs)) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_minmax(self): |
| def tmax(a, b): |
| return torch.max(2 * a, b) |
| |
| def tmin(a, b): |
| return torch.min(2 * a, b) |
| |
| a = torch.randn(4, 4, dtype=torch.float, device="cuda") |
| b = torch.randn(4, 4, dtype=torch.float, device="cuda") |
| nan = torch.tensor(float('nan'), dtype=torch.float, device="cuda") |
| |
| for f, inputs in product( |
| (tmax, tmin), |
| ([a, b], [a, nan], [b, nan])): |
| s = self.checkScript(f, inputs) |
| self.assertAllFused(s.graph_for(*inputs)) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_clamp(self): |
| def func2(a, b): |
| return torch.clamp(a + b, min=0, max=2) |
| |
| def funcInf(a, b): |
| return torch.clamp(a + b, min=0, max=float('inf')) |
| |
| def funcOptMin(a, b): |
| return torch.clamp(a + b, max=2) |
| |
| def funcOptMax(a, b): |
| return torch.clamp(a + b, min=0) |
| |
| a = torch.randn(4, 4, dtype=torch.float, device='cuda', requires_grad=True) |
| b = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| nan = torch.tensor(float('nan'), dtype=torch.float, device='cuda') |
| |
| funcs = (func2, funcInf, funcOptMin, funcOptMax) |
| for f, inputs in product(funcs, [[a, b], [a, nan]]): |
| f.__disable_jit_function_caching__ = True |
| inp1, inp2 = inputs |
| s = self.checkScript(f, (inp1, inp2), profiling=ProfilingMode.PROFILING) |
| self.assertAllFused(s.graph_for(inp1, inp2), except_for={'aten::size', 'aten::_size_if_not_equal'}) |
| c = s(inp1, inp2) |
| with enable_profiling_mode_for_profiling_tests(): |
| warmup_backward(c.sum()) |
| graph = backward_graph(s) |
| self.assertAllFused(graph, except_for={'aten::Float', 'aten::_grad_sum_to_size'}) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| @unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.LEGACY, "no half support with profiling on") |
| def test_dropout(self): |
| def func(x): |
| x = torch.nn.functional.dropout(x) |
| return torch.nn.functional.relu(x) |
| |
| a = torch.randn(4, 4, dtype=torch.float, device='cuda', requires_grad=True) |
| s = torch.jit.script(func) |
| c = s(a) |
| c = s(a) |
| warmup_backward(c.sum()) |
| # skip_check to skip extra bailout nodes in between |
| graph = backward_graph(s, skip_check=True) |
| self.assertAllFused(graph, except_for={'aten::div', 'prim::Constant'}) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_comparison_eq_ne(self): |
| def f(x, y): |
| mask = (x == 0).type_as(x) |
| z = x * mask + y |
| mask = (x != 0).type_as(x) |
| z = z * mask + y |
| return z |
| |
| x = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| y = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| |
| ge = self.checkTrace(f, (x, y)) |
| self.assertAllFused(ge.graph_for(x, y)) |
| |
| @staticmethod |
| def fn_test_comparison_gt_lt(x, y): |
| mask = (x > 0).type_as(x) |
| z = x * mask + y |
| mask = (x < 0).type_as(x) |
| z = z * mask + y |
| return z |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_comparison_gt_lt_cuda(self): |
| x = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| y = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| |
| ge = self.checkTrace(self.fn_test_comparison_gt_lt, (x, y)) |
| self.assertAllFused(ge.graph_for(x, y)) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_comparison_ge_le_cuda(self): |
| def f(x, y): |
| mask = (x >= 0).type_as(x) |
| z = x * mask + y |
| mask = (x <= 0).type_as(x) |
| z = z * mask + y |
| return z |
| |
| x = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| y = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| |
| ge = self.checkTrace(f, (x, y)) |
| self.assertAllFused(ge.graph_for(x, y)) |
| x.requires_grad_(True) |
| y.requires_grad_(True) |
| self.assertAllFused(ge.graph_for(x, y), except_for=("aten::size", "prim::BroadcastSizes", |
| "aten::_size_if_not_equal")) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_addcmul_cuda(self): |
| t = torch.randn(1, 4, dtype=torch.float, device='cuda') |
| t1 = torch.randn(4, 1, dtype=torch.float, device='cuda') |
| t2 = torch.randn(1, 4, dtype=torch.float, device='cuda') |
| |
| def foo(t, t1, t2): |
| return t.addcmul(t + 1, t2, value=0.1) |
| |
| ge = self.checkTrace(foo, (t, t1, t2), allow_unused=True) |
| graph = ge.graph_for(t, t1, t2) |
| self.assertAllFused(graph) |
| |
| # TODO: We leak CUDA memory here because the traced graph holds onto a |
| # constant-ified tensor. Since the Python-global CompilationUnit is alive |
| # until the end of the process, the memory is effectively leaked. |
| # Removed `_cuda` suffix from this test which disables leak-checking. |
| # If this is a real problem, we'll need to revisit Torchscript Function |
| # lifetimes in Python. |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_lerp(self): |
| start = torch.randn(4, 1, dtype=torch.float, device='cuda') |
| end = torch.randn(1, 4, dtype=torch.float, device='cuda') |
| weight = torch.tensor(0.5, dtype=torch.float, device='cuda') |
| |
| # scalar weight overload |
| def foo_weight_scalar(start, end): |
| return torch.lerp(start + 1, end, 0.5) |
| |
| # tensor weight overload |
| def foo_weight_tensor(start, end): |
| return torch.lerp(start + 1, end, weight) |
| |
| ge_weight_scalar = self.checkTrace(foo_weight_scalar, (start, end)) |
| graph = ge_weight_scalar.graph_for(start, end) |
| self.assertAllFused(graph) |
| |
| ge_weight_tensor = self.checkTrace(foo_weight_tensor, (start, end)) |
| graph = ge_weight_tensor.graph_for(start, end) |
| self.assertAllFused(graph) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_concat_cuda(self): |
| hx = torch.randn(3, 20, dtype=torch.float, device='cuda') |
| cx = torch.randn(3, 20, dtype=torch.float, device='cuda') |
| |
| def foo(hx, cx): |
| return torch.cat((hx + cx, hx * cx)) |
| |
| ge = self.checkTrace(foo, (hx, cx)) |
| graph = ge.graph_for(hx, cx) |
| self.assertAllFused(graph) |
| FileCheck().check("FusedConcat").check_next("return").run(str(graph)) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_concat_invariant_cuda(self): |
| # Invariant: the output of prim::FusedConcat may |
| # not be an input to any node inside the FusionGroup. |
| def fn(x, y, z): |
| x1 = x + y |
| y1 = x - y |
| w = torch.cat([x1, y1]) |
| return w + z |
| |
| x = torch.randn(2, 2, dtype=torch.float, device='cuda') |
| y = torch.randn(2, 2, dtype=torch.float, device='cuda') |
| z = torch.randn(4, 2, dtype=torch.float, device='cuda') |
| ge = self.checkTrace(fn, (x, y, z)) |
| graph = ge.graph_for(x, y, z) |
| self.assertAllFused(graph, except_for={'aten::add'}) |
| FileCheck().check("FusedConcat").check_next("return").run(str(graph)) |
| |
| @staticmethod |
| def fn_test_exp(x, y): |
| return (x + .5 * y).exp() |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_exp_cuda(self): |
| x = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| y = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| |
| ge = self.checkTrace(self.fn_test_exp, (x, y)) |
| self.assertAllFused(ge.graph_for(x, y)) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| @unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.LEGACY, "broken with profiling on") |
| @torch._jit_internal._disable_emit_hooks_decorator |
| @_inline_everything |
| def test_fuse_decompose_normalization(self): |
| class ResLike(torch.jit.ScriptModule): |
| def __init__(self, norm_module): |
| super(ResLike, self).__init__() |
| self.nm = norm_module |
| |
| @torch.jit.script_method |
| def forward(self, x, y): |
| return y + torch.relu(self.nm(x)) |
| |
| def test_norm_decompose(nm, in_opt_graph, not_in_opt_graph, in_fusegraph): |
| model = ResLike(nm).cuda() |
| model_noopt = ResLike(nm).cuda() |
| model_noopt.load_state_dict(model.state_dict()) |
| x = torch.randn(2, 16, 8, 8, device='cuda') |
| y = torch.randn(2, 16, 8, 8, device='cuda') |
| |
| # FIXME: We need differentiation for CNNs for this optimization to trigger |
| with torch.no_grad(): |
| out = model(x, y) |
| graph = model.graph_for(x, y) |
| rep = str(graph) |
| |
| with torch.jit.optimized_execution(False): |
| out_noopt = model_noopt(x, y) |
| rep_noopt = str(model_noopt.graph_for(x, y)) |
| self.assertEqual(out, out_noopt, atol=3e-5) |
| |
| # Check that normalization op has really been decomposed |
| for node_in_graph in in_opt_graph: |
| self.assertIn(node_in_graph, rep) |
| |
| for node_not_in_graph in not_in_opt_graph: |
| self.assertNotIn(node_not_in_graph, rep) |
| self.assertIn(node_not_in_graph, rep_noopt) |
| |
| fusion_groups = [node for node in graph.nodes() if node.kind() == 'prim::FusionGroup'] |
| self.assertEqual(len(fusion_groups), 1) |
| fused_graph = str(fusion_groups[0].g('Subgraph')) |
| for node_in_fusegraph in in_fusegraph: |
| self.assertIn(node_in_fusegraph, fused_graph) |
| |
| # test for batchnorm decompose |
| bm = nn.BatchNorm2d(16) |
| test_norm_decompose(bm, ['aten::batch_norm_update_stats'], |
| ['aten::batch_norm('], ['aten::sqrt']) |
| |
| # test for layernorm decompose |
| lm = nn.LayerNorm(8) |
| test_norm_decompose(lm, ['aten::batch_norm_stats'], |
| ['aten::layer_norm('], ['aten::sub', 'aten::mul', 'aten::add']) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_threshold(self): |
| def f(x): |
| return torch.threshold(x, 0, -10) + x + x + x |
| |
| x = torch.tensor([-1, -0.5, 0, 1, 2, 3], device='cuda') |
| scripted = self.checkScript(f, (x,)) |
| self.assertAllFused(scripted.graph_for(x)) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_scalar_arg_cuda(self): |
| def fn_test_scalar_arg(x: torch.Tensor, p: float) -> torch.Tensor: |
| return p * (x * x + x) |
| |
| x = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| p = 3 |
| scripted = self.checkScript(fn_test_scalar_arg, (x, p)) |
| self.assertAllFused(scripted.graph_for(x, p)) |
| |
| x.requires_grad_(True) |
| |
| # use another function otherwise we will bailout |
| # and won't be able to do fused checks |
| def fn_test_scalar_arg_requires_grad(x: torch.Tensor, p: float) -> torch.Tensor: |
| return p * (x * x + x) |
| |
| scripted = torch.jit.script(fn_test_scalar_arg_requires_grad) |
| out = scripted(x, p) |
| self.assertAllFused(scripted.graph_for(x, p), except_for=("aten::size", "prim::BroadcastSizes", |
| "aten::_size_if_not_equal")) |
| |
| @unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle") |
| @unittest.skip("deduplicating introduces aliasing in backward graph's outputs") |
| @enable_cpu_fuser |
| def test_fuser_deduplication(self): |
| # See that fusion kernel outputs are deduplicated when removing _grad_sum_to_size in the fuser's compilation |
| # see the discussion in PR #14957. |
| def f(x, y): |
| return torch.sigmoid(x + y) |
| |
| b = torch.randn(5, 5, requires_grad=True) |
| a = torch.randn(5, 5, requires_grad=True) |
| s = self.checkScript(f, (a, b)) |
| self.assertAllFused(s.graph_for(a, b), except_for={ |
| 'aten::size', 'aten::_size_if_not_equal', 'prim::BroadcastSizes'}) |
| |
| c = s(a, b) |
| results = warmup_backward(c.sum(), [a, b]) |
| ga2, gb2 = results.pop() |
| graph = backward_graph(s) |
| self.assertAllFused(graph) |
| # check that a, b share storage, i.e. were generated as a single output in the fuser |
| self.assertEqual(ga2.data_ptr(), gb2.data_ptr()) |
| |
| @unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle") |
| @enable_cpu_fuser |
| @unittest.skip("temporarily disabled because fusion was restricted in fixing #22833") |
| def test_fuser_iou(self): |
| # This checks if most of Intersection over Union is fused. |
| # In particular, the backward contains many _grad_sum_to_size. |
| def iou(b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2): |
| ltx = torch.max(b1x1, b2x1) # [N,M] |
| lty = torch.max(b1y1, b2y1) |
| rbx = torch.min(b1x2, b2x2) |
| rby = torch.min(b1y2, b2y2) |
| |
| w = (rbx - ltx).clamp(min=0, max=float('inf')) # [N,M] |
| h = (rby - lty).clamp(min=0, max=float('inf')) # [N,M] |
| inter = w * h # [N,M] |
| |
| area1 = (b1x2 - b1x1) * (b1y2 - b1y2) # [N,1] |
| area2 = (b2x2 - b2x1) * (b2y2 - b2y2) # [1,M] |
| iou = inter / (area1 + area2 - inter) |
| return iou |
| |
| box1 = torch.randn(5, 4, requires_grad=True) |
| box2 = torch.randn(5, 4, requires_grad=True) |
| # unsqueezing can currently not be fused |
| b1x1 = box1[:, 0].unsqueeze(1) # [N,1] |
| b1y1 = box1[:, 1].unsqueeze(1) |
| b1x2 = box1[:, 2].unsqueeze(1) |
| b1y2 = box1[:, 3].unsqueeze(1) |
| b2x1 = box2[:, 0].unsqueeze(0) # [1,N] |
| b2y1 = box2[:, 1].unsqueeze(0) |
| b2x2 = box2[:, 2].unsqueeze(0) |
| b2y2 = box2[:, 3].unsqueeze(0) |
| |
| s = self.checkScript(iou, (b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2)) |
| self.assertAllFused(s.graph_for(b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2), |
| except_for={'aten::size', 'prim::BroadcastSizes', 'aten::_size_if_not_equal'}) |
| |
| with enable_profiling_mode_for_profiling_tests(True): |
| c = s(b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2) |
| warmup_backward(c.sum(), [b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2]) |
| graph = backward_graph(s) |
| self.assertAllFused(graph, except_for={'aten::size', 'prim::BroadcastSizes', 'aten::_size_if_not_equal'}) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| @unittest.skipIf(not RUN_CUDA_MULTI_GPU, "needs non-zero device") |
| @enable_cpu_fuser |
| def test_fusion_reuse_multi_gpu(self): |
| def fn(x, y): |
| return x * y * x * y |
| |
| inputs_cpu = [ |
| torch.randn(4, 4, dtype=torch.float), |
| torch.randn(4, 4, dtype=torch.float), |
| ] |
| inputs_cuda0 = [x.cuda(0) for x in inputs_cpu] |
| inputs_cuda1 = [y.cuda(1) for y in inputs_cpu] |
| |
| # Should not crash; these should compile different kernels. |
| ge = self.checkScript(fn, inputs_cpu) |
| self.assertAllFused(ge.graph_for(*inputs_cpu)) |
| ge(*inputs_cuda0) |
| ge(*inputs_cuda1) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| @unittest.skipIf(not RUN_CUDA_MULTI_GPU, "needs non-zero device") |
| @enable_cpu_fuser |
| def test_kernel_cache_multi_gpu(self): |
| def not_fusible(x): |
| return x |
| |
| def fn(x, y, z): |
| x_out = x * x * x * x * x # fusion: lambda x. x * x * x * x * x |
| y_out = y * y * y * y * y |
| z_out = z * z * z * z * z |
| return not_fusible(x_out), not_fusible(y_out), not_fusible(z_out) |
| |
| inputs = [ |
| torch.randn(4, 4, dtype=torch.float), |
| torch.randn(4, 4, dtype=torch.float, device='cuda:0'), |
| torch.randn(4, 4, dtype=torch.float, device='cuda:1'), |
| ] |
| |
| prev_cache_size = torch._C._jit_debug_fuser_num_cached_kernel_specs() |
| |
| # There are 3 FusionGroups. Because they have the same graph, they |
| # should reuse the same KernelSpec in the KernelSpec cache. |
| ge = self.checkScript(fn, inputs) |
| self.assertGraphContainsExactly( |
| ge.graph_for(*inputs), 'prim::FusionGroup', 3, True) |
| new_cache_size = torch._C._jit_debug_fuser_num_cached_kernel_specs() |
| # XXX: This assumes that the same kernel isn't already used by another test |
| self.assertEqual(new_cache_size - prev_cache_size, 1) |
| |
| @unittest.skipIf(not RUN_CUDA_MULTI_GPU, "needs non-zero device") |
| def test_nonzero_device_cuda(self): |
| device = 'cuda:' + str(1) |
| x = torch.tensor([0.4], dtype=torch.float, device=device) |
| y = torch.tensor([0.7], dtype=torch.float, device=device) |
| |
| def doit(x, y): |
| return torch.sigmoid(torch.tanh(x * (x + y) + x)) |
| |
| ge = self.checkTrace(doit, (x, y)) |
| self.assertAllFused(ge.graph_for(x, y)) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_lstm_cuda(self): |
| inputs = get_lstm_inputs('cuda', training=True) |
| module = self.checkScript(LSTMCellS, inputs) |
| return |
| forward_graph = module.graph_for(*inputs) |
| self.assertGraphContainsExactly( |
| forward_graph, 'prim::FusionGroup', 1, consider_subgraphs=True) |
| self.assertTrue(len(strip_profiling_nodes(forward_graph.nodes())) == 2) |
| # Everything is differentiable but TupleConstruct return |
| FileCheck().check("DifferentiableGraph").check_next("TupleConstruct") \ |
| .check_next("return").run(str(forward_graph)) |
| |
| with enable_profiling_mode_for_profiling_tests(True): |
| hy, cy = module(*inputs) |
| warmup_backward((hy + cy).sum()) |
| backward = backward_graph(module) |
| self.assertAllFused(backward, except_for=("aten::t", "aten::mm", |
| "aten::_grad_sum_to_size")) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| # By default, on Ampere or later GPUs, LSTM computes float tensors at TF32 precision. |
| # We want float tensors to be computed at full precision in order to use the default precision |
| @with_tf32_off |
| def test_lstm_concat_cuda(self): |
| inputs = get_lstm_inputs('cuda') |
| ge = self.checkTrace(LSTMCellC, inputs) |
| graph = ge.graph_for(*inputs) |
| FileCheck().check("FusedConcat").check_next("return").run(str(graph)) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_lstm_gates_permutations_cuda(self): |
| # lstm has gates = x.mm(w_ih.t()) + hx.mm(w_hh.t()) + b_ih + b_hh. |
| # Test that any permutation of this will still result in one FusionGroup. |
| choices = ['x.mm(w_ih.t())', 'hx.mm(w_hh.t())', 'b_ih', 'b_hh'] |
| template = dedent(''' |
| def cell(x, hx, cx, w_ih, w_hh, b_ih, b_hh): |
| gates = {} + {} + {} + {} |
| ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1) |
| return ingate * forgetgate * cellgate * outgate |
| ''') |
| for permutation in permutations(choices, len(choices)): |
| code = template.format(*permutation) |
| scope = {} |
| exec(code, globals(), scope) |
| cu = torch.jit.CompilationUnit(code) |
| |
| inputs = get_lstm_inputs('cuda', training=False) |
| self.assertEqual(cu.cell(*inputs), scope['cell'](*inputs)) |
| forward_graph = cu.cell.graph_for(*inputs) |
| self.assertGraphContainsExactly(forward_graph, 'prim::FusionGroup', 1) |
| |
| # TODO: Fuser doesn't work at all when inputs require grad. Fix that |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| # By default, on Ampere or later GPUs, LSTM computes float tensors at TF32 precision. |
| # We want float tensors to be computed at full precision in order to use the default precision |
| @with_tf32_off |
| def test_lstm_traced_cuda(self): |
| inputs = get_lstm_inputs('cuda') |
| ge = self.checkTrace(LSTMCellF, inputs) |
| graph = ge.graph_for(*inputs) |
| # .check_not("aten::add") don't get pulled into FusionGroup because of BailOuts |
| FileCheck().check_not("Chunk").check_not("aten::sigmoid") \ |
| .check_not("aten::tanh").check("FusionGroup").check_next("TupleConstruct") \ |
| .check_next("return").check_not("FusionGroup_2").run(str(graph)) |
| |
| @unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle") |
| @unittest.skip("Test is flaky, see https://github.com/pytorch/pytorch/issues/8746") |
| @enable_cpu_fuser |
| def test_lstm_traced_cpu(self): |
| inputs = get_lstm_inputs('cpu') |
| try: |
| ge = self.checkTrace(LSTMCellF, inputs) |
| graph = ge.graph_for(*inputs) |
| FileCheck.check("FusionGroup").run(str(graph)) |
| except RuntimeError as e: |
| if 'Failed to compile' in e.args[0]: |
| warnings.warn('CPU fuser test has failed! This is not a hard failure, ' |
| 'because the kernels sometimes trigger bugs in compilers ' |
| '(most notably GCC 7.2).') |
| raise unittest.SkipTest('Failed to compile') from e |
| else: |
| raise |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_milstm_cuda(self): |
| inputs = get_milstm_inputs('cuda', training=True) |
| module = self.checkScript(MiLSTMCell, inputs) |
| forward_graph = module.graph_for(*inputs) |
| self.assertGraphContainsExactly( |
| forward_graph, 'prim::FusionGroup', 1, consider_subgraphs=True) |
| FileCheck().check("DifferentiableGraph").check_next("TupleConstruct") \ |
| .check_next("return").check("FusionGroup").run(str(forward_graph)) |
| hy, cy = module(*inputs) |
| warmup_backward((hy + cy).sum()) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| @unittest.skipIf(GRAPH_EXECUTOR == ProfilingMode.LEGACY, "borked on the legacy executor") |
| def test_rand_cuda(self): |
| class M(torch.jit.ScriptModule): |
| __constants__ = ['d'] |
| |
| def __init__(self): |
| super(M, self).__init__() |
| self.d = torch.device('cuda') |
| |
| @torch.jit.script_method |
| def create(self, x): |
| return x * x + x + torch.rand_like(x) |
| |
| x = torch.zeros([3, 4, 5], dtype=torch.float, device='cuda') |
| m = M() |
| out1 = m.create(x) |
| out2 = m.create(x) |
| self.assertNotEqual(out1, out2) |
| self.assertTrue(torch.all(out1 >= 0)) |
| self.assertTrue(torch.all(out1 < 1)) |
| self.assertTrue(torch.all(out2 >= 0)) |
| self.assertTrue(torch.all(out2 < 1)) |
| self.assertAllFused(m.create.graph_for(x)) |
| |
| @staticmethod |
| def fn_test_relu(x, y): |
| return F.relu(x + .5 * y) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_relu_cuda(self): |
| x = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| y = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| |
| ge = self.checkTrace(self.fn_test_relu, (x, y)) |
| self.assertAllFused(ge.graph_for(x, y)) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_erf_cuda(self): |
| def fn_test_erf(x): |
| return F.relu(torch.erf(x) - torch.erfc(x)) |
| |
| x = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| ge = self.checkTrace(fn_test_erf, (x,)) |
| self.assertAllFused(ge.graph_for(x)) |
| x.requires_grad_(True) |
| ge = self.checkTrace(fn_test_erf, (x,)) |
| self.assertAllFused(ge.graph_for(x), except_for=("aten::size", "prim::BroadcastSizes", |
| "aten::_size_if_not_equal")) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| @unittest.skipIf(GRAPH_EXECUTOR == ProfilingMode.LEGACY, "borked on the legacy executor") |
| def test_rand_broadcast_cuda(self): |
| def fn_test_rand(x, y): |
| r = torch.rand_like(y) |
| return r * x + x |
| |
| x = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| y = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| script_f = torch.jit.script(fn_test_rand) |
| out = script_f(x, y) |
| self.assertAllFused(script_f.graph_for(x, y)) |
| x.requires_grad_(True) |
| out = script_f(x, y) |
| self.assertAllFused(script_f.graph_for(x, y), except_for=("aten::size", "prim::BroadcastSizes", |
| "aten::_size_if_not_equal")) |
| # test that broadcasting random produces correct results |
| x = torch.ones(4, 4, dtype=torch.float, device='cuda') |
| y = torch.ones(4, dtype=torch.float, device='cuda') |
| out = script_f(x, y) |
| self.assertEqual(out[0], out[1]) |
| |
| @unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle") |
| @enable_cpu_fuser |
| def test_scalar(self): |
| def fn(x, y): |
| return 2 * x + y |
| |
| x = torch.tensor(0.1, dtype=torch.float, device='cpu') |
| y = torch.tensor(1, dtype=torch.float, device='cpu') |
| ge = self.checkScript(fn, (x, y)) |
| self.assertAllFused(ge.graph_for(x, y)) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_small_constant_cuda(self): |
| def fn_test_small_constant(x, y): |
| return (1e-8 * x + 5e-9 * y) * 1e8 |
| x = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| y = torch.randn(4, 4, dtype=torch.float, device='cuda') |
| |
| ge = self.checkTrace(fn_test_small_constant, (x, y)) |
| self.assertAllFused(ge.graph_for(x, y)) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| def test_tensor_scalar_ops_cuda(self): |
| def should_fuse(x): |
| z = 3. |
| y = x + z |
| return x * y |
| |
| # XXX: right now we only support fusing scalars if |
| # they're constant (#9940) |
| def should_not_fuse(x, z): |
| y = x + int(z) |
| return x * y |
| |
| inputs = [torch.randn(2, 2, dtype=torch.float, device='cuda')] |
| ge = self.checkScript(should_fuse, inputs) |
| self.assertAllFused(ge.graph_for(*inputs)) |
| |
| inputs = [ |
| torch.randn(2, 2, dtype=torch.float, device='cuda'), |
| torch.tensor(3., dtype=torch.float, device='cuda'), |
| ] |
| ge = self.checkScript(should_not_fuse, inputs) |
| self.assertGraphContainsExactly( |
| ge.graph_for(*inputs), 'prim::FusionGroup', 0, consider_subgraphs=True) |
| |
| @unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle") |
| @enable_cpu_fuser |
| def test_where_and_typing(self): |
| def f(x, y): |
| mask = x > y |
| res = torch.where(mask, x, y) |
| return mask, res |
| |
| x = torch.randn(4, 4, dtype=torch.double) |
| y = torch.randn(4, 4, dtype=torch.double) |
| |
| script_f = self.checkScript(f, (x, y)) |
| self.assertAllFused(script_f.graph_for(x, y), except_for={'prim::TupleConstruct'}) |
| |
| @unittest.skipIf(not RUN_CUDA, "fuser requires CUDA") |
| @unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.LEGACY, "no half support with profiling on") |
| def test_grad_sum_to_size_elimination(self): |
| |
| def my_broadcasted_cell(a, b, c): |
| return (a + b) + c |
| |
| s1 = torch.randn(5, 1, requires_grad=True, device='cuda') |
| s2 = torch.randn(5, 5, requires_grad=True, device='cuda') |
| |
| module = self.checkScript(my_broadcasted_cell, (s1, s1, s1), profiling=ProfilingMode.PROFILING) |
| forward_graph = module.graph_for(s1, s1, s1) |
| self.assertAllFused(forward_graph, except_for=("aten::size", "prim::BroadcastSizes", |
| "aten::_size_if_not_equal")) |
| |
| old_plans = set() |
| for i in range(3): |
| # if we have s2, then the s1 are _grad_sum_to_size'd |
| |
| args = s2 if i < 1 else s1, s2 if i < 2 else s1, s2 |
| args = [a.detach_().requires_grad_() for a in args] |
| # recompile, so we don't trigger bailouts |
| module = self.checkScript(my_broadcasted_cell, args, profiling=ProfilingMode.PROFILING) |
| res = module(s2 if i < 1 else s1, s2 if i < 2 else s1, s2) |
| warmup_backward(res.sum(), args) |
| grads = torch.autograd.grad(res.sum(), args) |
| for inp, gr in zip(args, grads): |
| self.assertEqual(inp.shape, gr.shape) |
| backward = None |
| # this is a workaround for the backward graphs not being |
| # in order for Python 2 |
| for g in all_backward_graphs(module): |
| if str(g) not in old_plans: |
| assert backward is None |
| backward = g |
| old_plans.add(str(backward)) |
| num_grads = 1 if i > 0 else 0 |
| self.assertEqual(len([n for n in backward.nodes() if n.kind() == 'aten::_grad_sum_to_size']), num_grads) |
| |
| |
| if __name__ == '__main__': |
| run_tests() |
