mirror of
https://github.com/zebrajr/pytorch.git
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a28bfb5ed5
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/127125 Approved by: https://github.com/Skylion007 ghstack dependencies: #127122, #127123, #127124
299 lines
9.1 KiB
Python
299 lines
9.1 KiB
Python
# Owner(s): ["module: dynamo"]
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import unittest
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import torch
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import torch._dynamo
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import torch._dynamo.test_case
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from torch._dynamo.backends.debugging import ExplainWithBackend
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from torch._dynamo.backends.onnxrt import has_onnxruntime
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from torch._dynamo.backends.tvm import has_tvm
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from torch._dynamo.testing import same
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from torch.fx._lazy_graph_module import _force_skip_lazy_graph_module
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from torch.testing._internal.inductor_utils import HAS_CUDA
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requires_cuda = unittest.skipUnless(HAS_CUDA, "requires cuda")
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class Seq(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.layers = torch.nn.Sequential(
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torch.nn.Linear(10, 10),
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torch.nn.ReLU(),
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torch.nn.Linear(10, 10),
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torch.nn.Sigmoid(),
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)
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def forward(self, x):
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return self.layers(x)
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class Conv_Bn_Relu(torch.nn.Module):
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def __init__(self, in_channels, out_channels, **kwargs):
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super().__init__()
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self.conv = torch.nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
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self.bn = torch.nn.BatchNorm2d(out_channels, eps=0.001)
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self.relu = torch.nn.ReLU()
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def forward(self, x):
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return self.relu(self.bn(self.conv(x)))
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class TestOptimizations(torch._dynamo.test_case.TestCase):
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def test_example_inputs(self):
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def fn(a, bc, d):
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b, c = bc
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return a / d - b / c
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def compiler_fn(graph, example_inputs):
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nonlocal r1
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r1 = graph(*example_inputs)[0]
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return graph.forward
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a = torch.empty(2).fill_(1)
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b = torch.empty(2).fill_(2)
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c = torch.empty(2).fill_(3)
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d = 4
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r1 = None
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r2 = fn(a, (b, c), d)
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opt_fn = torch._dynamo.optimize_assert(compiler_fn)(fn)
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r3 = opt_fn(a, (b, c), d)
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self.assertIsNotNone(r1)
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self.assertEqual(r1.size(), r2.size())
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self.assertEqual(r1.stride(), r2.stride())
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self.assertEqual(r1.dtype, r2.dtype)
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self.assertEqual(r1.size(), r3.size())
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self.assertEqual(r1.stride(), r3.stride())
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self.assertEqual(r1.dtype, r3.dtype)
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def test_example_inputs_runtime_use(self):
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def fn(a, bc, d):
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b, c = bc
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return a / d - b / c
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def compiler_fn(graph, example_inputs):
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def fwd(*args):
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nonlocal r1
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r = graph.forward(*args)
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r1 = r[0]
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return r
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return fwd
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a = torch.empty(2).fill_(1)
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b = torch.empty(2).fill_(2)
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c = torch.empty(2).fill_(3)
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d = 4
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r1 = None
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r2 = fn(a, (b, c), d)
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opt_fn = torch._dynamo.optimize_assert(compiler_fn)(fn)
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r3 = opt_fn(a, (b, c), d)
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self.assertIsNotNone(r1)
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self.assertTrue(same(r1, r2))
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self.assertTrue(same(r1, r3))
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def _check_backend_works(self, backend, options=None):
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model = Seq().eval()
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input = torch.randn(2, 10)
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r1 = model(input)
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r2 = torch.compile(model, backend=backend, options=options)(input)
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self.assertTrue(same(r1, r2.float(), tol=0.01))
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def test_eager(self):
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self._check_backend_works("eager")
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@_force_skip_lazy_graph_module()
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def test_torchscript(self):
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self._check_backend_works("ts")
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def test_aot_eager(self):
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self._check_backend_works("aot_eager")
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def test_aot_eager_decomp_partition(self):
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self._check_backend_works("aot_eager_decomp_partition")
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@_force_skip_lazy_graph_module()
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def test_aot_ts(self):
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self._check_backend_works("aot_ts")
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@requires_cuda
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def test_aot_cudagraphs(self):
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self._check_backend_works("cudagraphs")
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@unittest.skipIf(not has_onnxruntime(), "requires onnxruntime")
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def test_onnxrt(self):
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self._check_backend_works("onnxrt")
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@unittest.skipIf(not has_tvm(), "requires tvm")
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def test_tvm(self):
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self._check_backend_works("tvm")
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self._check_backend_works("tvm", options={"scheduler": None})
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def test_list_backends(self):
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self.assertIn("inductor", torch._dynamo.list_backends())
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self.assertIn("inductor", torch._dynamo.list_backends(exclude_tags=None))
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self.assertNotIn("eager", torch._dynamo.list_backends())
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self.assertNotIn("eager", torch._dynamo.list_backends(exclude_tags=["debug"]))
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self.assertIn("eager", torch._dynamo.list_backends(exclude_tags=[]))
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class NormalizeIRTests(torch._dynamo.test_case.TestCase):
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def test_inplace_normalize(self):
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def fn(a, b):
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x = torch.cos(a)
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x += b
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return torch.sin(x)
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a = torch.randn(10)
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b = torch.randn(10).to(torch.float64)
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ref = fn(a, b)
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optimized_fn = torch._dynamo.optimize("aot_eager")(fn)
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res = optimized_fn(a, b)
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self.assertTrue(same(ref, res))
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class MPSNotSupportedTest(torch._dynamo.test_case.TestCase):
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@unittest.skipIf(not torch.backends.mps.is_available(), "requires mps")
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def test_mps_not_supported(self):
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model = Seq().to("mps")
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example_input = torch.randn(1, 10).to("mps")
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self.assertRaises(
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RuntimeError,
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lambda: torch.compile(model, backend="inductor")(example_input),
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)
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class TestExplainWithBackend(torch._dynamo.test_case.TestCase):
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def test_explain_with_backend(self):
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def fn3(x):
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x = torch.sin(x)
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torch._dynamo.graph_break()
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x = torch.sin(x)
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return x
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def fn2(x):
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x = torch.cos(x)
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x = fn3(x)
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x = torch.cos(x)
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return x
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def fn1(x):
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x = torch.tan(x)
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x = fn2(x)
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x = torch.tan(x)
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return x
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def fn(x):
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x = torch.sigmoid(x)
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x = fn1(x)
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x = torch.sigmoid(x)
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return x
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# Wrap TorchInductor with explain backend
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eb = ExplainWithBackend("inductor")
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optimized_fn = torch.compile(fn, backend=eb)
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input_tensor = torch.randn(5)
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result = optimized_fn(input_tensor)
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# Check that fn still produces the same output when wrapped by ExplainWithBackend
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self.assertTrue(torch.allclose(result, fn(input_tensor)))
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# Verify ExplainOutput object contents, output might change but make sure these fields are present
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explain_output = eb.output()
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explain_str = str(explain_output)
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self.assertIn("Graph Count", explain_str)
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self.assertIn("Graph Break Count", explain_str)
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self.assertIn("Op Count", explain_str)
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self.assertIn("Break Reasons", explain_str)
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# Verify that for the given functions above, we report the correct number of graphs, graph breaks, and ops
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self.assertEqual(8, explain_output.graph_count)
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self.assertEqual(7, explain_output.graph_break_count)
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self.assertEqual(8, explain_output.op_count)
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class TestCustomBackendAPI(torch._dynamo.test_case.TestCase):
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"""Test APIs documented by https://pytorch.org/docs/main/torch.compiler_custom_backends.html"""
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def test_register_backend_api(self):
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from torch._dynamo import register_backend
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backend_run = False
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@register_backend
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def my_custom_backend(gm, example_inputs):
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nonlocal backend_run
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backend_run = True
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return gm.forward
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def f(x):
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return torch.relu(x)
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opt_f = torch.compile(f, backend="my_custom_backend")
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opt_f(torch.randn(3, 3))
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self.assertTrue(backend_run)
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def test_aot_autograd_api(self):
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from torch._dynamo.backends.common import aot_autograd
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from functorch.compile import make_boxed_func
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backend_run = False
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def my_compiler(gm, example_inputs):
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nonlocal backend_run
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backend_run = True
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return make_boxed_func(gm.forward)
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my_backend = aot_autograd(fw_compiler=my_compiler)
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def f(x):
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return torch.relu(x)
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opt_f = torch.compile(f, backend=my_backend)
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opt_f(torch.randn(3, 3))
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self.assertTrue(backend_run)
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def test_lookup_backend(self):
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from torch._dynamo import list_backends, lookup_backend
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backends = list_backends()
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backend_run = False
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def my_compiler(gm, example_inputs):
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nonlocal backend_run
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backend_run = True
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try:
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trt_compiled = lookup_backend("tensorrt")(gm, example_inputs)
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if trt_compiled is not None:
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return trt_compiled
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except Exception:
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pass
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# first backend failed, try something else...
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try:
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inductor_compiled = lookup_backend("inductor")(gm, example_inputs)
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if inductor_compiled is not None:
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return inductor_compiled
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except Exception:
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pass
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return gm.forward
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def f(x):
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return torch.relu(x)
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opt_f = torch.compile(f, backend=my_compiler)
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opt_f(torch.randn(3, 3))
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self.assertTrue(backend_run)
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if __name__ == "__main__":
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from torch._dynamo.test_case import run_tests
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run_tests()
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