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40b25578e4
Add a deterministic mode to skip the on device benchmarking that we know should affect numeric. This include - pad-mm - dynamic rblock scaling - template autotuning - coordinate descent tuning for reduction - reduction config autotuning in CachingAutotuner. For reduction both RBLOCK, num_warps should affect numeric. XBLOCK does not. We can still autotune XBLOCK for reductions. - benchmarking for computation communication reordering pass The mode definitely has perf hit. Pull Request resolved: https://github.com/pytorch/pytorch/pull/163589 Approved by: https://github.com/v0i0
102 lines
3.1 KiB
Python
102 lines
3.1 KiB
Python
# Owner(s): ["module: inductor"]
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import contextlib
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import unittest
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import torch
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import torch._inductor.config as inductor_config
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from torch._dynamo.utils import counters
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from torch._inductor.test_case import run_tests, TestCase
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from torch._inductor.utils import fresh_cache
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from torch.testing._internal.common_utils import (
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instantiate_parametrized_tests,
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parametrize,
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)
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from torch.testing._internal.inductor_utils import (
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GPU_TYPE,
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HAS_CUDA_AND_TRITON,
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IS_BIG_GPU,
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)
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@instantiate_parametrized_tests
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class DeterministicTest(TestCase):
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def setUp(self) -> None:
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super().setUp()
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self._exit_stack = contextlib.ExitStack()
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self._exit_stack.enter_context(fresh_cache())
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def tearDown(self) -> None:
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self._exit_stack.close()
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super().tearDown()
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@parametrize("deterministic", [False, True])
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def test_mm_padding(self, deterministic):
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with inductor_config.patch(deterministic=deterministic):
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@torch.compile()
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def foo(x, y):
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return x @ y
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inps = [torch.rand([2049, 2049], device=GPU_TYPE) for _ in range(2)]
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out = foo(*inps)
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self.assertEqual(out, inps[0] @ inps[1])
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if deterministic:
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self.assertTrue(counters["inductor"]["pad_mm_bench"] == 0)
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else:
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self.assertTrue(counters["inductor"]["pad_mm_bench"] > 0)
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@parametrize("deterministic", [False, True])
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@inductor_config.patch(max_autotune=True)
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@unittest.skipIf(not IS_BIG_GPU, "templates require big gpu")
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def test_max_autotune(self, deterministic):
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with inductor_config.patch(deterministic=deterministic):
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@torch.compile()
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def foo(x, y):
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return x @ y
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inps = [torch.rand([2048, 2048], device=GPU_TYPE) for _ in range(2)]
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out = foo(*inps)
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self.assertEqual(out, inps[0] @ inps[1])
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if deterministic:
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self.assertTrue(counters["inductor"]["select_algorithm_autotune"] == 0)
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else:
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self.assertTrue(counters["inductor"]["select_algorithm_autotune"] > 0)
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def test_pointwise_coordesc_tuning(self):
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@torch.compile(mode="max-autotune")
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def f(x):
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return x + 1
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x = torch.randn(2048, device=GPU_TYPE)
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self.assertEqual(f(x), x + 1)
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self.assertTrue(counters["inductor"]["coordesc_tuning_bench"] > 0)
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@parametrize("deterministic", [False, True])
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def test_reduction_coordesc_tuning(self, deterministic):
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with inductor_config.patch(
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deterministic=deterministic, coordinate_descent_tuning=True
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):
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@torch.compile()
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def foo(x):
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return x.sum(dim=-1)
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inp = torch.rand([2048, 2048], device=GPU_TYPE)
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out = foo(inp)
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self.assertEqual(out, inp.sum(dim=-1))
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if deterministic:
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self.assertTrue(counters["inductor"]["coordesc_tuning_bench"] == 0)
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else:
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self.assertTrue(counters["inductor"]["coordesc_tuning_bench"] > 0)
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if __name__ == "__main__":
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if HAS_CUDA_AND_TRITON:
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run_tests()
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