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1df14f1bf8
it without creating cyclic dependencies Pull Request resolved: https://github.com/pytorch/pytorch/pull/109832 Approved by: https://github.com/zou3519
127 lines
4.2 KiB
Python
127 lines
4.2 KiB
Python
# Owner(s): ["module: inductor"]
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import json
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import unittest
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import torch
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import torch._dynamo.test_case
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import torch._inductor.utils
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from torch._inductor import config
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from torch.profiler import ProfilerActivity
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from torch.testing._internal.common_utils import TemporaryFileName, TEST_WITH_ROCM
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from torch.utils._triton import has_triton
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HAS_TRITON = has_triton()
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class DynamoProfilerTests(torch._dynamo.test_case.TestCase):
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@unittest.skipIf(not HAS_TRITON, "requires cuda & triton")
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def test_inductor_profiling_triton_launch(self):
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# Verify that we get some sort of CPU-side indication of triton kernel launches
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# in the profile traces. Currently, those appear as `cuLaunchKernel`. If this
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# detail changes, the test can be updated or removed.
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@torch.compile
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def fn(x, y):
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return (x + y).sin().cos()
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x, y = (torch.rand((4, 4), device="cuda") for _ in range(2))
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with torch.profiler.profile() as prof:
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fn(x, y)
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with TemporaryFileName(mode="w+") as fname:
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prof.export_chrome_trace(fname)
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with open(fname) as f:
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trace_json = json.load(f)
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self.assertTrue("traceEvents" in trace_json)
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events = trace_json["traceEvents"]
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def nameMatchesLaunchKernel(event_name):
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return "cuLaunchKernel" in event_name
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self.assertTrue(
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any(
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("name" in event and "cuLaunchKernel" == event["name"])
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for event in events
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)
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)
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def _test_profiling_kernel_names(self, fn, args, kernel_name_str: str):
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"""
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We expect a record_function event to be added on the CPU side, surrounding
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the launch of each triton kernel.
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"""
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fn_opt = torch.compile(fn)
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for _ in range(2):
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fn_opt(*args)
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with torch.profiler.profile(activities=[ProfilerActivity.CPU]) as prof:
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fn_opt(*args)
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# The name of the kernel is expected to match the name of the kernel in debug
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# files etc. The name could change in the future, but it seems reasonable that
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# the name should always contain "triton" and "kernel_name_str" - e.g. if the
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# kernel contains a sin op, it should probably contain "str" in the name.
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# If this changes in the future, feel free to change the assertion here.
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# Debugging tips: you can add prof.export_chrome_trace("test.json") inline in
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# this test, and then view test.json in chrome://tracing to see the trace.
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self.assertTrue(
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any(
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(
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hasattr(event, "name")
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and kernel_name_str in event.name
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and "triton" in event.name
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)
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for event in prof.events()
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)
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)
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@unittest.skipIf(not HAS_TRITON, "requires cuda & triton")
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def test_inductor_profiling_kernel_names_pointwise(self):
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def fn(x, y):
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return (x + y).sin().cos()
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args = [torch.rand((4, 4), device="cuda") for _ in range(2)]
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self._test_profiling_kernel_names(fn, args, "sin")
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@unittest.skipIf(not HAS_TRITON, "requires cuda & triton")
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def test_inductor_profiling_kernel_names_template(self):
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with config.patch(
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{"max_autotune": True, "max_autotune_gemm_backends": "TRITON"}
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):
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def fn(x, y):
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return x @ y
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args = [torch.rand((4, 4), device="cuda") for _ in range(2)]
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self._test_profiling_kernel_names(fn, args, "mm")
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@unittest.skipIf(not HAS_TRITON, "requires cuda & triton")
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def test_inductor_profiling_kernel_names_foreach(self):
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with config.patch(
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{"max_autotune": True, "max_autotune_gemm_backends": "TRITON"}
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):
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def fn(x, y):
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return torch._foreach_add(x, y)
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x = [torch.rand((4, 4), device="cuda") for _ in range(3)]
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y = [torch.rand((4, 4), device="cuda") for _ in range(3)]
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args = (x, y)
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self._test_profiling_kernel_names(fn, args, "_for_")
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if __name__ == "__main__":
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from torch._dynamo.test_case import run_tests
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if not TEST_WITH_ROCM:
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run_tests()
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