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https://github.com/saymrwulf/pytorch.git
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9743e3a19c
As the design in RFC https://github.com/pytorch/pytorch/issues/114856, this PR implemented Intel GPU Inductor backend by: - Reuse WrapperCodegen and TritonScheduling for python wrapper and kernel code generation. And implenented device-specific code generation in XPUDeviceOpOverrides - Reuse fx_pass, lowering, codecache, triton kernel auto-tuning, and compilation. For the test case, this PR provided test/inductor/test_xpu_basic.py for basic inductor backend functionality testing. We'll reuse all the existing Inductor test case in the next PR. Pull Request resolved: https://github.com/pytorch/pytorch/pull/121895 Approved by: https://github.com/EikanWang, https://github.com/jansel, https://github.com/desertfire
65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
# Owner(s): ["module: inductor"]
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import importlib
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import os
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import sys
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import unittest
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import torch
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from torch.testing._internal.common_utils import IS_CI, IS_WINDOWS
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if IS_WINDOWS and IS_CI:
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sys.stderr.write(
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"Windows CI does not have necessary dependencies for test_xpu_basic yet\n"
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)
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if __name__ == "__main__":
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sys.exit(0)
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raise unittest.SkipTest("requires sympy/functorch/filelock")
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importlib.import_module("filelock")
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pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
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sys.path.append(pytorch_test_dir)
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from inductor.test_torchinductor import check_model_gpu, TestCase
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# TODO: Remove this file.
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# This is a temporary test case to test the base functionality of first Intel GPU Inductor integration.
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# We are working on reuse and pass the test cases in test/inductor/* step by step.
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# Will remove this file when pass full test in test/inductor/*.
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class XpuBasicTests(TestCase):
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common = check_model_gpu
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device = "xpu"
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def test_add(self):
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def fn(a, b):
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return a + b
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self.common(fn, (torch.rand(2, 3, 16, 16), torch.rand(2, 3, 16, 16)))
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def test_sub(self):
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def fn(a, b):
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return a - b
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self.common(fn, (torch.rand(2, 3, 16, 16), torch.rand(2, 3, 16, 16)))
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def test_mul(self):
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def fn(a, b):
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return a * b
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self.common(fn, (torch.rand(2, 3, 16, 16), torch.rand(2, 3, 16, 16)))
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def test_div(self):
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def fn(a, b):
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return a / b
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self.common(fn, (torch.rand(2, 3, 16, 16), torch.rand(2, 3, 16, 16)))
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if __name__ == "__main__":
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from torch._dynamo.test_case import run_tests
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from torch.testing._internal.inductor_utils import HAS_XPU
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if HAS_XPU:
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run_tests(needs="filelock")
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