mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
134bc4fc34
See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129763 Approved by: https://github.com/jansel
727 lines
21 KiB
Python
727 lines
21 KiB
Python
# Owner(s): ["module: inductor"]
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import sys
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import unittest
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import torch
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import torch._inductor
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from torch._inductor.test_case import TestCase
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from torch.testing._internal.common_utils import (
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instantiate_parametrized_tests,
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IS_FBCODE,
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parametrize,
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)
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from torch.testing._internal.inductor_utils import HAS_CPU, HAS_CUDA
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from torch.testing._internal.triton_utils import requires_cuda
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aten = torch.ops.aten
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try:
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try:
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from .test_torchinductor import check_model, check_model_cuda
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except ImportError:
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from test_torchinductor import check_model, check_model_cuda
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except (unittest.SkipTest, ImportError) as e:
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sys.stderr.write(f"{type(e)}: {e}\n")
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if __name__ == "__main__":
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sys.exit(0)
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raise
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inplace_bin_ops_under_test = [
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torch._foreach_add_,
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torch._foreach_mul_,
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torch._foreach_sub_,
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torch._foreach_div_,
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]
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bin_ops_under_test = [
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torch._foreach_add,
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torch._foreach_mul,
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torch._foreach_sub,
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torch._foreach_div,
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torch._foreach_maximum,
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torch._foreach_minimum,
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torch._foreach_clamp_max,
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torch._foreach_clamp_min,
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aten._foreach_copy,
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]
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un_ops_under_test = [
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torch._foreach_reciprocal,
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torch._foreach_neg,
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torch._foreach_sign,
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torch._foreach_abs,
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torch._foreach_sqrt,
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]
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compose_ops = [torch._foreach_addcdiv, torch._foreach_addcmul]
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all_ops = parametrize(
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"op", bin_ops_under_test + un_ops_under_test, name_fn=lambda f: f.__name__
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)
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bin_ops = parametrize("op", bin_ops_under_test, name_fn=lambda f: f.__name__)
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inplace_bin_ops = parametrize(
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"op", inplace_bin_ops_under_test, name_fn=lambda f: f.__name__
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)
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scalar_bin_ops = parametrize("op", bin_ops_under_test[:4], name_fn=lambda f: f.__name__)
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scalar_tensor_bin_ops = parametrize(
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"op", bin_ops_under_test[:2], name_fn=lambda f: f.__name__
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)
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decomp_ops = parametrize("op", compose_ops, name_fn=lambda f: f.__name__)
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def gen_args(op):
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if op in un_ops_under_test:
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return (
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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)
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else:
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return (
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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)
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@instantiate_parametrized_tests
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class ForeachTests(TestCase):
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check_model_cuda = check_model_cuda
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check_model_cpu = check_model
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check_kernel_count = True
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def setUp(self):
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super().setUp()
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torch._inductor.metrics.reset()
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def tearDown(self):
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super().tearDown()
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torch._inductor.metrics.reset()
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def _test_single_list(self, op):
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if op in un_ops_under_test:
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def fn(a0, a1):
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return op([a0, a1])
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else:
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def fn(a0, a1, b0, b1):
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return op([a0, a1], [b0, b1])
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self.check_model_cuda(
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fn,
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gen_args(op),
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)
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def _test_single_scalar(self, op):
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def fn(a0, a1):
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return op([a0, a1], 3.3)
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self.check_model_cuda(
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fn,
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(
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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),
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)
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def _test_single_scalar_tensor(self, op):
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def fn(a0, a1):
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return op([a0, a1], torch.tensor(3.3, device="cuda:0"))
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self.check_model_cuda(
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fn,
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(
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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),
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)
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# called in test_cuda_cpp_wrapper.py
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@requires_cuda
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def test_foreach_cpp_wrapper_cuda(self):
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self._test_single_list(op=torch._foreach_add)
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@requires_cuda
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@all_ops
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def test_single_list(self, op):
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self._test_single_list(op)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
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@requires_cuda
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@scalar_bin_ops
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def test_single_scalar(self, op):
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self._test_single_scalar(op)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
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@requires_cuda
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@scalar_tensor_bin_ops
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def test_single_scalar_tensor(self, op):
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self._test_single_scalar_tensor(op)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
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@requires_cuda
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@all_ops
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def test_scheduler_fusion_list(self, op):
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if op in un_ops_under_test:
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def fn(a0, a1):
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c = op([a0, a1])
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return torch._foreach_sqrt(c)
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else:
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def fn(a0, a1, b0, b1):
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c = op([a0, a1], [b0, b1])
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return c, torch._foreach_add([a0, a1], c)
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self.check_model_cuda(
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fn,
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gen_args(op),
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)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
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@requires_cuda
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@scalar_bin_ops
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def test_scheduler_fusion_scalar(self, op):
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def fn(a0, a1):
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c = op([a0, a1], 3.4)
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return c, torch._foreach_add([a0, a1], c)
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self.check_model_cuda(
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fn,
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(
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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),
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)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
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@requires_cuda
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@scalar_bin_ops
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def test_broadcasting(self, op):
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def fn(a0, a1, b0, b1):
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return op([a0, a1], [b0, b1])
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fn_opt = torch._dynamo.optimize()(fn)
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inputs = (
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torch.rand(10, 1, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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torch.rand(1, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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)
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actual = fn_opt(*inputs)
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expected = fn(*inputs)
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self.assertEqual(actual, expected)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
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@requires_cuda
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@all_ops
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def test_singleton_lists(self, op):
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if op in un_ops_under_test:
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def fn(a0):
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return op([a0])
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args = (torch.rand(10, 10, device="cuda:0"),)
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else:
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def fn(a0, b0):
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return op([a0], [b0])
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args = (
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(10, 10, device="cuda:0"),
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)
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self.check_model_cuda(
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fn,
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args,
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)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
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@requires_cuda
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@bin_ops
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def test_type_promotion(self, op):
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def fn(a0, a1, b0, b1):
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return op([a0, a1], [b0, b1])
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fn_opt = torch._dynamo.optimize()(fn)
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max32 = torch.iinfo(torch.int32).max
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max64 = torch.iinfo(torch.int64).max
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inputs = (
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torch.randint(max32, (10, 10), device="cuda:0", dtype=torch.int32),
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torch.randint(max32, (20, 20), device="cuda:0", dtype=torch.int32),
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torch.randint(max32, (10, 10), device="cuda:0", dtype=torch.int32),
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torch.randint(max64, (20, 20), device="cuda:0", dtype=torch.int64),
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)
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actual = fn_opt(*inputs)
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expected = fn(*inputs)
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self.assertEqual(actual, expected)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
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@requires_cuda
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@scalar_bin_ops
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def test_kernel_split_arg_limit_list(self, op):
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# NB: foeach_copy won't pass this test because it will dce one set of buffers
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def fn(a, b):
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return op(a, b)
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fn_opt = torch._dynamo.optimize()(fn)
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max_args = 370
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max_list_len = (max_args // 3) + 1
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inputs = (
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[torch.rand(10, 10, device="cuda:0") for _ in range(max_list_len)],
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[torch.rand(10, 10, device="cuda:0") for _ in range(max_list_len)],
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)
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actual = fn_opt(*inputs)
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expected = fn(*inputs)
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self.assertEqual(actual, expected)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 2)
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@requires_cuda
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@scalar_bin_ops
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@unittest.skip(
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"Triton recursion depth exceeded: https://github.com/openai/triton/issues/1763"
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)
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def test_kernel_split_arg_limit_scalar(self, op):
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def fn(a):
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return op(a, 3.3)
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fn_opt = torch._dynamo.optimize()(fn)
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max_args = 370
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max_list_len = (max_args // 2) + 1
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inputs = ([torch.rand(10, 10, device="cuda:0") for _ in range(max_list_len)],)
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actual = fn_opt(*inputs)
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expected = fn(*inputs)
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self.assertEqual(actual, expected)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 2)
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@requires_cuda
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@bin_ops
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def test_fusion_duplicate_buffer_list(self, op):
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def fn(a0, a1, b0, b1):
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c = op([a0, a1], [b0, b1])
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return op([a0, b0], [c[0], c[0]])
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self.check_model_cuda(
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fn,
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(
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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),
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reference_in_float=False,
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check_lowp=False,
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)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
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@requires_cuda
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@all_ops
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def test_non_foreach_consumer_list(self, op):
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if op in un_ops_under_test:
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def fn(a0, a1):
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c = op([a0, a1])
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return torch.mul(c[0], a0)
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else:
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def fn(a0, a1, b0, b1):
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c = op([a0, a1], [b0, b1])
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return torch.mul(c[0], a0)
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self.check_model_cuda(
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fn,
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gen_args(op),
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)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
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@requires_cuda
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@scalar_bin_ops
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def test_non_foreach_consumer_scalar(self, op):
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def fn(a0, a1):
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c = op([a0, a1], 4.7)
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return torch.mul(c[0], a0)
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self.check_model_cuda(
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fn,
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(
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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),
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)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
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@requires_cuda
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@all_ops
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def test_non_foreach_producer_list(self, op):
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if op in un_ops_under_test:
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def fn(a0, a1):
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c0 = torch.add(a0, a0)
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c1 = torch.add(a1, a1)
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return op([c0, c1])
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else:
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def fn(a0, a1, b0, b1):
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c0 = torch.add(a0, b0)
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c1 = torch.add(a1, b1)
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return op([a0, a1], [c0, c1])
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self.check_model_cuda(
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fn, gen_args(op), reference_in_float=False, check_lowp=False
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)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
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@requires_cuda
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@scalar_bin_ops
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def test_non_foreach_producer_scalar(self, op):
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def fn(a0, a1, b0, b1):
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c0 = torch.mul(a0, b0)
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c1 = torch.mul(a1, b1)
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return op([c0, c1], 5.6)
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self.check_model_cuda(
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fn,
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(
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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),
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)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
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@requires_cuda
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@all_ops
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def test_non_foreach_consumer_producer_list(self, op):
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if op in un_ops_under_test:
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def fn(a0, a1):
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c0 = torch.add(a0, a0)
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c1 = torch.mul(a1, a1)
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d = op([c0, c1])
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e0 = torch.mul(d[0], a0)
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e1 = torch.mul(d[1], a1)
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return [e0, e1]
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else:
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def fn(a0, a1, b0, b1):
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c0 = torch.add(a0, b0)
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c1 = torch.add(a1, b1)
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d = op([a0, a1], [c0, c1])
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e0 = torch.mul(d[0], a0)
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e1 = torch.mul(d[1], a1)
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return [e0, e1]
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self.check_model_cuda(
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fn,
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gen_args(op),
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reference_in_float=False,
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check_lowp=False,
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)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
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@requires_cuda
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@scalar_bin_ops
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def test_non_foreach_consumer_producer_scalar(self, op):
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def fn(a0, a1, b0, b1):
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c0 = torch.add(a0, b0)
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c1 = torch.add(a1, b1)
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d = op([c0, c1], 5.8)
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e0 = torch.mul(d[0], a0)
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e1 = torch.mul(d[1], a1)
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return [e0, e1]
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self.check_model_cuda(
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fn,
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(
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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),
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reference_in_float=False,
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check_lowp=False,
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)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
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@requires_cuda
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@bin_ops
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@torch._dynamo.config.patch("automatic_dynamic_shapes", False)
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@torch._dynamo.config.patch("assume_static_by_default", False)
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def test_dynamic_shapes_fallback(self, op):
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def fn(a0, a1, b0, b1):
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return op([a0, a1], [b0, b1])
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inputs = (
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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torch.rand(10, 10, device="cuda:0"),
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torch.rand(20, 20, device="cuda:0"),
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)
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self.check_model_cuda(fn, inputs)
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self.assertEqual(torch._inductor.metrics.generated_kernel_count, 2)
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@unittest.skipIf(IS_FBCODE, "cpp compile not supported in fbcode")
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@bin_ops
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def test_cpu_cpp_fallback(self, op):
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def fn(a0, a1, b0, b1):
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return op([a0, a1], [b0, b1])
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inputs = (
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torch.rand(10, 10, device="cpu"),
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torch.rand(20, 20, device="cpu"),
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torch.rand(10, 10, device="cpu"),
|
|
torch.rand(20, 20, device="cpu"),
|
|
)
|
|
|
|
self.check_model_cpu(fn, inputs)
|
|
|
|
self.assertEqual(torch._inductor.metrics.generated_kernel_count, 2)
|
|
|
|
@requires_cuda
|
|
@decomp_ops
|
|
def test_decomp(self, op):
|
|
def fn(a0, a1, b0, b1, c0, c1):
|
|
return op([a0, a1], [b0, b1], [c0, c1], value=0.5)
|
|
|
|
self.check_model_cuda(
|
|
fn,
|
|
(
|
|
torch.rand(10, 10, device="cuda:0"),
|
|
torch.rand(20, 20, device="cuda:0"),
|
|
torch.rand(10, 10, device="cuda:0"),
|
|
torch.rand(20, 20, device="cuda:0"),
|
|
torch.rand(10, 10, device="cuda:0"),
|
|
torch.rand(20, 20, device="cuda:0"),
|
|
),
|
|
)
|
|
|
|
self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
|
|
|
|
@requires_cuda
|
|
def test_fuse_concat(self):
|
|
def fn(x1, x2, x3, w1, w2, w3):
|
|
x = torch.stack([x1, x2, x3])
|
|
w = torch.stack([w1, w2, w3])
|
|
|
|
y = torch.bmm(x, w)
|
|
|
|
return y
|
|
|
|
x1 = torch.randn(5, 4).cuda()
|
|
x2 = x1 + 1
|
|
x3 = x1 + 2
|
|
w1 = torch.randn(4, 3).cuda()
|
|
w2 = w1 + 1
|
|
w3 = w1 + 2
|
|
|
|
args = (x1, x2, x3, w1, w2, w3)
|
|
|
|
self.check_model_cuda(fn, args)
|
|
|
|
self.assertEqual(torch._inductor.metrics.generated_kernel_count, 2)
|
|
|
|
@requires_cuda
|
|
def test_zero_elems(self):
|
|
def fn(a0, a1, b0, b1):
|
|
return torch._foreach_add([a0, a1], [b0, b1])
|
|
|
|
self.check_model_cuda(
|
|
fn,
|
|
(
|
|
torch.rand(0, device="cuda:0"),
|
|
torch.rand(10, 10, device="cuda:0"),
|
|
torch.rand(0, device="cuda:0"),
|
|
torch.rand(10, 10, device="cuda:0"),
|
|
),
|
|
)
|
|
|
|
self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
|
|
|
|
@requires_cuda
|
|
@bin_ops
|
|
def test_2d_blocking(self, op):
|
|
def fn(a0, a1, b0, b1):
|
|
return op([a0, a1], [b0, b1])
|
|
|
|
self.check_model_cuda(
|
|
fn,
|
|
(
|
|
torch.rand(10, 40, device="cuda:0"),
|
|
torch.rand(10, 30, device="cuda:0"),
|
|
torch.rand(40, 10, device="cuda:0").t(),
|
|
torch.rand(30, 10, device="cuda:0").t(),
|
|
),
|
|
)
|
|
|
|
self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
|
|
|
|
@requires_cuda
|
|
@bin_ops
|
|
def test_2d_blocking_partitioning(self, op):
|
|
def fn(a0, a1, b0, b1):
|
|
return op([a0, a1], [b0, b1])
|
|
|
|
self.check_model_cuda(
|
|
fn,
|
|
(
|
|
torch.rand(30, 20, device="cuda:0"),
|
|
torch.rand(40, 30, device="cuda:0"),
|
|
torch.rand(30, 20, device="cuda:0"),
|
|
torch.rand(30, 40, device="cuda:0").t(),
|
|
),
|
|
)
|
|
|
|
self.assertEqual(torch._inductor.metrics.generated_kernel_count, 2)
|
|
|
|
@requires_cuda
|
|
@bin_ops
|
|
def test_2d_blocking_partitioning_elems(self, op):
|
|
"""2D blocking should be grouped by number of yelems"""
|
|
|
|
def fn(a0, a1, a2, b0, b1, b2):
|
|
return op([a0, a1, a2], [b0, b1, b2])
|
|
|
|
self.check_model_cuda(
|
|
fn,
|
|
(
|
|
torch.rand(10, 20, device="cuda:0"),
|
|
torch.rand(30, 20, device="cuda:0"),
|
|
torch.rand(10, 30, device="cuda:0"),
|
|
torch.rand(20, 10, device="cuda:0").t(),
|
|
torch.rand(20, 30, device="cuda:0").t(),
|
|
torch.rand(30, 10, device="cuda:0").t(),
|
|
),
|
|
)
|
|
|
|
self.assertEqual(torch._inductor.metrics.generated_kernel_count, 2)
|
|
|
|
@requires_cuda
|
|
@inplace_bin_ops
|
|
def test_reinplacing(self, op):
|
|
def fn(a0, a1, b0, b1):
|
|
op([a0, a1], [b0, b1])
|
|
return [a0, a1]
|
|
|
|
inputs = (
|
|
torch.rand(10, 10, device="cuda:0"),
|
|
torch.rand(20, 20, device="cuda:0"),
|
|
torch.rand(10, 10, device="cuda:0"),
|
|
torch.rand(20, 20, device="cuda:0"),
|
|
)
|
|
|
|
self.check_model_cuda(fn, inputs, check_lowp=False)
|
|
|
|
self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
|
|
|
|
@requires_cuda
|
|
@inplace_bin_ops
|
|
def test_reinplacing_mut_before(self, op):
|
|
def fn(a0, a1, b0, b1):
|
|
a0.add_(torch.ones(10, 10, device="cuda:0"))
|
|
op([a0, a1], [b0, b1])
|
|
return [a0, a1]
|
|
|
|
inputs = (
|
|
torch.rand(10, 10, device="cuda:0"),
|
|
torch.rand(20, 20, device="cuda:0"),
|
|
torch.rand(10, 10, device="cuda:0"),
|
|
torch.rand(20, 20, device="cuda:0"),
|
|
)
|
|
|
|
self.check_model_cuda(fn, inputs, check_lowp=False)
|
|
|
|
self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
|
|
|
|
@requires_cuda
|
|
@inplace_bin_ops
|
|
def test_reinplacing_mut_after(self, op):
|
|
def fn(a0, a1, b0, b1):
|
|
op([a0, a1], [b0, b1])
|
|
a0.add_(torch.ones(10, 10, device="cuda:0"))
|
|
return [a0, a1]
|
|
|
|
inputs = (
|
|
torch.rand(10, 10, device="cuda:0"),
|
|
torch.rand(20, 20, device="cuda:0"),
|
|
torch.rand(10, 10, device="cuda:0"),
|
|
torch.rand(20, 20, device="cuda:0"),
|
|
)
|
|
|
|
self.check_model_cuda(fn, inputs, check_lowp=False)
|
|
|
|
self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
|
|
|
|
@requires_cuda
|
|
def test_multi_device(self):
|
|
def test_foreach_add(a0, a1, b0, b1):
|
|
return torch._foreach_add([a0, a1], [b0, b1])
|
|
|
|
inps = [
|
|
torch.ones(10, 10, device="cuda"),
|
|
torch.ones(20, 20, device="cpu"),
|
|
torch.zeros(10, 10, device="cuda"),
|
|
torch.zeros(20, 20, device="cpu"),
|
|
]
|
|
|
|
out_eager = test_foreach_add(*inps)
|
|
out_compiled = torch.compile(test_foreach_add)(*inps)
|
|
|
|
self.assertEqual(out_eager, out_compiled)
|
|
self.assertEqual(torch._inductor.metrics.generated_kernel_count, 2)
|
|
|
|
@requires_cuda
|
|
def test_aliasing(self):
|
|
def test_foreach_add(a0, a1, a2, b0, b1, b2):
|
|
return torch._foreach_add_([a0, a1, a2], [b0, b1, b2])
|
|
|
|
input = torch.ones(10, 10, device="cuda")
|
|
input2 = torch.ones(10, 10, device="cuda")
|
|
inps = [
|
|
input,
|
|
input.view(10, 10),
|
|
input.view(10, 10),
|
|
input2,
|
|
input2.view(10, 10),
|
|
input2.view(10, 10),
|
|
]
|
|
|
|
out_eager = test_foreach_add(*inps)
|
|
out_compiled = torch.compile(test_foreach_add)(*inps)
|
|
|
|
self.assertEqual(out_eager, out_compiled)
|
|
self.assertEqual(torch._inductor.metrics.generated_kernel_count, 4)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from torch._inductor.test_case import run_tests
|
|
|
|
if HAS_CPU or HAS_CUDA:
|
|
run_tests(needs="filelock")
|