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https://github.com/pytorch/pytorch.git
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918ece4f4d
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/129762 Approved by: https://github.com/anijain2305
323 lines
12 KiB
Python
323 lines
12 KiB
Python
# Owner(s): ["module: dynamo"]
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from unittest.mock import patch
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import torch
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import torch._dynamo.test_case
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import torch._dynamo.testing
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class RecompileTests(torch._dynamo.test_case.TestCase):
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def test_automatic_dynamic_reduce_recompiles(self):
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# Test the counterfactual, lots of recompiles without this config
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def foo(x, y):
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return x * y
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def run_foo_6_times_and_count_recompiles(dynamic=None):
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cnt = torch._dynamo.testing.CompileCounter()
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x = torch.randn([2])
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y = torch.randn([2])
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opt = torch._dynamo.optimize(cnt, dynamic=dynamic)(foo)
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opt(x, y)
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x = torch.randn([3])
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y = torch.randn([3])
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opt(x, y)
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x = torch.randn([4])
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y = torch.randn([4])
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opt(x, y)
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opt(x, y)
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x = torch.randn([5])
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y = torch.randn([5])
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opt(x, y)
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opt(x, y)
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x = torch.randn([6])
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y = torch.randn([6])
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opt(x, y)
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return cnt
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@patch.object(torch._dynamo.config, "automatic_dynamic_shapes", False)
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@patch.object(torch._dynamo.config, "assume_static_by_default", True)
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def run_without_automatic():
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return run_foo_6_times_and_count_recompiles()
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@patch.object(torch._dynamo.config, "automatic_dynamic_shapes", True)
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@patch.object(torch._dynamo.config, "assume_static_by_default", True)
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def run_with_automatic():
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return run_foo_6_times_and_count_recompiles()
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without = run_without_automatic()
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self.assertEqual(without.frame_count, 5)
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self.assertEqual(without.op_count, 5)
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torch._dynamo.reset()
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without = run_foo_6_times_and_count_recompiles(dynamic=False)
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self.assertEqual(without.frame_count, 5)
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self.assertEqual(without.op_count, 5)
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torch._dynamo.reset()
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with_automatic = run_with_automatic()
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self.assertEqual(with_automatic.frame_count, 2)
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self.assertEqual(with_automatic.op_count, 2)
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torch._dynamo.reset()
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with_automatic = run_foo_6_times_and_count_recompiles(dynamic=None)
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self.assertEqual(with_automatic.frame_count, 2)
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self.assertEqual(with_automatic.op_count, 2)
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torch._dynamo.reset()
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with_dynamic = run_foo_6_times_and_count_recompiles(dynamic=True)
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self.assertEqual(with_dynamic.frame_count, 1)
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self.assertEqual(with_dynamic.op_count, 1)
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@patch.object(torch._dynamo.config, "assume_static_by_default", True)
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def test_recompiles_true_false_flop(self):
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# Test the counterfactual, lots of recompiles without this config
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def foo(x, y):
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if x:
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return y * 2
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else:
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return y * y
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def run_foo_6_times_and_count_recompiles():
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cnt = torch._dynamo.testing.CompileCounter()
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opt = torch._dynamo.optimize(cnt, nopython=True)(foo)
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x = True
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y = torch.randn([2])
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opt(x, y)
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x = False
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y = torch.randn([2])
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opt(x, y)
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x = True
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y = torch.randn([3])
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opt(x, y)
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x = True
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y = torch.randn([4])
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opt(x, y)
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x = True
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y = torch.randn([5])
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opt(x, y)
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return cnt
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@patch.object(torch._dynamo.config, "automatic_dynamic_shapes", False)
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@patch.object(torch._dynamo.config, "assume_static_by_default", True)
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def run_without_automatic():
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return run_foo_6_times_and_count_recompiles()
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@patch.object(torch._dynamo.config, "automatic_dynamic_shapes", True)
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@patch.object(torch._dynamo.config, "assume_static_by_default", True)
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def run_with_automatic():
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return run_foo_6_times_and_count_recompiles()
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without = run_without_automatic()
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self.assertEqual(without.frame_count, 5)
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self.assertEqual(without.op_count, 5)
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torch._dynamo.reset()
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with_automatic = run_with_automatic()
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self.assertEqual(with_automatic.frame_count, 3)
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self.assertEqual(with_automatic.op_count, 3)
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def test_automatic_dynamic_tensor_scalar_change(self):
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# Test the counterfactual, lots of recompiles without this config
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def foo(x, y):
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return x * y
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def run_foo_6_times_and_count_recompiles_swap_types():
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cnt = torch._dynamo.testing.CompileCounter()
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x = torch.randn([2])
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y = torch.randn([2])
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opt = torch._dynamo.optimize(cnt)(foo)
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opt(x, y)
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x = torch.randn([3])
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y = 3
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opt(x, y)
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x = torch.randn([4])
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y = torch.randn([4])
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opt(x, y)
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opt(x, y)
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x = torch.randn([5])
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y = 4
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opt(x, y)
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opt(x, y)
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x = torch.randn([6])
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y = torch.randn([6])
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opt(x, y)
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return cnt
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@patch.object(torch._dynamo.config, "automatic_dynamic_shapes", False)
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@patch.object(torch._dynamo.config, "assume_static_by_default", True)
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def run_without_automatic():
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return run_foo_6_times_and_count_recompiles_swap_types()
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@patch.object(torch._dynamo.config, "automatic_dynamic_shapes", True)
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@patch.object(torch._dynamo.config, "assume_static_by_default", True)
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def run_with_automatic():
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return run_foo_6_times_and_count_recompiles_swap_types()
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without = run_without_automatic()
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self.assertEqual(without.frame_count, 5)
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self.assertEqual(without.op_count, 5)
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torch._dynamo.reset()
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with_automatic = run_with_automatic()
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self.assertEqual(with_automatic.frame_count, 3)
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self.assertEqual(with_automatic.op_count, 3)
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def test_aliasing_guard_failures(self):
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def foo(a, b, c):
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a.add_(b)
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return c + 1
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cnt = torch._dynamo.testing.CompileCounter()
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compiled_foo = torch._dynamo.optimize(cnt, nopython=True)(foo)
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x = torch.randn([3])
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y = torch.randn([3])
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z = torch.randn([3])
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cmp_result = compiled_foo(
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x.clone().detach(), y.clone().detach(), z.clone().detach()
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)
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eager_result = foo(x.clone().detach(), y.clone().detach(), z.clone().detach())
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self.assertEqual(cmp_result, eager_result)
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self.assertEqual(cnt.frame_count, 1)
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cmp_result = compiled_foo(
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z.clone().detach(), y.clone().detach(), x.clone().detach()
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)
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eager_result = foo(z.clone().detach(), y.clone().detach(), x.clone().detach())
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self.assertEqual(cmp_result, eager_result)
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# No recompile, alias preserved
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self.assertEqual(cnt.frame_count, 1)
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x_clone = x.clone().detach()
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cmp_result = compiled_foo(x_clone, y.clone().detach(), x_clone)
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x_clone = x.clone().detach()
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eager_result = compiled_foo(x_clone, y.clone().detach(), x_clone)
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self.assertEqual(cmp_result, eager_result)
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# Recompile, alias changed
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self.assertEqual(cnt.frame_count, 2)
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def test_aliasing_guard_failures_with_globals(self):
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g1 = torch.randn([3])
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g2 = torch.randn([3])
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def foo(a):
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a.add_(g1)
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return g2 + 1
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cnt = torch._dynamo.testing.CompileCounter()
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compiled_foo = torch._dynamo.optimize(cnt, nopython=True)(foo)
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z = torch.randn([3])
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cmp_result = compiled_foo(z.clone().detach())
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eager_result = foo(z.clone().detach())
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self.assertEqual(cmp_result, eager_result)
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self.assertEqual(cnt.frame_count, 1)
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g1 = g1.clone().detach()
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cmp_result = compiled_foo(g1)
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g1 = g1.clone().detach()
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eager_result = compiled_foo(g1)
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self.assertEqual(cmp_result, eager_result)
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# Recompile, alias changed
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self.assertEqual(cnt.frame_count, 2)
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def test_dynamic_shape_parameter_recompile(self):
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# Test the matrix multiplication with Parameters.
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# Without the config assume_parameters_shapes_static_by_default,
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# the torch.nn.Parameter shapes are assumed to be static which leads to recompilation
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w = torch.nn.Parameter(torch.randn(3, 2))
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def foo(x):
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return x @ w
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def run_foo_6_times_and_count_recompiles():
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cnt = torch._dynamo.testing.CompileCounter()
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opt = torch._dynamo.optimize(cnt, nopython=True)(foo)
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x = torch.nn.Parameter(torch.randn(1, 3))
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opt(x)
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x = torch.nn.Parameter(torch.randn(10, 3))
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opt(x)
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x = torch.nn.Parameter(torch.randn(11, 3))
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opt(x)
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x = torch.nn.Parameter(torch.randn(15, 3))
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opt(x)
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x = torch.nn.Parameter(torch.randn(15, 3))
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opt(x)
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return cnt
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@patch.object(torch._dynamo.config, "force_parameter_static_shapes", True)
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@patch.object(torch._dynamo.config, "automatic_dynamic_shapes", False)
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@patch.object(torch._dynamo.config, "assume_static_by_default", True)
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def run_static_comp_default_param():
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return run_foo_6_times_and_count_recompiles()
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@patch.object(torch._dynamo.config, "force_parameter_static_shapes", True)
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@patch.object(torch._dynamo.config, "automatic_dynamic_shapes", True)
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@patch.object(torch._dynamo.config, "assume_static_by_default", True)
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def run_dynamic_comp_default_param():
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return run_foo_6_times_and_count_recompiles()
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@patch.object(torch._dynamo.config, "force_parameter_static_shapes", False)
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@patch.object(torch._dynamo.config, "automatic_dynamic_shapes", False)
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@patch.object(torch._dynamo.config, "assume_static_by_default", True)
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def run_static_comp_dynamic_param():
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return run_foo_6_times_and_count_recompiles()
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@patch.object(torch._dynamo.config, "force_parameter_static_shapes", False)
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@patch.object(torch._dynamo.config, "automatic_dynamic_shapes", True)
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@patch.object(torch._dynamo.config, "assume_static_by_default", True)
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def run_dynamic_comp_dynamic_param():
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return run_foo_6_times_and_count_recompiles()
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torch._dynamo.reset()
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static_comp_default_param = run_static_comp_default_param()
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self.assertEqual(static_comp_default_param.frame_count, 4)
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self.assertEqual(static_comp_default_param.op_count, 4)
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torch._dynamo.reset()
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dynamic_comp_default_param = run_dynamic_comp_default_param()
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self.assertEqual(dynamic_comp_default_param.frame_count, 4)
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self.assertEqual(dynamic_comp_default_param.op_count, 4)
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torch._dynamo.reset()
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static_comp_dynamic_param = run_static_comp_dynamic_param()
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self.assertEqual(static_comp_dynamic_param.frame_count, 4)
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self.assertEqual(static_comp_dynamic_param.op_count, 4)
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torch._dynamo.reset()
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dynamic_comp_dynamic_param = run_dynamic_comp_dynamic_param()
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self.assertEqual(dynamic_comp_dynamic_param.frame_count, 2)
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self.assertEqual(dynamic_comp_dynamic_param.op_count, 2)
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def test_simple_module_recompile(self):
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class SimpleDropout(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.dropout = torch.nn.Dropout(0.5)
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self.linear = torch.nn.Linear(10, 1)
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def forward(self, x):
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return self.dropout(self.linear(x))
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model = SimpleDropout()
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x = torch.randn(10)
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counter = torch._dynamo.testing.CompileCounter()
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model = torch.compile(model, backend=counter, fullgraph=True)
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for _ in range(20):
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model.eval()
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model(x)
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model.train()
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model(x)
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self.assertEqual(counter.frame_count, 2)
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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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