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https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
Delete ifdyn and ifunspec combinators (#103596)
Replaced with expect tests for ease of updating. Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/103596 Approved by: https://github.com/voznesenskym
This commit is contained in:
Edward Z. Yang
PyTorch MergeBot
parent
e82616d900
commit
ddf4cd69ec
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@ -39,7 +39,7 @@ from torch._dynamo.testing import (
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unsupported,
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)
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from torch._dynamo.utils import CompileProfiler, ifdyn, ifdynstaticdefault, ifunspec
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from torch._dynamo.utils import CompileProfiler, ifdynstaticdefault
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from torch.ao.quantization import MinMaxObserver
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from torch.ao.quantization.fake_quantize import FakeQuantize
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from torch.ao.quantization.qconfig import QConfig
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@ -351,8 +351,10 @@ class MiscTests(torch._dynamo.test_case.TestCase):
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self.assertTrue(same(ref, res))
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self.assertEqual(counts.frame_count, 1)
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expected_op_count = ifdynstaticdefault(1, 11)
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self.assertEqual(counts.op_count, expected_op_count)
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if torch._dynamo.config.assume_static_by_default:
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self.assertExpectedInline(counts.op_count, """1""")
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else:
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self.assertExpectedInline(counts.op_count, """11""")
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@torch._dynamo.config.patch(dynamic_shapes=True)
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def test_user_defined_binop(self):
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@ -377,8 +379,10 @@ class MiscTests(torch._dynamo.test_case.TestCase):
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self.assertTrue(same(ref, res))
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self.assertEqual(counts.frame_count, 1)
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expected_op_count = ifdynstaticdefault(1, 4)
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self.assertEqual(counts.op_count, expected_op_count)
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if torch._dynamo.config.assume_static_by_default:
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self.assertExpectedInline(counts.op_count, """1""")
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else:
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self.assertExpectedInline(counts.op_count, """4""")
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def test_compare_shapes_eq(self):
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def compare_shapes(a, b, to_list):
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@ -941,8 +945,12 @@ class MiscTests(torch._dynamo.test_case.TestCase):
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# output anything and none of the traced operations have side
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# effects. Probably need better heuristic for bailing on
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# dynamo if there are no outputs
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self.assertEqual(cnts.frame_count, ifunspec(1, 0))
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self.assertEqual(cnts.op_count, ifunspec(2, 0))
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if torch._dynamo.config.assume_static_by_default:
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self.assertExpectedInline(cnts.frame_count, """0""")
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self.assertExpectedInline(cnts.op_count, """0""")
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else:
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self.assertExpectedInline(cnts.frame_count, """1""")
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self.assertExpectedInline(cnts.op_count, """2""")
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def test_list_slice_mul(self):
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def fn(count):
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@ -953,8 +961,12 @@ class MiscTests(torch._dynamo.test_case.TestCase):
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cnts = torch._dynamo.testing.CompileCounter()
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opt_fn = torch._dynamo.optimize(cnts)(fn)
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self.assertEqual(opt_fn(2), [2, 3] * 4)
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self.assertEqual(cnts.frame_count, ifunspec(1, 0))
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self.assertEqual(cnts.op_count, ifunspec(2, 0))
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if torch._dynamo.config.assume_static_by_default:
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self.assertExpectedInline(cnts.frame_count, """0""")
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self.assertExpectedInline(cnts.op_count, """0""")
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else:
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self.assertExpectedInline(cnts.frame_count, """1""")
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self.assertExpectedInline(cnts.op_count, """2""")
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def test_tuple_mul(self):
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def fn(count):
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@ -964,8 +976,12 @@ class MiscTests(torch._dynamo.test_case.TestCase):
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cnts = torch._dynamo.testing.CompileCounter()
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opt_fn = torch._dynamo.optimize(cnts)(fn)
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self.assertEqual(opt_fn(2), (2, 3) * 4)
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self.assertEqual(cnts.frame_count, ifunspec(1, 0))
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self.assertEqual(cnts.op_count, ifunspec(ifdynstaticdefault(2, 2), 0))
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if torch._dynamo.config.assume_static_by_default:
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self.assertExpectedInline(cnts.frame_count, """0""")
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self.assertExpectedInline(cnts.op_count, """0""")
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else:
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self.assertExpectedInline(cnts.frame_count, """1""")
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self.assertExpectedInline(cnts.op_count, """2""")
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def test_tuple_mul_with_shape(self):
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def fn(a):
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@ -2377,14 +2393,20 @@ def fn():
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opt_m(data, correct_ref_id)
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# Extra op is the recorded equality test (although once
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# the trace is flattened this is dead!)
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self.assertEqual(cnts.op_count, ifunspec(3, 2))
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if torch._dynamo.config.assume_static_by_default:
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self.assertExpectedInline(cnts.op_count, """2""")
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else:
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self.assertExpectedInline(cnts.op_count, """3""")
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torch._dynamo.reset()
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cnts = torch._dynamo.testing.CompileCounter()
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incorrect_ref_id = id(m) + 1
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opt_m = torch._dynamo.optimize(cnts, nopython=True)(m)
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opt_m(data, incorrect_ref_id)
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self.assertEqual(cnts.op_count, ifunspec(2, 1))
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if torch._dynamo.config.assume_static_by_default:
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self.assertExpectedInline(cnts.op_count, """1""")
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else:
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self.assertExpectedInline(cnts.op_count, """2""")
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def test_inline_func_jump_on_tensor_condition(self):
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def f1(input):
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@ -4441,7 +4463,10 @@ def fn():
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ref = fn(x, y)
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res = opt_fn(x, y)
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self.assertTrue(same(ref, res))
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self.assertEqual(cnt.frame_count, ifunspec(ifdyn(1, 5), 5))
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if torch._dynamo.config.assume_static_by_default:
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self.assertExpectedInline(cnt.frame_count, """5""")
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else:
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self.assertExpectedInline(cnt.frame_count, """1""")
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# specifically test for tensor.attribute -> torch.something()
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def test_real_imag_tensor_attribute(self):
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@ -31,7 +31,6 @@ import torch.library
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from torch import nn
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from torch._dynamo.debug_utils import same_two_models
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from torch._dynamo.testing import rand_strided, requires_static_shapes, same
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from torch._dynamo.utils import ifdyn, ifdynstaticdefault, ifunspec
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from torch.nn import functional as F
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@ -877,8 +876,12 @@ class ReproTests(torch._dynamo.test_case.TestCase):
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# repeat_interleave is a dynamic shape operator we do not execute/
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# In the future, we could reduce the frame_count down to 1
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# by guarding on the exact values of `Tensor repeats` arg
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self.assertEqual(cnt.frame_count, 4)
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self.assertEqual(cnt.op_count, ifdyn(16, 10))
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if torch._dynamo.config.assume_static_by_default:
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self.assertExpectedInline(cnt.frame_count, """4""")
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self.assertExpectedInline(cnt.op_count, """10""")
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else:
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self.assertExpectedInline(cnt.frame_count, """4""")
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self.assertExpectedInline(cnt.op_count, """16""")
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def test_boxes_len(self):
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def fn(boxes):
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@ -889,8 +892,12 @@ class ReproTests(torch._dynamo.test_case.TestCase):
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opt_fn = torch._dynamo.optimize_assert(cnt)(fn)
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self.assertTrue(same(opt_fn(boxes1), boxes1.tensor + 4.0))
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self.assertEqual(cnt.frame_count, 1)
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self.assertEqual(cnt.op_count, ifdyn(ifdynstaticdefault(1, 6), 1))
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if torch._dynamo.config.assume_static_by_default:
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self.assertExpectedInline(cnt.frame_count, """1""")
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self.assertExpectedInline(cnt.op_count, """1""")
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else:
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self.assertExpectedInline(cnt.frame_count, """1""")
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self.assertExpectedInline(cnt.op_count, """6""")
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def _reformer(self, nopython):
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input = torch.randn([1, 64, 256])
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@ -964,8 +971,12 @@ class ReproTests(torch._dynamo.test_case.TestCase):
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with torch.enable_grad():
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cnt = self._reformer(nopython=False)
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# cant inline torch.autograd.Function means graph break
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self.assertEqual(cnt.frame_count, ifunspec(ifdyn(3, 1), 3))
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self.assertEqual(cnt.op_count, ifunspec(ifdyn(10, 11), 10))
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if torch._dynamo.config.assume_static_by_default:
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self.assertExpectedInline(cnt.frame_count, """3""")
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self.assertExpectedInline(cnt.op_count, """10""")
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else:
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self.assertExpectedInline(cnt.frame_count, """3""")
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self.assertExpectedInline(cnt.op_count, """10""")
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def test_longformer_chunk(self):
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input1 = torch.randn([1, 4096, 1])
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@ -980,10 +991,12 @@ class ReproTests(torch._dynamo.test_case.TestCase):
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self.assertTrue(same(opt_fn(input1), correct1))
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self.assertTrue(same(opt_fn(input2), correct2))
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self.assertEqual(cnt.frame_count, 2)
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self.assertEqual(
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cnt.op_count, ifunspec(35, ifdyn(ifdynstaticdefault(4, 20), 4))
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)
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if torch._dynamo.config.assume_static_by_default:
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self.assertExpectedInline(cnt.frame_count, """2""")
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self.assertExpectedInline(cnt.op_count, """4""")
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else:
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self.assertExpectedInline(cnt.frame_count, """2""")
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self.assertExpectedInline(cnt.op_count, """35""")
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def test_hf_t5_forward(self):
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input = torch.randn([1, 2048, 512])
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@ -993,8 +1006,12 @@ class ReproTests(torch._dynamo.test_case.TestCase):
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opt_model = torch._dynamo.optimize_assert(cnt)(model)
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self.assertTrue(same(opt_model(input), correct))
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self.assertEqual(cnt.frame_count, 1)
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self.assertEqual(cnt.op_count, ifdyn(ifdynstaticdefault(11, 12), 11))
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if torch._dynamo.config.assume_static_by_default:
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self.assertExpectedInline(cnt.frame_count, """1""")
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self.assertExpectedInline(cnt.op_count, """11""")
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else:
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self.assertExpectedInline(cnt.frame_count, """1""")
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self.assertExpectedInline(cnt.op_count, """12""")
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def test_module_in_skipfiles(self):
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model = nn.Linear(10, 10)
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@ -1072,7 +1089,10 @@ class ReproTests(torch._dynamo.test_case.TestCase):
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for _ in range(10):
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self.assertTrue(same(opt_model(a, b, c, d), correct))
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# self.assertEqual(cnt.frame_count, ifdyn(3, 2))
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# if torch._dynamo.config.assume_static_by_default:
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# self.assertExpectedInline(cnt.frame_count, """2""")
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# else:
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# self.assertExpectedInline(cnt.frame_count, """3""")
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# TODO(jansel): figure out why op count depends on imports
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self.assertIn(cnt.op_count, (36, 35, 34, 29, 28, 27))
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@ -1091,9 +1111,9 @@ class ReproTests(torch._dynamo.test_case.TestCase):
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self.assertTrue(same(opt_model(a, b, c, d), correct))
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if torch._dynamo.config.assume_static_by_default:
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self.assertEqual(cnt.frame_count, ifdyn(2, 4))
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self.assertExpectedInline(cnt.frame_count, """2""")
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else:
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self.assertEqual(cnt.frame_count, ifdyn(3, 6))
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self.assertExpectedInline(cnt.frame_count, """3""")
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def test_hf_model_output(self):
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ex = ModelOutput(a=torch.randn(10), b=torch.randn(10), c=torch.randn(10))
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@ -1271,9 +1291,13 @@ class ReproTests(torch._dynamo.test_case.TestCase):
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cnt = torch._dynamo.testing.CompileCounter()
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opt_fn = torch._dynamo.optimize_assert(cnt)(fn)
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self.assertEqual(opt_fn(cfg), 64)
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self.assertEqual(cnt.frame_count, 1)
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# With unspec int, maximum computation is preserved
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self.assertEqual(cnt.op_count, ifunspec(4, 3))
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if torch._dynamo.config.assume_static_by_default:
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self.assertExpectedInline(cnt.frame_count, """1""")
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self.assertExpectedInline(cnt.op_count, """3""")
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else:
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self.assertExpectedInline(cnt.frame_count, """1""")
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self.assertExpectedInline(cnt.op_count, """4""")
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def test_reformer_sorting(self):
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x = torch.zeros([1, 12, 4096], dtype=torch.int64)
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@ -1283,8 +1307,12 @@ class ReproTests(torch._dynamo.test_case.TestCase):
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cnt = torch._dynamo.testing.CompileCounter()
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opt_fn = torch._dynamo.optimize_assert(cnt)(fn)
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self.assertTrue(same(opt_fn(x), correct))
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self.assertEqual(cnt.frame_count, 1)
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self.assertEqual(cnt.op_count, ifdyn(ifdynstaticdefault(14, 27), 14))
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if torch._dynamo.config.assume_static_by_default:
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self.assertExpectedInline(cnt.frame_count, """1""")
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self.assertExpectedInline(cnt.op_count, """14""")
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else:
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self.assertExpectedInline(cnt.frame_count, """1""")
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self.assertExpectedInline(cnt.op_count, """27""")
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def test_recursive_map(self):
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# https://github.com/pytorch/torchdynamo/issues/132
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@ -8,7 +8,7 @@ import torch._dynamo.test_case
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import torch._dynamo.testing
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from torch._dynamo import config
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from torch._dynamo.testing import unsupported
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from torch._dynamo.utils import ifdyn, ifdynstaticdefault, ifunspec
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from torch._dynamo.utils import ifdynstaticdefault
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globalmod = torch.nn.ReLU()
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@ -328,7 +328,7 @@ class SubGraphTests(torch._dynamo.test_case.TestCase):
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# means we fail to unroll the loop.
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# TODO: Consider forcing specialization when we iterate over
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# the loop
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self._common(fn, 2, ifunspec(1, 4))
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self._common(fn, 2, ifdynstaticdefault(4, 1))
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def test_restore_range_iter(self):
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def fn(a, b):
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@ -598,7 +598,7 @@ class SubGraphTests(torch._dynamo.test_case.TestCase):
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b = b + x * i
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return b
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self._common(fn, 1, ifdyn(ifdynstaticdefault(2, 7), 2))
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self._common(fn, 1, ifdynstaticdefault(2, 7))
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if __name__ == "__main__":
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@ -35,7 +35,9 @@ cache_size_limit = 64
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# whether or not to specialize on int inputs. This only has an effect with
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# dynamic_shapes; when dynamic_shapes is False, we ALWAYS specialize on int
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# inputs
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# inputs. Note that assume_static_by_default will also cause ints to get
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# specialized, so this is mostly useful for export, where we want inputs
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# to be dynamic, but accesses to ints should NOT get promoted into inputs.
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specialize_int = False
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# Assume these functions return constants
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@ -1398,13 +1398,6 @@ def fqn(obj: Any):
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return f"{obj.__module__}.{obj.__qualname__}"
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def ifdyn(count1, count2):
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if torch._dynamo.config.dynamic_shapes:
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return count1
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else:
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return count2
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def ifdynstaticdefault(count1, count2):
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if torch._dynamo.config.assume_static_by_default:
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return count1
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@ -1412,13 +1405,6 @@ def ifdynstaticdefault(count1, count2):
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return count2
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def ifunspec(count1, count2):
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if torch._dynamo.config.dynamic_shapes and not torch._dynamo.config.specialize_int:
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return count1
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else:
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return count2
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def import_submodule(mod: types.ModuleType):
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"""
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Ensure all the files in a given submodule are imported
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@ -2159,6 +2159,9 @@ class TestCase(expecttest.TestCase):
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def enforceNonDefaultStream(self):
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return CudaNonDefaultStream()
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def assertExpectedInline(self, actual, expect, skip=0):
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return super().assertExpectedInline(actual if isinstance(actual, str) else str(actual), expect, skip + 1)
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def assertLogs(self, logger=None, level=None):
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if logger is None:
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logger = logging.getLogger("torch")
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