| # Owner(s): ["module: dynamo"] |
| import re |
| from textwrap import dedent |
| from unittest.mock import patch |
| |
| import torch |
| |
| import torch._dynamo |
| import torch._dynamo.test_case |
| import torch.fx.traceback as fx_traceback |
| import torch.utils._pytree as pytree |
| from torch._dynamo.testing import CompileCounter, expectedFailureDynamic, rand_strided |
| from torch._functorch.aot_autograd import _aot_export_function, create_functional_call |
| from torch.profiler import profile |
| from torch.testing._internal.common_utils import compare_equal_outs_and_grads |
| |
| |
| def maybe_dupe_op(x): |
| y = x + 1 |
| z = x + 2 |
| if x.numel() < 5: |
| return y, y |
| else: |
| return y, z |
| |
| |
| aten = torch.ops.aten |
| lib = torch.library.Library("custom", "DEF") |
| lib.define("maybe_dupe_op(Tensor a) -> (Tensor, Tensor)") |
| lib.impl("maybe_dupe_op", maybe_dupe_op, "CPU") |
| lib.impl("maybe_dupe_op", maybe_dupe_op, "Meta") |
| |
| |
| class AotAutogradFallbackTests(torch._dynamo.test_case.TestCase): |
| def test_LSTM(self): |
| # https://github.com/pytorch/torchdynamo/issues/1147 |
| class Repro(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.self_mod_model_lstm_lstm = torch.nn.LSTM( |
| 64, 64, num_layers=2, bidirectional=True |
| ) |
| |
| def forward(self, permute: torch.Tensor): |
| self_mod_model_lstm_lstm = self.self_mod_model_lstm_lstm(permute) |
| return (self_mod_model_lstm_lstm,) |
| |
| mod = Repro() |
| |
| aot_mod = torch._dynamo.optimize("aot_eager")(mod) |
| |
| args = [((92, 4, 64), (1, 5888, 92), torch.float32, "cpu", False)] |
| args = [ |
| rand_strided(sh, st, dt, dev).requires_grad_(rg) |
| for (sh, st, dt, dev, rg) in args |
| ] |
| |
| eager_result = mod(*args) |
| aot_result = aot_mod(*args) |
| self.assertTrue(torch._dynamo.testing.same(eager_result, aot_result)) |
| |
| def test_mutation(self): |
| # https://github.com/pytorch/torchdynamo/issues/1301 |
| def fn(param, y): |
| prev_grad = torch.is_grad_enabled() |
| try: |
| torch.set_grad_enabled(False) |
| param.add_(y) |
| finally: |
| torch.set_grad_enabled(prev_grad) |
| return y |
| |
| y = torch.randn(4) |
| x = torch.nn.Parameter(torch.randn(4)) |
| aot_fn = torch._dynamo.optimize("aot_eager")(fn) |
| # This should not error: we mutated an autograd leaf under no_grad mode. |
| aot_fn(x, y) |
| |
| def test_mutation1(self): |
| def fn(_stack0: torch.Tensor, diagonal_chunked_attention_scores: torch.Tensor): |
| getitem = diagonal_chunked_attention_scores[ |
| ( |
| slice(None, None, None), |
| slice(None, None, None), |
| slice(None, 256, None), |
| slice(None, 257, None), |
| ) |
| ] |
| _stack0[ |
| ( |
| slice(None, None, None), |
| slice(None, -1, None), |
| slice(None, None, None), |
| slice(256, None, None), |
| ) |
| ] = getitem |
| view = _stack0.view(1, 12, 1024, 513) |
| return (view,) |
| |
| x = torch.randn(torch.Size([12, 4, 256, 513])) |
| y = torch.randn(torch.Size([12, 3, 512, 513])) |
| aot_fn = torch._dynamo.optimize("aot_eager")(fn) |
| aot_fn(x, y) |
| |
| def test_negative_testing_mutation(self): |
| def fn(_stack0: torch.Tensor, diagonal_chunked_attention_scores: torch.Tensor): |
| getitem = diagonal_chunked_attention_scores[ |
| ( |
| slice(None, None, None), |
| slice(None, None, None), |
| slice(None, 256, None), |
| slice(None, 257, None), |
| ) |
| ] |
| _stack0 = torch.sin(_stack0) |
| _stack0[ |
| ( |
| slice(None, None, None), |
| slice(None, -1, None), |
| slice(None, None, None), |
| slice(256, None, None), |
| ) |
| ] = getitem |
| view = _stack0.view(1, 12, 1024, 513) |
| return (view,) |
| |
| x = torch.randn(torch.Size([12, 4, 256, 513])) |
| y = torch.randn(torch.Size([12, 3, 512, 513])) |
| aot_fn = torch._dynamo.optimize("aot_eager")(fn) |
| aot_fn(x, y) |
| |
| def test_negative_testing(self): |
| def fn(x, y): |
| return torch.sin(x).add_(y) |
| |
| y = torch.randn(4) |
| x = torch.randn(4) |
| aot_fn = torch._dynamo.optimize("aot_eager")(fn) |
| aot_fn(x, y) |
| |
| def test_call_fn_with_non_const_inputs_aot_safe(self): |
| class ModuleSpecialFwd(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.conv = torch.nn.Conv2d( |
| in_channels=3, out_channels=20, kernel_size=(5, 5) |
| ) |
| |
| def _conv_forward(self, x): |
| return self.conv._conv_forward(x, self.conv.weight, self.conv.bias) |
| |
| def forward(self, x): |
| return self._conv_forward(x) |
| |
| # Init mod |
| mod = ModuleSpecialFwd() |
| rx = torch.randn([3, 10, 10]) |
| |
| # Run it for real |
| real = mod(rx) |
| |
| # Run it in export |
| graph, _ = torch._dynamo.export(mod)(rx) |
| |
| # Run exported graph with AOT |
| self.assertTrue(torch._dynamo.testing.same(real, graph(rx))) |
| |
| aot_fn = torch._dynamo.optimize("aot_eager")(graph) |
| aot_fn(rx) |
| |
| def test_call_fn_with_non_const_inputs_aot_unsafe(self): |
| class ModuleSpecialFwd(torch.nn.Module): |
| def _some_bad_fwd(self, param, y): |
| prev_grad = torch.is_grad_enabled() |
| try: |
| torch.set_grad_enabled(False) |
| param.add_(y) |
| finally: |
| torch.set_grad_enabled(prev_grad) |
| return y |
| |
| def forward(self, x, y): |
| return self._some_bad_fwd(x, y) |
| |
| # Init mod |
| mod = ModuleSpecialFwd() |
| x = torch.nn.Parameter(torch.randn(4)) |
| y = torch.randn([4]) |
| |
| # Run it for real |
| real = mod(x, y) |
| |
| # Run it in export |
| graph, _ = torch._dynamo.export(mod)(x, y) |
| |
| # Assert equal |
| self.assertTrue(torch._dynamo.testing.same(real, graph(x, y))) |
| |
| # Run exported graph with AOT |
| aot_fn = torch._dynamo.optimize("aot_eager")(graph) |
| # This should not error: we mutated an autograd leaf under no_grad mode. |
| aot_fn(x, y) |
| |
| def test_call_fn_with_non_const_inputs_aot_unsafe_control_flow(self): |
| class ModuleSpecialFwd(torch.nn.Module): |
| def _some_bad_fwd(self, param, y): |
| if y[0][0] < 3: |
| return y + param |
| return param * y |
| |
| def forward(self, x, y): |
| a = x * y |
| a = self._some_bad_fwd(a, a) |
| b = x + y |
| return a * b |
| |
| # Init mod |
| mod = ModuleSpecialFwd() |
| x = torch.nn.Parameter(torch.randn([2, 2])) |
| y = torch.randn([2, 2]) |
| |
| # Run it for real |
| real = mod(x, y) |
| |
| # Run it through optimize, with our capturing fn |
| |
| gms = [] |
| counter = CompileCounter() |
| |
| def capturing_fn(gm, inputs): |
| nonlocal gms |
| gms.append(gm) |
| return counter(gm, inputs) |
| |
| optimized_mod = torch._dynamo.optimize(capturing_fn)(mod) |
| |
| # Assert equal |
| self.assertTrue(torch._dynamo.testing.same(real, optimized_mod(x, y))) |
| |
| # Uncomment to reproduce commented out graphs below. |
| # for gm in gms: |
| # print("GM CODE", gm.code) |
| |
| self.assertEqual(counter.frame_count, 4) |
| self.assertEqual(counter.op_count, 7) |
| # Graph 1 |
| # def forward(self, x : torch.nn.parameter.Parameter, y : torch.Tensor): |
| # mul = x * y; x = y = None |
| # return (mul,) |
| # BREAK |
| # Graph 2 |
| # def forward(self, y : torch.Tensor): |
| # getitem = y[0]; y = None |
| # getitem_1 = getitem[0]; getitem = None |
| # lt = getitem_1 < 3; getitem_1 = None |
| # return (lt,) |
| # BREAK |
| # Graph 3 |
| # def forward(self, param : torch.Tensor, y : torch.Tensor): |
| # add = y + param; y = param = None |
| # return (add,) |
| # BREAK |
| # Graph 4 |
| # def forward(self, _stack0 : torch.Tensor, x : torch.nn.parameter.Parameter, y : torch.Tensor): |
| # add = x + y; x = y = None |
| # mul = _stack0 * add; _stack0 = add = None |
| # return (mul,) |
| |
| # Run fn with AOT |
| torch._dynamo.reset() |
| |
| aot_fn = torch._dynamo.optimize("aot_eager")(optimized_mod) |
| aot_fn(x, y) |
| |
| # Note: Dynamo recompilation guarding invalid grad |
| # |
| # This test is a spiritual equivalent to test_invalid_requires_grad_fake in test_autodispatch.py |
| # The point of this test is to invoke aot_autograd in a way that would normally trigger an assertion |
| # (This is what test_invalid_requires_grad_fake) does. However, the point of this test is to prove |
| # that we do not hit this asseriton, as dynamo recompiles correctly and protects this condition. |
| # |
| # Subnote: The reason for us having test_invalid_requires_grad_fake utilizing fake tenosrs |
| # is because dynamo sends fake tensors down to aot_autograd. |
| @patch("torch._functorch.config.debug_assert", True) |
| def test_requires_grad_fake_via_dynamo_recompiles(self): |
| class F(torch.nn.Module): |
| def forward(self, x, y): |
| return (x + y,) |
| |
| x = torch.randn(3, 3, requires_grad=True) |
| y = torch.randn(3, 3, requires_grad=True) |
| z = torch.randn(3, 3, requires_grad=False) |
| |
| cc = torch._dynamo.testing.CompileCounterWithBackend("aot_eager") |
| |
| failure_reason = None |
| |
| def guard_fail_fn(failure): |
| nonlocal failure_reason |
| failure_reason = failure[0] |
| |
| fxy = torch._dynamo.optimize(cc, guard_fail_fn=guard_fail_fn)(F()) |
| compare_equal_outs_and_grads(self, F(), fxy, (x, y)) |
| compare_equal_outs_and_grads(self, F(), fxy, (x, z)) |
| self.assertExpectedInline( |
| failure_reason, |
| """tensor 'L['y']' requires_grad mismatch. expected requires_grad=1""", |
| ) |
| |
| # Reset failure reason |
| failure_reason = None |
| |
| self.assertEqual(cc.frame_count, 2) |
| |
| torch._dynamo.reset() # for new backend |
| cc = torch._dynamo.testing.CompileCounterWithBackend("aot_eager") |
| |
| fxz = torch._dynamo.optimize(cc, guard_fail_fn=guard_fail_fn)(F()) |
| compare_equal_outs_and_grads(self, F(), fxz, (x, z)) |
| compare_equal_outs_and_grads(self, F(), fxz, (x, z)) |
| self.assertEqual(cc.frame_count, 1) |
| self.assertTrue(failure_reason is None) |
| |
| def test_double_backward_errors(self): |
| # Remove this test after we get double backward to actually work |
| for grad_output in (torch.tensor(1.0, requires_grad=True), None): |
| x = torch.tensor(1.0, requires_grad=True) |
| err = "torch.compile with aot_autograd does not currently support double backward" |
| |
| # The following cases should be equivalent: |
| |
| # (1) double backward entirely inside compiled function |
| def f1(x): |
| y = x.sin().exp() |
| (gx,) = torch.autograd.grad( |
| y, x, create_graph=True, grad_outputs=grad_output |
| ) |
| torch.autograd.grad(gx, x) |
| return gx |
| |
| compiled_f1 = torch.compile(backend="aot_eager")(f1) |
| f1(x) |
| with self.assertRaisesRegex(RuntimeError, err): |
| compiled_f1(x) |
| |
| # (2) the second half of double backward outside compiled function |
| def f2(x): |
| y = x.sin().exp() |
| (gx,) = torch.autograd.grad( |
| y, x, create_graph=True, grad_outputs=grad_output |
| ) |
| return gx |
| |
| compiled_f2 = torch.compile(backend="aot_eager")(f2) |
| gx = compiled_f2(x) |
| with self.assertRaisesRegex(RuntimeError, err): |
| torch.autograd.grad(gx, x) |
| |
| # (3) double backward entirely outside compiled function |
| def f3(x): |
| y = x.sin().exp() |
| return y |
| |
| compiled_f3 = torch.compile(backend="aot_eager")(f3) |
| y = compiled_f3(x) |
| (gx,) = torch.autograd.grad( |
| y, x, create_graph=True, grad_outputs=grad_output |
| ) |
| with self.assertRaisesRegex(RuntimeError, err): |
| torch.autograd.grad(gx, x) |
| |
| # create_graph=False |
| def f4(x): |
| y = x.sin().exp() |
| return y |
| |
| compiled_f4 = torch.compile(backend="aot_eager")(f4) |
| x = torch.tensor(1.0, requires_grad=True) |
| y = compiled_f4(x) |
| (gx,) = torch.autograd.grad(y, x, create_graph=False, grad_outputs=grad_output) |
| |
| @patch("torch._functorch.config.debug_assert", True) |
| def test_arg_dupe_via_dynamo_recompiles(self): |
| class F(torch.nn.Module): |
| def forward(self, x, y): |
| x = x.trunc_() |
| y = y.trunc_() |
| return (x + y,) |
| |
| x = torch.randn(3, 3, requires_grad=True) |
| x1, x2, x3, x4 = x.clone(), x.clone(), x.clone(), x.clone() |
| y = torch.randn(3, 3, requires_grad=True) |
| y1, y2, y4 = y.clone(), y.clone(), y.clone() |
| |
| cc = torch._dynamo.testing.CompileCounterWithBackend("aot_eager") |
| |
| failure_reason = None |
| |
| def guard_fail_fn(failure): |
| nonlocal failure_reason |
| failure_reason = failure[0] |
| |
| fxy = torch._dynamo.optimize(cc, guard_fail_fn=guard_fail_fn)(F()) |
| # Note: to prevent a recompilation between the two calls, |
| # we need to clone x and y on each use. |
| # fxy mutates the input's metadata, so otherwise dynamo will end up recompiling. |
| fxy(x1, y1) |
| fxy(x2, y2) |
| |
| self.assertTrue(failure_reason is None) |
| |
| # Reset failure reason |
| failure_reason = None |
| |
| self.assertEqual(cc.frame_count, 1) |
| |
| torch._dynamo.reset() # for new backend |
| cc = torch._dynamo.testing.CompileCounterWithBackend("aot_eager") |
| |
| fxx = torch._dynamo.optimize(cc, guard_fail_fn=guard_fail_fn)(F()) |
| fxx(x3, x3) |
| fxx(x4, y4) |
| self.assertEqual(cc.frame_count, 2) |
| self.assertExpectedInline(failure_reason, """L['x'] is L['y']""") |
| |
| @patch("torch._functorch.config.debug_assert", True) |
| def test_arg_dupe_via_dynamo_recompiles_many_args_param_non_tensor_arg(self): |
| class F(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.mean = torch.nn.Parameter(torch.randn(3, 3)) |
| |
| def forward(self, a, b, c, d, e, f): |
| a.trunc_() |
| b.trunc_() |
| c.trunc_() |
| d.trunc_() |
| return (a + b + c + d + self.mean) * e * f |
| |
| a = torch.randn(3, 3, requires_grad=True) |
| b = torch.randn(3, 3, requires_grad=True) |
| a1, a2, a3, a4 = a.clone(), a.clone(), a.clone(), a.clone() |
| b1, b2, b3, b4 = b.clone(), b.clone(), b.clone(), b.clone() |
| |
| failure_reason = None |
| |
| def guard_fail_fn(failure): |
| nonlocal failure_reason |
| failure_reason = failure[0] |
| |
| self.assertTrue(failure_reason is None) |
| |
| cc = torch._dynamo.testing.CompileCounterWithBackend("aot_eager") |
| |
| f = torch._dynamo.optimize(cc, guard_fail_fn=guard_fail_fn)(F()) |
| f(a1, a1, a1, a1, 2, 2) |
| f(a2, b2, b2, b2, 2, 2) |
| self.assertEqual(cc.frame_count, 2) |
| self.assertExpectedInline(failure_reason, """L['a'] is L['b']""") |
| |
| torch._dynamo.reset() |
| |
| cc = torch._dynamo.testing.CompileCounterWithBackend("aot_eager") |
| |
| c = torch.randn(3, 3, requires_grad=True) |
| d = torch.randn(3, 3, requires_grad=True) |
| c3, c4 = c.clone(), c.clone() |
| d3, d4 = d.clone(), d.clone() |
| |
| f = torch._dynamo.optimize(cc, guard_fail_fn=guard_fail_fn)(F()) |
| f(a3, b3, c3, c3, 3, 3) |
| f(a4, b4, c4, d4, 3, 3) |
| self.assertEqual(cc.frame_count, 2) |
| self.assertExpectedInline(failure_reason, """L['c'] is L['d']""") |
| |
| @patch("torch._functorch.config.debug_assert", True) |
| def test_arg_dupe_via_dynamo_recompiles_many_with_global(self): |
| z = None |
| |
| class F(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.mean = torch.nn.Parameter(torch.randn(3, 3)) |
| |
| def forward(self, a, b, c, d, e, f): |
| a.trunc_() |
| b.trunc_() |
| c.trunc_() |
| d.trunc_() |
| return (a + b + c + d + z + self.mean) * e * f |
| |
| a = torch.randn(3, 3, requires_grad=True) |
| b = torch.randn(3, 3, requires_grad=True) |
| z = a |
| a1, a2, a3, a4 = a.clone(), a.clone(), a.clone(), a.clone() |
| b1, b2, b3, b4 = b.clone(), b.clone(), b.clone(), b.clone() |
| |
| failure_reason = None |
| |
| def guard_fail_fn(failure): |
| nonlocal failure_reason |
| failure_reason = failure[0] |
| |
| self.assertTrue(failure_reason is None) |
| |
| cc = torch._dynamo.testing.CompileCounterWithBackend("aot_eager") |
| |
| f = torch._dynamo.optimize(cc, guard_fail_fn=guard_fail_fn)(F()) |
| f(a1, a1, a1, a1, 2, 2) |
| f(a2, b2, b2, b2, 2, 2) |
| self.assertEqual(cc.frame_count, 2) |
| self.assertExpectedInline(failure_reason, """L['a'] is L['b']""") |
| |
| @patch("torch._functorch.config.debug_assert", True) |
| def test_arg_dupe_via_dynamo_recompiles_many_args_param_non_tensor_arg_list(self): |
| class F(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.mean = torch.nn.Parameter(torch.randn(3, 3)) |
| |
| def forward(self, e, f, a, b, c, d): |
| a.trunc_() |
| b.trunc_() |
| c.trunc_() |
| d.trunc_() |
| return (a + b + c + d + self.mean) * e[0] * f[0] |
| |
| a = torch.randn(3, 3, requires_grad=True) |
| b = torch.randn(3, 3, requires_grad=True) |
| a1, a2, a3, a4 = a.clone(), a.clone(), a.clone(), a.clone() |
| b1, b2, b3, b4 = b.clone(), b.clone(), b.clone(), b.clone() |
| |
| failure_reason = None |
| |
| def guard_fail_fn(failure): |
| nonlocal failure_reason |
| failure_reason = failure[0] |
| |
| self.assertTrue(failure_reason is None) |
| |
| cc = torch._dynamo.testing.CompileCounterWithBackend("aot_eager") |
| |
| f = torch._dynamo.optimize(cc, guard_fail_fn=guard_fail_fn)(F()) |
| f([3, 2, 1], [4, 5, 6], a1, a1, a1, a1) |
| f([3, 2, 1], [4, 5, 6], a2, b2, b2, b2) |
| self.assertEqual(cc.frame_count, 2) |
| self.assertExpectedInline(failure_reason, """L['a'] is L['b']""") |
| |
| torch._dynamo.reset() |
| |
| cc = torch._dynamo.testing.CompileCounterWithBackend("aot_eager") |
| |
| c = torch.randn(3, 3, requires_grad=True) |
| d = torch.randn(3, 3, requires_grad=True) |
| c3, c4 = c.clone(), c.clone() |
| d3, d4 = d.clone(), d.clone() |
| |
| f = torch._dynamo.optimize(cc, guard_fail_fn=guard_fail_fn)(F()) |
| f([3, 2, 1], [4, 5, 6], a3, b3, c3, c3) |
| f([3, 2, 1], [4, 5, 6], a4, b4, c4, d4) |
| self.assertEqual(cc.frame_count, 2) |
| |
| @patch("torch._functorch.config.debug_assert", True) |
| def test_arg_dupe_via_dynamo_recompiles_many_args_param(self): |
| class F(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.mean = torch.nn.Parameter(torch.randn(3, 3)) |
| |
| def forward(self, a, b, c, d): |
| a.trunc_() |
| b.trunc_() |
| c.trunc_() |
| d.trunc_() |
| return a + b + c + d + self.mean |
| |
| a = torch.randn(3, 3, requires_grad=True) |
| b = torch.randn(3, 3, requires_grad=True) |
| a1, a2, a3, a4 = a.clone(), a.clone(), a.clone(), a.clone() |
| b1, b2, b3, b4 = b.clone(), b.clone(), b.clone(), b.clone() |
| |
| failure_reason = None |
| |
| def guard_fail_fn(failure): |
| nonlocal failure_reason |
| failure_reason = failure[0] |
| |
| self.assertTrue(failure_reason is None) |
| |
| cc = torch._dynamo.testing.CompileCounterWithBackend("aot_eager") |
| |
| f = torch._dynamo.optimize(cc, guard_fail_fn=guard_fail_fn)(F()) |
| f(a1, a1, a1, a1) |
| f(a2, b2, b2, b2) |
| self.assertEqual(cc.frame_count, 2) |
| self.assertExpectedInline(failure_reason, """L['a'] is L['b']""") |
| |
| torch._dynamo.reset() |
| |
| cc = torch._dynamo.testing.CompileCounterWithBackend("aot_eager") |
| |
| c = torch.randn(3, 3, requires_grad=True) |
| d = torch.randn(3, 3, requires_grad=True) |
| c3, c4 = c.clone(), c.clone() |
| d3, d4 = d.clone(), d.clone() |
| |
| f = torch._dynamo.optimize(cc, guard_fail_fn=guard_fail_fn)(F()) |
| f(a3, b3, c3, c3) |
| f(a4, b4, c4, d4) |
| self.assertEqual(cc.frame_count, 2) |
| self.assertExpectedInline(failure_reason, """L['c'] is L['d']""") |
| |
| @patch("torch._functorch.config.debug_assert", True) |
| def test_arg_dupe_via_dynamo_recompiles_many_args(self): |
| class F(torch.nn.Module): |
| def forward(self, a, b, c, d): |
| a.trunc_() |
| b.trunc_() |
| c.trunc_() |
| d.trunc_() |
| return (a + b + c + d,) |
| |
| a = torch.randn(3, 3, requires_grad=True) |
| b = torch.randn(3, 3, requires_grad=True) |
| a1, a2, a3, a4 = a.clone(), a.clone(), a.clone(), a.clone() |
| b1, b2, b3, b4 = b.clone(), b.clone(), b.clone(), b.clone() |
| |
| failure_reason = None |
| |
| def guard_fail_fn(failure): |
| nonlocal failure_reason |
| failure_reason = failure[0] |
| |
| self.assertTrue(failure_reason is None) |
| |
| cc = torch._dynamo.testing.CompileCounterWithBackend("aot_eager") |
| |
| f = torch._dynamo.optimize(cc, guard_fail_fn=guard_fail_fn)(F()) |
| f(a1, a1, a1, a1) |
| f(a2, b2, b2, b2) |
| self.assertEqual(cc.frame_count, 2) |
| self.assertExpectedInline(failure_reason, """L['a'] is L['b']""") |
| |
| torch._dynamo.reset() |
| |
| cc = torch._dynamo.testing.CompileCounterWithBackend("aot_eager") |
| |
| c = torch.randn(3, 3, requires_grad=True) |
| d = torch.randn(3, 3, requires_grad=True) |
| c3, c4 = c.clone(), c.clone() |
| d3, d4 = d.clone(), d.clone() |
| |
| f = torch._dynamo.optimize(cc, guard_fail_fn=guard_fail_fn)(F()) |
| f(a3, b3, c3, c3) |
| f(a4, b4, c4, d4) |
| self.assertEqual(cc.frame_count, 2) |
| self.assertExpectedInline(failure_reason, """L['c'] is L['d']""") |
| |
| @expectedFailureDynamic # https://github.com/pytorch/pytorch/issues/103539 |
| @torch._dynamo.config.patch(automatic_dynamic_shapes=False) |
| @patch("torch._functorch.config.debug_assert", True) |
| def test_multiple_aot_autograd_calls_dupe_args(self): |
| # this is just dealing with the fact that |
| # aot_module_simplified expects submods to always return tuples/lists |
| class WrapperModule(torch.nn.Module): |
| def __init__(self, mod): |
| super().__init__() |
| self.mod = mod |
| |
| def forward(self, *args): |
| out = self.mod(*args) |
| if isinstance(out, (list, tuple)): |
| return out |
| return (out,) |
| |
| def compile_submod(input_mod, args): |
| from functorch.compile import nop |
| from torch._functorch.aot_autograd import aot_module_simplified |
| |
| class WrapperModule(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.original = input_mod |
| self.submod = aot_module_simplified(input_mod, args, nop) |
| |
| def forward(self, *args): |
| return self.submod(*args) |
| |
| return WrapperModule() |
| |
| def test_compile(fx_g, example_inps): |
| split_gm = torch.fx.passes.split_module.split_module( |
| fx_g, None, lambda node: 1 if "mul" in str(node) else 0 |
| ) |
| submod_1_inps = split_gm.submod_0(*example_inps) |
| split_gm.submod_0 = compile_submod( |
| WrapperModule(split_gm.submod_0), example_inps |
| ) |
| split_gm.submod_1 = compile_submod( |
| WrapperModule(split_gm.submod_1), submod_1_inps |
| ) |
| return split_gm |
| |
| @torch._dynamo.optimize(test_compile) |
| def f(a): |
| b, c = torch.ops.custom.maybe_dupe_op(a) |
| return (b.mul_(c),) |
| |
| f(torch.ones(4)) |
| f(torch.ones(6)) |
| |
| def test_nn_parameter_construction(self): |
| # https://github.com/pytorch/pytorch/issues/99569 |
| def fn(x): |
| y = x.sin() |
| z = torch.nn.Parameter(torch.ones(1)) |
| return y + z |
| |
| x = torch.rand((4, 4)) |
| |
| opt_fn = torch._dynamo.optimize("aot_eager")(fn) |
| self.assertTrue(torch._dynamo.testing.same(fn(x), opt_fn(x))) |
| |
| def test_aot_sequence_nr(self): |
| class Model(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.conv1 = torch.nn.Conv2d( |
| in_channels=16, |
| out_channels=16, |
| kernel_size=(1, 1), |
| stride=1, |
| padding="same", |
| bias=True, |
| ) |
| self.bn1 = torch.nn.BatchNorm2d(num_features=16) |
| self.relu1 = torch.nn.ReLU() |
| self.fc1 = torch.nn.Linear(in_features=1638400, out_features=1) |
| self.loss_fn = torch.nn.L1Loss() |
| |
| def forward(self, x, target): |
| y = x |
| x = self.conv1(x) |
| x = self.bn1(x) |
| x = self.relu1(x) |
| x = x + y |
| x = torch.flatten(x) |
| x = self.fc1(x) |
| output = self.loss_fn(x, target) |
| |
| return (output,) |
| |
| mod = Model() |
| mod.train() |
| x = torch.rand(100, 16, 32, 32, requires_grad=True) |
| target = torch.rand(1) |
| |
| # Use dynamo export to get the fx graph module |
| g_mod, _ = torch._dynamo.export(mod, x, target) |
| |
| def _prepare_model_args(): |
| named_parameters = dict(g_mod.named_parameters(remove_duplicate=False)) |
| named_buffers = dict(g_mod.named_buffers(remove_duplicate=False)) |
| params_and_buffers = { |
| **dict(named_parameters), |
| **dict(named_buffers), |
| } |
| params_and_buffers_flat, params_spec = pytree.tree_flatten( |
| params_and_buffers |
| ) |
| params_len = len(params_and_buffers_flat) |
| functional_call = create_functional_call(g_mod, params_spec, params_len) |
| return params_and_buffers_flat, functional_call |
| |
| full_args, fn_to_trace = _prepare_model_args() |
| param_and_buf_len = len(full_args) |
| full_args.extend([x, target]) |
| |
| # aot_export requires a graph mod input of fwd graph |
| # returns the full fwd/bwd graph in graph mod format |
| with torch.enable_grad(), fx_traceback.preserve_node_meta(): |
| fx_g, _, _, _ = _aot_export_function( |
| fn_to_trace, |
| full_args, |
| decompositions=None, |
| num_params_buffers=param_and_buf_len, |
| no_tangents=True, |
| ) |
| |
| # Walk all the nodes in fx graph. |
| # Write the resulting ops to a table |
| min_seq_nr = -1 |
| seq_table = "SeqNr|OrigAten|SrcFn\n" |
| for node in fx_g.graph.nodes: |
| if "call_" in node.op and "getitem" not in str(node.target): |
| seq_nr = node.meta.get("seq_nr", -1) |
| if seq_nr < 0: |
| continue |
| if min_seq_nr < 0: |
| min_seq_nr = seq_nr |
| mod_name = node.meta.get("source_fn", "") |
| orig_aten = node.meta.get("original_aten", "") |
| if isinstance(mod_name, tuple): |
| mod_name = mod_name[0] |
| # Make all seq_nr relative so it starts at 0 |
| seq_nr = seq_nr - min_seq_nr |
| seq_table = seq_table + f"{seq_nr}|{orig_aten}|{mod_name}\n" |
| |
| self.maxDiff = None |
| self.assertExpectedInline( |
| seq_table, |
| dedent( |
| """\ |
| SeqNr|OrigAten|SrcFn |
| 0|aten.convolution.default|l__self___conv1 |
| 0|aten.add.Tensor|l__self___bn1 |
| 1|aten._native_batch_norm_legit_functional.default|l__self___bn1 |
| 2|aten.relu.default|l__self___relu1 |
| 3|aten.add.Tensor|add |
| 4|aten.view.default|flatten |
| 5|aten.view.default|l__self___fc1 |
| 6|aten.t.default|l__self___fc1 |
| 7|aten.addmm.default|l__self___fc1 |
| 8|aten.view.default|l__self___fc1 |
| 9|aten.sub.Tensor|l__self___loss_fn |
| 10|aten.abs.default|l__self___loss_fn |
| 11|aten.mean.default|l__self___loss_fn |
| 11|aten.ones_like.default| |
| 11|aten.expand.default| |
| 11|aten.div.Scalar| |
| 10|aten.sgn.default| |
| 10|aten.mul.Tensor| |
| 8|aten.view.default| |
| 7|aten.t.default| |
| 7|aten.mm.default| |
| 7|aten.t.default| |
| 7|aten.mm.default| |
| 7|aten.t.default| |
| 7|aten.sum.dim_IntList| |
| 7|aten.view.default| |
| 6|aten.t.default| |
| 5|aten.view.default| |
| 4|aten.view.default| |
| 2|aten.threshold_backward.default| |
| 1|aten.native_batch_norm_backward.default| |
| 0|aten.convolution_backward.default| |
| 11|aten.add.Tensor| |
| """ |
| ), |
| ) |
| |
| def test_eager_sequence_nr(self): |
| class Model(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.conv1 = torch.nn.Conv2d( |
| in_channels=16, |
| out_channels=16, |
| kernel_size=(1, 1), |
| stride=1, |
| padding="same", |
| bias=True, |
| ) |
| self.bn1 = torch.nn.BatchNorm2d(num_features=16) |
| self.relu1 = torch.nn.ReLU() |
| self.fc1 = torch.nn.Linear(in_features=1638400, out_features=1) |
| self.loss_fn = torch.nn.L1Loss() |
| |
| def forward(self, x, target): |
| y = x |
| x = self.conv1(x) |
| x = self.bn1(x) |
| x = self.relu1(x) |
| x = x + y |
| x = torch.flatten(x) |
| x = self.fc1(x) |
| output = self.loss_fn(x, target) |
| |
| return (output,) |
| |
| def grad_with_create_graph(mod, x, target): |
| y = mod(x, target) |
| # Set create_graph=True to ensure that the sequence_nr |
| # for backward ops continues to count down. |
| (gx,) = torch.autograd.grad( |
| y[0], x, create_graph=True, grad_outputs=grad_output |
| ) |
| return gx |
| |
| x = torch.rand(100, 16, 32, 32, requires_grad=True) |
| target = torch.rand(1) |
| mod = Model() |
| args = [mod, x, target] |
| grad_output = torch.tensor(1.0, requires_grad=True) |
| compiled_f1 = torch.compile(backend="aot_eager")(grad_with_create_graph) |
| model_instance = compiled_f1 |
| with profile( |
| activities=[torch.profiler.ProfilerActivity.CPU], |
| record_shapes=True, |
| ) as kineto_prof: |
| res = model_instance(*args) |
| bwd_set = set() |
| prof_str = "SeqNr|Thread|FwdThread|Name\n" |
| for event in kineto_prof.events(): |
| if event.sequence_nr >= 0: |
| prof_str = ( |
| prof_str + f"{event.sequence_nr}|{event.thread}" |
| f"|{event.fwd_thread}|{event.name}|\n" |
| ) |
| if re.search(r"Backward[01]", event.name): |
| bwd_set.add(event.sequence_nr) |
| self.assertTrue(len(bwd_set), 13) |
| |
| |
| if __name__ == "__main__": |
| from torch._dynamo.test_case import run_tests |
| |
| run_tests() |
