| # Copyright (c) Facebook, Inc. and its affiliates. |
| # All rights reserved. |
| # |
| # This source code is licensed under the BSD-style license found in the |
| # LICENSE file in the root directory of this source tree. |
| |
| from torch.testing._internal.common_utils import TestCase, run_tests |
| import torch |
| import torch.nn as nn |
| import torch.utils._pytree as pytree |
| import unittest |
| import warnings |
| from torch.testing._internal.common_device_type import instantiate_device_type_tests |
| from functorch import ( |
| grad, vjp, vmap, jacrev, |
| make_fx |
| ) |
| from functorch.compile import ( |
| nnc_jit, compiled_function, compiled_module, |
| partition_with_recompute_fwd_in_bwd, pythonkey_decompose, aot_function, aot_module |
| ) |
| |
| from torch.testing._internal.common_device_type import ops |
| from functorch_lagging_op_db import functorch_lagging_op_db |
| from functorch_additional_op_db import additional_op_db |
| from common_utils import ( |
| xfail, |
| skip, |
| skipOps, |
| ) |
| |
| USE_TORCHVISION = False |
| try: |
| import torchvision |
| USE_TORCHVISION = True |
| except ImportError: |
| warnings.warn("Couldn't import torchvision. Some of our tests use it, try " |
| "to install it with commands from pytorch.org, post-fixed with " |
| "`--no-deps` to avoid overwriting the pytorch installation", |
| UserWarning) |
| |
| # NB: numpy is a testing dependency! |
| |
| |
| class TestPythonKey(TestCase): |
| def test_make_fx(self, device): |
| def f(x): |
| return torch.sin(x) |
| inp = torch.randn(3) |
| fx_f = make_fx(f)(inp) |
| |
| new_inp = torch.randn(3) |
| self.assertEqual(fx_f(new_inp), f(new_inp)) |
| |
| def test_make_fx_grad(self, device): |
| def f(x): |
| return torch.sin(x).sum() |
| inp = torch.randn(3) |
| f = grad(f) |
| fx_f = make_fx(f)(inp) |
| |
| new_inp = torch.randn(3) |
| self.assertEqual(fx_f(new_inp), f(new_inp)) |
| |
| def test_scalar_device(self): |
| def f(a, b): |
| return a + b |
| inps = [torch.randn(3, device='cuda'), torch.tensor(5)] |
| fx_f = make_fx(f)(*inps) |
| self.assertEqual(fx_f(*inps), f(*inps)) |
| |
| def test_make_fx_vmap(self, device): |
| def f(x): |
| return torch.sin(x) |
| inp = torch.randn(5, 3) |
| f = vmap(f) |
| fx_f = make_fx(f)(inp) |
| new_inp = torch.randn(5, 3) |
| self.assertEqual(fx_f(new_inp), f(new_inp)) |
| |
| def test_make_fx_jacrev(self, device): |
| def f(x): |
| return x.sin().sum() |
| inp = torch.randn(3) |
| f = jacrev(jacrev(f)) |
| fx_f = make_fx(f)(inp) |
| new_inp = torch.randn(3) |
| self.assertEqual(fx_f(new_inp), f(new_inp)) |
| |
| def test_make_fx_vjp(self, device): |
| def f(x): |
| return torch.sin(x).sum() |
| |
| primals = torch.randn(3) |
| _, vjp_fn = vjp(f, primals) |
| cotangent = torch.randn(()) |
| fx_f = make_fx(vjp_fn)(cotangent, True, True) |
| new_cotangent = torch.randn(()) |
| self.assertEqual(fx_f(new_cotangent, True, True), vjp_fn(new_cotangent)) |
| |
| def test_make_fx_no_decompose(self, device): |
| def f(x): |
| return torch.tanh(x).sum() |
| |
| fx_f = make_fx(grad(f))(torch.randn(5)) |
| ops = set([i.target for i in fx_f.graph.nodes]) |
| |
| self.assertEqual(torch.ops.aten.tanh_backward in ops, True) |
| |
| with pythonkey_decompose(): |
| fx_f = make_fx(grad(f))(torch.randn(5)) |
| ops = set([i.target for i in fx_f.graph.nodes]) |
| self.assertEqual(torch.ops.aten.tanh_backward in ops, False) |
| |
| @unittest.expectedFailure |
| def test_nnc_jit(self, device): |
| def f(x): |
| return torch.sin(x) |
| |
| jit_f = nnc_jit(f) |
| |
| inp = torch.randn(3) |
| self.assertEqual(jit_f(inp), f(inp)) |
| |
| @unittest.expectedFailure |
| def test_nnc_jit_warns_on_recompilation(self, device): |
| def f(x): |
| return torch.sin(x) |
| |
| jit_f = nnc_jit(f) |
| |
| inp = torch.randn(3) |
| jit_f(inp) |
| inp2 = torch.randn(5) |
| |
| with warnings.catch_warnings(record=True) as warns: |
| warnings.simplefilter("always") |
| jit_f(inp2) |
| |
| self.assertEqual(len(warns), 1) |
| self.assertTrue("Recompiling" in str(warns[-1].message)) |
| |
| @unittest.expectedFailure |
| def test_nnc_scalar(self, device): |
| def f(x): |
| return torch.sin(x) |
| |
| jit_f = nnc_jit(f) |
| |
| inp = torch.randn(()) |
| self.assertEqual(jit_f(inp), f(inp)) |
| |
| @unittest.expectedFailure |
| def test_nnc_pytrees(self, device): |
| def f(x): |
| return [torch.sin(x[0])] |
| |
| jit_f = nnc_jit(f) |
| |
| inp = [torch.randn(3)] |
| self.assertEqual(jit_f(inp), f(inp)) |
| |
| def test_external_calls(self, device): |
| def f(a, b): |
| return torch.mv(a, b) |
| jit_f = nnc_jit(f) |
| inp = [torch.randn(3, 3), torch.randn(3)] |
| self.assertEqual(jit_f(*inp), f(*inp)) |
| |
| @unittest.expectedFailure |
| def test_nnc_passthrough(self, device): |
| def f(x, y): |
| return x + y, y |
| inp = (torch.randn(3), torch.randn(3)) |
| jit_f = nnc_jit(f) |
| self.assertEqual(jit_f(*inp), f(*inp)) |
| |
| def f(x): |
| x['a'] = x['a'] * 2 |
| return x |
| inp = ({'a': torch.randn(3), 'b': torch.randn(3)},) |
| jit_f = nnc_jit(f) |
| self.assertEqual(jit_f(*inp), f(*inp)) |
| |
| @unittest.skipIf(not USE_TORCHVISION, "test requires torchvision") |
| def test_resnet18_backward_trace(self, device): |
| mod = torchvision.models.resnet18() |
| |
| def f(x): |
| out = mod(x) |
| out.sum().backward() |
| return [a.grad for a in mod.parameters()] |
| |
| inp = torch.randn(3, 3, 250, 250, requires_grad=True) |
| grads = f(inp) |
| |
| mod.zero_grad() |
| mod(inp).sum().backward() |
| grads2 = [a.grad for a in mod.parameters()] |
| self.assertEqual(grads, grads2) |
| |
| |
| make_fx_failures = { |
| xfail('to_sparse'), |
| xfail('allclose'), |
| xfail('rsub', 'rsub_scalar'), |
| xfail('linalg.matrix_power'), |
| xfail('linalg.inv'), |
| xfail('linalg.cholesky'), |
| xfail('nn.functional.dropout'), |
| xfail('linalg.eigvals'), |
| xfail('nn.functional.ctc_loss'), |
| xfail('nn.functional.fractional_max_pool3d', device_type='cpu'), |
| xfail('randn_like'), # randomness |
| xfail('rand_like'), # randomness |
| xfail('randint_like'), # randomness |
| skip('new_empty'), # nondeterministic |
| skip('empty_like'), # nondeterministic |
| skip('linalg.lstsq', 'grad_oriented'), # flaky |
| } |
| |
| |
| class TestPythonKeyOperatorsOpInfo(TestCase): |
| @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,)) |
| @skipOps('TestPythonKeyOperatorsOpInfo', 'test_make_fx_exhaustive', make_fx_failures |
| ) |
| def test_make_fx_exhaustive(self, device, dtype, op): |
| |
| def f(args, kwargs): |
| return op.op(*args, **kwargs) |
| sample_inputs_itr = op.sample_inputs(device, dtype, requires_grad=False) |
| new_f = None |
| for sample_input in sample_inputs_itr: |
| args = [sample_input.input] + list(sample_input.args) |
| kwargs = sample_input.kwargs |
| t = f(args, kwargs) |
| # just since pytrees with torch.return_types doesn't work |
| if isinstance(t, tuple): |
| self.skipTest("output is a tuple that pytree doesn't work with") |
| |
| new_f = make_fx(f)(args, kwargs) |
| for arg in args: |
| if isinstance(arg, torch.Tensor) and arg.dtype == torch.float: |
| arg.uniform_(0, 1) |
| try: |
| old_out = f(args, kwargs) |
| except Exception: |
| continue |
| new_out = new_f(args, kwargs) |
| self.assertEqual(new_out, old_out) |
| pass |
| |
| |
| def _nop_compile(x, _): |
| return x |
| |
| |
| def _outs_and_grads(fn, inps): |
| outs = fn(*inps) |
| [out.sum().backward(retain_graph=True) for out in pytree.tree_flatten(outs)[0]] |
| grads = [inp.grad for inp in pytree.tree_flatten(inps)[0]] |
| for inp in pytree.tree_flatten(inps)[0]: |
| inp.grad = None |
| return outs, grads |
| |
| |
| class TestAOTAutograd(TestCase): |
| def verify_aot_autograd(self, f, inp): |
| if isinstance(f, nn.Module): |
| compiled_f = aot_module(f, _nop_compile, _nop_compile) |
| else: |
| compiled_f = aot_function(f, _nop_compile, _nop_compile) |
| ref_out, ref_grad = _outs_and_grads(f, inp) |
| test_out, test_grad = _outs_and_grads(compiled_f, inp) |
| self.assertEqual(ref_out, test_out) |
| self.assertEqual(ref_grad, test_grad) |
| |
| def test_single_output(self): |
| def f(a, b): |
| return a + b |
| inp = [torch.randn(3, 3, requires_grad=True), torch.randn(3, 3)] |
| self.verify_aot_autograd(f, inp) |
| |
| |
| def test_multi_output(self): |
| def f(a, b): |
| return a + b, a - b |
| inp = [torch.randn(3, 3, requires_grad=True), torch.randn(3, 3)] |
| self.verify_aot_autograd(f, inp) |
| |
| def test_multi_output_list(self): |
| def f(a, b): |
| return [a + b, a - b] |
| inp = [torch.randn(3, 3, requires_grad=True), torch.randn(3, 3)] |
| self.verify_aot_autograd(f, inp) |
| |
| def test_output_dict(self): |
| def f(x): |
| return {'a': x, 'b': x} |
| inp = [torch.randn(3, 3, requires_grad=True)] |
| self.verify_aot_autograd(f, inp) |
| |
| def f(x, y): |
| return {'a': x, 'b': y + x} |
| inp = [torch.randn(3, requires_grad=True), torch.randn(3)] |
| self.verify_aot_autograd(f, inp) |
| |
| def f(x): |
| new_d = {} |
| for k in x: |
| new_d[k] = x[k] * 2 |
| return new_d |
| inp = [{'a': torch.randn(3, requires_grad=True), 'b': torch.randn(3, requires_grad=True)}] |
| self.verify_aot_autograd(f, inp) |
| |
| def test_module(self): |
| mod = nn.Sequential(nn.Linear(32, 32), nn.ReLU()) |
| compiled_mod = compiled_module(mod, _nop_compile, _nop_compile) |
| inp = torch.randn(32, 32) |
| ref_out = mod(inp) |
| ref_out.sum().backward() |
| ref_grads = [p.grad for p in mod.parameters()] |
| out = compiled_mod(inp) |
| out.sum().backward() |
| grads = [p.grad for p in compiled_mod.parameters()] |
| self.assertEqual((out, grads), (ref_out, ref_grads)) |
| |
| def test_batchnorm(self): |
| mod = compiled_module(nn.BatchNorm2d(4), _nop_compile, _nop_compile) |
| x = torch.ones(1, 4, 2, 2) |
| mod(x).sum().backward() |
| |
| |
| class TestEagerFusionOpInfo(TestCase): |
| @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,)) |
| # entries in here need don't work and need to be fixed. |
| # Each one of these is a bug (or needs to be investigated) |
| @skipOps('TestEagerFusionOpInfo', 'test_aot_autograd_exhaustive', { |
| xfail('__rmatmul__'), |
| xfail('linalg.cholesky'), |
| xfail('linalg.det'), |
| xfail('linalg.inv'), |
| xfail('matmul'), |
| xfail('nn.functional.linear'), |
| xfail('nn.functional.dropout'), |
| xfail('polar'), |
| xfail('special.zeta', 'grad'), |
| xfail('to_sparse'), |
| xfail('addcdiv'), |
| xfail('angle'), |
| xfail('cholesky'), |
| xfail('cumulative_trapezoid'), |
| xfail('diag_embed'), |
| xfail('linalg.householder_product'), |
| xfail('logit'), |
| xfail('matrix_exp'), |
| xfail('sgn'), |
| xfail('trapezoid'), |
| xfail('trapz'), |
| }) |
| def test_aot_autograd_exhaustive(self, device, dtype, op): |
| |
| def f(args, kwargs): |
| return op.op(*args, **kwargs) |
| if not op.supports_autograd: |
| return |
| sample_inputs_itr = op.sample_inputs(device, dtype, requires_grad=True) |
| for sample_input in sample_inputs_itr: |
| args = [sample_input.input] + list(sample_input.args) |
| kwargs = sample_input.kwargs |
| if not all([isinstance(i, torch.Tensor) and i.dtype == torch.float for i in args]): |
| self.skipTest("not all inputs are float tensors") |
| if not all([isinstance(i, torch.Tensor) and i.dtype == torch.float for i in kwargs.values()]): |
| self.skipTest("not all inputs are float tensors") |
| continue |
| t = f(args, kwargs) |
| if isinstance(t, tuple): |
| self.skipTest("output is a tuple") |
| continue |
| |
| def reset_grads(): |
| def f(x): |
| x.grad = None |
| pytree.tree_map(f, args) |
| |
| def get_grads(args): |
| return pytree.tree_map(lambda x: x.grad, args) |
| |
| compiled_f = compiled_function(f, lambda x, _: x, lambda x, _: x) |
| |
| reset_grads() |
| compiled_f(args, kwargs).sum().backward() |
| compiled_grad = get_grads(args) |
| |
| reset_grads() |
| f(args, kwargs).sum().backward() |
| orig_grad = get_grads(args) |
| self.assertEqual(orig_grad, compiled_grad) |
| |
| def create_new_arg(x): |
| return x.detach().uniform_(0, 1).requires_grad_(x.requires_grad) |
| |
| args = pytree.tree_map(create_new_arg, args) |
| |
| reset_grads() |
| compiled_f(args, kwargs).sum().backward() |
| compiled_grad = get_grads(args) |
| |
| reset_grads() |
| f(args, kwargs).sum().backward() |
| orig_grad = get_grads(args) |
| self.assertEqual(orig_grad, compiled_grad) |
| |
| |
| class TestPartitioning(TestCase): |
| def test_recompute_partitioning(self): |
| def fn(a, b): |
| return torch.sin(torch.sin(a)) + b |
| |
| # Reference calculation |
| ref_a = torch.rand(10, 10, requires_grad=True) |
| ref_b = torch.rand(10, 10, requires_grad=True) |
| ref = fn(ref_a, ref_b) |
| ref.sum().backward() |
| |
| # Compiled function calculation |
| res_a = ref_a.clone().detach().requires_grad_(True) |
| res_b = ref_b.clone().detach().requires_grad_(True) |
| |
| def compile_fn(x, _): |
| return x |
| |
| compiled_fn = compiled_function(fn, compile_fn, compile_fn, partition_with_recompute_fwd_in_bwd) |
| res = compiled_fn(res_a, res_b) |
| res.sum().backward() |
| assert torch.allclose(ref, res, atol=1e-3, rtol=1e-3) |
| assert torch.allclose(ref_a.grad, res_a.grad, atol=1e-3, rtol=1e-3) |
| assert torch.allclose(ref_b.grad, res_b.grad, atol=1e-3, rtol=1e-3) |
| |
| |
| only_for = ("cpu") |
| instantiate_device_type_tests( |
| TestPythonKey, |
| globals(), |
| only_for=only_for, |
| ) |
| instantiate_device_type_tests(TestPythonKeyOperatorsOpInfo, globals(), only_for=only_for) |
| instantiate_device_type_tests(TestEagerFusionOpInfo, globals(), only_for=only_for) |
| |
| |
| if __name__ == '__main__': |
| run_tests() |
