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# Owner(s): ["oncall: export"]
# flake8: noqa
import unittest
from typing import Dict, List, Tuple
import torch
import torch._dynamo
from torch._dynamo.test_case import run_tests, TestCase
from torch._functorch.aot_autograd import aot_export_module
from torch.export import export, export_for_training
from torch.export._trace import _convert_ts_to_export_experimental
from torch.export.experimental import _export_forward_backward
from torch.testing import FileCheck
@unittest.skipIf(not torch._dynamo.is_dynamo_supported(), "dynamo isn't supported")
class TestExperiment(TestCase):
def test_torchscript_module_export(self):
class M(torch.nn.Module):
def forward(self, x):
return x.cos() + x.sin()
model_to_trace = M()
inps = (torch.randn(4, 4),)
traced_module_by_torchscript = torch.jit.trace(M(), example_inputs=inps)
exported_module = _convert_ts_to_export_experimental(
traced_module_by_torchscript, inps
)
self.assertTrue(torch.allclose(exported_module(*inps), model_to_trace(*inps)))
def test_torchscript_module_export_single_input(self):
class M(torch.nn.Module):
def forward(self, x):
return x.cos() + x.sin()
model_to_trace = M()
inps = torch.randn(4, 4)
traced_module_by_torchscript = torch.jit.trace(M(), example_inputs=inps)
exported_module = _convert_ts_to_export_experimental(
traced_module_by_torchscript, inps
)
self.assertTrue(torch.allclose(exported_module(inps), model_to_trace(inps)))
def test_torchscript_module_export_various_inputs_with_annotated_input_names(self):
def _check_equality_and_annotations(m_func, inps):
# Original module.
model_to_trace = m_func()
# ExportedProgram from TorchScript module.
traced_module_by_torchscript = torch.jit.trace(
m_func(), example_inputs=inps
)
exported_module = _convert_ts_to_export_experimental(
traced_module_by_torchscript, inps
)
# ExportedProgram from original module.
original_exported_module = torch.export.export_for_training(m_func(), inps)
# Check whether input annotations are the same as tracing the original module.
orig_ph_name_list = [
n.name
for n in original_exported_module.graph.nodes
if n.op == "placeholder"
]
ph_name_list = [
n.name for n in exported_module.graph.nodes if n.op == "placeholder"
]
self.assertEqual(orig_ph_name_list, ph_name_list)
# Check results equality.
self.assertTrue(
torch.allclose(exported_module(*inps), model_to_trace(*inps))
)
# Tuple
class MTuple(torch.nn.Module):
def forward(self, x: Tuple[torch.Tensor]):
return x[0] + x[1]
_check_equality_and_annotations(MTuple, ((torch.randn(4), torch.randn(4)),))
# List
class MList(torch.nn.Module):
def forward(self, x: List[torch.Tensor]):
return x[0] + x[1]
_check_equality_and_annotations(MList, ([torch.randn(4), torch.randn(4)],))
# Dict
class MDict(torch.nn.Module):
def forward(self, x: Dict[str, torch.Tensor]):
return x["0"] + x["1"]
_check_equality_and_annotations(
MDict, ({"0": torch.randn(4), "1": torch.randn(4)},)
)
def test_joint_basic(self) -> None:
class Module(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear = torch.nn.Linear(3, 3)
self.loss = torch.nn.CrossEntropyLoss()
def forward(self, x):
return self.loss(
self.linear(x).softmax(dim=0), torch.tensor([1.0, 0.0, 0.0])
)
m = Module()
example_inputs = (torch.randn(3),)
m(*example_inputs)
ep = torch.export.export_for_training(m, example_inputs)
joint_ep = _export_forward_backward(ep)
self.assertExpectedInline(
str(joint_ep.graph_module.code).strip(),
"""\
def forward(self, p_linear_weight, p_linear_bias, c_lifted_tensor_0, x):
view = torch.ops.aten.view.default(x, [1, 3]); x = None
permute = torch.ops.aten.permute.default(p_linear_weight, [1, 0]); p_linear_weight = None
addmm = torch.ops.aten.addmm.default(p_linear_bias, view, permute); p_linear_bias = permute = None
view_1 = torch.ops.aten.view.default(addmm, [3]); addmm = None
_softmax = torch.ops.aten._softmax.default(view_1, 0, False); view_1 = None
alias = torch.ops.aten.alias.default(_softmax)
alias_1 = torch.ops.aten.alias.default(alias); alias = None
clone = torch.ops.aten.clone.default(c_lifted_tensor_0); c_lifted_tensor_0 = None
_log_softmax = torch.ops.aten._log_softmax.default(_softmax, 0, False); _softmax = None
alias_2 = torch.ops.aten.alias.default(_log_softmax)
alias_3 = torch.ops.aten.alias.default(alias_2); alias_2 = None
mul = torch.ops.aten.mul.Tensor(_log_softmax, clone); _log_softmax = None
sum_1 = torch.ops.aten.sum.dim_IntList(mul, []); mul = None
neg = torch.ops.aten.neg.default(sum_1); sum_1 = None
div = torch.ops.aten.div.Scalar(neg, 1); neg = None
full_like = torch.ops.aten.full_like.default(div, 1, pin_memory = False, memory_format = torch.preserve_format)
div_1 = torch.ops.aten.div.Scalar(full_like, 1); full_like = None
neg_1 = torch.ops.aten.neg.default(div_1); div_1 = None
expand = torch.ops.aten.expand.default(neg_1, [3]); neg_1 = None
mul_1 = torch.ops.aten.mul.Tensor(expand, clone); expand = clone = None
alias_4 = torch.ops.aten.alias.default(alias_3); alias_3 = None
alias_5 = torch.ops.aten.alias.default(alias_4); alias_4 = None
exp = torch.ops.aten.exp.default(alias_5); alias_5 = None
sum_2 = torch.ops.aten.sum.dim_IntList(mul_1, [0], True)
mul_2 = torch.ops.aten.mul.Tensor(exp, sum_2); exp = sum_2 = None
sub = torch.ops.aten.sub.Tensor(mul_1, mul_2); mul_1 = mul_2 = None
alias_6 = torch.ops.aten.alias.default(alias_1); alias_1 = None
alias_7 = torch.ops.aten.alias.default(alias_6); alias_6 = None
mul_3 = torch.ops.aten.mul.Tensor(sub, alias_7); sub = None
sum_3 = torch.ops.aten.sum.dim_IntList(mul_3, [0], True)
mul_4 = torch.ops.aten.mul.Tensor(alias_7, sum_3); alias_7 = sum_3 = None
sub_1 = torch.ops.aten.sub.Tensor(mul_3, mul_4); mul_3 = mul_4 = None
view_2 = torch.ops.aten.view.default(sub_1, [1, 3]); sub_1 = None
permute_1 = torch.ops.aten.permute.default(view_2, [1, 0])
mm = torch.ops.aten.mm.default(permute_1, view); permute_1 = view = None
permute_2 = torch.ops.aten.permute.default(mm, [1, 0]); mm = None
sum_4 = torch.ops.aten.sum.dim_IntList(view_2, [0], True); view_2 = None
view_3 = torch.ops.aten.view.default(sum_4, [3]); sum_4 = None
permute_3 = torch.ops.aten.permute.default(permute_2, [1, 0]); permute_2 = None
return (div, permute_3, view_3)""",
)
ep = joint_ep.run_decompositions()
self.assertExpectedInline(
str(ep.graph_module.code).strip(),
"""\
def forward(self, p_linear_weight, p_linear_bias, c_lifted_tensor_0, x):
view = torch.ops.aten.view.default(x, [1, 3]); x = None
permute = torch.ops.aten.permute.default(p_linear_weight, [1, 0]); p_linear_weight = None
addmm = torch.ops.aten.addmm.default(p_linear_bias, view, permute); p_linear_bias = permute = None
view_1 = torch.ops.aten.view.default(addmm, [3]); addmm = None
_softmax = torch.ops.aten._softmax.default(view_1, 0, False); view_1 = None
alias = torch.ops.aten.alias.default(_softmax)
alias_1 = torch.ops.aten.alias.default(alias); alias = None
clone = torch.ops.aten.clone.default(c_lifted_tensor_0); c_lifted_tensor_0 = None
_log_softmax = torch.ops.aten._log_softmax.default(_softmax, 0, False); _softmax = None
alias_2 = torch.ops.aten.alias.default(_log_softmax)
alias_3 = torch.ops.aten.alias.default(alias_2); alias_2 = None
mul = torch.ops.aten.mul.Tensor(_log_softmax, clone); _log_softmax = None
sum_1 = torch.ops.aten.sum.dim_IntList(mul, []); mul = None
neg = torch.ops.aten.neg.default(sum_1); sum_1 = None
div = torch.ops.aten.div.Scalar(neg, 1); neg = None
full_like = torch.ops.aten.full_like.default(div, 1, pin_memory = False, memory_format = torch.preserve_format)
div_1 = torch.ops.aten.div.Scalar(full_like, 1); full_like = None
neg_1 = torch.ops.aten.neg.default(div_1); div_1 = None
expand = torch.ops.aten.expand.default(neg_1, [3]); neg_1 = None
mul_1 = torch.ops.aten.mul.Tensor(expand, clone); expand = clone = None
alias_4 = torch.ops.aten.alias.default(alias_3); alias_3 = None
alias_5 = torch.ops.aten.alias.default(alias_4); alias_4 = None
exp = torch.ops.aten.exp.default(alias_5); alias_5 = None
sum_2 = torch.ops.aten.sum.dim_IntList(mul_1, [0], True)
mul_2 = torch.ops.aten.mul.Tensor(exp, sum_2); exp = sum_2 = None
sub = torch.ops.aten.sub.Tensor(mul_1, mul_2); mul_1 = mul_2 = None
alias_6 = torch.ops.aten.alias.default(alias_1); alias_1 = None
alias_7 = torch.ops.aten.alias.default(alias_6); alias_6 = None
mul_3 = torch.ops.aten.mul.Tensor(sub, alias_7); sub = None
sum_3 = torch.ops.aten.sum.dim_IntList(mul_3, [0], True)
mul_4 = torch.ops.aten.mul.Tensor(alias_7, sum_3); alias_7 = sum_3 = None
sub_1 = torch.ops.aten.sub.Tensor(mul_3, mul_4); mul_3 = mul_4 = None
view_2 = torch.ops.aten.view.default(sub_1, [1, 3]); sub_1 = None
permute_1 = torch.ops.aten.permute.default(view_2, [1, 0])
mm = torch.ops.aten.mm.default(permute_1, view); permute_1 = view = None
permute_2 = torch.ops.aten.permute.default(mm, [1, 0]); mm = None
sum_4 = torch.ops.aten.sum.dim_IntList(view_2, [0], True); view_2 = None
view_3 = torch.ops.aten.view.default(sum_4, [3]); sum_4 = None
permute_3 = torch.ops.aten.permute.default(permute_2, [1, 0]); permute_2 = None
return (div, permute_3, view_3)""",
)
def test_joint_dynamic(self) -> None:
from torch.export import Dim
class Module(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.y = torch.nn.Parameter(torch.randn(3))
def forward(self, x):
x = torch.ones(x.shape[0], 3)
return (self.y + x).sum()
m = Module()
example_inputs = (torch.randn(3),)
m(*example_inputs)
ep = torch.export.export_for_training(
m, example_inputs, dynamic_shapes={"x": {0: Dim("x0")}}
)
joint_ep = _export_forward_backward(ep)
def test_joint_cifar10_backwards(self) -> None:
import torch.nn as nn
import torch.nn.functional as F
# From Pytorch's CIFAR10 example:
# https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
self.loss = nn.CrossEntropyLoss()
def forward(self, x, labels):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = torch.flatten(x, 1) # flatten all dimensions except batch
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return self.loss(x, labels)
net = Net()
x = torch.randn(4, 3, 32, 32)
labels = torch.ones(4, dtype=torch.int64)
inputs = (x, labels)
ep = export_for_training(net, inputs)
ep = _export_forward_backward(ep)
def test_joint_loss_index(self):
from torch.export.graph_signature import OutputKind
class Foo(torch.nn.Module):
def __init__(self, index):
super().__init__()
self.l = torch.nn.Linear(4, 4)
self.index = index
def forward(self, x):
x = self.l(x)
x = x.sum()
if self.index == 0:
return x, -x.detach()
else:
return x.detach(), x
inputs = (torch.randn(4, 4),)
for i in [0, 1]:
ep = export_for_training(Foo(i), inputs)
ep_joint = _export_forward_backward(ep, joint_loss_index=i)
for j, spec in enumerate(ep_joint.graph_signature.output_specs):
if i == j:
self.assertTrue(spec.kind == OutputKind.LOSS_OUTPUT)
else:
self.assertTrue(spec.kind != OutputKind.LOSS_OUTPUT)
if __name__ == "__main__":
run_tests()
|
