mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
For the following program.
```
def my_relu(a):
return a.relu()
def func(a, b):
a = torch.nn.Linear(10, 10)(a)
d = torch.square(b)
d = my_relu(d)
loss = d.sum()
return loss
with torchdynamo.optimize("aot_nop"):
x = torch.rand(10, 10, requires_grad=True)
y = torch.rand(10, 10, requires_grad=True)
out = func(x, y)
```
It would generate the following fx graph with stack_trace populated in both forward and backward nodes.
```
def forward(self, primals, tangents):
primals_1, primals_2, primals_3, primals_4, tangents_1, = fx_pytree.tree_flatten_spec([primals, tangents], self._in_spec)
t_default = torch.ops.aten.t.default(primals_3); primals_3 = None
addmm_default = torch.ops.aten.addmm.default(primals_4, primals_1, t_default); primals_4 = primals_1 = t_default = None
pow_tensor_scalar = torch.ops.aten.pow.Tensor_Scalar(primals_2, 2)
relu_default = torch.ops.aten.relu.default(pow_tensor_scalar); pow_tensor_scalar = None
detach_default = torch.ops.aten.detach.default(relu_default)
sum_default = torch.ops.aten.sum.default(relu_default); relu_default = None
is_same_size_default = torch.ops.aten.is_same_size.default(sum_default, tangents_1)
expand_default = torch.ops.aten.expand.default(tangents_1, [10, 10]); tangents_1 = None
detach_default_1 = torch.ops.aten.detach.default(detach_default); detach_default = None
threshold_backward_default = torch.ops.aten.threshold_backward.default(expand_default, detach_default_1, 0); expand_default = detach_default_1 = None
pow_tensor_scalar_1 = torch.ops.aten.pow.Tensor_Scalar(primals_2, 1.0); primals_2 = None
mul_scalar = torch.ops.aten.mul.Scalar(pow_tensor_scalar_1, 2.0); pow_tensor_scalar_1 = None
mul_tensor = torch.ops.aten.mul.Tensor(threshold_backward_default, mul_scalar); threshold_backward_default = mul_scalar = None
return pytree.tree_unflatten([sum_default, None, mul_tensor, None, None], self._out_spec)
====== joint graph =======
primals_1 None
primals_2 None
primals_3 None
primals_4 None
tangents_1 None
t_default File "/fsx/users/bahuang/repos/pytorch_fsx/test.py", line 12, in func
def func(a, b):
File "/fsx/users/bahuang/repos/pytorch_fsx/torch/nn/modules/linear.py", line 114, in forward
return F.linear(input, self.weight, self.bias)
addmm_default File "/fsx/users/bahuang/repos/pytorch_fsx/test.py", line 12, in func
def func(a, b):
File "/fsx/users/bahuang/repos/pytorch_fsx/torch/nn/modules/linear.py", line 114, in forward
return F.linear(input, self.weight, self.bias)
pow_tensor_scalar File "/fsx/users/bahuang/repos/pytorch_fsx/test.py", line 14, in func
d = torch.square(b)
relu_default File "/fsx/users/bahuang/repos/pytorch_fsx/test.py", line 15, in func
d = my_relu(d)
File "/fsx/users/bahuang/repos/pytorch_fsx/test.py", line 10, in my_relu
return a.relu()
detach_default File "/fsx/users/bahuang/repos/pytorch_fsx/test.py", line 15, in func
d = my_relu(d)
File "/fsx/users/bahuang/repos/pytorch_fsx/test.py", line 10, in my_relu
return a.relu()
sum_default
is_same_size_default
expand_default
detach_default_1 File "/fsx/users/bahuang/repos/pytorch_fsx/test.py", line 15, in func
d = my_relu(d)
File "/fsx/users/bahuang/repos/pytorch_fsx/test.py", line 10, in my_relu
return a.relu()
threshold_backward_default File "/fsx/users/bahuang/repos/pytorch_fsx/test.py", line 15, in func
d = my_relu(d)
File "/fsx/users/bahuang/repos/pytorch_fsx/test.py", line 10, in my_relu
return a.relu()
pow_tensor_scalar_1 File "/fsx/users/bahuang/repos/pytorch_fsx/test.py", line 14, in func
d = torch.square(b)
mul_scalar File "/fsx/users/bahuang/repos/pytorch_fsx/test.py", line 14, in func
d = torch.square(b)
mul_tensor File "/fsx/users/bahuang/repos/pytorch_fsx/test.py", line 14, in func
d = torch.square(b)
output None
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83558
Approved by: https://github.com/albanD
133 lines
3.9 KiB
C++
133 lines
3.9 KiB
C++
#include <c10/util/Exception.h>
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#include <pybind11/pybind11.h>
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#include <torch/csrc/Exceptions.h>
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#include <torch/csrc/autograd/python_anomaly_mode.h>
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#include <torch/csrc/autograd/python_cpp_function.h>
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#include <torch/csrc/python_headers.h>
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#include <torch/csrc/utils/auto_gil.h>
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#include <torch/csrc/utils/object_ptr.h>
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#include <torch/csrc/utils/pybind.h>
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#include <torch/csrc/utils/python_strings.h>
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#include <iostream>
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namespace torch {
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namespace autograd {
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void PyAnomalyMetadata::store_stack() {
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pybind11::gil_scoped_acquire gil;
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THPObjectPtr mod(PyImport_ImportModule("torch.fx.traceback"));
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if (!mod) {
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throw python_error();
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}
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THPObjectPtr list(PyObject_CallMethod(mod.get(), "format_stack", ""));
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if (!list) {
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throw python_error();
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}
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if (PyDict_SetItemString(dict(), ANOMALY_TRACE_KEY, list.get())) {
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throw python_error();
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}
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}
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void PyAnomalyMetadata::print_stack(const std::string& current_node_name) {
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pybind11::gil_scoped_acquire gil;
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if (!PyDict_Check(dict())) {
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throw std::runtime_error("Anomaly metadata is not a python dictionary.");
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}
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PyObject* trace_stack = PyDict_GetItemString(dict(), ANOMALY_TRACE_KEY);
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_print_stack(trace_stack, current_node_name, false);
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PyObject* pyparent(PyDict_GetItemString(dict(), ANOMALY_PARENT_KEY));
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// if there is no "parent_" in metadata, then it means this metadata's node
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// is the root and stop printing the traceback
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while (pyparent) {
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THPObjectPtr parent_metadata(PyObject_GetAttrString(pyparent, "metadata"));
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if (!parent_metadata) {
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throw python_error();
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}
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THPObjectPtr parent_name_pyobj(PyObject_CallMethod(pyparent, "name", ""));
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if (!parent_name_pyobj) {
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throw python_error();
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}
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const char* parent_name_char = PyUnicode_AsUTF8(parent_name_pyobj.get());
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if (!parent_name_char) {
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throw python_error();
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}
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const std::string parent_name(parent_name_char);
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PyObject* parent_stack =
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PyDict_GetItemString(parent_metadata.get(), ANOMALY_TRACE_KEY);
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_print_stack(parent_stack, parent_name, true);
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// get the parent of this node, if this node is a root, pyparent is simply
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// null
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pyparent = PyDict_GetItemString(parent_metadata.get(), ANOMALY_PARENT_KEY);
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}
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}
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void PyAnomalyMetadata::assign_parent(
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const std::shared_ptr<Node>& parent_node) {
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// assign the python object of parent_node in metadata["parent_"]
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// if parent_node is nullptr, then do nothing (it can mean that "parent_" key
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// is not in metadata)
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pybind11::gil_scoped_acquire gil;
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if (!parent_node)
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return;
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THPObjectPtr parent_node_(functionToPyObject(parent_node));
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if (!parent_node_) {
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throw python_error();
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}
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if (PyDict_SetItemString(dict(), ANOMALY_PARENT_KEY, parent_node_.get())) {
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throw python_error();
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}
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}
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void _print_stack(
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PyObject* stack,
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const std::string& current_node_name,
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bool is_parent) {
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if (!stack) {
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TORCH_WARN(
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"Error detected in ",
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current_node_name,
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". ",
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"No forward pass information available. Enable detect anomaly "
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"during forward pass for more information.");
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return;
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}
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THPObjectPtr empty_string(PyUnicode_FromString(""));
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if (!empty_string) {
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throw python_error();
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}
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// stack is a list of Python strings ending with newlines. Use join to convert
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// to a single string.
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THPObjectPtr msg(PyUnicode_Join(empty_string, stack));
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if (!msg) {
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throw python_error();
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}
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if (!is_parent) {
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TORCH_WARN(
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"Error detected in ",
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current_node_name,
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". ",
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"Traceback of forward call that caused the error:\n",
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THPUtils_unpackString(msg.get()));
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} else {
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TORCH_WARN(
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"\n\n",
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"Previous calculation was induced by ",
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current_node_name,
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". "
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"Traceback of forward call that induced the previous calculation:\n",
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THPUtils_unpackString(msg.get()));
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}
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}
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} // namespace autograd
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} // namespace torch
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