mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
517c7c9861
Summary:
Anywhere we used #include "foo.h", we now say #include <foo.h>
Paths are adjusted to be rooted out of aten/src, torch/lib, or
the root level directory.
I modified CMakeLists.txt by hand to remove TH and THC from
the include paths.
I used the following script to do the canonicalization:
```
import subprocess
import re
import os.path
files = subprocess.check_output(['git', 'ls-files']).decode('utf-8').rstrip().split('\n')
for fn in files:
if not any(fn.endswith(suff) for suff in ['.cu', '.cpp', '.in', '.h', '.hpp', '.cu', '.cuh', '.cc']):
continue
if not any(fn.startswith(pref) for pref in ["aten/", "torch/"]):
continue
with open(fn, 'r') as f:
c = f.read()
def fmt(p):
return "#include <{}>".format(p)
def repl(m):
p = m.group(1)
if p in ["dlfcn.h", "unistd.h", "nvrtc.h", "cuda.h", "cuda_runtime.h", "cstdint", "cudnn.h", "Python.h", "cusparse.h", "cuda_runtime_api.h", "cuda_fp16.h", "cublas_v2.h", "stdint.h", "curand_kernel.h"]:
return fmt(p)
if any(p.startswith(pref) for pref in ["torch/csrc", "c10/", "ATen/", "caffe2/", "TH/", "THC/", "Eigen/", "gtest/", "zdl/", "gloo/", "onnx/", "miopen/"]):
return fmt(p)
for root in ["aten/src", "torch/lib", ""]:
for bad_root in [os.path.dirname(fn), "aten/src/TH", "aten/src/THC", "torch/csrc"]:
new_p = os.path.relpath(os.path.join(bad_root, p), root)
if not new_p.startswith("../") and (os.path.exists(os.path.join(root, new_p)) or os.path.exists(os.path.join(root, new_p + ".in"))):
return fmt(new_p)
print("ERROR: ", fn, p)
return m.group(0)
new_c = re.sub(r'#include "([^"]+)"', repl, c)
if new_c != c:
print(fn)
with open(fn, 'w') as f:
f.write(new_c)
```
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14849
Reviewed By: dzhulgakov
Differential Revision: D13363445
Pulled By: ezyang
fbshipit-source-id: 52361f878a672785f9306c9e9ab2513128092b68
106 lines
3.1 KiB
C++
106 lines
3.1 KiB
C++
#pragma once
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#include <torch/csrc/python_headers.h>
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#include <torch/csrc/Exceptions.h>
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#include <torch/csrc/autograd/function.h>
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#include <torch/csrc/autograd/variable.h>
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#include <torch/csrc/autograd/saved_variable.h>
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#include <torch/csrc/utils/object_ptr.h>
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#include <c10/util/Optional.h>
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#include <c10/DeviceGuard.h>
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#include <vector>
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#include <utility>
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#include <memory>
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namespace torch { namespace jit { struct Graph; }}
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namespace torch { namespace autograd {
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struct VariableInfo {
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explicit VariableInfo(const Variable& var);
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Variable zeros(at::OptionalDeviceGuard& device_guard) const;
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at::Type* type;
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at::Device device = at::kCPU;
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std::vector<int64_t> size;
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bool requires_grad;
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};
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// A Function which is implemented by a Python object (i.e., a THPFunction).
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// Calls to 'apply' are forwarded to the Python method implementation.
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struct PyFunction : public Function {
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PyFunction(PyObject* obj) : obj(obj) {}
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variable_list apply(variable_list&& inputs) override;
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variable_list legacy_apply(const variable_list& inputs);
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void release_variables() override;
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std::string name() const override;
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std::shared_ptr<Function> get_shared_ptr() override;
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bool is_traceable() override;
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// THPFunction this Function is wrapping.
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PyObject* obj;
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};
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/**
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* Cast an object into a tuple, if it is not a tuple already. Returns true
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* if the original object was not a tuple.
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*/
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inline bool ensure_tuple(THPObjectPtr& obj) {
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if (PyTuple_Check(obj.get()))
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return false;
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PyObject *tuple = PyTuple_New(1);
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if (!tuple) throw python_error();
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PyTuple_SET_ITEM(tuple, 0, obj.release());
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obj = tuple;
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return true;
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}
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}} // namespace torch::autograd
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struct THPFunction {
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PyObject_HEAD
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PyObject *needs_input_grad;
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// Python tuple of tensors whose variables we should save. Set
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// by Python with 'save_for_backward'. If nullptr, no tensors were
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// saved.
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PyObject *to_save;
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// Python tuple of tensors which are not differentiable. Set by
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// Python with 'mark_non_differentiable'. If nullptr, no tensors were
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// non-differentiable.
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PyObject *non_differentiable;
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// Python tuple of tensors which had inplace updates in the forward()
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// pass. Set by Python with 'mark_dirty'. If nullptr, no tensors were
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// modified inplace.
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PyObject *dirty_tensors;
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std::vector<torch::autograd::VariableInfo> output_info;
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std::vector<torch::autograd::VariableInfo> input_info;
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std::vector<torch::autograd::SavedVariable> saved_variables;
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// For each input, true if the input is a THPVariable
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std::vector<bool> is_variable_input;
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char has_freed_buffers;
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// The C++ wrapper for this Python function.
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// See a comment in THPFunction_asFunction for details about this field.
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torch::autograd::PyFunction cdata;
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};
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bool THPFunction_initModule(PyObject *module);
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extern PyTypeObject THPFunctionType;
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extern PyObject *THPFunctionClass;
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// XXX: this function requires the GIL (it can have side effects).
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std::shared_ptr<torch::autograd::PyFunction> THPFunction_asFunction(THPFunction* self);
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inline bool THPFunction_Check(PyObject* obj) {
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return PyObject_IsInstance(obj, (PyObject*)&THPFunctionType);
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}
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