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Commit 6569c84e authored by rusty1s's avatar rusty1s
Browse files

cpu port to new extension api

parent aa80bb88
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Showing with 250 additions and 29 deletions
.coveragerc
View file @ 6569c84e
[report]
exclude_lines =
pragma: no cover
def backward
cuda
.gitignore
View file @ 6569c84e
__pycache__/
_ext/
build/
dist/
.cache/
.eggs/
*.egg-info/
.coverage
*.so
*.aux
*.log
*.pdf
......
.travis.yml
View file @ 6569c84e
......@@ -6,14 +6,15 @@ matrix:
- python: 2.7
- python: 3.5
- python: 3.6
- python: 3.7
install:
- if [[ $TRAVIS_PYTHON_VERSION == 2.7 ]]; then pip install http://download.pytorch.org/whl/cpu/torch-0.4.0-cp27-cp27mu-linux_x86_64.whl; fi
- if [[ $TRAVIS_PYTHON_VERSION == 3.5 ]]; then pip install http://download.pytorch.org/whl/cpu/torch-0.4.0-cp35-cp35m-linux_x86_64.whl; fi
- if [[ $TRAVIS_PYTHON_VERSION == 3.6 ]]; then pip install http://download.pytorch.org/whl/cpu/torch-0.4.0-cp36-cp36m-linux_x86_64.whl; fi
- if [[ $TRAVIS_PYTHON_VERSION == 2.7 ]]; then pip install http://download.pytorch.org/whl/cpu/torch-0.4.1-cp27-cp27mu-linux_x86_64.whl; fi
- if [[ $TRAVIS_PYTHON_VERSION == 3.5 ]]; then pip install http://download.pytorch.org/whl/cpu/torch-0.4.1-cp35-cp35m-linux_x86_64.whl; fi
- if [[ $TRAVIS_PYTHON_VERSION == 3.6 ]]; then pip install http://download.pytorch.org/whl/cpu/torch-0.4.1-cp36-cp36m-linux_x86_64.whl; fi
- if [[ $TRAVIS_PYTHON_VERSION == 3.7 ]]; then pip install http://download.pytorch.org/whl/cpu/torch-0.4.1.post2-cp37-cp37m-linux_x86_64.whl; fi
- pip install pycodestyle
- pip install flake8
- pip install codecov
- pip install cffi
script:
- pycodestyle .
- flake8 .
......
README.md
View file @ 6569c84e
......@@ -40,7 +40,7 @@ If not, add cuda (`/usr/local/cuda/bin`) to your `$PATH`.
Then run:
```
pip install cffi torch-scatter
pip install torch-scatter
```
## Example
......
cpu/dim_apply.h 0 → 100644
View file @ 6569c84e
#pragma once
#include <torch/torch.h>
#define DIM_APPLY3(TYPE1, TENSOR1, TYPE2, TENSOR2, TYPE3, TENSOR3, DIM, CODE) \
TYPE1 *TENSOR1##_data = TENSOR1.data<TYPE1>(); \
auto TENSOR1##_size = TENSOR1.size(DIM); \
auto TENSOR1##_stride = TENSOR1.stride(DIM); \
\
TYPE2 *TENSOR2##_data = TENSOR2.data<TYPE2>(); \
auto TENSOR2##_size = TENSOR2.size(DIM); \
auto TENSOR2##_stride = TENSOR2.stride(DIM); \
\
TYPE3 *TENSOR3##_data = TENSOR3.data<TYPE3>(); \
auto TENSOR3##_size = TENSOR3.size(DIM); \
auto TENSOR3##_stride = TENSOR3.stride(DIM); \
\
auto dims = TENSOR1.dim(); \
auto zeros = at::zeros(torch::CPU(at::kLong), {dims}); \
auto counter = zeros.data<int64_t>(); \
bool has_finished = false; \
\
while (!has_finished) { \
CODE; \
if (dims == 1) \
break; \
\
for (int64_t cur_dim = 0; cur_dim < dims; cur_dim++) { \
if (cur_dim == DIM) { \
if (cur_dim == dims - 1) { \
has_finished = true; \
break; \
} \
continue; \
} \
\
counter[cur_dim]++; \
TENSOR1##_data += TENSOR1.stride(cur_dim); \
TENSOR2##_data += TENSOR2.stride(cur_dim); \
TENSOR3##_data += TENSOR3.stride(cur_dim); \
\
if (counter[cur_dim] == TENSOR1.size(cur_dim)) { \
if (cur_dim == dims - 1) { \
has_finished = true; \
break; \
} else { \
TENSOR1##_data -= counter[cur_dim] * TENSOR1.stride(cur_dim); \
TENSOR2##_data -= counter[cur_dim] * TENSOR2.stride(cur_dim); \
TENSOR3##_data -= counter[cur_dim] * TENSOR3.stride(cur_dim); \
counter[cur_dim] = 0; \
} \
} else \
break; \
} \
}
#define DIM_APPLY4(TYPE1, TENSOR1, TYPE2, TENSOR2, TYPE3, TENSOR3, TYPE4, \
TENSOR4, DIM, CODE) \
TYPE1 *TENSOR1##_data = TENSOR1.data<TYPE1>(); \
auto TENSOR1##_size = TENSOR1.size(DIM); \
auto TENSOR1##_stride = TENSOR1.stride(DIM); \
\
TYPE2 *TENSOR2##_data = TENSOR2.data<TYPE2>(); \
auto TENSOR2##_size = TENSOR2.size(DIM); \
auto TENSOR2##_stride = TENSOR2.stride(DIM); \
\
TYPE3 *TENSOR3##_data = TENSOR3.data<TYPE3>(); \
auto TENSOR3##_size = TENSOR3.size(DIM); \
auto TENSOR3##_stride = TENSOR3.stride(DIM); \
\
TYPE4 *TENSOR4##_data = TENSOR4.data<TYPE4>(); \
auto TENSOR4##_size = TENSOR4.size(DIM); \
auto TENSOR4##_stride = TENSOR4.stride(DIM); \
\
auto dims = TENSOR1.dim(); \
auto zeros = at::zeros(torch::CPU(at::kLong), {dims}); \
auto counter = zeros.data<int64_t>(); \
bool has_finished = false; \
\
while (!has_finished) { \
CODE; \
if (dims == 1) \
break; \
\
for (int64_t cur_dim = 0; cur_dim < dims; cur_dim++) { \
if (cur_dim == DIM) { \
if (cur_dim == dims - 1) { \
has_finished = true; \
break; \
} \
continue; \
} \
\
counter[cur_dim]++; \
TENSOR1##_data += TENSOR1.stride(cur_dim); \
TENSOR2##_data += TENSOR2.stride(cur_dim); \
TENSOR3##_data += TENSOR3.stride(cur_dim); \
TENSOR4##_data += TENSOR4.stride(cur_dim); \
\
if (counter[cur_dim] == TENSOR1.size(cur_dim)) { \
if (cur_dim == dims - 1) { \
has_finished = true; \
break; \
} else { \
TENSOR1##_data -= counter[cur_dim] * TENSOR1.stride(cur_dim); \
TENSOR2##_data -= counter[cur_dim] * TENSOR2.stride(cur_dim); \
TENSOR3##_data -= counter[cur_dim] * TENSOR3.stride(cur_dim); \
TENSOR4##_data -= counter[cur_dim] * TENSOR4.stride(cur_dim); \
counter[cur_dim] = 0; \
} \
} else \
break; \
} \
}
cpu/scatter.cpp 0 → 100644
View file @ 6569c84e
#include <torch/torch.h>
#include "dim_apply.h"
void scatter_mul(at::Tensor src, at::Tensor index, at::Tensor out,
int64_t dim) {
int64_t elems_per_row = index.size(dim), i, idx;
AT_DISPATCH_ALL_TYPES(src.type(), "scatter_mul", [&] {
DIM_APPLY3(scalar_t, src, int64_t, index, scalar_t, out, dim, {
for (i = 0; i < elems_per_row; i++) {
idx = index_data[i * index_stride];
out_data[idx * out_stride] *= src_data[i * src_stride];
}
})
});
}
void scatter_div(at::Tensor src, at::Tensor index, at::Tensor out,
int64_t dim) {
int64_t elems_per_row = index.size(dim), i, idx;
AT_DISPATCH_ALL_TYPES(src.type(), "scatter_div", [&] {
DIM_APPLY3(scalar_t, src, int64_t, index, scalar_t, out, dim, {
for (i = 0; i < elems_per_row; i++) {
idx = index_data[i * index_stride];
out_data[idx * out_stride] /= src_data[i * src_stride];
}
})
});
}
void scatter_max(at::Tensor src, at::Tensor index, at::Tensor out,
at::Tensor arg, int64_t dim) {
int64_t elems_per_row = index.size(dim), i, idx;
AT_DISPATCH_ALL_TYPES(src.type(), "scatter_max", [&] {
DIM_APPLY4(scalar_t, src, int64_t, index, scalar_t, out, int64_t, arg, dim,
{
for (i = 0; i < elems_per_row; i++) {
idx = index_data[i * index_stride];
if (src_data[i * src_stride] >= out_data[idx * out_stride]) {
out_data[idx * out_stride] = src_data[i * src_stride];
arg_data[idx * arg_stride] = i;
}
}
})
});
}
void scatter_min(at::Tensor src, at::Tensor index, at::Tensor out,
at::Tensor arg, int64_t dim) {
int64_t elems_per_row = index.size(dim), i, idx;
AT_DISPATCH_ALL_TYPES(src.type(), "scatter_min", [&] {
DIM_APPLY4(scalar_t, src, int64_t, index, scalar_t, out, int64_t, arg, dim,
{
for (i = 0; i < elems_per_row; i++) {
idx = index_data[i * index_stride];
if (src_data[i * src_stride] <= out_data[idx * out_stride]) {
out_data[idx * out_stride] = src_data[i * src_stride];
arg_data[idx * arg_stride] = i;
}
}
})
});
}
void index_backward(at::Tensor grad, at::Tensor index, at::Tensor arg,
at::Tensor out, int64_t dim) {
int64_t elems_per_row = index.size(dim), i, idx;
AT_DISPATCH_ALL_TYPES(grad.type(), "index_backward", [&] {
DIM_APPLY4(scalar_t, grad, int64_t, index, int64_t, arg, scalar_t, out, dim,
{
for (i = 0; i < elems_per_row; i++) {
idx = index_data[i * index_stride];
if (arg_data[idx * arg_stride] == i) {
out_data[i * out_stride] = grad_data[idx * grad_stride];
}
}
})
});
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("scatter_mul", &scatter_mul, "Scatter Mul (CPU)");
m.def("scatter_div", &scatter_div, "Scatter Div (CPU)");
m.def("scatter_max", &scatter_max, "Scatter Max (CPU)");
m.def("scatter_min", &scatter_min, "Scatter Min (CPU)");
m.def("index_backward", &index_backward, "Index Backward (CPU)");
}
setup.py
View file @ 6569c84e
from os import path as osp
import glob
from setuptools import setup, find_packages
__version__ = '1.0.3'
import torch.cuda
from torch.utils.cpp_extension import CppExtension, CUDAExtension
ext_modules = [CppExtension('scatter_cpu', ['cpu/scatter.cpp'])]
cmdclass = {'build_ext': torch.utils.cpp_extension.BuildExtension}
if torch.cuda.is_available():
ext_modules += [
CUDAExtension('scatter_cuda',
['cuda/scatter.cpp'] + glob.glob('cuda/*.cu'))
]
__version__ = '1.0.4'
url = 'https://github.com/rusty1s/pytorch_scatter'
install_requires = ['cffi']
install_requires = []
setup_requires = ['pytest-runner', 'cffi']
tests_require = ['pytest', 'pytest-cov']
......@@ -21,7 +32,7 @@ setup(
install_requires=install_requires,
setup_requires=setup_requires,
tests_require=tests_require,
packages=find_packages(exclude=['build']),
ext_package='',
cffi_modules=[osp.join(osp.dirname(__file__), 'build.py:ffi')],
ext_modules=ext_modules,
cmdclass=cmdclass,
packages=find_packages(),
)
test/test_backward.py
View file @ 6569c84e
......@@ -5,8 +5,9 @@ import torch
from torch.autograd import gradcheck
import torch_scatter
from .utils import dtypes, devices, tensor
from .utils import devices, tensor
dtypes = [torch.float, torch.double]
funcs = ['add', 'sub', 'mul', 'div', 'mean']
indices = [2, 0, 1, 1, 0]
......
test/utils.py
View file @ 6569c84e
......@@ -3,9 +3,6 @@ from torch.testing import get_all_dtypes
dtypes = get_all_dtypes()
dtypes.remove(torch.half)
dtypes.remove(torch.short) # TODO: PyTorch `atomicAdd` bug with short type.
dtypes.remove(torch.uint8) # We cannot properly test unsigned values.
dtypes.remove(torch.int8) # Overflow on gradient computations :(
devices = [torch.device('cpu')]
if torch.cuda.is_available(): # pragma: no cover
......
torch_scatter/__init__.py
View file @ 6569c84e
......@@ -6,7 +6,7 @@ from .mean import scatter_mean
from .max import scatter_max
from .min import scatter_min
__version__ = '1.0.3'
__version__ = '1.0.4'
__all__ = [
'scatter_add', 'scatter_sub', 'scatter_mul', 'scatter_div', 'scatter_mean',
......
torch_scatter/div.py
View file @ 6569c84e
from torch.autograd import Function
from .utils.ffi import get_func
from .utils.ext import get_func
from .utils.gen import gen
......@@ -8,7 +8,7 @@ class ScatterDiv(Function):
@staticmethod
def forward(ctx, out, src, index, dim):
func = get_func('scatter_div', src)
func(dim, out, index, src)
func(src, index, out, dim)
ctx.mark_dirty(out)
ctx.save_for_backward(out, src, index)
......
torch_scatter/max.py
View file @ 6569c84e
from torch.autograd import Function
from .utils.ffi import get_func
from .utils.ext import get_func
from .utils.gen import gen
......@@ -9,7 +9,7 @@ class ScatterMax(Function):
def forward(ctx, out, src, index, dim):
arg = index.new_full(out.size(), -1)
func = get_func('scatter_max', src)
func(dim, out, index, src, arg)
func(src, index, out, arg, dim)
ctx.mark_dirty(out)
ctx.dim = dim
......@@ -25,7 +25,7 @@ class ScatterMax(Function):
if ctx.needs_input_grad[1]:
grad_src = grad_out.new_zeros(index.size())
func = get_func('index_backward', grad_out)
func(ctx.dim, grad_src, index, grad_out, arg)
func(grad_out, index, arg, grad_src, ctx.dim)
return None, grad_src, None, None
......
torch_scatter/min.py
View file @ 6569c84e
from torch.autograd import Function
from .utils.ffi import get_func
from .utils.ext import get_func
from .utils.gen import gen
......@@ -9,7 +9,7 @@ class ScatterMin(Function):
def forward(ctx, out, src, index, dim):
arg = index.new_full(out.size(), -1)
func = get_func('scatter_min', src)
func(dim, out, index, src, arg)
func(src, index, out, arg, dim)
ctx.mark_dirty(out)
ctx.dim = dim
......@@ -25,7 +25,7 @@ class ScatterMin(Function):
if ctx.needs_input_grad[1]:
grad_src = grad_out.new_zeros(index.size())
func = get_func('index_backward', grad_out)
func(ctx.dim, grad_src, index, grad_out, arg)
func(grad_out, index, arg, grad_src, ctx.dim)
return None, grad_src, None, None
......
torch_scatter/mul.py
View file @ 6569c84e
from torch.autograd import Function
from .utils.ffi import get_func
from .utils.ext import get_func
from .utils.gen import gen
......@@ -8,7 +8,7 @@ class ScatterMul(Function):
@staticmethod
def forward(ctx, out, src, index, dim):
func = get_func('scatter_mul', src)
func(dim, out, index, src)
func(src, index, out, dim)
ctx.mark_dirty(out)
ctx.save_for_backward(out, src, index)
......
torch_scatter/utils/ext.py 0 → 100644
View file @ 6569c84e
import torch
import scatter_cpu
if torch.cuda.is_available():
import scatter_cuda
def get_func(name, tensor):
scatter = scatter_cuda if tensor.is_cuda else scatter_cpu
return getattr(scatter, name)
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