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Metadata-Version: 2.1
Name: torch_sparse
Version: 0.6.13
Summary: PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations
Home-page: https://github.com/rusty1s/pytorch_sparse
Author: Matthias Fey
Author-email: matthias.fey@tu-dortmund.de
License: UNKNOWN
Download-URL: https://github.com/rusty1s/pytorch_sparse/archive/0.6.13.tar.gz
Description: [pypi-image]: https://badge.fury.io/py/torch-sparse.svg
[pypi-url]: https://pypi.python.org/pypi/torch-sparse
[testing-image]: https://github.com/rusty1s/pytorch_sparse/actions/workflows/testing.yml/badge.svg
[testing-url]: https://github.com/rusty1s/pytorch_sparse/actions/workflows/testing.yml
[linting-image]: https://github.com/rusty1s/pytorch_sparse/actions/workflows/linting.yml/badge.svg
[linting-url]: https://github.com/rusty1s/pytorch_sparse/actions/workflows/linting.yml
[coverage-image]: https://codecov.io/gh/rusty1s/pytorch_sparse/branch/master/graph/badge.svg
[coverage-url]: https://codecov.io/github/rusty1s/pytorch_sparse?branch=master
# PyTorch Sparse
[![PyPI Version][pypi-image]][pypi-url]
[![Testing Status][testing-image]][testing-url]
[![Linting Status][linting-image]][linting-url]
[![Code Coverage][coverage-image]][coverage-url]
--------------------------------------------------------------------------------
This package consists of a small extension library of optimized sparse matrix operations with autograd support.
This package currently consists of the following methods:
* **[Coalesce](#coalesce)**
* **[Transpose](#transpose)**
* **[Sparse Dense Matrix Multiplication](#sparse-dense-matrix-multiplication)**
* **[Sparse Sparse Matrix Multiplication](#sparse-sparse-matrix-multiplication)**
All included operations work on varying data types and are implemented both for CPU and GPU.
To avoid the hazzle of creating [`torch.sparse_coo_tensor`](https://pytorch.org/docs/stable/torch.html?highlight=sparse_coo_tensor#torch.sparse_coo_tensor), this package defines operations on sparse tensors by simply passing `index` and `value` tensors as arguments ([with same shapes as defined in PyTorch](https://pytorch.org/docs/stable/sparse.html)).
Note that only `value` comes with autograd support, as `index` is discrete and therefore not differentiable.
## Installation
### Anaconda
**Update:** You can now install `pytorch-sparse` via [Anaconda](https://anaconda.org/pyg/pytorch-sparse) for all major OS/PyTorch/CUDA combinations 🤗
Given that you have [`pytorch >= 1.8.0` installed](https://pytorch.org/get-started/locally/), simply run
```
conda install pytorch-sparse -c pyg
```
### Binaries
We alternatively provide pip wheels for all major OS/PyTorch/CUDA combinations, see [here](https://data.pyg.org/whl).
#### PyTorch 1.11
To install the binaries for PyTorch 1.11.0, simply run
```
pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-1.11.0+${CUDA}.html
```
where `${CUDA}` should be replaced by either `cpu`, `cu102`, `cu113`, or `cu115` depending on your PyTorch installation.
| | `cpu` | `cu102` | `cu113` | `cu115` |
|-------------|-------|---------|---------|---------|
| **Linux** | ✅ | ✅ | ✅ | ✅ |
| **Windows** | ✅ | | ✅ | ✅ |
| **macOS** | ✅ | | | |
#### PyTorch 1.10
To install the binaries for PyTorch 1.10.0, PyTorch 1.10.1 and PyTorch 1.10.2, simply run
```
pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-1.10.0+${CUDA}.html
```
where `${CUDA}` should be replaced by either `cpu`, `cu102`, `cu111`, or `cu113` depending on your PyTorch installation.
| | `cpu` | `cu102` | `cu111` | `cu113` |
|-------------|-------|---------|---------|---------|
| **Linux** | ✅ | ✅ | ✅ | ✅ |
| **Windows** | ✅ | ✅ | ✅ | ✅ |
| **macOS** | ✅ | | | |
**Note:** Binaries of older versions are also provided for PyTorch 1.4.0, PyTorch 1.5.0, PyTorch 1.6.0, PyTorch 1.7.0/1.7.1, PyTorch 1.8.0/1.8.1 and PyTorch 1.9.0 (following the same procedure).
For older versions, you might need to explicitly specify the latest supported version number in order to prevent a manual installation from source.
You can look up the latest supported version number [here](https://data.pyg.org/whl).
### From source
Ensure that at least PyTorch 1.7.0 is installed and verify that `cuda/bin` and `cuda/include` are in your `$PATH` and `$CPATH` respectively, *e.g.*:
```
$ python -c "import torch; print(torch.__version__)"
>>> 1.7.0
$ echo $PATH
>>> /usr/local/cuda/bin:...
$ echo $CPATH
>>> /usr/local/cuda/include:...
```
If you want to additionally build `torch-sparse` with METIS support, *e.g.* for partioning, please download and install the [METIS library](http://glaros.dtc.umn.edu/gkhome/metis/metis/download) by following the instructions in the `Install.txt` file.
Note that METIS needs to be installed with 64 bit `IDXTYPEWIDTH` by changing `include/metis.h`.
Afterwards, set the environment variable `WITH_METIS=1`.
Then run:
```
pip install torch-scatter torch-sparse
```
When running in a docker container without NVIDIA driver, PyTorch needs to evaluate the compute capabilities and may fail.
In this case, ensure that the compute capabilities are set via `TORCH_CUDA_ARCH_LIST`, *e.g.*:
```
export TORCH_CUDA_ARCH_LIST="6.0 6.1 7.2+PTX 7.5+PTX"
```
## Functions
### Coalesce
```
torch_sparse.coalesce(index, value, m, n, op="add") -> (torch.LongTensor, torch.Tensor)
```
Row-wise sorts `index` and removes duplicate entries.
Duplicate entries are removed by scattering them together.
For scattering, any operation of [`torch_scatter`](https://github.com/rusty1s/pytorch_scatter) can be used.
#### Parameters
* **index** *(LongTensor)* - The index tensor of sparse matrix.
* **value** *(Tensor)* - The value tensor of sparse matrix.
* **m** *(int)* - The first dimension of sparse matrix.
* **n** *(int)* - The second dimension of sparse matrix.
* **op** *(string, optional)* - The scatter operation to use. (default: `"add"`)
#### Returns
* **index** *(LongTensor)* - The coalesced index tensor of sparse matrix.
* **value** *(Tensor)* - The coalesced value tensor of sparse matrix.
#### Example
```python
import torch
from torch_sparse import coalesce
index = torch.tensor([[1, 0, 1, 0, 2, 1],
[0, 1, 1, 1, 0, 0]])
value = torch.Tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7]])
index, value = coalesce(index, value, m=3, n=2)
```
```
print(index)
tensor([[0, 1, 1, 2],
[1, 0, 1, 0]])
print(value)
tensor([[6.0, 8.0],
[7.0, 9.0],
[3.0, 4.0],
[5.0, 6.0]])
```
### Transpose
```
torch_sparse.transpose(index, value, m, n) -> (torch.LongTensor, torch.Tensor)
```
Transposes dimensions 0 and 1 of a sparse matrix.
#### Parameters
* **index** *(LongTensor)* - The index tensor of sparse matrix.
* **value** *(Tensor)* - The value tensor of sparse matrix.
* **m** *(int)* - The first dimension of sparse matrix.
* **n** *(int)* - The second dimension of sparse matrix.
* **coalesced** *(bool, optional)* - If set to `False`, will not coalesce the output. (default: `True`)
#### Returns
* **index** *(LongTensor)* - The transposed index tensor of sparse matrix.
* **value** *(Tensor)* - The transposed value tensor of sparse matrix.
#### Example
```python
import torch
from torch_sparse import transpose
index = torch.tensor([[1, 0, 1, 0, 2, 1],
[0, 1, 1, 1, 0, 0]])
value = torch.Tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7]])
index, value = transpose(index, value, 3, 2)
```
```
print(index)
tensor([[0, 0, 1, 1],
[1, 2, 0, 1]])
print(value)
tensor([[7.0, 9.0],
[5.0, 6.0],
[6.0, 8.0],
[3.0, 4.0]])
```
### Sparse Dense Matrix Multiplication
```
torch_sparse.spmm(index, value, m, n, matrix) -> torch.Tensor
```
Matrix product of a sparse matrix with a dense matrix.
#### Parameters
* **index** *(LongTensor)* - The index tensor of sparse matrix.
* **value** *(Tensor)* - The value tensor of sparse matrix.
* **m** *(int)* - The first dimension of sparse matrix.
* **n** *(int)* - The second dimension of sparse matrix.
* **matrix** *(Tensor)* - The dense matrix.
#### Returns
* **out** *(Tensor)* - The dense output matrix.
#### Example
```python
import torch
from torch_sparse import spmm
index = torch.tensor([[0, 0, 1, 2, 2],
[0, 2, 1, 0, 1]])
value = torch.Tensor([1, 2, 4, 1, 3])
matrix = torch.Tensor([[1, 4], [2, 5], [3, 6]])
out = spmm(index, value, 3, 3, matrix)
```
```
print(out)
tensor([[7.0, 16.0],
[8.0, 20.0],
[7.0, 19.0]])
```
### Sparse Sparse Matrix Multiplication
```
torch_sparse.spspmm(indexA, valueA, indexB, valueB, m, k, n) -> (torch.LongTensor, torch.Tensor)
```
Matrix product of two sparse tensors.
Both input sparse matrices need to be **coalesced** (use the `coalesced` attribute to force).
#### Parameters
* **indexA** *(LongTensor)* - The index tensor of first sparse matrix.
* **valueA** *(Tensor)* - The value tensor of first sparse matrix.
* **indexB** *(LongTensor)* - The index tensor of second sparse matrix.
* **valueB** *(Tensor)* - The value tensor of second sparse matrix.
* **m** *(int)* - The first dimension of first sparse matrix.
* **k** *(int)* - The second dimension of first sparse matrix and first dimension of second sparse matrix.
* **n** *(int)* - The second dimension of second sparse matrix.
* **coalesced** *(bool, optional)*: If set to `True`, will coalesce both input sparse matrices. (default: `False`)
#### Returns
* **index** *(LongTensor)* - The output index tensor of sparse matrix.
* **value** *(Tensor)* - The output value tensor of sparse matrix.
#### Example
```python
import torch
from torch_sparse import spspmm
indexA = torch.tensor([[0, 0, 1, 2, 2], [1, 2, 0, 0, 1]])
valueA = torch.Tensor([1, 2, 3, 4, 5])
indexB = torch.tensor([[0, 2], [1, 0]])
valueB = torch.Tensor([2, 4])
indexC, valueC = spspmm(indexA, valueA, indexB, valueB, 3, 3, 2)
```
```
print(indexC)
tensor([[0, 1, 2],
[0, 1, 1]])
print(valueC)
tensor([8.0, 6.0, 8.0])
```
## C++ API
`torch-sparse` also offers a C++ API that contains C++ equivalent of python models.
```
mkdir build
cd build
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install
```
## Running tests
```
pytest
```
Keywords: pytorch,sparse,sparse-matrices,autograd
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: test
csrc/cpu/spspmm_cpu.cpp deleted 100644 → 0
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#include "spspmm_cpu.h"
#include "utils.h"
std::tuple<torch::Tensor, torch::Tensor, torch::optional<torch::Tensor>>
spspmm_cpu(torch::Tensor rowptrA, torch::Tensor colA,
torch::optional<torch::Tensor> optional_valueA,
torch::Tensor rowptrB, torch::Tensor colB,
torch::optional<torch::Tensor> optional_valueB, int64_t K,
std::string reduce) {
CHECK_CPU(rowptrA);
CHECK_CPU(colA);
if (optional_valueA.has_value())
CHECK_CPU(optional_valueA.value());
CHECK_CPU(rowptrB);
CHECK_CPU(colB);
if (optional_valueB.has_value())
CHECK_CPU(optional_valueB.value());
CHECK_INPUT(rowptrA.dim() == 1);
CHECK_INPUT(colA.dim() == 1);
if (optional_valueA.has_value()) {
CHECK_INPUT(optional_valueA.value().dim() == 1);
CHECK_INPUT(optional_valueA.value().size(0) == colA.size(0));
}
CHECK_INPUT(rowptrB.dim() == 1);
CHECK_INPUT(colB.dim() == 1);
if (optional_valueB.has_value()) {
CHECK_INPUT(optional_valueB.value().dim() == 1);
CHECK_INPUT(optional_valueB.value().size(0) == colB.size(0));
}
if (!optional_valueA.has_value() && optional_valueB.has_value())
optional_valueA =
torch::ones(colA.numel(), optional_valueB.value().options());
if (!optional_valueB.has_value() && optional_valueA.has_value())
optional_valueB =
torch::ones(colB.numel(), optional_valueA.value().options());
auto scalar_type = torch::ScalarType::Float;
if (optional_valueA.has_value())
scalar_type = optional_valueA.value().scalar_type();
auto rowptrA_data = rowptrA.data_ptr<int64_t>();
auto colA_data = colA.data_ptr<int64_t>();
auto rowptrB_data = rowptrB.data_ptr<int64_t>();
auto colB_data = colB.data_ptr<int64_t>();
auto rowptrC = torch::empty_like(rowptrA);
auto rowptrC_data = rowptrC.data_ptr<int64_t>();
rowptrC_data[0] = 0;
torch::Tensor colC;
torch::optional<torch::Tensor> optional_valueC = torch::nullopt;
AT_DISPATCH_ALL_TYPES(scalar_type, "spspmm", [&] {
AT_DISPATCH_HAS_VALUE(optional_valueA, [&] {
scalar_t *valA_data = nullptr, *valB_data = nullptr;
if (HAS_VALUE) {
valA_data = optional_valueA.value().data_ptr<scalar_t>();
valB_data = optional_valueB.value().data_ptr<scalar_t>();
}
int64_t nnz = 0, cA, cB;
std::vector<scalar_t> tmp_vals(K, 0);
std::vector<int64_t> cols;
std::vector<scalar_t> vals;
for (auto rA = 0; rA < rowptrA.numel() - 1; rA++) {
for (auto eA = rowptrA_data[rA]; eA < rowptrA_data[rA + 1]; eA++) {
cA = colA_data[eA];
for (auto eB = rowptrB_data[cA]; eB < rowptrB_data[cA + 1]; eB++) {
cB = colB_data[eB];
if (HAS_VALUE)
tmp_vals[cB] += valA_data[eA] * valB_data[eB];
else
tmp_vals[cB]++;
}
}
for (auto k = 0; k < K; k++) {
if (tmp_vals[k] != 0) {
cols.push_back(k);
if (HAS_VALUE)
vals.push_back(tmp_vals[k]);
nnz++;
}
tmp_vals[k] = (scalar_t)0;
}
rowptrC_data[rA + 1] = nnz;
}
colC = torch::from_blob(cols.data(), {nnz}, colA.options()).clone();
if (HAS_VALUE) {
optional_valueC = torch::from_blob(vals.data(), {nnz},
optional_valueA.value().options());
optional_valueC = optional_valueC.value().clone();
}
});
});
return std::make_tuple(rowptrC, colC, optional_valueC);
}
csrc/cpu/spspmm_cpu.h deleted 100644 → 0
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#pragma once
#include "../extensions.h"
std::tuple<torch::Tensor, torch::Tensor, torch::optional<torch::Tensor>>
spspmm_cpu(torch::Tensor rowptrA, torch::Tensor colA,
torch::optional<torch::Tensor> optional_valueA,
torch::Tensor rowptrB, torch::Tensor colB,
torch::optional<torch::Tensor> optional_valueB, int64_t K,
std::string reduce);
csrc/hip/atomics.cuh deleted 100644 → 0
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#pragma once
static inline __device__ void atomAdd(float *address, float val) {
atomicAdd(address, val);
}
static inline __device__ void atomAdd(double *address, double val) {
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ < 600 || TORCH_HIP_VERSION < 8000)
unsigned long long int *address_as_ull = (unsigned long long int *)address;
unsigned long long int old = *address_as_ull;
unsigned long long int assumed;
do {
assumed = old;
old = atomicCAS(address_as_ull, assumed,
__double_as_longlong(val + __longlong_as_double(assumed)));
} while (assumed != old);
#else
atomicAdd(address, val);
#endif
}
csrc/hip/convert_hip.h deleted 100644 → 0
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#pragma once
#include "../extensions.h"
torch::Tensor ind2ptr_cuda(torch::Tensor ind, int64_t M);
torch::Tensor ptr2ind_cuda(torch::Tensor ptr, int64_t E);
csrc/hip/convert_hip.hip deleted 100644 → 0
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#include "hip/hip_runtime.h"
#include "convert_hip.h"
#include <ATen/hip/HIPContext.h>
#include "utils.cuh"
#define THREADS 256
__global__ void ind2ptr_kernel(const int64_t *ind_data, int64_t *out_data,
int64_t M, int64_t numel) {
int64_t thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
if (thread_idx == 0) {
for (int64_t i = 0; i <= ind_data[0]; i++)
out_data[i] = 0;
} else if (thread_idx < numel) {
for (int64_t i = ind_data[thread_idx - 1]; i < ind_data[thread_idx]; i++)
out_data[i + 1] = thread_idx;
} else if (thread_idx == numel) {
for (int64_t i = ind_data[numel - 1] + 1; i < M + 1; i++)
out_data[i] = numel;
}
}
torch::Tensor ind2ptr_cuda(torch::Tensor ind, int64_t M) {
CHECK_CUDA(ind);
hipSetDevice(ind.get_device());
auto out = torch::empty(M + 1, ind.options());
if (ind.numel() == 0)
return out.zero_();
auto ind_data = ind.data_ptr<int64_t>();
auto out_data = out.data_ptr<int64_t>();
auto stream = at::cuda::getCurrentCUDAStream();
ind2ptr_kernel<<<(ind.numel() + 2 + THREADS - 1) / THREADS, THREADS, 0,
stream>>>(ind_data, out_data, M, ind.numel());
return out;
}
__global__ void ptr2ind_kernel(const int64_t *ptr_data, int64_t *out_data,
int64_t E, int64_t numel) {
int64_t thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
if (thread_idx < numel) {
int64_t idx = ptr_data[thread_idx], next_idx = ptr_data[thread_idx + 1];
for (int64_t i = idx; i < next_idx; i++) {
out_data[i] = thread_idx;
}
}
}
torch::Tensor ptr2ind_cuda(torch::Tensor ptr, int64_t E) {
CHECK_CUDA(ptr);
hipSetDevice(ptr.get_device());
auto out = torch::empty(E, ptr.options());
auto ptr_data = ptr.data_ptr<int64_t>();
auto out_data = out.data_ptr<int64_t>();
auto stream = at::cuda::getCurrentCUDAStream();
ptr2ind_kernel<<<(ptr.numel() - 1 + THREADS - 1) / THREADS, THREADS, 0,
stream>>>(ptr_data, out_data, E, ptr.numel() - 1);
return out;
}
csrc/hip/convert_hip_hip.hip deleted 100644 → 0
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#include "hip/hip_runtime.h"
#include "convert_hip.h"
#include <ATen/hip/HIPContext.h>
#include "utils.cuh"
#define THREADS 256
__global__ void ind2ptr_kernel(const int64_t *ind_data, int64_t *out_data,
int64_t M, int64_t numel) {
int64_t thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
if (thread_idx == 0) {
for (int64_t i = 0; i <= ind_data[0]; i++)
out_data[i] = 0;
} else if (thread_idx < numel) {
for (int64_t i = ind_data[thread_idx - 1]; i < ind_data[thread_idx]; i++)
out_data[i + 1] = thread_idx;
} else if (thread_idx == numel) {
for (int64_t i = ind_data[numel - 1] + 1; i < M + 1; i++)
out_data[i] = numel;
}
}
torch::Tensor ind2ptr_cuda(torch::Tensor ind, int64_t M) {
CHECK_CUDA(ind);
hipSetDevice(ind.get_device());
auto out = torch::empty(M + 1, ind.options());
if (ind.numel() == 0)
return out.zero_();
auto ind_data = ind.data_ptr<int64_t>();
auto out_data = out.data_ptr<int64_t>();
auto stream = at::hip::getCurrentHIPStreamMasqueradingAsCUDA();
hipLaunchKernelGGL(( ind2ptr_kernel), dim3((ind.numel() + 2 + THREADS - 1) / THREADS), dim3(THREADS), 0,
stream, ind_data, out_data, M, ind.numel());
return out;
}
__global__ void ptr2ind_kernel(const int64_t *ptr_data, int64_t *out_data,
int64_t E, int64_t numel) {
int64_t thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
if (thread_idx < numel) {
int64_t idx = ptr_data[thread_idx], next_idx = ptr_data[thread_idx + 1];
for (int64_t i = idx; i < next_idx; i++) {
out_data[i] = thread_idx;
}
}
}
torch::Tensor ptr2ind_cuda(torch::Tensor ptr, int64_t E) {
CHECK_CUDA(ptr);
hipSetDevice(ptr.get_device());
auto out = torch::empty(E, ptr.options());
auto ptr_data = ptr.data_ptr<int64_t>();
auto out_data = out.data_ptr<int64_t>();
auto stream = at::hip::getCurrentHIPStreamMasqueradingAsCUDA();
hipLaunchKernelGGL(( ptr2ind_kernel), dim3((ptr.numel() - 1 + THREADS - 1) / THREADS), dim3(THREADS), 0,
stream, ptr_data, out_data, E, ptr.numel() - 1);
return out;
}
csrc/hip/diag_hip.h deleted 100644 → 0
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#pragma once
#include "../extensions.h"
torch::Tensor non_diag_mask_cuda(torch::Tensor row, torch::Tensor col,
int64_t M, int64_t N, int64_t k);
csrc/hip/diag_hip.hip deleted 100644 → 0
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#include "hip/hip_runtime.h"
#include "diag_hip.h"
#include <ATen/hip/HIPContext.h>
#include "utils.cuh"
#define THREADS 1024
__global__ void non_diag_mask_kernel(const int64_t *row_data,
const int64_t *col_data, bool *out_data,
int64_t N, int64_t k, int64_t num_diag,
int64_t numel) {
int64_t thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
if (thread_idx < numel) {
int64_t r = row_data[thread_idx], c = col_data[thread_idx];
if (k < 0) {
if (r + k < 0) {
out_data[thread_idx] = true;
} else if (r + k >= N) {
out_data[thread_idx + num_diag] = true;
} else if (r + k > c) {
out_data[thread_idx + r + k] = true;
} else if (r + k < c) {
out_data[thread_idx + r + k + 1] = true;
}
} else {
if (r + k >= N) {
out_data[thread_idx + num_diag] = true;
} else if (r + k > c) {
out_data[thread_idx + r] = true;
} else if (r + k < c) {
out_data[thread_idx + r + 1] = true;
}
}
}
}
torch::Tensor non_diag_mask_cuda(torch::Tensor row, torch::Tensor col,
int64_t M, int64_t N, int64_t k) {
CHECK_CUDA(row);
CHECK_CUDA(col);
hipSetDevice(row.get_device());
auto E = row.size(0);
auto num_diag = k < 0 ? std::min(M + k, N) : std::min(M, N - k);
auto row_data = row.data_ptr<int64_t>();
auto col_data = col.data_ptr<int64_t>();
auto mask = torch::zeros(E + num_diag, row.options().dtype(torch::kBool));
auto mask_data = mask.data_ptr<bool>();
if (E == 0)
return mask;
auto stream = at::cuda::getCurrentCUDAStream();
non_diag_mask_kernel<<<(E + THREADS - 1) / THREADS, THREADS, 0, stream>>>(
row_data, col_data, mask_data, N, k, num_diag, E);
return mask;
}
csrc/hip/diag_hip_hip.hip deleted 100644 → 0
View file @ 2951b12d
#include "hip/hip_runtime.h"
#include "diag_hip.h"
#include <ATen/hip/HIPContext.h>
#include "utils.cuh"
#define THREADS 1024
__global__ void non_diag_mask_kernel(const int64_t *row_data,
const int64_t *col_data, bool *out_data,
int64_t N, int64_t k, int64_t num_diag,
int64_t numel) {
int64_t thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
if (thread_idx < numel) {
int64_t r = row_data[thread_idx], c = col_data[thread_idx];
if (k < 0) {
if (r + k < 0) {
out_data[thread_idx] = true;
} else if (r + k >= N) {
out_data[thread_idx + num_diag] = true;
} else if (r + k > c) {
out_data[thread_idx + r + k] = true;
} else if (r + k < c) {
out_data[thread_idx + r + k + 1] = true;
}
} else {
if (r + k >= N) {
out_data[thread_idx + num_diag] = true;
} else if (r + k > c) {
out_data[thread_idx + r] = true;
} else if (r + k < c) {
out_data[thread_idx + r + 1] = true;
}
}
}
}
torch::Tensor non_diag_mask_cuda(torch::Tensor row, torch::Tensor col,
int64_t M, int64_t N, int64_t k) {
CHECK_CUDA(row);
CHECK_CUDA(col);
hipSetDevice(row.get_device());
auto E = row.size(0);
auto num_diag = k < 0 ? std::min(M + k, N) : std::min(M, N - k);
auto row_data = row.data_ptr<int64_t>();
auto col_data = col.data_ptr<int64_t>();
auto mask = torch::zeros(E + num_diag, row.options().dtype(torch::kBool));
auto mask_data = mask.data_ptr<bool>();
if (E == 0)
return mask;
auto stream = at::hip::getCurrentHIPStreamMasqueradingAsCUDA();
hipLaunchKernelGGL(( non_diag_mask_kernel), dim3((E + THREADS - 1) / THREADS), dim3(THREADS), 0, stream,
row_data, col_data, mask_data, N, k, num_diag, E);
return mask;
}
csrc/hip/reducer.cuh deleted 100644 → 0
View file @ 2951b12d
#pragma once
#include <limits>
#include <map>
enum ReductionType { SUM, MEAN, MUL, DIV, MIN, MAX };
const std::map<std::string, ReductionType> reduce2REDUCE = {
{"sum", SUM}, {"mean", MEAN}, {"mul", MUL},
{"div", DIV}, {"min", MIN}, {"max", MAX},
};
#define AT_DISPATCH_REDUCTION_TYPES(reduce, ...) \
[&] { \
switch (reduce2REDUCE.at(reduce)) { \
case SUM: { \
const ReductionType REDUCE = SUM; \
return __VA_ARGS__(); \
} \
case MEAN: { \
const ReductionType REDUCE = MEAN; \
return __VA_ARGS__(); \
} \
case MUL: { \
const ReductionType REDUCE = MUL; \
return __VA_ARGS__(); \
} \
case DIV: { \
const ReductionType REDUCE = DIV; \
return __VA_ARGS__(); \
} \
case MIN: { \
const ReductionType REDUCE = MIN; \
return __VA_ARGS__(); \
} \
case MAX: { \
const ReductionType REDUCE = MAX; \
return __VA_ARGS__(); \
} \
} \
}()
template <typename scalar_t, ReductionType REDUCE> struct Reducer {
static inline __host__ __device__ scalar_t init() {
if (REDUCE == MUL || REDUCE == DIV)
return (scalar_t)1;
else if (REDUCE == MIN)
return std::numeric_limits<scalar_t>::max();
else if (REDUCE == MAX)
return std::numeric_limits<scalar_t>::lowest();
else
return (scalar_t)0;
}
static inline __host__ __device__ void update(scalar_t *val, scalar_t new_val,
int64_t *arg, int64_t new_arg) {
if (REDUCE == SUM || REDUCE == MEAN)
*val = *val + new_val;
else if (REDUCE == MUL)
*val = *val * new_val;
else if (REDUCE == DIV)
*val = *val / new_val;
else if ((REDUCE == MIN && new_val < *val) ||
(REDUCE == MAX && new_val > *val)) {
*val = new_val;
*arg = new_arg;
}
}
static inline __host__ __device__ void write(scalar_t *address, scalar_t val,
int64_t *arg_address,
int64_t arg, int count) {
if (REDUCE == SUM || REDUCE == MUL || REDUCE == DIV)
*address = val;
else if (REDUCE == MEAN)
*address = val / (scalar_t)(count > 0 ? count : 1);
else if (REDUCE == MIN || REDUCE == MAX) {
if (count > 0) {
*address = val;
*arg_address = arg;
} else
*address = (scalar_t)0;
}
}
};
csrc/hip/rw_hip.h deleted 100644 → 0
View file @ 2951b12d
#pragma once
#include "../extensions.h"
torch::Tensor random_walk_cuda(torch::Tensor rowptr, torch::Tensor col,
torch::Tensor start, int64_t walk_length);
csrc/hip/rw_hip.hip deleted 100644 → 0
View file @ 2951b12d
#include "hip/hip_runtime.h"
#include "rw_hip.h"
#include <ATen/hip/HIPContext.h>
#include "utils.cuh"
#define THREADS 1024
#define BLOCKS(N) (N + THREADS - 1) / THREADS
__global__ void uniform_random_walk_kernel(const int64_t *rowptr,
const int64_t *col,
const int64_t *start,
const float *rand, int64_t *out,
int64_t walk_length, int64_t numel) {
const int64_t thread_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (thread_idx < numel) {
int64_t cur = start[thread_idx];
out[thread_idx] = cur;
int64_t row_start, row_end;
for (int64_t l = 0; l < walk_length; l++) {
row_start = rowptr[cur], row_end = rowptr[cur + 1];
cur = col[row_start +
int64_t(rand[l * numel + thread_idx] * (row_end - row_start))];
out[(l + 1) * numel + thread_idx] = cur;
}
}
}
torch::Tensor random_walk_cuda(torch::Tensor rowptr, torch::Tensor col,
torch::Tensor start, int64_t walk_length) {
CHECK_CUDA(rowptr);
CHECK_CUDA(col);
CHECK_CUDA(start);
hipSetDevice(rowptr.get_device());
CHECK_INPUT(rowptr.dim() == 1);
CHECK_INPUT(col.dim() == 1);
CHECK_INPUT(start.dim() == 1);
auto rand = torch::rand({walk_length, start.size(0)},
start.options().dtype(torch::kFloat));
auto out = torch::full({walk_length + 1, start.size(0)}, -1, start.options());
auto stream = at::cuda::getCurrentCUDAStream();
uniform_random_walk_kernel<<<BLOCKS(start.numel()), THREADS, 0, stream>>>(
rowptr.data_ptr<int64_t>(), col.data_ptr<int64_t>(),
start.data_ptr<int64_t>(), rand.data_ptr<float>(),
out.data_ptr<int64_t>(), walk_length, start.numel());
return out.t().contiguous();
}
csrc/hip/rw_hip_hip.hip deleted 100644 → 0
View file @ 2951b12d
#include "hip/hip_runtime.h"
#include "rw_hip.h"
#include <ATen/hip/HIPContext.h>
#include "utils.cuh"
#define THREADS 1024
#define BLOCKS(N) (N + THREADS - 1) / THREADS
__global__ void uniform_random_walk_kernel(const int64_t *rowptr,
const int64_t *col,
const int64_t *start,
const float *rand, int64_t *out,
int64_t walk_length, int64_t numel) {
const int64_t thread_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (thread_idx < numel) {
int64_t cur = start[thread_idx];
out[thread_idx] = cur;
int64_t row_start, row_end;
for (int64_t l = 0; l < walk_length; l++) {
row_start = rowptr[cur], row_end = rowptr[cur + 1];
cur = col[row_start +
int64_t(rand[l * numel + thread_idx] * (row_end - row_start))];
out[(l + 1) * numel + thread_idx] = cur;
}
}
}
torch::Tensor random_walk_cuda(torch::Tensor rowptr, torch::Tensor col,
torch::Tensor start, int64_t walk_length) {
CHECK_CUDA(rowptr);
CHECK_CUDA(col);
CHECK_CUDA(start);
hipSetDevice(rowptr.get_device());
CHECK_INPUT(rowptr.dim() == 1);
CHECK_INPUT(col.dim() == 1);
CHECK_INPUT(start.dim() == 1);
auto rand = torch::rand({walk_length, start.size(0)},
start.options().dtype(torch::kFloat));
auto out = torch::full({walk_length + 1, start.size(0)}, -1, start.options());
auto stream = at::hip::getCurrentHIPStreamMasqueradingAsCUDA();
hipLaunchKernelGGL(( uniform_random_walk_kernel), dim3(BLOCKS(start.numel())), dim3(THREADS), 0, stream,
rowptr.data_ptr<int64_t>(), col.data_ptr<int64_t>(),
start.data_ptr<int64_t>(), rand.data_ptr<float>(),
out.data_ptr<int64_t>(), walk_length, start.numel());
return out.t().contiguous();
}
csrc/hip/spmm_hip.h deleted 100644 → 0
View file @ 2951b12d
#pragma once
#include "../extensions.h"
std::tuple<torch::Tensor, torch::optional<torch::Tensor>>
spmm_cuda(torch::Tensor rowptr, torch::Tensor col,
torch::optional<torch::Tensor> optional_value, torch::Tensor mat,
std::string reduce);
torch::Tensor spmm_value_bw_cuda(torch::Tensor row, torch::Tensor rowptr,
torch::Tensor col, torch::Tensor mat,
torch::Tensor grad, std::string reduce);
template<typename T>
__device__ T __ldg(const T* ptr) {
return *ptr;
}
csrc/hip/spmm_hip.hip deleted 100644 → 0
View file @ 2951b12d
#include "hip/hip_runtime.h"
#include "spmm_hip.h"
#include <ATen/hip/HIPContext.h>
#include "reducer.cuh"
#include "utils.cuh"
#define THREADS 256
#define FULL_MASK 0xffffffff
// Paper: Design Principles for Sparse Matrix Multiplication on the GPU
// Code: https://github.com/owensgroup/merge-spmm
template <typename scalar_t, ReductionType REDUCE, bool HAS_VALUE>
__global__ void spmm_kernel(const int64_t *rowptr_data, const int64_t *col_data,
const scalar_t *value_data,
const scalar_t *mat_data, scalar_t *out_data,
int64_t *arg_out_data, int B, int M, int N, int K) {
// We ignore blockIdx.y here, because threads
// across `blockIdx.y` are treated equally.
int thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
int row = thread_idx >> 5; // thread_idx / 32
int lane_idx = thread_idx & (32 - 1); // thread_idx % 32
int batch_idx = row / M;
// Compute the column index of `mat` in which the thread is operating.
int mat_col_idx = lane_idx + (blockIdx.y << 5);
// Compute the output index (row-major order).
int out_idx = row * K + mat_col_idx;
// Helper arrays for warp communication.
int mat_row, mat_rows[32];
scalar_t val, vals[HAS_VALUE ? 32 : 1];
// Do not aggregate/write across the Y-axis (lane_idx < leftover).
int leftover = K - (blockIdx.y << 5);
if (batch_idx < B) {
int row_start = __ldg(rowptr_data + (row % M));
int row_end = __ldg(rowptr_data + (row % M) + 1);
int col_idx = row_start + lane_idx;
scalar_t result = Reducer<scalar_t, REDUCE>::init();
int64_t arg;
// Iterate over all `col` indices in parallel within a warp.
for (int c = row_start; c < row_end; c += 32) {
if (col_idx < row_end) {
// Coalesced memory access into `col` and `val`.
mat_row = __ldg(col_data + col_idx) * K;
if (HAS_VALUE)
val = __ldg(value_data + col_idx);
} else {
mat_row = -1;
if (HAS_VALUE)
val = (scalar_t)0;
}
col_idx += 32;
#pragma unroll
for (int i = 0; i < 32; i++) {
// Communication between all threads in a warp.
mat_rows[i] = __shfl_sync(FULL_MASK, mat_row, i);
if (HAS_VALUE)
vals[i] = __shfl_sync(FULL_MASK, val, i);
}
#pragma unroll
for (int i = 0; i < 32; i++) {
if (lane_idx < leftover && mat_rows[i] != -1) {
// Coalesced memory access into `mat`.
val = __ldg(mat_data + batch_idx * N * K + mat_rows[i] + mat_col_idx);
if (HAS_VALUE)
val = vals[i] * val;
Reducer<scalar_t, REDUCE>::update(&result, val, &arg, c + i);
}
}
}
if (lane_idx < leftover) {
// Coalesced write into `out`.
Reducer<scalar_t, REDUCE>::write(out_data + out_idx, result,
arg_out_data + out_idx, arg,
row_end - row_start);
}
}
}
std::tuple<torch::Tensor, torch::optional<torch::Tensor>>
spmm_cuda(torch::Tensor rowptr, torch::Tensor col,
torch::optional<torch::Tensor> optional_value, torch::Tensor mat,
std::string reduce) {
CHECK_CUDA(rowptr);
CHECK_CUDA(col);
if (optional_value.has_value())
CHECK_CUDA(optional_value.value());
CHECK_CUDA(mat);
hipSetDevice(rowptr.get_device());
CHECK_INPUT(rowptr.dim() == 1);
CHECK_INPUT(col.dim() == 1);
if (optional_value.has_value()) {
CHECK_INPUT(optional_value.value().dim() == 1);
CHECK_INPUT(optional_value.value().size(0) == col.size(0));
}
CHECK_INPUT(mat.dim() >= 2);
mat = mat.contiguous();
auto sizes = mat.sizes().vec();
sizes[mat.dim() - 2] = rowptr.numel() - 1;
auto out = torch::empty(sizes, mat.options());
torch::optional<torch::Tensor> arg_out = torch::nullopt;
int64_t *arg_out_data = nullptr;
if (reduce2REDUCE.at(reduce) == MIN || reduce2REDUCE.at(reduce) == MAX) {
arg_out = torch::full_like(out, col.numel(), rowptr.options());
arg_out_data = arg_out.value().data_ptr<int64_t>();
}
auto rowptr_data = rowptr.data_ptr<int64_t>();
auto col_data = col.data_ptr<int64_t>();
auto M = rowptr.numel() - 1;
auto N = mat.size(-2);
auto K = mat.size(-1);
auto B = mat.numel() / (N * K);
auto BLOCKS = dim3((32 * B * M + THREADS - 1) / THREADS, (K + 31) / 32);
auto stream = at::cuda::getCurrentCUDAStream();
AT_DISPATCH_ALL_TYPES_AND(at::ScalarType::Half, mat.scalar_type(), "_", [&] {
auto mat_data = mat.data_ptr<scalar_t>();
auto out_data = out.data_ptr<scalar_t>();
AT_DISPATCH_REDUCTION_TYPES(reduce, [&] {
if (optional_value.has_value()) {
auto value_data = optional_value.value().data_ptr<scalar_t>();
spmm_kernel<scalar_t, REDUCE, true><<<BLOCKS, THREADS, 0, stream>>>(
rowptr_data, col_data, value_data, mat_data, out_data, arg_out_data,
B, M, N, K);
} else {
spmm_kernel<scalar_t, REDUCE, false><<<BLOCKS, THREADS, 0, stream>>>(
rowptr_data, col_data, nullptr, mat_data, out_data, arg_out_data, B,
M, N, K);
}
});
});
return std::make_tuple(out, arg_out);
}
template <typename scalar_t, ReductionType REDUCE>
__global__ void
spmm_value_bw_kernel(const int64_t *row_data, const int64_t *rowptr_data,
const int64_t *col_data, const scalar_t *mat_data,
const scalar_t *grad_data, scalar_t *out_data, int B,
int M, int N, int E, int K) {
int thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
int index_idx = (thread_idx >> 5); // thread_idx / 32
int lane_idx = thread_idx & (32 - 1); // thread_idx % 32
if (index_idx < E) {
int row = __ldg(row_data + index_idx);
int col = __ldg(col_data + index_idx);
scalar_t val = (scalar_t)0;
for (int b = 0; b < B; b++) {
for (int k = lane_idx; k < K; k += 32) {
val += mat_data[b * N * K + col * K + k] *
grad_data[b * M * K + row * K + k];
}
}
#pragma unroll
for (int i = 32 / 2; i > 0; i /= 2) { // Parallel reduction inside a warp.
val += __shfl_down_sync(FULL_MASK, val, i);
}
if (lane_idx == 0) {
if (REDUCE == MEAN) {
int row_start = __ldg(rowptr_data + row);
int row_end = __ldg(rowptr_data + row + 1);
val /= (scalar_t)max(row_end - row_start, 1);
}
out_data[index_idx] = val;
}
}
}
torch::Tensor spmm_value_bw_cuda(torch::Tensor row, torch::Tensor rowptr,
torch::Tensor col, torch::Tensor mat,
torch::Tensor grad, std::string reduce) {
CHECK_CUDA(row);
CHECK_CUDA(rowptr);
CHECK_CUDA(col);
CHECK_CUDA(mat);
CHECK_CUDA(grad);
hipSetDevice(row.get_device());
mat = mat.contiguous();
grad = grad.contiguous();
auto M = grad.size(-2);
auto N = mat.size(-2);
auto E = row.numel();
auto K = mat.size(-1);
auto B = mat.numel() / (N * K);
auto BLOCKS = dim3((E * 32 + THREADS - 1) / THREADS);
auto out = torch::zeros(row.numel(), grad.options());
auto row_data = row.data_ptr<int64_t>();
auto rowptr_data = rowptr.data_ptr<int64_t>();
auto col_data = col.data_ptr<int64_t>();
auto stream = at::cuda::getCurrentCUDAStream();
AT_DISPATCH_ALL_TYPES_AND(at::ScalarType::Half, mat.scalar_type(), "_", [&] {
auto mat_data = mat.data_ptr<scalar_t>();
auto grad_data = grad.data_ptr<scalar_t>();
auto out_data = out.data_ptr<scalar_t>();
AT_DISPATCH_REDUCTION_TYPES(reduce, [&] {
spmm_value_bw_kernel<scalar_t, REDUCE><<<BLOCKS, THREADS, 0, stream>>>(
row_data, rowptr_data, col_data, mat_data, grad_data, out_data, B, M,
N, E, K);
});
});
return out;
}
csrc/hip/spmm_hip_hip.hip deleted 100644 → 0
View file @ 2951b12d
#include "hip/hip_runtime.h"
#include "spmm_hip.h"
#include <ATen/hip/HIPContext.h>
#include "reducer.cuh"
#include "utils.cuh"
#define THREADS 256
#define FULL_MASK 0xffffffff
// Paper: Design Principles for Sparse Matrix Multiplication on the GPU
// Code: https://github.com/owensgroup/merge-spmm
template <typename scalar_t, ReductionType REDUCE, bool HAS_VALUE>
__global__ void spmm_kernel(const int64_t *rowptr_data, const int64_t *col_data,
const scalar_t *value_data,
const scalar_t *mat_data, scalar_t *out_data,
int64_t *arg_out_data, int B, int M, int N, int K) {
// We ignore blockIdx.y here, because threads
// across `blockIdx.y` are treated equally.
int thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
int row = thread_idx >> 5; // thread_idx / 32
int lane_idx = thread_idx & (32 - 1); // thread_idx % 32
int batch_idx = row / M;
// Compute the column index of `mat` in which the thread is operating.
int mat_col_idx = lane_idx + (blockIdx.y << 5);
// Compute the output index (row-major order).
int out_idx = row * K + mat_col_idx;
// Helper arrays for warp communication.
int mat_row, mat_rows[32];
scalar_t val, vals[HAS_VALUE ? 32 : 1];
// Do not aggregate/write across the Y-axis (lane_idx < leftover).
int leftover = K - (blockIdx.y << 5);
if (batch_idx < B) {
int row_start = __ldg(rowptr_data + (row % M));
int row_end = __ldg(rowptr_data + (row % M) + 1);
int col_idx = row_start + lane_idx;
scalar_t result = Reducer<scalar_t, REDUCE>::init();
int64_t arg;
// Iterate over all `col` indices in parallel within a warp.
for (int c = row_start; c < row_end; c += 32) {
if (col_idx < row_end) {
// Coalesced memory access into `col` and `val`.
mat_row = __ldg(col_data + col_idx) * K;
if (HAS_VALUE)
val = __ldg(value_data + col_idx);
} else {
mat_row = -1;
if (HAS_VALUE)
val = (scalar_t)0;
}
col_idx += 32;
#pragma unroll
for (int i = 0; i < 32; i++) {
// Communication between all threads in a warp.
mat_rows[i] = __shfl_sync(FULL_MASK, mat_row, i);
if (HAS_VALUE)
vals[i] = __shfl_sync(FULL_MASK, val, i);
}
#pragma unroll
for (int i = 0; i < 32; i++) {
if (lane_idx < leftover && mat_rows[i] != -1) {
// Coalesced memory access into `mat`.
val = __ldg(mat_data + batch_idx * N * K + mat_rows[i] + mat_col_idx);
if (HAS_VALUE)
val = vals[i] * val;
Reducer<scalar_t, REDUCE>::update(&result, val, &arg, c + i);
}
}
}
if (lane_idx < leftover) {
// Coalesced write into `out`.
Reducer<scalar_t, REDUCE>::write(out_data + out_idx, result,
arg_out_data + out_idx, arg,
row_end - row_start);
}
}
}
std::tuple<torch::Tensor, torch::optional<torch::Tensor>>
spmm_cuda(torch::Tensor rowptr, torch::Tensor col,
torch::optional<torch::Tensor> optional_value, torch::Tensor mat,
std::string reduce) {
CHECK_CUDA(rowptr);
CHECK_CUDA(col);
if (optional_value.has_value())
CHECK_CUDA(optional_value.value());
CHECK_CUDA(mat);
hipSetDevice(rowptr.get_device());
CHECK_INPUT(rowptr.dim() == 1);
CHECK_INPUT(col.dim() == 1);
if (optional_value.has_value()) {
CHECK_INPUT(optional_value.value().dim() == 1);
CHECK_INPUT(optional_value.value().size(0) == col.size(0));
}
CHECK_INPUT(mat.dim() >= 2);
mat = mat.contiguous();
auto sizes = mat.sizes().vec();
sizes[mat.dim() - 2] = rowptr.numel() - 1;
auto out = torch::empty(sizes, mat.options());
torch::optional<torch::Tensor> arg_out = torch::nullopt;
int64_t *arg_out_data = nullptr;
if (reduce2REDUCE.at(reduce) == MIN || reduce2REDUCE.at(reduce) == MAX) {
arg_out = torch::full_like(out, col.numel(), rowptr.options());
arg_out_data = arg_out.value().data_ptr<int64_t>();
}
auto rowptr_data = rowptr.data_ptr<int64_t>();
auto col_data = col.data_ptr<int64_t>();
auto M = rowptr.numel() - 1;
auto N = mat.size(-2);
auto K = mat.size(-1);
auto B = mat.numel() / (N * K);
auto BLOCKS = dim3((32 * B * M + THREADS - 1) / THREADS, (K + 31) / 32);
auto stream = at::hip::getCurrentHIPStreamMasqueradingAsCUDA();
AT_DISPATCH_ALL_TYPES_AND(at::ScalarType::Half, mat.scalar_type(), "_", [&] {
auto mat_data = mat.data_ptr<scalar_t>();
auto out_data = out.data_ptr<scalar_t>();
AT_DISPATCH_REDUCTION_TYPES(reduce, [&] {
if (optional_value.has_value()) {
auto value_data = optional_value.value().data_ptr<scalar_t>();
hipLaunchKernelGGL(( spmm_kernel<scalar_t, REDUCE, true>), dim3(BLOCKS), dim3(THREADS), 0, stream,
rowptr_data, col_data, value_data, mat_data, out_data, arg_out_data,
B, M, N, K);
} else {
hipLaunchKernelGGL(( spmm_kernel<scalar_t, REDUCE, false>), dim3(BLOCKS), dim3(THREADS), 0, stream,
rowptr_data, col_data, nullptr, mat_data, out_data, arg_out_data, B,
M, N, K);
}
});
});
return std::make_tuple(out, arg_out);
}
template <typename scalar_t, ReductionType REDUCE>
__global__ void
spmm_value_bw_kernel(const int64_t *row_data, const int64_t *rowptr_data,
const int64_t *col_data, const scalar_t *mat_data,
const scalar_t *grad_data, scalar_t *out_data, int B,
int M, int N, int E, int K) {
int thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
int index_idx = (thread_idx >> 5); // thread_idx / 32
int lane_idx = thread_idx & (32 - 1); // thread_idx % 32
if (index_idx < E) {
int row = __ldg(row_data + index_idx);
int col = __ldg(col_data + index_idx);
scalar_t val = (scalar_t)0;
for (int b = 0; b < B; b++) {
for (int k = lane_idx; k < K; k += 32) {
val += mat_data[b * N * K + col * K + k] *
grad_data[b * M * K + row * K + k];
}
}
#pragma unroll
for (int i = 32 / 2; i > 0; i /= 2) { // Parallel reduction inside a warp.
val += __shfl_down_sync(FULL_MASK, val, i);
}
if (lane_idx == 0) {
if (REDUCE == MEAN) {
int row_start = __ldg(rowptr_data + row);
int row_end = __ldg(rowptr_data + row + 1);
val /= (scalar_t)max(row_end - row_start, 1);
}
out_data[index_idx] = val;
}
}
}
torch::Tensor spmm_value_bw_cuda(torch::Tensor row, torch::Tensor rowptr,
torch::Tensor col, torch::Tensor mat,
torch::Tensor grad, std::string reduce) {
CHECK_CUDA(row);
CHECK_CUDA(rowptr);
CHECK_CUDA(col);
CHECK_CUDA(mat);
CHECK_CUDA(grad);
hipSetDevice(row.get_device());
mat = mat.contiguous();
grad = grad.contiguous();
auto M = grad.size(-2);
auto N = mat.size(-2);
auto E = row.numel();
auto K = mat.size(-1);
auto B = mat.numel() / (N * K);
auto BLOCKS = dim3((E * 32 + THREADS - 1) / THREADS);
auto out = torch::zeros(row.numel(), grad.options());
auto row_data = row.data_ptr<int64_t>();
auto rowptr_data = rowptr.data_ptr<int64_t>();
auto col_data = col.data_ptr<int64_t>();
auto stream = at::hip::getCurrentHIPStreamMasqueradingAsCUDA();
AT_DISPATCH_ALL_TYPES_AND(at::ScalarType::Half, mat.scalar_type(), "_", [&] {
auto mat_data = mat.data_ptr<scalar_t>();
auto grad_data = grad.data_ptr<scalar_t>();
auto out_data = out.data_ptr<scalar_t>();
AT_DISPATCH_REDUCTION_TYPES(reduce, [&] {
hipLaunchKernelGGL(( spmm_value_bw_kernel<scalar_t, REDUCE>), dim3(BLOCKS), dim3(THREADS), 0, stream,
row_data, rowptr_data, col_data, mat_data, grad_data, out_data, B, M,
N, E, K);
});
});
return out;
}
csrc/hip/spspmm_hip.h deleted 100644 → 0
View file @ 2951b12d
#pragma once
#include "../extensions.h"
std::tuple<torch::Tensor, torch::Tensor, torch::optional<torch::Tensor>>
spspmm_cuda(torch::Tensor rowptrA, torch::Tensor colA,
torch::optional<torch::Tensor> optional_valueA,
torch::Tensor rowptrB, torch::Tensor colB,
torch::optional<torch::Tensor> optional_valueB, int64_t K,
std::string reduce);
csrc/hip/spspmm_hip.hip deleted 100644 → 0
View file @ 2951b12d
#include "spspmm_hip.h"
#include <ATen/hip/HIPContext.h>
#include <hipsparse.h>
#include "utils.cuh"
#define AT_DISPATCH_CUSPARSE_TYPES(TYPE, ...) \
[&] { \
switch (TYPE) { \
case torch::ScalarType::Float: { \
using scalar_t = float; \
const auto &cusparsecsrgemm2_bufferSizeExt = \
hipsparseScsrgemm2_bufferSizeExt; \
const auto &cusparsecsrgemm2 = hipsparseScsrgemm2; \
return __VA_ARGS__(); \
} \
case torch::ScalarType::Double: { \
using scalar_t = double; \
const auto &cusparsecsrgemm2_bufferSizeExt = \
hipsparseDcsrgemm2_bufferSizeExt; \
const auto &cusparsecsrgemm2 = hipsparseDcsrgemm2; \
return __VA_ARGS__(); \
} \
default: \
AT_ERROR("Not implemented for '", toString(TYPE), "'"); \
} \
}()
std::tuple<torch::Tensor, torch::Tensor, torch::optional<torch::Tensor>>
spspmm_cuda(torch::Tensor rowptrA, torch::Tensor colA,
torch::optional<torch::Tensor> optional_valueA,
torch::Tensor rowptrB, torch::Tensor colB,
torch::optional<torch::Tensor> optional_valueB, int64_t K,
std::string reduce) {
CHECK_CUDA(rowptrA);
CHECK_CUDA(colA);
if (optional_valueA.has_value())
CHECK_CUDA(optional_valueA.value());
CHECK_CUDA(rowptrB);
CHECK_CUDA(colB);
if (optional_valueB.has_value())
CHECK_CUDA(optional_valueB.value());
hipSetDevice(rowptrA.get_device());
CHECK_INPUT(rowptrA.dim() == 1);
CHECK_INPUT(colA.dim() == 1);
if (optional_valueA.has_value()) {
CHECK_INPUT(optional_valueA.value().dim() == 1);
CHECK_INPUT(optional_valueA.value().size(0) == colA.size(0));
}
CHECK_INPUT(rowptrB.dim() == 1);
CHECK_INPUT(colB.dim() == 1);
if (optional_valueB.has_value()) {
CHECK_INPUT(optional_valueB.value().dim() == 1);
CHECK_INPUT(optional_valueB.value().size(0) == colB.size(0));
}
if (!optional_valueA.has_value() && optional_valueB.has_value())
optional_valueA =
torch::ones(colA.numel(), optional_valueB.value().options());
if (!optional_valueB.has_value() && optional_valueA.has_value())
optional_valueB =
torch::ones(colB.numel(), optional_valueA.value().options());
auto scalar_type = torch::ScalarType::Float;
if (optional_valueA.has_value())
scalar_type = optional_valueA.value().scalar_type();
auto handle = at::cuda::getCurrentCUDASparseHandle();
hipsparseMatDescr_t descr;
hipsparseCreateMatDescr(&descr);
rowptrA = rowptrA.toType(torch::kInt);
colA = colA.toType(torch::kInt);
rowptrB = rowptrB.toType(torch::kInt);
colB = colB.toType(torch::kInt);
int64_t M = rowptrA.numel() - 1, N = rowptrB.numel() - 1;
auto rowptrA_data = rowptrA.data_ptr<int>();
auto colA_data = colA.data_ptr<int>();
auto rowptrB_data = rowptrB.data_ptr<int>();
auto colB_data = colB.data_ptr<int>();
torch::Tensor rowptrC, colC;
torch::optional<torch::Tensor> optional_valueC = torch::nullopt;
int nnzC;
int *nnzTotalDevHostPtr = &nnzC;
// Step 1: Create an opaque structure.
csrgemm2Info_t info = NULL;
hipsparseCreateCsrgemm2Info(&info);
// Step 2: Allocate buffer for `csrgemm2Nnz` and `csrgemm2`.
size_t bufferSize;
AT_DISPATCH_CUSPARSE_TYPES(scalar_type, [&] {
scalar_t alpha = (scalar_t)1.0;
cusparsecsrgemm2_bufferSizeExt(handle, M, N, K, &alpha, descr, colA.numel(),
rowptrA_data, colA_data, descr, colB.numel(),
rowptrB_data, colB_data, NULL, descr, 0,
NULL, NULL, info, &bufferSize);
void *buffer = NULL;
hipMalloc(&buffer, bufferSize);
// Step 3: Compute CSR row pointer.
rowptrC = torch::empty(M + 1, rowptrA.options());
auto rowptrC_data = rowptrC.data_ptr<int>();
hipsparseXcsrgemm2Nnz(handle, M, N, K, descr, colA.numel(), rowptrA_data,
colA_data, descr, colB.numel(), rowptrB_data,
colB_data, descr, 0, NULL, NULL, descr, rowptrC_data,
nnzTotalDevHostPtr, info, buffer);
// Step 4: Compute CSR entries.
colC = torch::empty(nnzC, rowptrC.options());
auto colC_data = colC.data_ptr<int>();
if (optional_valueA.has_value())
optional_valueC = torch::empty(nnzC, optional_valueA.value().options());
scalar_t *valA_data = NULL, *valB_data = NULL, *valC_data = NULL;
if (optional_valueA.has_value()) {
valA_data = optional_valueA.value().data_ptr<scalar_t>();
valB_data = optional_valueB.value().data_ptr<scalar_t>();
valC_data = optional_valueC.value().data_ptr<scalar_t>();
}
cusparsecsrgemm2(handle, M, N, K, &alpha, descr, colA.numel(), valA_data,
rowptrA_data, colA_data, descr, colB.numel(), valB_data,
rowptrB_data, colB_data, NULL, descr, 0, NULL, NULL, NULL,
descr, valC_data, rowptrC_data, colC_data, info, buffer);
hipFree(buffer);
});
// Step 5: Destroy the opaque structure.
hipsparseDestroyCsrgemm2Info(info);
rowptrC = rowptrC.toType(torch::kLong);
colC = colC.toType(torch::kLong);
return std::make_tuple(rowptrC, colC, optional_valueC);
}
csrc/hip/utils.cuh deleted 100644 → 0
View file @ 2951b12d
#pragma once
#include "../extensions.h"
#define CHECK_CUDA(x) \
AT_ASSERTM(x.device().is_cuda(), #x " must be CUDA tensor")
#define CHECK_INPUT(x) AT_ASSERTM(x, "Input mismatch")
__device__ __inline__ at::Half
__shfl_sync(const unsigned mask, const at::Half var, const int srcLane) {
return __shfl_sync(mask, (__half)var, srcLane);
}
__device__ __inline__ at::Half __shfl_down_sync(const unsigned mask,
const at::Half var,
const unsigned int delta) {
return __shfl_down_sync(mask, (__half)var, delta);
}
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