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【Sparse】add new API/OP(csr->csr) of SparseTensor softmax (PaddlePaddl…
…e#43475) * add new API/OP(csr->csr) of SparseTensor softmax * fix comment
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/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
Licensed under the Apache License, Version 2.0 (the "License"); | ||
you may not use this file except in compliance with the License. | ||
You may obtain a copy of the License at | ||
http://www.apache.org/licenses/LICENSE-2.0 | ||
Unless required by applicable law or agreed to in writing, software | ||
distributed under the License is distributed on an "AS IS" BASIS, | ||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
See the License for the specific language governing permissions and | ||
limitations under the License. */ | ||
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#include "paddle/phi/kernels/sparse/softmax_grad_kernel.h" | ||
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#include "paddle/fluid/platform/cpu_info.h" | ||
#include "paddle/phi/backends/cpu/cpu_context.h" | ||
#include "paddle/phi/core/kernel_registry.h" | ||
#include "paddle/phi/core/visit_type.h" | ||
#include "paddle/phi/kernels/funcs/cpu_vec.h" | ||
#include "paddle/phi/kernels/sparse/empty_kernel.h" | ||
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namespace plt = paddle::platform; | ||
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namespace phi { | ||
namespace sparse { | ||
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template <typename T, typename Context> | ||
void SoftmaxCsrGradKernel(const Context& dev_ctx, | ||
const SparseCsrTensor& out, | ||
const SparseCsrTensor& dout, | ||
int axis, | ||
SparseCsrTensor* dx) { | ||
PADDLE_ENFORCE_EQ(axis, | ||
-1, | ||
phi::errors::Unimplemented( | ||
"SparseCsrTensor only support axis=-1 for softmax, " | ||
"which is faster when reading data by row (axis=-1)")); | ||
EmptyLikeCsrKernel<T, Context>(dev_ctx, dout, dx); | ||
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auto out_dim = out.dims(); | ||
int rows = 1; | ||
for (int i = 0; i < out_dim.size() - 1; ++i) { | ||
rows *= out_dim[i]; | ||
} | ||
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const DenseTensor& out_crows = out.non_zero_crows(); | ||
const DenseTensor& out_values = out.non_zero_elements(); | ||
const DenseTensor& dout_values = dout.non_zero_elements(); | ||
DenseTensor* dx_values = dx->mutable_non_zero_elements(); | ||
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int row_first = 0; | ||
int row_nnz = 0; | ||
const T* out_data = out_values.data<T>(); | ||
const T* dout_data = dout_values.data<T>(); | ||
T* dx_data = dx_values->data<T>(); | ||
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// dx = (dout - sum(dout * out)) * out | ||
PD_VISIT_INTEGRAL_TYPES( | ||
out.non_zero_crows().dtype(), "SoftmaxCsrGradKernel", ([&] { | ||
const data_t* out_crows_data = out_crows.data<data_t>(); | ||
for (int i = 0; i < rows; ++i) { | ||
row_first = static_cast<int>(out_crows_data[i]); | ||
row_nnz = static_cast<int>(out_crows_data[i + 1] - out_crows_data[i]); | ||
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out_data = out_data + row_first; | ||
dout_data = dout_data + row_first; | ||
dx_data = dx_data + row_first; | ||
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T sum = 0; | ||
phi::funcs::vec_mul_reduce<T, plt::avx>( | ||
row_nnz, dout_data, out_data, &sum); | ||
phi::funcs::vec_add_bias<T, plt::avx>( | ||
row_nnz, static_cast<T>(-1) * sum, dout_data, dx_data); | ||
phi::funcs::vec_mul<T, plt::avx>(row_nnz, dx_data, out_data, dx_data); | ||
} | ||
})); | ||
} | ||
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} // namespace sparse | ||
} // namespace phi | ||
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PD_REGISTER_KERNEL(softmax_csr_grad, | ||
CPU, | ||
ALL_LAYOUT, | ||
phi::sparse::SoftmaxCsrGradKernel, | ||
float, | ||
double) { | ||
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); | ||
} |
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/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
Licensed under the Apache License, Version 2.0 (the "License"); | ||
you may not use this file except in compliance with the License. | ||
You may obtain a copy of the License at | ||
http://www.apache.org/licenses/LICENSE-2.0 | ||
Unless required by applicable law or agreed to in writing, software | ||
distributed under the License is distributed on an "AS IS" BASIS, | ||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
See the License for the specific language governing permissions and | ||
limitations under the License. */ | ||
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#include "paddle/phi/kernels/sparse/softmax_kernel.h" | ||
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#include "paddle/fluid/platform/cpu_info.h" | ||
#include "paddle/phi/backends/cpu/cpu_context.h" | ||
#include "paddle/phi/core/kernel_registry.h" | ||
#include "paddle/phi/core/visit_type.h" | ||
#include "paddle/phi/kernels/funcs/cpu_vec.h" | ||
#include "paddle/phi/kernels/sparse/empty_kernel.h" | ||
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namespace plt = paddle::platform; | ||
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namespace phi { | ||
namespace sparse { | ||
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template <typename T, typename Context> | ||
void SoftmaxCsrKernel(const Context& dev_ctx, | ||
const SparseCsrTensor& x, | ||
int axis, | ||
SparseCsrTensor* out) { | ||
PADDLE_ENFORCE_EQ(axis, | ||
-1, | ||
phi::errors::Unimplemented( | ||
"SparseCsrTensor only support axis=-1 for softmax, " | ||
"which is faster when reading data by row (axis=-1)")); | ||
EmptyLikeCsrKernel<T, Context>(dev_ctx, x, out); | ||
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auto x_dim = x.dims(); | ||
int row_number = 1; | ||
for (int i = 0; i < x_dim.size() - 1; ++i) { | ||
row_number *= x_dim[i]; | ||
} | ||
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const DenseTensor& x_crows = x.non_zero_crows(); | ||
const DenseTensor& x_values = x.non_zero_elements(); | ||
DenseTensor* out_values = out->mutable_non_zero_elements(); | ||
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int row_first = 0; | ||
int row_nnz = 0; | ||
T row_max_val = 0; | ||
const T* x_data = x_values.data<T>(); | ||
T* out_data = out_values->data<T>(); | ||
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// out = exp(x-x_max) / sum( exp(x-x_max )) | ||
PD_VISIT_INTEGRAL_TYPES( | ||
x.non_zero_crows().dtype(), "CsrSoftmaxKernel", ([&] { | ||
const data_t* x_crows_data = x_crows.data<data_t>(); | ||
for (int i = 0; i < row_number; ++i) { | ||
row_first = static_cast<int>(x_crows_data[i]); | ||
row_nnz = static_cast<int>(x_crows_data[i + 1] - x_crows_data[i]); | ||
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x_data = x_data + row_first; | ||
out_data = out_data + row_first; | ||
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row_max_val = *std::max_element(x_data, x_data + row_nnz); | ||
phi::funcs::vec_add_bias<T, plt::avx>( | ||
row_nnz, static_cast<T>(-1) * row_max_val, x_data, out_data); | ||
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phi::funcs::vec_exp<T>(row_nnz, out_data, out_data); | ||
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T sum = 0; | ||
phi::funcs::vec_sum<T, plt::avx>(row_nnz, out_data, &sum); | ||
phi::funcs::vec_scal<T, plt::avx>( | ||
row_nnz, static_cast<T>(1) / sum, out_data, out_data); | ||
} | ||
})); | ||
} | ||
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} // namespace sparse | ||
} // namespace phi | ||
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PD_REGISTER_KERNEL(softmax_csr, | ||
CPU, | ||
ALL_LAYOUT, | ||
phi::sparse::SoftmaxCsrKernel, | ||
float, | ||
double) { | ||
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); | ||
} |
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/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
Licensed under the Apache License, Version 2.0 (the "License"); | ||
you may not use this file except in compliance with the License. | ||
You may obtain a copy of the License at | ||
http://www.apache.org/licenses/LICENSE-2.0 | ||
Unless required by applicable law or agreed to in writing, software | ||
distributed under the License is distributed on an "AS IS" BASIS, | ||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
See the License for the specific language governing permissions and | ||
limitations under the License. */ | ||
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#include "paddle/phi/kernels/sparse/empty_kernel.h" | ||
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#include "paddle/phi/backends/cpu/cpu_context.h" | ||
#include "paddle/phi/backends/gpu/gpu_context.h" | ||
#include "paddle/phi/core/kernel_registry.h" | ||
#include "paddle/phi/kernels/copy_kernel.h" | ||
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namespace phi { | ||
namespace sparse { | ||
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template <typename T, typename Context> | ||
void EmptyLikeCsrKernel(const Context& dev_ctx, | ||
const SparseCsrTensor& x, | ||
SparseCsrTensor* out) { | ||
const DenseTensor& x_crows = x.non_zero_crows(); | ||
const DenseTensor& x_cols = x.non_zero_cols(); | ||
const DenseTensor& x_values = x.non_zero_elements(); | ||
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DenseTensor* out_crows = out->mutable_non_zero_crows(); | ||
DenseTensor* out_cols = out->mutable_non_zero_cols(); | ||
DenseTensor* out_values = out->mutable_non_zero_elements(); | ||
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out->set_dims(x.dims()); | ||
phi::Copy(dev_ctx, x_crows, dev_ctx.GetPlace(), false, out_crows); | ||
phi::Copy(dev_ctx, x_cols, dev_ctx.GetPlace(), false, out_cols); | ||
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out_values->Resize(x_values.dims()); | ||
dev_ctx.template Alloc<T>(out_values); | ||
} | ||
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} // namespace sparse | ||
} // namespace phi | ||
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PD_REGISTER_KERNEL(empty_like_csr, | ||
CPU, | ||
ALL_LAYOUT, | ||
phi::sparse::EmptyLikeCsrKernel, | ||
float, | ||
double, | ||
int8_t, | ||
uint8_t, | ||
int16_t, | ||
int, | ||
int64_t, | ||
bool) { | ||
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); | ||
} | ||
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) | ||
PD_REGISTER_KERNEL(empty_like_csr, | ||
GPU, | ||
ALL_LAYOUT, | ||
phi::sparse::EmptyLikeCsrKernel, | ||
float, | ||
double, | ||
int8_t, | ||
uint8_t, | ||
int16_t, | ||
int, | ||
int64_t, | ||
bool) { | ||
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); | ||
} | ||
#endif |
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/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
Licensed under the Apache License, Version 2.0 (the "License"); | ||
you may not use this file except in compliance with the License. | ||
You may obtain a copy of the License at | ||
http://www.apache.org/licenses/LICENSE-2.0 | ||
Unless required by applicable law or agreed to in writing, software | ||
distributed under the License is distributed on an "AS IS" BASIS, | ||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
See the License for the specific language governing permissions and | ||
limitations under the License. */ | ||
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#pragma once | ||
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#include "paddle/phi/core/sparse_csr_tensor.h" | ||
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namespace phi { | ||
namespace sparse { | ||
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template <typename T, typename Context> | ||
void EmptyLikeCsrKernel(const Context& dev_ctx, | ||
const SparseCsrTensor& x, | ||
SparseCsrTensor* out); | ||
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} // namespace sparse | ||
} // namespace phi |
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@@ -0,0 +1,102 @@ | ||
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
Licensed under the Apache License, Version 2.0 (the "License"); | ||
you may not use this file except in compliance with the License. | ||
You may obtain a copy of the License at | ||
http://www.apache.org/licenses/LICENSE-2.0 | ||
Unless required by applicable law or agreed to in writing, software | ||
distributed under the License is distributed on an "AS IS" BASIS, | ||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
See the License for the specific language governing permissions and | ||
limitations under the License. */ | ||
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#include "paddle/phi/backends/gpu/gpu_context.h" | ||
#include "paddle/phi/core/kernel_registry.h" | ||
#include "paddle/phi/core/visit_type.h" | ||
#include "paddle/phi/kernels/funcs/math_cuda_utils.h" | ||
#include "paddle/phi/kernels/sparse/empty_kernel.h" | ||
#include "paddle/phi/kernels/sparse/softmax_grad_kernel.h" | ||
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namespace phi { | ||
namespace sparse { | ||
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template <typename T, typename IntT = int> | ||
__global__ void SoftmaxGradGpuKernel(const IntT* out_crows, | ||
const T* out_values, | ||
const T* dout_values, | ||
T* dx_values, | ||
int row_number) { | ||
// dx = (dout - sum(dout * out)) * out | ||
int row = blockIdx.x * blockDim.y + threadIdx.y; | ||
int non_zero_idx = threadIdx.x; | ||
if (row >= row_number) return; | ||
int row_first = static_cast<int>(out_crows[row]); | ||
int row_nnz = static_cast<int>(out_crows[row + 1] - out_crows[row]); | ||
if (row_nnz == 0) return; | ||
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int kIteration = (row_nnz + warpSize - 1) / warpSize; | ||
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T mul_result = 0; | ||
for (int i = 0; i < kIteration; ++i) { | ||
int idx = non_zero_idx + i * warpSize; | ||
if (idx >= row_nnz) break; | ||
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mul_result += out_values[row_first + idx] * dout_values[row_first + idx]; | ||
} | ||
T sum = phi::funcs::warpReduceSum<T>(mul_result, 0xFFFFFFFF); | ||
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for (int i = 0; i < kIteration; ++i) { | ||
int idx = non_zero_idx + i * warpSize; | ||
if (idx >= row_nnz) break; | ||
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dx_values[row_first + idx] = | ||
(dout_values[row_first + idx] - sum) * out_values[row_first + idx]; | ||
} | ||
} | ||
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template <typename T, typename Context> | ||
void SoftmaxCsrGradKernel(const Context& dev_ctx, | ||
const SparseCsrTensor& out, | ||
const SparseCsrTensor& dout, | ||
int axis, | ||
SparseCsrTensor* dx) { | ||
PADDLE_ENFORCE_EQ(axis, | ||
-1, | ||
phi::errors::Unimplemented( | ||
"SparseCsrTensor only support axis=-1 for softmax, " | ||
"which is faster when reading data by row (axis=-1)")); | ||
EmptyLikeCsrKernel<T, Context>(dev_ctx, dout, dx); | ||
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auto out_dim = out.dims(); | ||
int row_number = 1; | ||
for (int i = 0; i < out_dim.size() - 1; ++i) { | ||
row_number *= out_dim[i]; | ||
} | ||
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dim3 grid((row_number + 3) / 4); | ||
dim3 block(32, 4); | ||
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PD_VISIT_INTEGRAL_TYPES( | ||
out.non_zero_crows().dtype(), "SoftmaxCsrGradKernel", ([&] { | ||
SoftmaxGradGpuKernel<T, data_t><<<grid, block, 0, dev_ctx.stream()>>>( | ||
out.non_zero_crows().data<data_t>(), | ||
out.non_zero_elements().data<T>(), | ||
dout.non_zero_elements().data<T>(), | ||
dx->mutable_non_zero_elements()->data<T>(), | ||
row_number); | ||
})); | ||
} | ||
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} // namespace sparse | ||
} // namespace phi | ||
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PD_REGISTER_KERNEL(softmax_csr_grad, | ||
GPU, | ||
ALL_LAYOUT, | ||
phi::sparse::SoftmaxCsrGradKernel, | ||
float, | ||
double) { | ||
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); | ||
} |
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