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[Sparse] add SparseCsrTensor fused_attention kernel and API (#43966)
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* [Sparse] add SparseCsrTensor fused_attention kernel and API

* fix comment
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zhwesky2010 committed Jul 5, 2022
1 parent 7d3b08d commit 59813de
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Showing 14 changed files with 840 additions and 4 deletions.
9 changes: 9 additions & 0 deletions paddle/phi/api/yaml/sparse_api.yaml
Expand Up @@ -141,6 +141,15 @@
layout : x
data_type : dtype

- api: fused_attention
args : (Tensor query, Tensor key, Tensor value, Tensor sparse_mask, Tensor key_padding_mask, Tensor attn_mask)
output : Tensor(out), Tensor(softmax)
kernel :
func : fused_attention_csr{dense, dense, dense, sparse_csr, dense, dense -> dense, sparse_csr}
layout : sparse_mask
intermediate : softmax
backward: fused_attention_grad

- api: masked_matmul
args : (Tensor x, Tensor y, Tensor mask)
output : Tensor(out)
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7 changes: 7 additions & 0 deletions paddle/phi/api/yaml/sparse_bw_api.yaml
Expand Up @@ -127,3 +127,10 @@
output : Tensor(x_grad)
kernel :
func : coo_values_grad{sparse_coo, dense-> sparse_coo}

- backward_api: fused_attention_grad
forward : fused_attention_csr(Tensor query, Tensor key, Tensor value, Tensor sparse_mask, Tensor key_padding_mask, Tensor attn_mask) -> Tensor(out), Tensor(softmax)
args: (Tensor query, Tensor key, Tensor value, Tensor softmax, Tensor out_grad)
output : Tensor(query_grad), Tensor(key_grad), Tensor(value_grad)
kernel :
func : fused_attention_csr_grad{dense, dense, dense, sparse_csr, dense -> dense, dense, dense}
38 changes: 38 additions & 0 deletions paddle/phi/kernels/sparse/cpu/fused_attention_grad_kernel.cc
@@ -0,0 +1,38 @@
/* 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. */

#include "paddle/phi/kernels/sparse/fused_attention_grad_kernel.h"

#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"

namespace phi {
namespace sparse {

template <typename T, typename Context>
void FusedAttentionCsrGradKernel(const Context& dev_ctx,
const DenseTensor& query,
const DenseTensor& key,
const DenseTensor& value,
const SparseCsrTensor& softmax,
const DenseTensor& dout,
DenseTensor* dquery,
DenseTensor* dkey,
DenseTensor* dvalue) {
PD_THROW(
"Not support CPU kernel of 'sparse.nn.functional.fused_attention' now");
}

} // namespace sparse
} // namespace phi
38 changes: 38 additions & 0 deletions paddle/phi/kernels/sparse/cpu/fused_attention_kernel.cc
@@ -0,0 +1,38 @@
/* 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. */

#include "paddle/phi/kernels/sparse/fused_attention_kernel.h"

#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"

namespace phi {
namespace sparse {

template <typename T, typename Context>
void FusedAttentionCsrKernel(const Context& dev_ctx,
const DenseTensor& query,
const DenseTensor& key,
const DenseTensor& value,
const SparseCsrTensor& sparse_mask,
const DenseTensor& key_padding_mask,
const DenseTensor& attn_mask,
DenseTensor* out,
SparseCsrTensor* softmax) {
PD_THROW(
"Not support CPU kernel of 'sparse.nn.functional.fused_attention' now");
}

} // namespace sparse
} // namespace phi
35 changes: 35 additions & 0 deletions paddle/phi/kernels/sparse/fused_attention_grad_kernel.h
@@ -0,0 +1,35 @@
/* 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. */

#pragma once

#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"

namespace phi {
namespace sparse {

template <typename T, typename Context>
void FusedAttentionCsrGradKernel(const Context& dev_ctx,
const DenseTensor& query,
const DenseTensor& key,
const DenseTensor& value,
const SparseCsrTensor& softmax,
const DenseTensor& dout,
DenseTensor* dquery,
DenseTensor* dkey,
DenseTensor* dvalue);

} // namespace sparse
} // namespace phi
35 changes: 35 additions & 0 deletions paddle/phi/kernels/sparse/fused_attention_kernel.h
@@ -0,0 +1,35 @@
/* 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. */

#pragma once

#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"

namespace phi {
namespace sparse {

template <typename T, typename Context>
void FusedAttentionCsrKernel(const Context& dev_ctx,
const DenseTensor& query,
const DenseTensor& key,
const DenseTensor& value,
const SparseCsrTensor& sparse_mask,
const DenseTensor& key_padding_mask,
const DenseTensor& attn_mask,
DenseTensor* out,
SparseCsrTensor* softmax);

} // namespace sparse
} // namespace phi
153 changes: 153 additions & 0 deletions paddle/phi/kernels/sparse/gpu/fused_attention_grad_kernel.cu
@@ -0,0 +1,153 @@
// 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.

#include "paddle/phi/kernels/sparse/fused_attention_grad_kernel.h"

#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/math_cuda_utils.h"
#include "paddle/phi/kernels/funcs/sparse/sparse_blas.h"
#include "paddle/phi/kernels/sparse/empty_kernel.h"
#include "paddle/phi/kernels/sparse/matmul_grad_kernel.h"

namespace phi {
namespace sparse {

template <typename T>
__global__ void AttnSoftmaxGpuGradKernel(const int64_t* out_crows,
const T* out_values,
const T* dout_values,
T* dx_values,
int M,
int total_row_num,
float scale,
int batch_nnz) {
// dx = (dout - sum(dout * out)) * out
int row = blockIdx.x * blockDim.y + threadIdx.y;
if (row >= total_row_num) return;

int cur_batch = row / M;
int crow_idx = cur_batch * (M + 1) + (row % M);
int row_first = cur_batch * batch_nnz + static_cast<int>(out_crows[crow_idx]);
int row_nnz = static_cast<int>(out_crows[crow_idx + 1] - out_crows[crow_idx]);
if (row_nnz == 0) return;

int kIteration = (row_nnz + WARP_SIZE - 1) / WARP_SIZE;
T mul_result = 0;
for (int i = 0; i < kIteration; ++i) {
int idx = threadIdx.x + i * WARP_SIZE;
if (idx >= row_nnz) break;

mul_result += out_values[row_first + idx] * dout_values[row_first + idx];
}
T sum = phi::funcs::warpReduceSum<T>(mul_result, 0xFFFFFFFF);

for (int i = 0; i < kIteration; ++i) {
int idx = threadIdx.x + i * WARP_SIZE;
if (idx >= row_nnz) break;

dx_values[row_first + idx] = (dout_values[row_first + idx] - sum) *
out_values[row_first + idx] / scale;
}
}

template <typename T, typename Context>
void FusedAttentionCsrGradKernel(const Context& dev_ctx,
const DenseTensor& query,
const DenseTensor& key,
const DenseTensor& value,
const SparseCsrTensor& softmax,
const DenseTensor& dout,
DenseTensor* dquery,
DenseTensor* dkey,
DenseTensor* dvalue) {
#if CUDA_VERSION >= 11070
/* Step1: Forward: softmax{CSR} * value{Dense} -> out{Dense}, reuse */
SparseCsrTensor dsoftmax;
CsrDenseMatmulGradKernel<T, Context>(
dev_ctx, softmax, value, dout, &dsoftmax, dvalue);

/* Step2: Calculate grad of sdd_result, manualy not reuse */
SparseCsrTensor d_sdd_result;
EmptyLikeCsrKernel<T, Context>(dev_ctx, dsoftmax, &d_sdd_result);
auto q_dim = query.dims();
auto q_rank = q_dim.size();

int total_row_num = 1;
int batch_num = 1;
for (int i = 0; i < q_rank - 1; ++i) {
total_row_num *= q_dim[i];
if (i < q_rank - 2) {
batch_num *= q_dim[i];
}
}
int M = q_dim[q_rank - 2];
int N = q_dim[q_rank - 1];
int batch_nnz = softmax.nnz() / batch_num;

dim3 grid((total_row_num + 3) / 4);
dim3 block(WARP_SIZE, 4);

AttnSoftmaxGpuGradKernel<T><<<grid, block, 0, dev_ctx.stream()>>>(
softmax.non_zero_crows().data<int64_t>(),
softmax.non_zero_elements().data<T>(),
dsoftmax.mutable_non_zero_elements()->data<T>(),
d_sdd_result.mutable_non_zero_elements()->data<T>(),
M,
total_row_num,
std::sqrt(N),
batch_nnz);

/* Step3: Forward: query{Dense} * key'{Dense} -> sdd_result{SparseCsr} */
auto sparse_blas = phi::funcs::sparse::GetSparseBlas<Context, T>(dev_ctx);
// dquery{Dense} = d_sdd_result{SparseCsr} * key{Dense} //
dquery->Resize(query.dims());
dev_ctx.template Alloc<T>(dquery);
sparse_blas.SPMM(false,
false,
static_cast<T>(1.f),
d_sdd_result,
key,
static_cast<T>(0.f),
dquery);

// dkey{Dense} = d_sdd_result'{SparseCsr} * query{Dense} //
dkey->Resize(key.dims());
dev_ctx.template Alloc<T>(dkey);
sparse_blas.SPMM(true,
false,
static_cast<T>(1.f),
d_sdd_result,
query,
static_cast<T>(0.f),
dkey);
#else
PADDLE_THROW(
phi::errors::Unimplemented("backward of 'sparse.nn.functional.attention' "
"use 'cusparseCsrSetStridedBatch', which is "
"completed supported from CUDA 11.7"));
#endif
}

} // namespace sparse
} // namespace phi

PD_REGISTER_KERNEL(fused_attention_csr_grad,
GPU,
ALL_LAYOUT,
phi::sparse::FusedAttentionCsrGradKernel,
float,
double) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR);
}

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