wehub-resource-sync
91 lines
3.5 KiB
Plaintext
91 lines
3.5 KiB
Plaintext
// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/backends/context_pool.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/flash_attn_kernel.h"
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#include "paddle/phi/kernels/funcs/broadcast_function.h"
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#include "paddle/phi/kernels/funcs/elementwise_functor.h"
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#include "paddle/phi/kernels/fusion/cutlass/variable_length_memory_efficient_attention.h"
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#include "paddle/phi/kernels/fusion/gpu/block_attn.h"
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#include "paddle/phi/kernels/gpu/flash_attn_utils.h"
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#include "paddle/phi/kernels/memcpy_kernel.h"
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#include "paddle/utils/none.h"
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namespace phi {
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namespace fusion {
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void GetMaxLenTensor(const GPUContext& dev_ctx,
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const DenseTensor& seq_lens_tensor,
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const DenseTensor& batch_size,
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DenseTensor* out) {
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DenseTensor max_len_tensor;
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max_len_tensor.Resize({1});
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auto* max_len_tensor_data = dev_ctx.template Alloc<int>(
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&max_len_tensor, max_len_tensor.numel() * sizeof(int));
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const int64_t bsz = batch_size.dims()[0];
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constexpr int blockSize = 128;
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int max_len_cpu = 0;
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GetMaxLenKernel<blockSize><<<1, blockSize, 0, dev_ctx.stream()>>>(
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seq_lens_tensor.data<int>(), max_len_tensor.data<int>(), bsz);
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MemcpyD2HKernel(dev_ctx, max_len_tensor, 0, out);
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}
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template <typename T, typename Context>
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void BlhaGetMaxLenKernel(const Context& dev_ctx,
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const DenseTensor& seq_lens_encoder,
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const DenseTensor& seq_lens_decoder,
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const DenseTensor& batch_size,
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DenseTensor* max_enc_len_this_time,
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DenseTensor* max_dec_len_this_time) {
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phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance();
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auto& dev_ctx_cpu = *pool.Get(CPUPlace());
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// decoder
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max_dec_len_this_time->Resize({1});
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if (seq_lens_decoder.numel() > 0) {
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GetMaxLenTensor(
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dev_ctx, seq_lens_decoder, batch_size, max_dec_len_this_time);
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} else {
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Full<int, CPUContext>(reinterpret_cast<const CPUContext&>(dev_ctx_cpu),
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max_dec_len_this_time->dims(),
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0,
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max_dec_len_this_time);
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}
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// encoder
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max_enc_len_this_time->Resize({1});
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if (seq_lens_encoder.numel() > 0) {
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GetMaxLenTensor(
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dev_ctx, seq_lens_encoder, batch_size, max_enc_len_this_time);
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} else {
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Full<int, CPUContext>(reinterpret_cast<const CPUContext&>(dev_ctx_cpu),
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max_enc_len_this_time->dims(),
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0,
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max_enc_len_this_time);
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}
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}
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} // namespace fusion
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} // namespace phi
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PD_REGISTER_KERNEL(blha_get_max_len,
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GPU,
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ALL_LAYOUT,
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phi::fusion::BlhaGetMaxLenKernel,
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int,
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int64_t) {
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kernel->OutputAt(0).SetBackend(phi::Backend::CPU);
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kernel->OutputAt(1).SetBackend(phi::Backend::CPU);
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}
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