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4403a21d4b
* [Metax] refactor moe and flash attention backend --------- Co-authored-by: zhangchenyi_dl <16219492+zhangchenyidl@user.noreply.gitee.com>
392 lines
14 KiB
C++
392 lines
14 KiB
C++
// Copyright (c) 2025 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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#pragma once
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#include "fused_moe_gemm_kernels.h"
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#include "fused_moe_imp_op.h"
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#include "fused_moe_op.h"
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#include "mctlass/numeric_conversion.h"
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#include "mctlassEx/mctlassEx.h"
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namespace phi {
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template <typename T, int VecSize>
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__global__ void moe_token_type_ids_kernel(T* gating_output,
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const int* moe_token_type_ids_out,
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const int num_rows,
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const int num_experts,
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const int k) {
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const int moe_token_index = blockIdx.x * blockDim.x + threadIdx.x;
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if (moe_token_index >= num_rows) {
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return;
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}
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gating_output[moe_token_index * 2] =
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gating_output[moe_token_index * 2] +
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(moe_token_type_ids_out[moe_token_index]) * -1e10;
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gating_output[moe_token_index * 2 + 1] =
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gating_output[moe_token_index * 2 + 1] +
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(1 - moe_token_type_ids_out[moe_token_index]) * -1e10;
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}
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template <typename T>
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void moe_token_type_ids_kernelLauncher(T* gating_output,
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const int* moe_token_type_ids_out,
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const int num_rows,
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const int num_experts,
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const int k,
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cudaStream_t stream) {
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const int blocks = num_rows * k / 512 + 1;
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const int threads = 512;
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moe_token_type_ids_kernel<T, 1><<<blocks, 512, 0, stream>>>(
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gating_output, moe_token_type_ids_out, num_rows, num_experts, k);
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}
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template <typename T, typename MacaType>
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class McMoeHelper {
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public:
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McMoeHelper(const std::string gemm_method,
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McMoeGemmRunner<MacaType, int8_t>* int8_moe_gemm_runner)
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: gemm_method_(gemm_method),
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int8_moe_gemm_runner_(int8_moe_gemm_runner) {}
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// -------- getWorkspaceSize -------- //
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template <typename KeyT>
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size_t getWorkspaceSize(const int64_t num_rows,
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const int64_t hidden_size,
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const int64_t inter_size,
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const int64_t num_experts,
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const int64_t k) {
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const size_t buf_size = AlignTo16(k * num_rows * hidden_size);
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const size_t interbuf_size = AlignTo16(k * num_rows * inter_size);
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const size_t padded_experts = AlignTo16(num_experts);
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const size_t num_moe_inputs = AlignTo16(k * num_rows);
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// softmax output, permuted_rows and permuted_experts have moved to outside
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// of moe kernel, allocate them in Encoder or Decoder before invoking
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// FfnLayer forward.
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size_t total_ws_bytes =
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5 * num_moe_inputs *
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sizeof(int); // source_rows_, permuted_rows_, permuted_experts_
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total_ws_bytes += buf_size * sizeof(KeyT); // permuted_data
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total_ws_bytes +=
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padded_experts * sizeof(int32_t); // Hold total_rows_before_expert_
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const size_t bytes_for_fc1_result = interbuf_size * sizeof(KeyT);
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const size_t sorter_ws_size_bytes =
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AlignTo16(sorter_.getWorkspaceSize(num_rows));
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sorter_.update_num_experts(num_experts);
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int64_t bytes_for_intermediate_and_sorting = bytes_for_fc1_result;
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if (sorter_ws_size_bytes > bytes_for_fc1_result) {
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int64_t remaining_bytes =
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AlignTo16(sorter_ws_size_bytes - bytes_for_fc1_result);
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bytes_for_intermediate_and_sorting += remaining_bytes;
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}
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total_ws_bytes +=
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bytes_for_intermediate_and_sorting; // intermediate (fc1) output + cub
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// sorting workspace
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int64_t num_softmax_outs = 0;
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const bool is_pow_2 =
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(num_experts != 0) && ((num_experts & (num_experts - 1)) == 0);
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if (!is_pow_2 || num_experts > 256) {
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num_softmax_outs = AlignTo16(num_rows * num_experts);
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}
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total_ws_bytes += num_softmax_outs * sizeof(float);
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return total_ws_bytes;
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}
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void computeFFN(const paddle::Tensor* input,
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const paddle::Tensor* gate_weight,
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const paddle::Tensor* up_gate_proj_weight,
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const paddle::Tensor* up_gate_proj_scale,
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const paddle::Tensor* up_gate_proj_bias,
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const paddle::Tensor* down_proj_weight,
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const paddle::Tensor* down_proj_scale,
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const paddle::Tensor* down_proj_bias,
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const paddle::Tensor* moe_token_type_ids,
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const int moe_topk,
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const bool group_moe,
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const bool norm_topk_prob,
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const float routed_scaling_factor,
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const std::string moe_type,
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paddle::Tensor* output) {
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auto* input_activations = input->data<T>();
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auto* gating_weights = gate_weight->data<float>();
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const T* fc1_expert_biases =
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up_gate_proj_bias ? up_gate_proj_bias->data<T>() : nullptr;
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const T* fc2_expert_biases =
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down_proj_bias ? down_proj_bias->data<T>() : nullptr;
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auto* output_ = output->data<T>();
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auto stream = input->stream();
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auto place = input->place();
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auto input_type = input->dtype();
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auto input_dims = input->dims();
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auto up_gate_proj_dims = up_gate_proj_weight->dims();
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int64_t token_num = 0;
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if (input_dims.size() == 3) {
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token_num = input_dims[0] * input_dims[1];
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} else {
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token_num = input_dims[0];
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}
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const int64_t num_rows = token_num;
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const int64_t hidden_size = up_gate_proj_dims[2];
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int64_t inter_dim = 0;
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if (moe_type == "qkv") {
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inter_dim =
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up_gate_proj_dims[2] * up_gate_proj_dims[3] * up_gate_proj_dims[4];
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} else {
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inter_dim = up_gate_proj_dims[1];
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}
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// if (gemm_method_ == "weight_only_int4") {
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// inter_dim = inter_dim * 2;
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// }
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const int64_t inter_size = inter_dim;
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const int64_t num_experts = up_gate_proj_dims[0];
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const int64_t k = moe_topk;
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int64_t bytes =
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getWorkspaceSize<T>(num_rows, hidden_size, inter_size, num_experts, k);
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// Pointers
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int* expert_for_source_row;
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int* source_rows_;
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int* permuted_rows_;
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int* permuted_experts_;
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int* expanded_source_row_to_expanded_dest_row;
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T* permuted_data_;
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int32_t* total_rows_before_expert_;
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T* fc1_result_;
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float* softmax_out_;
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paddle::Tensor ws_ptr_tensor =
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GetEmptyTensor({bytes}, paddle::DataType::INT8, place);
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int8_t* ws_ptr = ws_ptr_tensor.data<int8_t>();
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const int64_t buf_size = AlignTo16(k * num_rows * hidden_size);
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const int64_t interbuf_size = AlignTo16(k * num_rows * inter_size);
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const int64_t padded_experts = AlignTo16(num_experts);
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const int64_t num_moe_inputs = AlignTo16(k * num_rows);
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expert_for_source_row = reinterpret_cast<int*>(ws_ptr);
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source_rows_ = expert_for_source_row + num_moe_inputs;
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permuted_rows_ = source_rows_ + num_moe_inputs;
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permuted_experts_ = permuted_rows_ + num_moe_inputs;
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expanded_source_row_to_expanded_dest_row =
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permuted_experts_ + num_moe_inputs;
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permuted_data_ = reinterpret_cast<T*>(
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expanded_source_row_to_expanded_dest_row + num_moe_inputs);
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total_rows_before_expert_ =
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reinterpret_cast<int32_t*>(permuted_data_ + buf_size);
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fc1_result_ =
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reinterpret_cast<T*>(total_rows_before_expert_ + padded_experts);
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const bool is_pow_2 =
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(num_experts != 0) && ((num_experts & (num_experts - 1)) == 0);
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if (!is_pow_2 || num_experts > 256) {
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softmax_out_ = reinterpret_cast<float*>(fc1_result_ + interbuf_size);
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} else {
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softmax_out_ = nullptr;
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}
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paddle::Tensor expert_scales_float_tensor =
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GetEmptyTensor({num_rows, moe_topk}, paddle::DataType::FLOAT32, place);
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float* expert_scales_float = expert_scales_float_tensor.data<float>();
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float* softmax_max_prob = nullptr;
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if (group_moe) {
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paddle::Tensor softmax_max_prob_tensor = GetEmptyTensor(
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{num_rows, moe_topk}, paddle::DataType::FLOAT32, place);
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// (TODO: check fill success ?)
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paddle::experimental::fill(softmax_max_prob_tensor, 0.f);
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softmax_max_prob = softmax_max_prob_tensor.data<float>();
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}
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paddle::Tensor fc1_out_tensor =
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GetEmptyTensor({num_rows * k, inter_size}, input_type, place);
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T* fc1_out = fc1_out_tensor.data<T>();
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auto input_cast_tensor =
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paddle::experimental::cast(*input, paddle::DataType::FLOAT32);
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auto gate_tensor =
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paddle::experimental::matmul(input_cast_tensor, *gate_weight);
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float* gating_output = gate_tensor.data<float>();
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if (moe_token_type_ids) {
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auto* moe_token_type_ids_out = moe_token_type_ids->data<int>();
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moe_token_type_ids_kernelLauncher<float>(gating_output,
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moe_token_type_ids_out,
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num_rows,
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num_experts,
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k,
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stream);
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}
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topk_gating_softmax_kernelLauncher<float, int>(gating_output,
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nullptr,
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expert_scales_float,
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softmax_out_,
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expert_for_source_row,
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source_rows_,
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softmax_max_prob,
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num_rows,
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num_experts,
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k,
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group_moe,
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stream);
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const int64_t sorter_ws_size_bytes =
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AlignTo16(sorter_.getWorkspaceSize(int64_t(k * num_rows)));
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sorter_.run(fc1_result_,
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sorter_ws_size_bytes,
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expert_for_source_row,
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permuted_experts_,
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source_rows_,
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permuted_rows_,
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k * num_rows,
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false,
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stream);
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initialize_moe_routing_kernelLauncher(
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input_activations,
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permuted_data_,
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permuted_rows_,
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nullptr,
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nullptr,
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expanded_source_row_to_expanded_dest_row,
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num_rows,
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num_rows,
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hidden_size,
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k,
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stream);
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const int64_t expanded_active_expert_rows = k * num_rows;
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compute_total_rows_before_expert(permuted_experts_,
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expanded_active_expert_rows,
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num_experts,
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total_rows_before_expert_,
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stream);
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mctlassExOrder_t row_major = mctlassExOrder_t::MCTLASS_EX_ORDER_ROW_MAJOR;
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mctlassExOrder_t column_major =
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mctlassExOrder_t::MCTLASS_EX_ORDER_COLUMN_MAJOR;
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auto m_num_tile =
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GetEmptyTensor({num_experts}, paddle::DataType::INT32, place);
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int* m_num_tile_ptr = reinterpret_cast<int*>(m_num_tile.data<int>());
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if (gemm_method_ == "weight_only_int8") {
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int8_moe_gemm_runner_->mc_grouped_gemm_basic_kernel(
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reinterpret_cast<const MacaType*>(permuted_data_),
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row_major,
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reinterpret_cast<const int8_t*>(up_gate_proj_weight->data<int8_t>()),
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column_major,
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reinterpret_cast<const MacaType*>(up_gate_proj_scale->data<T>()),
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reinterpret_cast<const MacaType*>(fc1_expert_biases),
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reinterpret_cast<MacaType*>(fc1_out),
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row_major,
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total_rows_before_expert_,
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m_num_tile_ptr,
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num_experts,
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expanded_active_expert_rows,
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inter_size,
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hidden_size,
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stream);
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} else {
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throw std::runtime_error("Unsupported gemm method: " + gemm_method_);
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}
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if (moe_type == "ffn") {
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auto act_out_tensor =
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paddle::experimental::swiglu(fc1_out_tensor, nullptr);
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auto act_out = act_out_tensor.data<T>();
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paddle::Tensor fc2_output_tensor =
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GetEmptyTensor({k * num_rows, hidden_size}, input_type, place);
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T* fc2_result = fc2_output_tensor.data<T>();
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if (gemm_method_ == "weight_only_int8") {
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int8_moe_gemm_runner_->mc_grouped_gemm_basic_kernel(
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reinterpret_cast<const MacaType*>(act_out),
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row_major,
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reinterpret_cast<const int8_t*>(down_proj_weight->data<int8_t>()),
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column_major,
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reinterpret_cast<const MacaType*>(down_proj_scale->data<T>()),
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nullptr,
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reinterpret_cast<MacaType*>(fc2_result),
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row_major,
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total_rows_before_expert_,
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m_num_tile_ptr,
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num_experts,
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expanded_active_expert_rows,
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hidden_size,
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inter_size / 2,
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stream);
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} else {
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throw std::runtime_error("Unsupported gemm method: " + gemm_method_);
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}
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finalize_moe_routing_kernelLauncher(
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fc2_result,
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output_,
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fc2_expert_biases,
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reinterpret_cast<float*>(expert_scales_float),
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expanded_source_row_to_expanded_dest_row,
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expert_for_source_row,
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num_rows,
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hidden_size,
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k,
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static_cast<int>(1),
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norm_topk_prob,
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routed_scaling_factor,
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stream);
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} else {
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finalize_moe_routing_kernelLauncher(
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// fc2_result,
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fc1_out,
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output_,
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fc1_expert_biases, // fc2_expert_biases,
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reinterpret_cast<float*>(expert_scales_float),
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expanded_source_row_to_expanded_dest_row,
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expert_for_source_row,
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num_rows,
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inter_size,
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k,
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static_cast<int>(0),
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norm_topk_prob,
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routed_scaling_factor,
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stream);
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}
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}
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private:
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McMoeGemmRunner<MacaType, int8_t>* int8_moe_gemm_runner_;
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std::string gemm_method_;
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CubKeyValueSorter sorter_;
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};
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} // namespace phi
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