mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-12-24 13:28:13 +08:00
398 lines
18 KiB
Plaintext
398 lines
18 KiB
Plaintext
// Copyright (c) 2025 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 "cutlass/numeric_conversion.h"
|
|
#include "group_swiglu_with_masked.h"
|
|
#include "helper.h"
|
|
#include "moe/fused_moe_helper.h"
|
|
|
|
template <typename DataT,
|
|
typename NvType,
|
|
typename WeightSavedT,
|
|
cutlass::WintQuantMethod QuantMethod>
|
|
void WeightOnlyMoeFFNKernel(const paddle::Tensor& permute_input,
|
|
const paddle::Tensor& tokens_expert_prefix_sum,
|
|
const paddle::Tensor& up_gate_proj_weight,
|
|
const paddle::Tensor& down_proj_weight,
|
|
const paddle::Tensor* up_gate_proj_bias,
|
|
const paddle::Tensor* up_gate_proj_super_scale,
|
|
const paddle::Tensor* down_proj_super_scale,
|
|
const paddle::Tensor* up_gate_proj_local_scale,
|
|
const paddle::Tensor* up_gate_proj_code_scale,
|
|
const paddle::Tensor* up_gate_proj_code_zp,
|
|
const paddle::Tensor* down_proj_local_scale,
|
|
const paddle::Tensor* down_proj_code_scale,
|
|
const paddle::Tensor* down_proj_code_zp,
|
|
paddle::Tensor fc1_out,
|
|
paddle::Tensor ffn_out,
|
|
const int64_t total_rows_in_ll_else_minus1,
|
|
const int64_t actual_total_rows,
|
|
const int64_t inter_size,
|
|
const int64_t hidden_size,
|
|
const int num_experts,
|
|
bool used_in_ep_low_latency) {
|
|
using namespace phi;
|
|
using WeightOnlyTraits = cutlass::WintQuantTraits<NvType, QuantMethod>;
|
|
using WeightType = typename WeightOnlyTraits::WeightType;
|
|
|
|
typename WeightOnlyTraits::Arguments up_gate_proj_quant_args;
|
|
typename WeightOnlyTraits::Arguments down_proj_quant_args;
|
|
if constexpr (QuantMethod == cutlass::WintQuantMethod::kWeightOnlyInt2) {
|
|
up_gate_proj_quant_args.local_scale_ptr =
|
|
const_cast<uint8_t*>(up_gate_proj_local_scale->data<uint8_t>());
|
|
up_gate_proj_quant_args.code_scale_ptr =
|
|
const_cast<float*>(up_gate_proj_code_scale->data<float>());
|
|
up_gate_proj_quant_args.code_zp_ptr =
|
|
const_cast<float*>(up_gate_proj_code_zp->data<float>());
|
|
|
|
down_proj_quant_args.local_scale_ptr =
|
|
const_cast<uint8_t*>(down_proj_local_scale->data<uint8_t>());
|
|
down_proj_quant_args.code_scale_ptr =
|
|
const_cast<float*>(down_proj_code_scale->data<float>());
|
|
down_proj_quant_args.code_zp_ptr =
|
|
const_cast<float*>(down_proj_code_zp->data<float>());
|
|
}
|
|
|
|
auto moe_gemm_runner = MoeGemmRunner<NvType, WeightOnlyTraits>();
|
|
auto stream = permute_input.stream();
|
|
|
|
moe_gemm_runner.moe_gemm_bias_act(
|
|
reinterpret_cast<const NvType*>(permute_input.data<DataT>()),
|
|
reinterpret_cast<const WeightType*>(
|
|
up_gate_proj_weight.data<WeightSavedT>()),
|
|
reinterpret_cast<const NvType*>(
|
|
up_gate_proj_super_scale ? up_gate_proj_super_scale->data<DataT>()
|
|
: nullptr),
|
|
reinterpret_cast<const NvType*>(
|
|
up_gate_proj_bias ? up_gate_proj_bias->data<DataT>() : nullptr),
|
|
reinterpret_cast<NvType*>(fc1_out.data<DataT>()),
|
|
const_cast<int64_t*>(tokens_expert_prefix_sum.data<int64_t>()),
|
|
total_rows_in_ll_else_minus1,
|
|
actual_total_rows,
|
|
inter_size,
|
|
hidden_size,
|
|
num_experts,
|
|
up_gate_proj_quant_args,
|
|
"none",
|
|
stream);
|
|
|
|
paddle::Tensor act_out;
|
|
if (used_in_ep_low_latency) {
|
|
act_out = GroupSwigluWithMasked(fc1_out, tokens_expert_prefix_sum);
|
|
} else {
|
|
act_out = paddle::experimental::swiglu(fc1_out, nullptr);
|
|
}
|
|
|
|
moe_gemm_runner.moe_gemm(
|
|
reinterpret_cast<const NvType*>(act_out.data<DataT>()),
|
|
reinterpret_cast<const WeightType*>(
|
|
down_proj_weight.data<WeightSavedT>()),
|
|
reinterpret_cast<const NvType*>(down_proj_super_scale
|
|
? down_proj_super_scale->data<DataT>()
|
|
: nullptr),
|
|
reinterpret_cast<NvType*>(ffn_out.data<DataT>()),
|
|
const_cast<int64_t*>(tokens_expert_prefix_sum.data<int64_t>()),
|
|
total_rows_in_ll_else_minus1,
|
|
actual_total_rows,
|
|
hidden_size,
|
|
inter_size / 2,
|
|
num_experts,
|
|
down_proj_quant_args,
|
|
stream);
|
|
}
|
|
|
|
template <paddle::DataType T>
|
|
void MoeFFNWint2Kernel(
|
|
const paddle::Tensor& permute_input,
|
|
const paddle::Tensor& tokens_expert_prefix_sum,
|
|
const paddle::Tensor& up_gate_proj_weight,
|
|
const paddle::Tensor& down_proj_weight,
|
|
const paddle::optional<paddle::Tensor>& up_gate_proj_bias,
|
|
const paddle::optional<paddle::Tensor>& up_gate_proj_scale,
|
|
const paddle::optional<paddle::Tensor>& down_proj_scale,
|
|
const paddle::optional<paddle::Tensor>& up_gate_proj_local_scale,
|
|
const paddle::optional<paddle::Tensor>& up_gate_proj_code_scale,
|
|
const paddle::optional<paddle::Tensor>& up_gate_proj_code_zp,
|
|
const paddle::optional<paddle::Tensor>& down_proj_local_scale,
|
|
const paddle::optional<paddle::Tensor>& down_proj_code_scale,
|
|
const paddle::optional<paddle::Tensor>& down_proj_code_zp,
|
|
paddle::Tensor ffn_out,
|
|
bool used_in_ep_low_latency) {
|
|
using namespace phi;
|
|
using data_t = typename PDTraits<T>::data_t;
|
|
using NvType = typename PDTraits<T>::DataType;
|
|
|
|
auto place = permute_input.place();
|
|
|
|
assert(permute_input.dims().size() == 3 || permute_input.dims().size() == 2);
|
|
assert(up_gate_proj_weight.dims().size() == 3);
|
|
|
|
const int num_experts = up_gate_proj_weight.dims()[0];
|
|
const int hidden_size = permute_input.dims()[permute_input.dims().size() - 1];
|
|
|
|
int inter_dim = up_gate_proj_weight.dims()[1] *
|
|
up_gate_proj_weight.dims()[2] / hidden_size;
|
|
|
|
const int64_t inter_size = inter_dim * 4;
|
|
|
|
int num_experts_ = num_experts;
|
|
int num_max_tokens_per_expert = 0;
|
|
int expanded_active_expert_rows = 0;
|
|
|
|
paddle::Tensor fc1_out_tensor;
|
|
if (permute_input.dims().size() == 3) {
|
|
num_experts_ = permute_input.dims()[0];
|
|
assert(num_experts == num_experts_);
|
|
|
|
num_max_tokens_per_expert = permute_input.dims()[1];
|
|
expanded_active_expert_rows = num_experts_ * num_max_tokens_per_expert;
|
|
fc1_out_tensor = GetEmptyTensor(
|
|
{num_experts_, num_max_tokens_per_expert, inter_size}, T, place);
|
|
} else {
|
|
expanded_active_expert_rows = permute_input.dims()[0];
|
|
fc1_out_tensor =
|
|
GetEmptyTensor({expanded_active_expert_rows, inter_size}, T, place);
|
|
}
|
|
|
|
// This is a trick.
|
|
// expanded_active_expert_rows is not needed in variable group gemm.
|
|
// but is needed in accommodating deepep low latency mode
|
|
const int64_t total_rows_in_ll_else_minus1 =
|
|
used_in_ep_low_latency ? expanded_active_expert_rows : -1;
|
|
|
|
// When we tune the optimal configuration, we need the actual total_rows.
|
|
const int64_t actual_total_rows = expanded_active_expert_rows;
|
|
|
|
WeightOnlyMoeFFNKernel<data_t,
|
|
NvType,
|
|
uint8_t,
|
|
cutlass::WintQuantMethod::kWeightOnlyInt2>(
|
|
permute_input,
|
|
tokens_expert_prefix_sum,
|
|
up_gate_proj_weight,
|
|
down_proj_weight,
|
|
const_cast<paddle::Tensor*>(up_gate_proj_bias.get_ptr()),
|
|
const_cast<paddle::Tensor*>(up_gate_proj_scale.get_ptr()),
|
|
const_cast<paddle::Tensor*>(down_proj_scale.get_ptr()),
|
|
const_cast<paddle::Tensor*>(up_gate_proj_local_scale.get_ptr()),
|
|
const_cast<paddle::Tensor*>(up_gate_proj_code_scale.get_ptr()),
|
|
const_cast<paddle::Tensor*>(up_gate_proj_code_zp.get_ptr()),
|
|
const_cast<paddle::Tensor*>(down_proj_local_scale.get_ptr()),
|
|
const_cast<paddle::Tensor*>(down_proj_code_scale.get_ptr()),
|
|
const_cast<paddle::Tensor*>(down_proj_code_zp.get_ptr()),
|
|
fc1_out_tensor,
|
|
ffn_out,
|
|
total_rows_in_ll_else_minus1,
|
|
actual_total_rows,
|
|
inter_size,
|
|
hidden_size,
|
|
num_experts,
|
|
used_in_ep_low_latency);
|
|
}
|
|
|
|
paddle::Tensor MoeExpertFFNWint2Func(
|
|
const paddle::Tensor& permute_input,
|
|
const paddle::Tensor& tokens_expert_prefix_sum,
|
|
const paddle::Tensor& up_gate_proj_weight,
|
|
const paddle::Tensor& down_proj_weight,
|
|
const paddle::optional<paddle::Tensor>& up_gate_proj_bias,
|
|
const paddle::optional<paddle::Tensor>& up_gate_proj_scale,
|
|
const paddle::optional<paddle::Tensor>& down_proj_scale,
|
|
const paddle::optional<paddle::Tensor>& up_gate_proj_local_scale,
|
|
const paddle::optional<paddle::Tensor>& up_gate_proj_code_scale,
|
|
const paddle::optional<paddle::Tensor>& up_gate_proj_code_zp,
|
|
const paddle::optional<paddle::Tensor>& down_proj_local_scale,
|
|
const paddle::optional<paddle::Tensor>& down_proj_code_scale,
|
|
const paddle::optional<paddle::Tensor>& down_proj_code_zp,
|
|
const bool used_in_ep_low_latency) {
|
|
const auto dtype = permute_input.dtype();
|
|
auto ffn_out = paddle::empty_like(permute_input, dtype);
|
|
|
|
switch (dtype) {
|
|
case paddle::DataType::BFLOAT16:
|
|
MoeFFNWint2Kernel<paddle::DataType::BFLOAT16>(permute_input,
|
|
tokens_expert_prefix_sum,
|
|
up_gate_proj_weight,
|
|
down_proj_weight,
|
|
up_gate_proj_bias,
|
|
up_gate_proj_scale,
|
|
down_proj_scale,
|
|
up_gate_proj_local_scale,
|
|
up_gate_proj_code_scale,
|
|
up_gate_proj_code_zp,
|
|
down_proj_local_scale,
|
|
down_proj_code_scale,
|
|
down_proj_code_zp,
|
|
ffn_out,
|
|
used_in_ep_low_latency);
|
|
break;
|
|
case paddle::DataType::FLOAT16:
|
|
MoeFFNWint2Kernel<paddle::DataType::FLOAT16>(permute_input,
|
|
tokens_expert_prefix_sum,
|
|
up_gate_proj_weight,
|
|
down_proj_weight,
|
|
up_gate_proj_bias,
|
|
up_gate_proj_scale,
|
|
down_proj_scale,
|
|
up_gate_proj_local_scale,
|
|
up_gate_proj_code_scale,
|
|
up_gate_proj_code_zp,
|
|
down_proj_local_scale,
|
|
down_proj_code_scale,
|
|
down_proj_code_zp,
|
|
ffn_out,
|
|
used_in_ep_low_latency);
|
|
break;
|
|
default:
|
|
PD_THROW("Unsupported data type for MoeExpertFFN");
|
|
}
|
|
return ffn_out;
|
|
}
|
|
|
|
std::vector<paddle::Tensor> MoeExpertFFNWint2(
|
|
const paddle::Tensor& permute_input,
|
|
const paddle::Tensor& tokens_expert_prefix_sum,
|
|
const paddle::Tensor& up_gate_proj_weight,
|
|
const paddle::Tensor& down_proj_weight,
|
|
const paddle::optional<paddle::Tensor>& up_gate_proj_bias,
|
|
const paddle::optional<paddle::Tensor>& up_gate_proj_scale,
|
|
const paddle::optional<paddle::Tensor>& down_proj_scale,
|
|
const paddle::optional<paddle::Tensor>& up_gate_proj_local_scale,
|
|
const paddle::optional<paddle::Tensor>& up_gate_proj_code_scale,
|
|
const paddle::optional<paddle::Tensor>& up_gate_proj_code_zp,
|
|
const paddle::optional<paddle::Tensor>& down_proj_local_scale,
|
|
const paddle::optional<paddle::Tensor>& down_proj_code_scale,
|
|
const paddle::optional<paddle::Tensor>& down_proj_code_zp,
|
|
const bool used_in_ep_low_latency) {
|
|
return {MoeExpertFFNWint2Func(permute_input,
|
|
tokens_expert_prefix_sum,
|
|
up_gate_proj_weight,
|
|
down_proj_weight,
|
|
up_gate_proj_bias,
|
|
up_gate_proj_scale,
|
|
down_proj_scale,
|
|
up_gate_proj_local_scale,
|
|
up_gate_proj_code_scale,
|
|
up_gate_proj_code_zp,
|
|
down_proj_local_scale,
|
|
down_proj_code_scale,
|
|
down_proj_code_zp,
|
|
used_in_ep_low_latency)};
|
|
}
|
|
|
|
std::vector<std::vector<int64_t>> MoeExpertFFNWint2InferShape(
|
|
const std::vector<int64_t>& permute_input_shape,
|
|
const std::vector<int64_t>& tokens_expert_prefix_sum_shape,
|
|
const std::vector<int64_t>& up_gate_proj_weight_shape,
|
|
const std::vector<int64_t>& down_proj_weight_shape,
|
|
const paddle::optional<std::vector<int64_t>>& up_gate_proj_bias_shape,
|
|
const paddle::optional<std::vector<int64_t>>& up_gate_proj_scale_shape,
|
|
const paddle::optional<std::vector<int64_t>>& down_proj_scale_shape,
|
|
const paddle::optional<std::vector<int64_t>>&
|
|
up_gate_proj_local_scale_shape,
|
|
const paddle::optional<std::vector<int64_t>>& up_gate_proj_code_scale_shape,
|
|
const paddle::optional<std::vector<int64_t>>& up_gate_proj_code_zp_shape,
|
|
const paddle::optional<std::vector<int64_t>>& down_proj_local_scale_shape,
|
|
const paddle::optional<std::vector<int64_t>>& down_proj_code_scale_shape,
|
|
const paddle::optional<std::vector<int64_t>>& down_proj_code_zp_shape,
|
|
const bool used_in_ep_low_latency) {
|
|
return {permute_input_shape};
|
|
}
|
|
|
|
std::vector<paddle::DataType> MoeExpertFFNWint2InferDtype(
|
|
const paddle::DataType& permute_input_dtype,
|
|
const paddle::DataType& tokens_expert_prefix_sum_dtype,
|
|
const paddle::DataType& up_gate_proj_weight_dtype,
|
|
const paddle::DataType& down_proj_weight_dtype,
|
|
const paddle::optional<paddle::DataType>& up_gate_proj_bias_dtype,
|
|
const paddle::optional<paddle::DataType>& up_gate_proj_scale_dtype,
|
|
const paddle::optional<paddle::DataType>& down_proj_scale_dtype,
|
|
const paddle::optional<paddle::DataType>& up_gate_proj_local_scale_dtype,
|
|
const paddle::optional<paddle::DataType>& up_gate_proj_code_scale_dtype,
|
|
const paddle::optional<paddle::DataType>& up_gate_proj_code_zp_dtype,
|
|
const paddle::optional<paddle::DataType>& down_proj_local_scale_dtype,
|
|
const paddle::optional<paddle::DataType>& down_proj_code_scale_dtype,
|
|
const paddle::optional<paddle::DataType>& down_proj_code_zp_dtype,
|
|
const bool used_in_ep_low_latency) {
|
|
return {permute_input_dtype};
|
|
}
|
|
|
|
/**
|
|
* @brief Weight-Only Quantized Mixture of Experts (MoE) Feed-Forward Network
|
|
* Operator
|
|
*
|
|
* This operator performs the expert computation in MoE architecture, including:
|
|
* 1. First linear transformation (up_gate_proj) with optional quantization
|
|
* 2. SwiGLU activation function
|
|
* 3. Second linear transformation (down_proj) with optional quantization
|
|
*
|
|
* Supports multiple quantization methods including weight-only int4/int8 and
|
|
* w4a8 quantization.
|
|
*
|
|
* Inputs:
|
|
* - permute_input: Permuted input tensor organized by expert
|
|
* Shape: [total_tokens * top_k, hidden_size]
|
|
* dtype: bfloat16/float16 (or int8 for w4a8)
|
|
* - tokens_expert_prefix_sum: Prefix sum array of token counts per expert for
|
|
* group_gemm Shape: [num_experts] dtype: int64
|
|
* - up_gate_proj_weight: First FFN layer weights
|
|
* Shape: [num_experts, inter_size * 2, hidden_size]
|
|
* dtype: Same as input (unquantized) or int8 (quantized)
|
|
* - down_proj_weight: Second FFN layer weights
|
|
* Shape: [num_experts, hidden_size, inter_size]
|
|
* dtype: Same as input (unquantized) or int8 (quantized)
|
|
* - up_gate_proj_bias: Optional bias for first FFN layer
|
|
* Shape: [num_experts, inter_size * 2]
|
|
* dtype: Same as input
|
|
* - up_gate_proj_scale: Quantization scales for first FFN layer
|
|
* Shape: [num_experts, inter_size * 2]
|
|
* dtype: Same as input
|
|
* - down_proj_scale: Quantization scales for second FFN layer
|
|
* Shape: [num_experts, hidden_size]
|
|
* dtype: Same as input
|
|
*
|
|
* Outputs:
|
|
* - output_tensor: Output tensor after MoE FFN computation
|
|
* Shape: Same as permute_input
|
|
* dtype: Same as input (or up_gate_proj_scale dtype for w4a8)
|
|
*
|
|
* Attributes:
|
|
* - used_in_ep_low_latency: Whether running in low latency mode
|
|
* Affects activation function implementation
|
|
*
|
|
* Note:
|
|
* - Low latency mode uses specialized grouped SwiGLU implementation
|
|
*/
|
|
PD_BUILD_STATIC_OP(moe_expert_ffn_wint2)
|
|
.Inputs({"permute_input",
|
|
"tokens_expert_prefix_sum",
|
|
"up_gate_proj_weight",
|
|
"down_proj_weight",
|
|
paddle::Optional("up_gate_proj_bias"),
|
|
paddle::Optional("up_gate_proj_scale"),
|
|
paddle::Optional("down_proj_scale"),
|
|
paddle::Optional("up_gate_proj_local_scale"),
|
|
paddle::Optional("up_gate_proj_code_scale"),
|
|
paddle::Optional("up_gate_proj_code_zp"),
|
|
paddle::Optional("down_proj_local_scale"),
|
|
paddle::Optional("down_proj_code_scale"),
|
|
paddle::Optional("down_proj_code_zp")})
|
|
.Outputs({"output_tensor"})
|
|
.Attrs({"used_in_ep_low_latency:bool"})
|
|
.SetKernelFn(PD_KERNEL(MoeExpertFFNWint2))
|
|
.SetInferShapeFn(PD_INFER_SHAPE(MoeExpertFFNWint2InferShape))
|
|
.SetInferDtypeFn(PD_INFER_DTYPE(MoeExpertFFNWint2InferDtype));
|