""" # Copyright (c) 2024 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. """ from dataclasses import dataclass from paddle import nn from paddlenlp.utils.log import logger from fastdeploy.model_executor.layers.utils import get_tensor from .cutlass_fused_moe import CutlassFusedMoeMethod @dataclass class MoEComputeParams: """ some params for computing MoE. it is given to different compute methods. """ global_num_experts: int = -1 top_k: int = -1 hidden_size: int = -1 num_local_experts: int = -1 moe_intermediate_size: int = -1 tp_size: int = -1 ep_size: int = -1 dp_size: int = -1 moe_quant_type: str = "" class FusedMoE(nn.Layer): """ FusedMoE is a layer that performs MoE (Mixture of Experts) computation. """ def __init__( self, llm_config, moe_intermediate_size: int = -1, num_experts: int = -1, top_k: int = -1, moe_use_gate_correction_bias: bool = False, moe_quant_type: str = "weight_only_int4", layer_idx: int = -1, gate_weight_key=None, gate_correction_bias_key=None, ffn1_expert_weight_key=None, ffn2_expert_weight_key=None, moe_ffn1_bias_keys=None, moe_ffn2_bias_keys=None, moe_ffn1_weight_scale_keys=None, moe_ffn2_weight_scale_keys=None, moe_ffn1_in_scale_keys=None, moe_ffn2_in_scale_keys=None, ): """ Initialize the Moe layer with given parameters. Args: llm_config (LLMConfig): Arguments related to inference, containing attributes such as weight_dtype, act_dtype, mp_size, hidden_size, head_dim, num_attention_heads, and ffn_hidden_size. """ super().__init__() self.llm_config = llm_config self.layer_idx = layer_idx self.tp_size = llm_config.parallel_config.mp_size self.ep_size = llm_config.parallel_config.ep_size self.moe_use_gate_correction_bias = moe_use_gate_correction_bias self.hidden_size = llm_config.model_config.hidden_size self.moe_config = llm_config.moe_config self.use_offline_quant = llm_config.tmp_config.use_offline_quant moe_tag = self.llm_config.moe_config.moe_tag logger.info(f"{moe_tag}MoE is running in {moe_quant_type} mode") self.moe_quant_type = moe_quant_type self.num_experts = num_experts self.num_local_experts = self.num_experts // self.ep_size logger.info(f'''MoE config is num_experts:{num_experts}, top_k:{top_k}, hidden_size:{self.hidden_size}, moe_intermediate_size:{moe_intermediate_size}''') logger.info( f"MoE is running on moe_quant_type: {self.moe_quant_type}, ep:{self.ep_size}, tp:{self.tp_size} mode" ) self.moe_intermediate_size = moe_intermediate_size // self.tp_size self.gate_weight_key = gate_weight_key self.gate_correction_bias_key = gate_correction_bias_key self.ffn1_expert_weight_key = ffn1_expert_weight_key self.ffn2_expert_weight_key = ffn2_expert_weight_key self.ffn1_bias_key = moe_ffn1_bias_keys self.ffn2_bias_key = moe_ffn2_bias_keys if self.moe_quant_type == "w4a8": # below keys are only used in MoE W4A8! self.ffn1_expert_weight_scale_key = moe_ffn1_weight_scale_keys self.ffn2_expert_weight_scale_key = moe_ffn2_weight_scale_keys self.ffn1_expert_in_scale_key = moe_ffn1_in_scale_keys self.ffn2_expert_in_scale_key = moe_ffn2_in_scale_keys self.compute_method = CutlassFusedMoeMethod() self.moe_compute_params = MoEComputeParams() self.moe_compute_params.global_num_experts = self.num_experts self.moe_compute_params.top_k = top_k self.moe_compute_params.hidden_size = self.hidden_size self.moe_compute_params.num_local_experts = self.num_local_experts self.moe_compute_params.moe_quant_type = self.moe_quant_type self.moe_compute_params.moe_intermediate_size = self.moe_intermediate_size self.moe_compute_params.ep_size = self.ep_size self.moe_compute_params.tp_size = self.tp_size def load_gate_state_dict(self, state_dict): """ load_gate_state_dict function. """ up_gate_proj_weight = [] up_gate_proj_weight_scale = [] down_proj_weight = [] down_proj_weight_scale = [] for j in range(self.num_experts): up_gate_proj_weight.append( get_tensor( state_dict.pop(self.ffn1_expert_weight_key.format(j)))) down_proj_weight.append( get_tensor( state_dict.pop(self.ffn2_expert_weight_key.format(j)))) return up_gate_proj_weight, down_proj_weight def load_state_dict(self, state_dict, is_update: bool = False): """ load_state_dict function. """ # gate if not is_update: gate_weight_tensor = get_tensor(state_dict.pop(self.gate_weight_key)) self.gate_weight = self.create_parameter( shape=gate_weight_tensor.shape, dtype="float32", ) self.gate_weight.set_value(gate_weight_tensor) # gate_correction_bias if self.moe_use_gate_correction_bias: gate_correction_bias_tensor = get_tensor( state_dict.pop(self.gate_correction_bias_key)) self.gate_correction_bias = self.create_parameter( shape=gate_correction_bias_tensor.shape, dtype="float32", ) self.gate_correction_bias.set_value(gate_correction_bias_tensor) else: self.gate_correction_bias = None up_gate_proj_weight, down_proj_weight = self.load_gate_state_dict( state_dict) weight1_scale = None weight2_scale = None ffn1_in_scale = None ffn2_in_scale = None if self.moe_quant_type == "w4a8": weight1_scale = [] weight2_scale = [] ffn1_in_scale = [] ffn2_in_scale = [] for j in range(self.num_experts): weight1_scale.append( get_tensor( state_dict.pop( self.ffn1_expert_weight_scale_key.format( self.layer_idx, j)))) weight2_scale.append( get_tensor( state_dict.pop( self.ffn2_expert_weight_scale_key.format( self.layer_idx, j)))) ffn1_in_scale.append( get_tensor( state_dict.pop( self.ffn1_expert_in_scale_key.format( self.layer_idx, j)))) ffn2_in_scale.append( get_tensor( state_dict.pop( self.ffn2_expert_in_scale_key.format( self.layer_idx, j)))) # other weight is with compute_method # different method may have different way to create weights self.compute_method.create_weights(self, self.moe_compute_params, up_gate_proj_weight, down_proj_weight, None, None, weight1_scale, weight2_scale, ffn1_in_scale, ffn2_in_scale) def forward(self, x, **kwargs): """ Defines the forward computation of the moe layer. Args: x (Tensor): Input tensor to the moe layer. Returns: Tensor: Output tensor. """ out = self.compute_method.apply(self, self.moe_compute_params, x) if self.tp_size > 1: from fastdeploy.distributed.communication_op import \ tensor_model_parallel_all_reduce tensor_model_parallel_all_reduce(out) return out