""" # 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. """ import paddle from paddle import nn from paddleformers.utils.log import logger from fastdeploy import envs from fastdeploy.model_executor.layers.utils import get_tensor class FusedMoE(nn.Layer): """ FusedMoE is a layer that performs MoE (Mixture of Experts) computation. """ def __init__( self, fd_config, moe_intermediate_size: int = -1, num_experts: int = -1, expert_id_offset: int = 0, top_k: int = -1, layer_idx: int = -1, moe_tag: str = "", weight_key_map: dict = {}, ): """ Initialize the Moe layer with given parameters. Args: fd_config (FDConfig): 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.fd_config = fd_config self.layer_idx = layer_idx self.tp_size = fd_config.parallel_config.tensor_parallel_degree self.ep_size = fd_config.parallel_config.expert_parallel_degree self.ep_rank = fd_config.parallel_config.expert_parallel_rank assert (self.tp_size >= 1 and self.ep_size == 1) or \ (self.tp_size == 1 and self.ep_size > 1), \ 'MoE only support parallelism on TP or EP dimension.' self.hidden_size = fd_config.model_config.hidden_size self.moe_config = fd_config.moe_config self.num_experts = num_experts self.num_local_experts = self.num_experts // self.ep_size self.moe_intermediate_size = moe_intermediate_size // self.tp_size self.top_k = top_k self.hidden_size = self.hidden_size self.moe_intermediate_size = moe_intermediate_size // self.tp_size self.weight_key_map = weight_key_map self.use_method = envs.FD_MOE_BACKEND.lower() self.gate_correction_bias = None self.moe_tag = moe_tag if self.ep_size > 1: expert_id_offset = expert_id_offset + self.ep_rank * self.num_local_experts self.expert_id_offset = expert_id_offset if fd_config.quant_config: self.quant_method = fd_config.quant_config.get_quant_method(self) else: # now, no quant method(w_fp16 a_fp16) can't get from quant_config, we will optimize it in future from .fused_moe_cutlass_backend import CutlassMoEMethod self.quant_method = CutlassMoEMethod(None) if self.ep_size > 1: self.quant_method.init_ep(self) logger.info( f"{moe_tag}MoE config is {num_experts=}[{expert_id_offset}, {expert_id_offset+self.num_local_experts}), \ {top_k=}, hidden_size={self.hidden_size}, {moe_intermediate_size=}, \ , ep_size={self.ep_size}, \ tp_size={self.tp_size}.") def load_experts_weight(self, state_dict: dict, ffn1_expert_weight_key: str, ffn2_expert_weight_key: str): """ Load experts weight from state_dict. Args: state_dict (dict): The state_dict of model. ffn1_expert_weight_key (str): The key of ffn1 expert weight. ffn2_expert_weight_key (str): The key of ffn2 expert weight. """ ffn1_weights = [] ffn2_weights = [] is_ffn_merged = ffn1_expert_weight_key.format( self.expert_id_offset) in state_dict if is_ffn_merged: for i in range(self.num_local_experts): expert_idx = self.expert_id_offset + i ffn1_weights.append( get_tensor( state_dict.pop( ffn1_expert_weight_key.format(expert_idx)))) ffn2_weights.append( get_tensor( state_dict.pop( ffn2_expert_weight_key.format(expert_idx)))) else: gate_expert_weight_key = ffn1_expert_weight_key.replace( "up_gate_proj", "gate_proj") up_expert_weight_key = ffn1_expert_weight_key.replace( "up_gate_proj", "up_proj") for j in range(self.num_local_experts): expert_idx = self.expert_id_offset + j gate = get_tensor( state_dict.pop(gate_expert_weight_key.format(expert_idx))) up = get_tensor( state_dict.pop(up_expert_weight_key.format(expert_idx))) ffn1_weights.append(paddle.concat([gate, up], axis=-1)) ffn2_weights.append( get_tensor( state_dict.pop( ffn2_expert_weight_key.format(expert_idx)))) return ffn1_weights, ffn2_weights def extract_moe_ffn_weights(self, state_dict: dict): """ Extract MoE FFN weights from state dict based on weight key mapping. Args: state_dict (dict): Model state dictionary containing the weights. Returns: tuple: A tuple containing two lists: - ffn1_weights: List of tensors for first FFN layer weights - ffn2_weights: List of tensors for second FFN layer weights Raises: AssertionError: If required weight keys are missing or number of weights doesn't match number of local experts. """ ffn1_expert_weight_key = self.weight_key_map.get( "ffn1_expert_weight_key", None) ffn2_expert_weight_key = self.weight_key_map.get( "ffn2_expert_weight_key", None) assert ffn1_expert_weight_key is not None, "ffn1_expert_weight_key should not be none." assert ffn2_expert_weight_key is not None, "ffn2_expert_weight_key should not be none." ffn1_weights, ffn2_weights = self.load_experts_weight( state_dict, ffn1_expert_weight_key, ffn2_expert_weight_key) assert len( ffn1_weights ) == self.num_local_experts, "ffn1_weights length should be equal to num_local_experts." assert len( ffn2_weights ) == self.num_local_experts, "ffn2_weights length should be equal to num_local_experts." return ffn1_weights, ffn2_weights def extract_gate_correction_bias(self, gate_correction_bias_key, state_dict): """ extract_gate_correction_bias function. """ gate_correction_bias_tensor = get_tensor( state_dict.pop(gate_correction_bias_key)).astype("float32") return gate_correction_bias_tensor def load_state_dict(self, state_dict): """ load_state_dict function. """ self.gate_correction_bias_key = self.weight_key_map.get( "gate_correction_bias_key", None) if self.gate_correction_bias_key is not None and self.gate_correction_bias_key in state_dict: self.moe_use_gate_correction_bias = True else: self.moe_use_gate_correction_bias = False if self.moe_use_gate_correction_bias: gate_correction_bias_tensor = self.extract_gate_correction_bias( self.gate_correction_bias_key, state_dict) 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) gate_weight_key = self.weight_key_map.get("gate_weight_key", None) assert gate_weight_key is not None, "gate_weight_key should not be None, please check model checkpoints" gate_weight_tensor = get_tensor(state_dict.pop(gate_weight_key)) self.gate_weight = self.create_parameter( shape=gate_weight_tensor.shape, dtype="float32", ) self.gate_weight.set_value(gate_weight_tensor.astype("float32")) if self.fd_config.model_config.is_quantized: self.quant_method.process_prequanted_weights(self, state_dict) else: self.quant_method.create_weights(self, state_dict) def forward(self, x: paddle.Tensor): """ Defines the forward computation of the moe layer. Args: x (Tensor): Input tensor to the moe layer. Returns: Tensor: Output tensor.s """ gate_out = paddle.matmul(x.cast("float32"), self.gate_weight) out = self.quant_method.apply(self, x, gate_out) return out