mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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Sync v2.0 version of code to github repo
This commit is contained in:
Jiang-Jia-Jun
@@ -1,5 +1,5 @@
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"""
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# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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# 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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@@ -14,34 +14,13 @@
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# limitations under the License.
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"""
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from dataclasses import dataclass
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import paddle
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from paddle import nn
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from paddlenlp.utils.log import logger
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from paddleformers.utils.log import logger
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from fastdeploy import envs
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from fastdeploy.model_executor.layers.utils import get_tensor
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from .cutlass_fused_moe import CutlassFusedMoeMethod
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@dataclass
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class MoEComputeParams:
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"""
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some params for computing MoE.
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it is given to different compute methods.
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"""
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global_num_experts: int = -1
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top_k: int = -1
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hidden_size: int = -1
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num_local_experts: int = -1
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moe_intermediate_size: int = -1
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tp_size: int = -1
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ep_size: int = -1
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dp_size: int = -1
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moe_quant_type: str = ""
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class FusedMoE(nn.Layer):
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"""
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@@ -50,174 +29,195 @@ class FusedMoE(nn.Layer):
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def __init__(
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self,
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llm_config,
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fd_config,
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moe_intermediate_size: int = -1,
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num_experts: int = -1,
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expert_id_offset: int = 0,
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top_k: int = -1,
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moe_use_gate_correction_bias: bool = False,
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moe_quant_type: str = "weight_only_int4",
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layer_idx: int = -1,
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gate_weight_key=None,
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gate_correction_bias_key=None,
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ffn1_expert_weight_key=None,
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ffn2_expert_weight_key=None,
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moe_ffn1_bias_keys=None,
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moe_ffn2_bias_keys=None,
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moe_ffn1_weight_scale_keys=None,
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moe_ffn2_weight_scale_keys=None,
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moe_ffn1_in_scale_keys=None,
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moe_ffn2_in_scale_keys=None,
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moe_tag: str = "",
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weight_key_map: dict = {},
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):
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"""
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Initialize the Moe layer with given parameters.
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Args:
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llm_config (LLMConfig): Arguments related to inference, containing
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fd_config (FDConfig): Arguments related to inference, containing
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attributes such as weight_dtype, act_dtype, mp_size, hidden_size, head_dim,
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num_attention_heads, and ffn_hidden_size.
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"""
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super().__init__()
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self.llm_config = llm_config
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self.fd_config = fd_config
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self.layer_idx = layer_idx
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self.tp_size = llm_config.parallel_config.mp_size
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self.ep_size = llm_config.parallel_config.ep_size
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self.moe_use_gate_correction_bias = moe_use_gate_correction_bias
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self.tp_size = fd_config.parallel_config.tensor_parallel_degree
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self.ep_size = fd_config.parallel_config.expert_parallel_degree
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self.ep_rank = fd_config.parallel_config.expert_parallel_rank
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assert (self.tp_size >= 1 and self.ep_size == 1) or \
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(self.tp_size == 1 and self.ep_size > 1), \
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'MoE only support parallelism on TP or EP dimension.'
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self.hidden_size = fd_config.model_config.hidden_size
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self.moe_config = fd_config.moe_config
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self.hidden_size = llm_config.model_config.hidden_size
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self.moe_config = llm_config.moe_config
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self.use_offline_quant = llm_config.tmp_config.use_offline_quant
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moe_tag = self.llm_config.moe_config.moe_tag
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logger.info(f"{moe_tag}MoE is running in {moe_quant_type} mode")
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self.moe_quant_type = moe_quant_type
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self.num_experts = num_experts
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self.num_local_experts = self.num_experts // self.ep_size
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logger.info(f'''MoE config is num_experts:{num_experts},
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top_k:{top_k},
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hidden_size:{self.hidden_size},
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moe_intermediate_size:{moe_intermediate_size}''')
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logger.info(
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f"MoE is running on moe_quant_type: {self.moe_quant_type}, ep:{self.ep_size}, tp:{self.tp_size} mode"
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)
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self.moe_intermediate_size = moe_intermediate_size // self.tp_size
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self.gate_weight_key = gate_weight_key
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self.gate_correction_bias_key = gate_correction_bias_key
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self.top_k = top_k
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self.hidden_size = self.hidden_size
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self.moe_intermediate_size = moe_intermediate_size // self.tp_size
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self.weight_key_map = weight_key_map
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self.ffn1_expert_weight_key = ffn1_expert_weight_key
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self.ffn2_expert_weight_key = ffn2_expert_weight_key
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self.ffn1_bias_key = moe_ffn1_bias_keys
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self.ffn2_bias_key = moe_ffn2_bias_keys
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self.use_method = envs.FD_MOE_BACKEND.lower()
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self.gate_correction_bias = None
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self.moe_tag = moe_tag
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if self.moe_quant_type == "w4a8":
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# below keys are only used in MoE W4A8!
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self.ffn1_expert_weight_scale_key = moe_ffn1_weight_scale_keys
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self.ffn2_expert_weight_scale_key = moe_ffn2_weight_scale_keys
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self.ffn1_expert_in_scale_key = moe_ffn1_in_scale_keys
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self.ffn2_expert_in_scale_key = moe_ffn2_in_scale_keys
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if self.ep_size > 1:
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expert_id_offset = expert_id_offset + self.ep_rank * self.num_local_experts
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self.compute_method = CutlassFusedMoeMethod()
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self.expert_id_offset = expert_id_offset
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self.moe_compute_params = MoEComputeParams()
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self.moe_compute_params.global_num_experts = self.num_experts
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self.moe_compute_params.top_k = top_k
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self.moe_compute_params.hidden_size = self.hidden_size
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self.moe_compute_params.num_local_experts = self.num_local_experts
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self.moe_compute_params.moe_quant_type = self.moe_quant_type
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self.moe_compute_params.moe_intermediate_size = self.moe_intermediate_size
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self.moe_compute_params.ep_size = self.ep_size
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self.moe_compute_params.tp_size = self.tp_size
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if fd_config.quant_config:
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self.quant_method = fd_config.quant_config.get_quant_method(self)
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else:
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# now, no quant method(w_fp16 a_fp16) can't get from quant_config, we will optimize it in future
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from .fused_moe_cutlass_backend import CutlassMoEMethod
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self.quant_method = CutlassMoEMethod(None)
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def load_gate_state_dict(self, state_dict):
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if self.ep_size > 1:
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self.quant_method.init_ep(self)
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logger.info(
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f"{moe_tag}MoE config is {num_experts=}[{expert_id_offset}, {expert_id_offset+self.num_local_experts}), \
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{top_k=}, hidden_size={self.hidden_size}, {moe_intermediate_size=}, \
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, ep_size={self.ep_size}, \
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tp_size={self.tp_size}.")
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def load_experts_weight(self, state_dict: dict,
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ffn1_expert_weight_key: str,
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ffn2_expert_weight_key: str):
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"""
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load_gate_state_dict function.
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Load experts weight from state_dict.
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Args:
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state_dict (dict): The state_dict of model.
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ffn1_expert_weight_key (str): The key of ffn1 expert weight.
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ffn2_expert_weight_key (str): The key of ffn2 expert weight.
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"""
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up_gate_proj_weight = []
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up_gate_proj_weight_scale = []
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down_proj_weight = []
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down_proj_weight_scale = []
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for j in range(self.num_experts):
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up_gate_proj_weight.append(
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get_tensor(
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state_dict.pop(self.ffn1_expert_weight_key.format(j))))
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down_proj_weight.append(
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get_tensor(
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state_dict.pop(self.ffn2_expert_weight_key.format(j))))
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return up_gate_proj_weight, down_proj_weight
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ffn1_weights = []
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ffn2_weights = []
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is_ffn_merged = ffn1_expert_weight_key.format(
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self.expert_id_offset) in state_dict
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if is_ffn_merged:
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for i in range(self.num_local_experts):
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expert_idx = self.expert_id_offset + i
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ffn1_weights.append(
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get_tensor(
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state_dict.pop(
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ffn1_expert_weight_key.format(expert_idx))))
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ffn2_weights.append(
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get_tensor(
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state_dict.pop(
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ffn2_expert_weight_key.format(expert_idx))))
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else:
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gate_expert_weight_key = ffn1_expert_weight_key.replace(
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"up_gate_proj", "gate_proj")
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up_expert_weight_key = ffn1_expert_weight_key.replace(
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"up_gate_proj", "up_proj")
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for j in range(self.num_local_experts):
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expert_idx = self.expert_id_offset + j
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gate = get_tensor(
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state_dict.pop(gate_expert_weight_key.format(expert_idx)))
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up = get_tensor(
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state_dict.pop(up_expert_weight_key.format(expert_idx)))
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ffn1_weights.append(paddle.concat([gate, up], axis=-1))
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ffn2_weights.append(
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get_tensor(
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state_dict.pop(
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ffn2_expert_weight_key.format(expert_idx))))
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return ffn1_weights, ffn2_weights
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def load_state_dict(self, state_dict, is_update: bool = False):
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def extract_moe_ffn_weights(self, state_dict: dict):
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"""
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Extract MoE FFN weights from state dict based on weight key mapping.
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Args:
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state_dict (dict): Model state dictionary containing the weights.
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Returns:
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tuple: A tuple containing two lists:
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- ffn1_weights: List of tensors for first FFN layer weights
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- ffn2_weights: List of tensors for second FFN layer weights
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Raises:
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AssertionError: If required weight keys are missing or number of weights
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doesn't match number of local experts.
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"""
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ffn1_expert_weight_key = self.weight_key_map.get(
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"ffn1_expert_weight_key", None)
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ffn2_expert_weight_key = self.weight_key_map.get(
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"ffn2_expert_weight_key", None)
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assert ffn1_expert_weight_key is not None, "ffn1_expert_weight_key should not be none."
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assert ffn2_expert_weight_key is not None, "ffn2_expert_weight_key should not be none."
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ffn1_weights, ffn2_weights = self.load_experts_weight(
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state_dict, ffn1_expert_weight_key, ffn2_expert_weight_key)
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assert len(
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ffn1_weights
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) == self.num_local_experts, "ffn1_weights length should be equal to num_local_experts."
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assert len(
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ffn2_weights
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) == self.num_local_experts, "ffn2_weights length should be equal to num_local_experts."
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return ffn1_weights, ffn2_weights
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def extract_gate_correction_bias(self, gate_correction_bias_key,
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state_dict):
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"""
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extract_gate_correction_bias function.
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"""
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gate_correction_bias_tensor = get_tensor(
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state_dict.pop(gate_correction_bias_key)).astype("float32")
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return gate_correction_bias_tensor
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def load_state_dict(self, state_dict):
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"""
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load_state_dict function.
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"""
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# gate
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if not is_update:
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gate_weight_tensor = get_tensor(state_dict.pop(self.gate_weight_key))
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self.gate_weight = self.create_parameter(
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shape=gate_weight_tensor.shape,
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dtype="float32",
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)
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self.gate_weight.set_value(gate_weight_tensor)
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# gate_correction_bias
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self.gate_correction_bias_key = self.weight_key_map.get(
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"gate_correction_bias_key", None)
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if self.gate_correction_bias_key is not None and self.gate_correction_bias_key in state_dict:
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self.moe_use_gate_correction_bias = True
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else:
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self.moe_use_gate_correction_bias = False
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if self.moe_use_gate_correction_bias:
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gate_correction_bias_tensor = get_tensor(
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state_dict.pop(self.gate_correction_bias_key))
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gate_correction_bias_tensor = self.extract_gate_correction_bias(
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self.gate_correction_bias_key, state_dict)
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self.gate_correction_bias = self.create_parameter(
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shape=gate_correction_bias_tensor.shape,
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dtype="float32",
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)
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self.gate_correction_bias.set_value(gate_correction_bias_tensor)
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gate_weight_key = self.weight_key_map.get("gate_weight_key", None)
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assert gate_weight_key is not None, "gate_weight_key should not be None, please check model checkpoints"
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gate_weight_tensor = get_tensor(state_dict.pop(gate_weight_key))
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self.gate_weight = self.create_parameter(
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shape=gate_weight_tensor.shape,
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dtype="float32",
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)
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self.gate_weight.set_value(gate_weight_tensor.astype("float32"))
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if self.fd_config.model_config.is_quantized:
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self.quant_method.process_prequanted_weights(self, state_dict)
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else:
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self.gate_correction_bias = None
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self.quant_method.create_weights(self, state_dict)
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up_gate_proj_weight, down_proj_weight = self.load_gate_state_dict(
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state_dict)
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weight1_scale = None
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weight2_scale = None
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ffn1_in_scale = None
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ffn2_in_scale = None
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if self.moe_quant_type == "w4a8":
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weight1_scale = []
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weight2_scale = []
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ffn1_in_scale = []
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ffn2_in_scale = []
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for j in range(self.num_experts):
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weight1_scale.append(
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get_tensor(
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state_dict.pop(
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self.ffn1_expert_weight_scale_key.format(
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self.layer_idx, j))))
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weight2_scale.append(
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get_tensor(
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state_dict.pop(
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self.ffn2_expert_weight_scale_key.format(
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self.layer_idx, j))))
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ffn1_in_scale.append(
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get_tensor(
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state_dict.pop(
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self.ffn1_expert_in_scale_key.format(
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self.layer_idx, j))))
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ffn2_in_scale.append(
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get_tensor(
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state_dict.pop(
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self.ffn2_expert_in_scale_key.format(
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self.layer_idx, j))))
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# other weight is with compute_method
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# different method may have different way to create weights
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self.compute_method.create_weights(self, self.moe_compute_params,
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up_gate_proj_weight,
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down_proj_weight, None, None,
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weight1_scale, weight2_scale,
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ffn1_in_scale, ffn2_in_scale)
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def forward(self, x, **kwargs):
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def forward(self, x: paddle.Tensor):
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"""
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Defines the forward computation of the moe layer.
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@@ -225,13 +225,9 @@ class FusedMoE(nn.Layer):
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x (Tensor): Input tensor to the moe layer.
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Returns:
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Tensor: Output tensor.
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Tensor: Output tensor.s
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"""
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out = self.compute_method.apply(self, self.moe_compute_params, x)
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if self.tp_size > 1:
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from fastdeploy.distributed.communication_op import \
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tensor_model_parallel_all_reduce
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tensor_model_parallel_all_reduce(out)
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gate_out = paddle.matmul(x.cast("float32"), self.gate_weight)
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out = self.quant_method.apply(self, x, gate_out)
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return out
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