mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 11:56:44 +08:00
09c979f3dd
* new loader support 0.3B * fix weight * support parallel load * support parallel load * fix slice * support moe * delete code * perfect code * perfect code
584 lines
24 KiB
Python
584 lines
24 KiB
Python
"""
|
|
# 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.
|
|
"""
|
|
|
|
from typing import Optional
|
|
|
|
import numpy as np
|
|
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
|
|
from fastdeploy.platforms import current_platform
|
|
from fastdeploy.worker.experts_manager import RedundantExpertManger
|
|
|
|
# TODO(lulinjun): remove this import after supporting all backends
|
|
is_supported_moe_backend = None
|
|
if current_platform.is_cuda():
|
|
from .check_backend_supported import is_supported_moe_backend
|
|
|
|
|
|
def get_moe_method():
|
|
"""
|
|
return moe method based on device platform
|
|
"""
|
|
from fastdeploy.platforms import current_platform
|
|
|
|
if current_platform.is_cuda():
|
|
from .fused_moe_cutlass_backend import CutlassMoEMethod
|
|
|
|
return CutlassMoEMethod(None)
|
|
elif current_platform.is_xpu():
|
|
from .fused_moe_xpu_backend import XPUMoEMethod
|
|
|
|
return XPUMoEMethod(None)
|
|
elif current_platform.is_gcu():
|
|
from fastdeploy.model_executor.layers.backends import GCUFusedMoeMethod
|
|
|
|
return GCUFusedMoeMethod(None)
|
|
elif current_platform.is_maca():
|
|
from fastdeploy.model_executor.layers.backends import (
|
|
MetaxTritonWeightOnlyMoEMethod,
|
|
)
|
|
|
|
return MetaxTritonWeightOnlyMoEMethod(None)
|
|
raise NotImplementedError
|
|
|
|
|
|
class FusedMoE(nn.Layer):
|
|
"""
|
|
FusedMoE is a layer that performs MoE (Mixture of Experts) computation.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
fd_config,
|
|
reduce_results: bool = True,
|
|
moe_intermediate_size: int = -1,
|
|
num_experts: int = -1,
|
|
expert_id_offset: int = 0,
|
|
top_k: int = -1,
|
|
topk_method: str = "",
|
|
topk_group: int = -1,
|
|
n_group: int = -1,
|
|
routed_scaling_factor: float = 1.0,
|
|
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.reduce_results = reduce_results
|
|
self.tp_rank = fd_config.parallel_config.tensor_parallel_rank
|
|
self.tp_size = fd_config.parallel_config.tensor_parallel_size
|
|
self.ep_size = fd_config.parallel_config.expert_parallel_size
|
|
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.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.weight_key_map = weight_key_map
|
|
|
|
self.use_method = envs.FD_MOE_BACKEND.lower()
|
|
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
|
|
|
|
self.gate_correction_bias_key = self.weight_key_map.get("gate_correction_bias_key", None)
|
|
if self.gate_correction_bias_key is not None:
|
|
self.moe_use_gate_correction_bias = True
|
|
else:
|
|
self.moe_use_gate_correction_bias = False
|
|
|
|
# used for deepseek_v3
|
|
self.topk_method = topk_method
|
|
self.topk_group = topk_group
|
|
self.n_group = n_group
|
|
self.routed_scaling_factor = routed_scaling_factor
|
|
|
|
self._dtype = self._helper.get_default_dtype()
|
|
self.weight_dtype = self._dtype
|
|
|
|
moe_quant_config = fd_config.quant_config
|
|
self.moe_quant_config = moe_quant_config
|
|
self.moe_quant_type = None
|
|
if moe_quant_config:
|
|
self.quant_method = moe_quant_config.get_quant_method(self)
|
|
self.moe_quant_type = moe_quant_config.name()
|
|
else:
|
|
self.quant_method = get_moe_method()
|
|
self.redundant_table_manger = None
|
|
if self.ep_size > 1:
|
|
if fd_config.model_config.enable_redundant_experts is True:
|
|
self.redundant_table_manger = RedundantExpertManger(
|
|
n_routed_experts=fd_config.model_config.moe_num_experts,
|
|
num_hidden_layers=fd_config.model_config.num_hidden_layers,
|
|
redundant_experts_num=fd_config.model_config.redundant_experts_num,
|
|
ep_size=self.ep_size,
|
|
)
|
|
self.quant_method.init_ep(self)
|
|
|
|
if fd_config.load_config.dynamic_load_weight:
|
|
# It's for RL to build model
|
|
self.init_moe_weights()
|
|
else:
|
|
self.gate_correction_bias_key = self.weight_key_map.get("gate_correction_bias_key", None)
|
|
if self.gate_correction_bias_key is not None:
|
|
self.gate_correction_bias = self.create_parameter(shape=[1, self.num_experts], dtype="float32")
|
|
if moe_quant_config:
|
|
if (
|
|
moe_quant_config
|
|
and is_supported_moe_backend is not None
|
|
and is_supported_moe_backend(self.quant_method)
|
|
):
|
|
self.quant_method.create_weights(self, weight_loader=self.weight_loader)
|
|
else:
|
|
# w_fp16 a_fp16
|
|
self.quant_method.create_weights(self, weight_loader=self.weight_loader)
|
|
|
|
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 weight_loader(self, param, loaded_weight, expert_id, shard_id: Optional[str] = None):
|
|
from fastdeploy.platforms import current_platform
|
|
|
|
if shard_id is None:
|
|
# 1.gate up fused in disk
|
|
if self.tp_size > 1:
|
|
shard_offsets = [
|
|
# (shard_id, shard_offset, shard_size)
|
|
("gate", 0, self.moe_intermediate_size * self.tp_size),
|
|
("up", self.moe_intermediate_size * self.tp_size, self.moe_intermediate_size * self.tp_size),
|
|
]
|
|
for shard_id, shard_offset, shard_size in shard_offsets:
|
|
loaded_weight_shard = loaded_weight[..., shard_offset : shard_offset + shard_size]
|
|
self.weight_loader(param, loaded_weight_shard, expert_id, shard_id)
|
|
else:
|
|
expert_param = param[expert_id]
|
|
loaded_weight = get_tensor(loaded_weight)
|
|
expert_param.copy_(loaded_weight, False)
|
|
else:
|
|
# 2.gate up splited in disk
|
|
assert shard_id in ["gate", "down", "up"]
|
|
if current_platform.is_cuda():
|
|
SHARD_ID_TO_SHARDED_DIM = {"gate": 1, "down": 0, "up": 1}
|
|
else:
|
|
SHARD_ID_TO_SHARDED_DIM = {"gate": 0, "down": 1, "up": 0}
|
|
self._load_expert_weight(
|
|
param=param,
|
|
expert_id=expert_id,
|
|
shard_dim=SHARD_ID_TO_SHARDED_DIM[shard_id],
|
|
loaded_weight=loaded_weight,
|
|
shard_id=shard_id,
|
|
)
|
|
|
|
def _load_gate_up_weight(self, expert_param, shard_dim, loaded_weight, shard_id):
|
|
tensor_size = expert_param.shape[shard_dim] // 2
|
|
if shard_id == "gate":
|
|
expert_param = expert_param[..., :tensor_size] if shard_dim else expert_param[:tensor_size, ...]
|
|
elif shard_id == "up":
|
|
expert_param = expert_param[..., tensor_size:] if shard_dim else expert_param[tensor_size:, ...]
|
|
|
|
if self.tp_size > 1:
|
|
if isinstance(loaded_weight, np.ndarray):
|
|
size = loaded_weight.shape[-1]
|
|
else:
|
|
size = loaded_weight.get_shape()[-1]
|
|
block_size = size // self.tp_size
|
|
shard_offset = self.tp_rank * block_size
|
|
shard_size = (self.tp_rank + 1) * block_size
|
|
loaded_weight = loaded_weight[..., shard_offset:shard_size]
|
|
|
|
loaded_weight = get_tensor(loaded_weight)
|
|
# To ensure compatibility across backends, apply an extra transpose for GCU and XPU
|
|
if expert_param.shape != loaded_weight.shape:
|
|
loaded_weight = loaded_weight.transpose([1, 0])
|
|
assert expert_param.shape == loaded_weight.shape, (
|
|
f"Attempted to load weight ({loaded_weight.shape}) " f"into parameter ({expert_param.shape})"
|
|
)
|
|
expert_param.copy_(loaded_weight, False)
|
|
|
|
def _load_down_weight(self, expert_param, shard_dim, loaded_weight, shard_id):
|
|
if self.tp_size > 1:
|
|
if isinstance(loaded_weight, np.ndarray):
|
|
size = loaded_weight.shape[shard_dim]
|
|
else:
|
|
size = loaded_weight.get_shape()[shard_dim]
|
|
block_size = size // self.tp_size
|
|
shard_offset = self.tp_rank * block_size
|
|
shard_size = (self.tp_rank + 1) * block_size
|
|
loaded_weight = loaded_weight[shard_offset:shard_size, ...]
|
|
loaded_weight = get_tensor(loaded_weight)
|
|
# To ensure compatibility across backends, apply an extra transpose for GCU and XPU
|
|
if expert_param.shape != loaded_weight.shape:
|
|
loaded_weight = loaded_weight.transpose([1, 0])
|
|
assert expert_param.shape == loaded_weight.shape, (
|
|
f"Attempted to load weight ({loaded_weight.shape}) " f"into parameter ({expert_param.shape})"
|
|
)
|
|
expert_param.copy_(loaded_weight, False)
|
|
|
|
def _load_expert_weight(
|
|
self,
|
|
param,
|
|
expert_id,
|
|
shard_dim,
|
|
loaded_weight,
|
|
shard_id,
|
|
):
|
|
expert_param = param[expert_id]
|
|
if shard_id == "down":
|
|
self._load_down_weight(expert_param, shard_dim, loaded_weight, shard_id)
|
|
elif shard_id in ["gate", "up"]:
|
|
self._load_gate_up_weight(expert_param, shard_dim, loaded_weight, shard_id)
|
|
|
|
@classmethod
|
|
def make_expert_params_mapping(
|
|
cls,
|
|
num_experts: int,
|
|
ckpt_gate_proj_name: Optional[str] = None,
|
|
ckpt_up_proj_name: Optional[str] = None,
|
|
ckpt_down_proj_name: Optional[str] = None,
|
|
ckpt_gate_up_proj_name: Optional[str] = None,
|
|
param_gate_up_proj_name: Optional[str] = None,
|
|
param_down_proj_name: Optional[str] = None,
|
|
ckpt_expert_key_name: str = "experts",
|
|
) -> list[tuple[str, str, int, str]]:
|
|
param_name_maping = []
|
|
|
|
if ckpt_gate_up_proj_name:
|
|
param_name_maping.append((None, ckpt_gate_up_proj_name))
|
|
if ckpt_gate_proj_name:
|
|
param_name_maping.append(("gate", ckpt_gate_proj_name))
|
|
if ckpt_down_proj_name:
|
|
param_name_maping.append(("down", ckpt_down_proj_name))
|
|
if ckpt_up_proj_name:
|
|
param_name_maping.append(("up", ckpt_up_proj_name))
|
|
|
|
return [
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
(
|
|
(
|
|
param_gate_up_proj_name
|
|
if weight_name in [ckpt_gate_proj_name, ckpt_up_proj_name, ckpt_gate_up_proj_name]
|
|
else param_down_proj_name
|
|
),
|
|
f"{ckpt_expert_key_name}.{expert_id}.{weight_name}.",
|
|
expert_id,
|
|
shard_id,
|
|
)
|
|
for expert_id in range(num_experts)
|
|
for shard_id, weight_name in param_name_maping
|
|
]
|
|
|
|
def init_moe_weights(self):
|
|
"""
|
|
Initialize the weight shapes and parameters for the MoE layer.
|
|
Combines weight shape initialization and parameter creation into a single function.
|
|
"""
|
|
# Initialize weight shapes
|
|
gate_correction_bias_shape = [1, self.num_experts]
|
|
|
|
if self.fd_config.model_config.moe_use_aux_free:
|
|
self.gate_correction_bias = self.create_parameter(
|
|
shape=gate_correction_bias_shape,
|
|
dtype="float32",
|
|
)
|
|
up_gate_proj_output_dim = self.moe_intermediate_size * 2
|
|
if self.moe_quant_type in ["block_wise_fp8", "wint8"]:
|
|
up_gate_proj_weight_shape = [
|
|
self.num_local_experts,
|
|
up_gate_proj_output_dim,
|
|
self.hidden_size,
|
|
]
|
|
down_proj_weight_shape = [
|
|
self.num_local_experts,
|
|
self.hidden_size,
|
|
self.moe_intermediate_size,
|
|
]
|
|
else:
|
|
up_gate_proj_weight_shape = [
|
|
self.num_local_experts,
|
|
self.hidden_size,
|
|
up_gate_proj_output_dim,
|
|
]
|
|
down_proj_weight_shape = [
|
|
self.num_local_experts,
|
|
self.moe_intermediate_size,
|
|
self.hidden_size,
|
|
]
|
|
|
|
# Create parameters
|
|
if self.moe_quant_type == "block_wise_fp8":
|
|
# (TODO:gaoziyuan)
|
|
self.weight_dtype = "float8_e4m3fn"
|
|
self.init_block_wise_fp8_scale()
|
|
elif self.moe_quant_type == "wint8":
|
|
self.weight_dtype = "int8"
|
|
self.init_weight_only_scale()
|
|
|
|
# up_gate_proj parameters
|
|
self.up_gate_proj_weight = self.create_parameter(
|
|
shape=up_gate_proj_weight_shape,
|
|
dtype=self.weight_dtype,
|
|
default_initializer=paddle.nn.initializer.Constant(0),
|
|
)
|
|
# down_proj parameters
|
|
self.down_proj_weight = self.create_parameter(
|
|
shape=down_proj_weight_shape,
|
|
dtype=self.weight_dtype,
|
|
default_initializer=paddle.nn.initializer.Constant(0),
|
|
)
|
|
|
|
def init_weight_only_scale(self):
|
|
"""
|
|
Initialize the weight scale.
|
|
"""
|
|
self.up_gate_proj_weight_scale = self.create_parameter(
|
|
shape=[self.num_local_experts, self.moe_intermediate_size * 2],
|
|
dtype=self._dtype,
|
|
)
|
|
self.down_proj_weight_scale = self.create_parameter(
|
|
shape=[self.num_local_experts, self.hidden_size],
|
|
dtype=self._dtype,
|
|
)
|
|
|
|
def init_block_wise_fp8_scale(self):
|
|
"""
|
|
Initialize the weight scale.
|
|
"""
|
|
self.up_gate_proj_weight_scale = self.create_parameter(
|
|
shape=[self.num_local_experts, self.moe_intermediate_size * 2 // 128, self.hidden_size // 128],
|
|
dtype="float32",
|
|
is_bias=False,
|
|
)
|
|
self.down_proj_weight_scale = self.create_parameter(
|
|
shape=[self.num_local_experts, self.hidden_size // 128, self.moe_intermediate_size // 128],
|
|
dtype="float32",
|
|
is_bias=False,
|
|
)
|
|
|
|
def load_experts_weight(
|
|
self,
|
|
state_dict: dict,
|
|
up_gate_proj_expert_weight_key: str,
|
|
down_proj_expert_weight_key: str,
|
|
):
|
|
"""
|
|
Load experts weight from state_dict.
|
|
Args:
|
|
state_dict (dict): The state_dict of model.
|
|
up_gate_proj_expert_weight_key (str): The key of up_gate_proj expert weight.
|
|
down_proj_expert_weight_key (str): The key of down_proj expert weight.
|
|
"""
|
|
logical_expert_ids = [
|
|
i
|
|
for i in range(
|
|
self.expert_id_offset,
|
|
self.expert_id_offset + self.num_local_experts,
|
|
)
|
|
]
|
|
ep_rank_to_expert_id_list = [i for i in range(self.num_experts)]
|
|
if self.redundant_table_manger is not None:
|
|
(
|
|
ep_rank_to_expert_id_list,
|
|
expert_id_to_ep_rank_array,
|
|
expert_in_rank_num_list,
|
|
tokens_per_expert_stats_list,
|
|
) = self.redundant_table_manger.get_ep_rank_to_expert_id_list_by_layer(self.layer_idx)
|
|
logical_expert_ids = ep_rank_to_expert_id_list[
|
|
self.expert_id_offset : self.expert_id_offset + self.num_local_experts
|
|
]
|
|
up_gate_proj_weights = []
|
|
down_proj_weights = []
|
|
is_ffn_merged = up_gate_proj_expert_weight_key.format(self.expert_id_offset) in state_dict
|
|
if is_ffn_merged:
|
|
for expert_idx in logical_expert_ids:
|
|
down_proj_expert_weight_key_name = down_proj_expert_weight_key.format(expert_idx)
|
|
up_gate_proj_expert_weight_key_name = up_gate_proj_expert_weight_key.format(expert_idx)
|
|
up_gate_proj_weights.append(
|
|
get_tensor(
|
|
(
|
|
state_dict.pop(up_gate_proj_expert_weight_key_name)
|
|
if up_gate_proj_expert_weight_key_name in state_dict
|
|
else up_gate_proj_expert_weight_key_name
|
|
),
|
|
self.fd_config.model_config.model,
|
|
)
|
|
)
|
|
down_proj_weights.append(
|
|
get_tensor(
|
|
(
|
|
state_dict.pop(down_proj_expert_weight_key_name)
|
|
if down_proj_expert_weight_key_name in state_dict
|
|
else down_proj_expert_weight_key_name
|
|
),
|
|
self.fd_config.model_config.model,
|
|
)
|
|
)
|
|
else:
|
|
gate_expert_weight_key = up_gate_proj_expert_weight_key.replace("up_gate_proj", "gate_proj")
|
|
up_expert_weight_key = up_gate_proj_expert_weight_key.replace("up_gate_proj", "up_proj")
|
|
for expert_idx in logical_expert_ids:
|
|
gate_expert_weight_key_name = gate_expert_weight_key.format(expert_idx)
|
|
up_expert_weight_key_name = up_expert_weight_key.format(expert_idx)
|
|
down_proj_expert_weight_key_name = down_proj_expert_weight_key.format(expert_idx)
|
|
gate = get_tensor(
|
|
(
|
|
state_dict.pop(gate_expert_weight_key_name)
|
|
if gate_expert_weight_key_name in state_dict
|
|
else gate_expert_weight_key_name
|
|
),
|
|
self.fd_config.model_config.model,
|
|
)
|
|
up = get_tensor(
|
|
(
|
|
state_dict.pop(up_expert_weight_key_name)
|
|
if up_expert_weight_key_name in state_dict
|
|
else up_expert_weight_key_name
|
|
),
|
|
self.fd_config.model_config.model,
|
|
)
|
|
up_gate_proj_weights.append(paddle.concat([gate, up], axis=-1))
|
|
down_proj_weights.append(
|
|
get_tensor(
|
|
(
|
|
state_dict.pop(down_proj_expert_weight_key_name)
|
|
if down_proj_expert_weight_key_name in state_dict
|
|
else down_proj_expert_weight_key_name
|
|
),
|
|
self.fd_config.model_config.model,
|
|
)
|
|
)
|
|
return up_gate_proj_weights, down_proj_weights, logical_expert_ids, ep_rank_to_expert_id_list
|
|
|
|
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:
|
|
- up_gate_proj_weights: List of tensors for first FFN layer weights
|
|
- down_proj_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.
|
|
"""
|
|
up_gate_proj_expert_weight_key = self.weight_key_map.get("up_gate_proj_expert_weight_key", None)
|
|
down_proj_expert_weight_key = self.weight_key_map.get("down_proj_expert_weight_key", None)
|
|
assert up_gate_proj_expert_weight_key is not None, "up_gate_proj_expert_weight_key should not be none."
|
|
assert down_proj_expert_weight_key is not None, "down_proj_expert_weight_key should not be none."
|
|
|
|
up_gate_proj_weights, down_proj_weights, logical_expert_ids, _ = self.load_experts_weight(
|
|
state_dict,
|
|
up_gate_proj_expert_weight_key,
|
|
down_proj_expert_weight_key,
|
|
)
|
|
assert (
|
|
len(up_gate_proj_weights) == self.num_local_experts
|
|
), "up_gate_proj_weights length should be equal to num_local_experts."
|
|
assert (
|
|
len(down_proj_weights) == self.num_local_experts
|
|
), "down_proj_weights length should be equal to num_local_experts."
|
|
|
|
return up_gate_proj_weights, down_proj_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, is_rearrange: bool = False):
|
|
"""
|
|
load_state_dict function.
|
|
"""
|
|
if not is_rearrange:
|
|
if self.moe_use_gate_correction_bias:
|
|
gate_correction_bias_tensor = self.extract_gate_correction_bias(
|
|
self.gate_correction_bias_key, state_dict
|
|
)
|
|
if self.gate_correction_bias.shape != gate_correction_bias_tensor.shape:
|
|
gate_correction_bias_tensor = gate_correction_bias_tensor.reshape(self.gate_correction_bias.shape)
|
|
self.gate_correction_bias.set_value(gate_correction_bias_tensor)
|
|
else:
|
|
self.gate_correction_bias = None
|
|
else:
|
|
self.gate_correction_bias = None
|
|
|
|
if is_supported_moe_backend is not None and is_supported_moe_backend(self.quant_method):
|
|
if self.fd_config.model_config.is_quantized:
|
|
if getattr(self.fd_config.quant_config, "is_permuted", True):
|
|
self.quant_method.process_prequanted_weights(self, state_dict)
|
|
else:
|
|
self.quant_method.process_loaded_weights(self, state_dict)
|
|
else:
|
|
self.quant_method.process_loaded_weights(self, state_dict)
|
|
else:
|
|
if self.fd_config.model_config.is_quantized:
|
|
if getattr(self.fd_config.quant_config, "is_permuted", True):
|
|
self.quant_method.process_prequanted_weights(self, state_dict)
|
|
else:
|
|
self.quant_method.create_weights(self, state_dict)
|
|
else:
|
|
if self.moe_quant_config:
|
|
self.quant_method.create_weights(self, state_dict)
|
|
else:
|
|
# w_fp16 a_fp16
|
|
self.quant_method.process_loaded_weights(self, state_dict)
|
|
|
|
def forward(self, x: paddle.Tensor, gate: nn.Layer):
|
|
"""
|
|
Defines the forward computation of the moe layer.
|
|
|
|
Args:
|
|
x (Tensor): Input tensor to the moe layer.
|
|
|
|
Returns:
|
|
Tensor: Output tensor.s
|
|
|
|
"""
|
|
out = self.quant_method.apply(self, x, gate)
|
|
return out
|