mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 00:33:03 +08:00
418 lines
15 KiB
Python
418 lines
15 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 __future__ import annotations
|
|
|
|
from functools import partial
|
|
from typing import Dict, Union
|
|
|
|
import numpy as np
|
|
import paddle
|
|
from paddle import nn
|
|
from paddleformers.transformers import PretrainedModel
|
|
from paddleformers.utils.log import logger
|
|
|
|
from fastdeploy.config import FDConfig, ModelConfig
|
|
from fastdeploy.model_executor.layers.lm_head import ParallelLMHead
|
|
from fastdeploy.model_executor.layers.normalization import RMSNorm
|
|
from fastdeploy.model_executor.models.ernie4_5_moe import Ernie4_5_DecoderLayer
|
|
from fastdeploy.model_executor.models.model_base import ModelForCasualLM
|
|
from fastdeploy.worker.forward_meta import ForwardMeta
|
|
|
|
|
|
class Ernie4_5_MTPPretrainedModel(PretrainedModel):
|
|
"""
|
|
Ernie4_5_MTPPretrainedModel
|
|
"""
|
|
|
|
config_class = FDConfig
|
|
|
|
def _init_weight(self, layer):
|
|
"""
|
|
_init_weight
|
|
"""
|
|
return None
|
|
|
|
@classmethod
|
|
def _get_tensor_parallel_mappings(cls, config: ModelConfig, is_split=True):
|
|
"""
|
|
get_tensor_parallel_mappings
|
|
"""
|
|
logger.info("erine inference model _get_tensor_parallel_mappings")
|
|
|
|
from paddleformers.transformers.conversion_utils import \
|
|
split_or_merge_func
|
|
|
|
fn = split_or_merge_func(
|
|
is_split=is_split,
|
|
tensor_parallel_degree=config.tensor_parallel_degree,
|
|
tensor_parallel_rank=config.tensor_parallel_rank,
|
|
num_attention_heads=config.num_attention_heads,
|
|
)
|
|
|
|
def gqa_qkv_split_func(
|
|
weight,
|
|
tensor_parallel_degree,
|
|
tensor_parallel_rank,
|
|
num_attention_heads,
|
|
num_key_value_heads,
|
|
head_dim,
|
|
):
|
|
|
|
def get_shape(tensor):
|
|
return (tensor.get_shape()
|
|
if hasattr(tensor, "get_shape") else tensor.shape)
|
|
|
|
def slice_tensor(tensor, start, end):
|
|
shape = get_shape(tensor)
|
|
if len(shape) == 1:
|
|
return tensor[start:end]
|
|
else:
|
|
return tensor[..., start:end]
|
|
|
|
q_end = num_attention_heads * head_dim
|
|
k_end = q_end + num_key_value_heads * head_dim
|
|
v_end = k_end + num_key_value_heads * head_dim
|
|
|
|
q = slice_tensor(weight, 0, q_end)
|
|
k = slice_tensor(weight, q_end, k_end)
|
|
v = slice_tensor(weight, k_end, v_end)
|
|
|
|
def split_tensor(tensor, degree):
|
|
shape = get_shape(tensor)
|
|
size = shape[-1]
|
|
block_size = size // degree
|
|
if hasattr(tensor, "get_shape"):
|
|
return [
|
|
slice_tensor(tensor, i * block_size,
|
|
(i + 1) * block_size)
|
|
for i in range(degree)
|
|
]
|
|
else:
|
|
return np.split(tensor, degree, axis=-1)
|
|
|
|
q_list = split_tensor(q, tensor_parallel_degree)
|
|
k_list = split_tensor(k, tensor_parallel_degree)
|
|
v_list = split_tensor(v, tensor_parallel_degree)
|
|
|
|
if tensor_parallel_rank is None:
|
|
return [
|
|
np.concatenate([q_i, k_i, v_i], axis=-1)
|
|
for q_i, k_i, v_i in zip(q_list, k_list, v_list)
|
|
]
|
|
else:
|
|
return np.concatenate(
|
|
[
|
|
q_list[tensor_parallel_rank],
|
|
k_list[tensor_parallel_rank],
|
|
v_list[tensor_parallel_rank],
|
|
],
|
|
axis=-1,
|
|
)
|
|
|
|
def gqa_qkv_merge_func(weight_list, num_attention_heads,
|
|
num_key_value_heads, head_dim):
|
|
tensor_parallel_degree = len(weight_list)
|
|
num_attention_heads = num_attention_heads // tensor_parallel_degree
|
|
num_key_value_heads = num_key_value_heads // tensor_parallel_degree
|
|
|
|
is_paddle_tensor = not isinstance(weight_list[0], np.ndarray)
|
|
|
|
def get_shape(tensor):
|
|
return (tensor.get_shape()
|
|
if hasattr(tensor, "get_shape") else tensor.shape)
|
|
|
|
def slice_tensor(tensor, start, end):
|
|
if len(get_shape(tensor)) == 1:
|
|
return tensor[start:end]
|
|
else:
|
|
return tensor[..., start:end]
|
|
|
|
q_list, k_list, v_list = [], [], []
|
|
|
|
for weight in weight_list:
|
|
q_end = num_attention_heads * head_dim
|
|
k_end = q_end + num_key_value_heads * head_dim
|
|
v_end = k_end + num_key_value_heads * head_dim
|
|
|
|
q = slice_tensor(weight, 0, q_end)
|
|
k = slice_tensor(weight, q_end, k_end)
|
|
v = slice_tensor(weight, k_end, v_end)
|
|
|
|
q_list.append(q)
|
|
k_list.append(k)
|
|
v_list.append(v)
|
|
|
|
merged = q_list + k_list + v_list
|
|
|
|
if is_paddle_tensor:
|
|
tensor = paddle.concat(merged, axis=-1)
|
|
if tensor.place.is_gpu_place():
|
|
tensor = tensor._copy_to(paddle.CUDAPinnedPlace(), False)
|
|
return tensor
|
|
else:
|
|
return np.concatenate(merged, axis=-1)
|
|
|
|
if (config.num_key_value_heads is not None
|
|
and config.num_key_value_heads != config.num_attention_heads):
|
|
if is_split:
|
|
qkv_fn = partial(
|
|
gqa_qkv_split_func,
|
|
tensor_parallel_degree=config.tensor_parallel_degree,
|
|
tensor_parallel_rank=config.tensor_parallel_rank,
|
|
num_attention_heads=config.num_attention_heads,
|
|
num_key_value_heads=config.num_key_value_heads,
|
|
head_dim=config.hidden_size // config.num_attention_heads,
|
|
)
|
|
else:
|
|
qkv_fn = partial(
|
|
gqa_qkv_merge_func,
|
|
num_attention_heads=config.num_attention_heads,
|
|
num_key_value_heads=config.num_key_value_heads,
|
|
head_dim=config.hidden_size // config.num_attention_heads,
|
|
)
|
|
else:
|
|
qkv_fn = partial(fn, is_column=True)
|
|
|
|
def get_tensor_parallel_split_mappings(num_layers, moe_num_experts,
|
|
moe_layer_start_index):
|
|
"""
|
|
get tensor from parallel-split-mappings
|
|
"""
|
|
final_actions = {}
|
|
base_model_prefix = "ernie.mtp_block"
|
|
|
|
base_actions = {}
|
|
|
|
base_actions["ernie.mtp_linear_proj.0.weight"] = partial(
|
|
fn, is_column=True)
|
|
base_actions[
|
|
f"{base_model_prefix}.0.self_attn.qkv_proj.weight"] = qkv_fn
|
|
base_actions[
|
|
f"{base_model_prefix}.0.self_attn.o_proj.weight"] = partial(
|
|
fn, is_column=False)
|
|
base_actions[
|
|
f"{base_model_prefix}.0.mlp.up_gate_proj.weight"] = partial(
|
|
fn, is_column=True, is_naive_2fuse=True)
|
|
base_actions[f"{base_model_prefix}.0.mlp.down_proj.weight"] = (
|
|
partial(fn, is_column=False))
|
|
|
|
for expert_idx in range(moe_num_experts):
|
|
base_actions[
|
|
f"{base_model_prefix}.{moe_layer_start_index}"
|
|
f".mlp.experts.{expert_idx}.up_gate_proj.weight"] = partial(
|
|
fn, is_column=True, is_naive_2fuse=True)
|
|
base_actions[
|
|
f"{base_model_prefix}.{moe_layer_start_index}"
|
|
f".mlp.experts.{expert_idx}.down_proj.weight"] = partial(
|
|
fn, is_column=False)
|
|
|
|
for key, action in base_actions.items():
|
|
if (f"{base_model_prefix}.0.mlp.up_gate_proj.weight" in key or
|
|
f"{base_model_prefix}.0.mlp.down_proj.weight" in key):
|
|
for i in range(moe_layer_start_index):
|
|
final_actions[key.replace("0.", f"{i}.")] = action
|
|
elif f"{moe_layer_start_index}.mlp.experts." in key:
|
|
for i in range(moe_layer_start_index, num_layers):
|
|
final_actions[key.replace(f"{moe_layer_start_index}.",
|
|
f"{i}.")] = action
|
|
elif f"{base_model_prefix}.0." in key:
|
|
for i in range(num_layers):
|
|
final_actions[key.replace("0.", f"{i}.")] = action
|
|
final_actions[key] = action
|
|
return final_actions
|
|
|
|
moe_num_experts = 0
|
|
mappings = get_tensor_parallel_split_mappings(
|
|
config.num_layers,
|
|
moe_num_experts,
|
|
config.moe_layer_start_index,
|
|
)
|
|
|
|
return mappings
|
|
|
|
|
|
class Ernie4_5_MTPModel(nn.Layer):
|
|
"""
|
|
Ernie4_5_MTPModel
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
fd_config: FDConfig = None,
|
|
):
|
|
"""
|
|
Initializer for the Ernie4_5_MTPModel class.
|
|
|
|
Args:
|
|
|
|
"""
|
|
super().__init__()
|
|
|
|
self.num_layers = fd_config.model_config.num_layers
|
|
self.embeddings = fd_config.speculative_config.sharing_model.model.embeddings
|
|
|
|
self.hidden_layers = [
|
|
Ernie4_5_DecoderLayer(
|
|
fd_config=fd_config,
|
|
prefix=f"{fd_config.model_config.prefix_name}.{i}")
|
|
for i in range(self.num_layers)
|
|
]
|
|
|
|
self.enorm = RMSNorm(
|
|
fd_config,
|
|
hidden_size=fd_config.model_config.hidden_size,
|
|
eps=1e-5,
|
|
prefix="ernie.mtp_emb_norm.0",
|
|
)
|
|
|
|
self.hnorm = RMSNorm(
|
|
fd_config,
|
|
hidden_size=fd_config.model_config.hidden_size,
|
|
eps=1e-5,
|
|
prefix="ernie.mtp_hidden_norm.0",
|
|
)
|
|
|
|
self.eh_proj = ParallelLMHead(
|
|
fd_config=fd_config,
|
|
num_embeddings=fd_config.model_config.hidden_size,
|
|
embedding_dim=fd_config.model_config.hidden_size * 2,
|
|
prefix="ernie.mtp_linear_proj.0",
|
|
)
|
|
|
|
def load_state_dict(self, state_dict):
|
|
"""
|
|
Load model parameters from a given state dictionary.
|
|
|
|
Args:
|
|
state_dict (dict[str, np.ndarray | paddle.Tensor]):
|
|
A dictionary containing model parameters, where keys are parameter names
|
|
and values are NumPy arrays or PaddlePaddle tensors.
|
|
"""
|
|
# self.embeddings.load_state_dict(state_dict)
|
|
self.enorm.load_state_dict(state_dict)
|
|
self.hnorm.load_state_dict(state_dict)
|
|
self.eh_proj.load_state_dict(state_dict)
|
|
for i in range(self.num_layers):
|
|
logger.info(f"Start load layer {i}")
|
|
self.hidden_layers[i].load_state_dict(state_dict)
|
|
|
|
def forward(
|
|
self,
|
|
ids_remove_padding: paddle.Tensor,
|
|
previous_hidden_states: paddle.Tensor,
|
|
forward_meta: ForwardMeta,
|
|
):
|
|
"""
|
|
forward
|
|
"""
|
|
inputs_embedding = self.embeddings(
|
|
ids_remove_padding=ids_remove_padding)
|
|
inputs_embedding = paddle.concat(
|
|
[self.enorm(inputs_embedding),
|
|
self.hnorm(previous_hidden_states)],
|
|
axis=-1)
|
|
hidden_states = self.eh_proj(inputs_embedding)
|
|
residual = None
|
|
for i in range(self.num_layers):
|
|
hidden_states, residual = self.hidden_layers[i](forward_meta,
|
|
hidden_states,
|
|
residual)
|
|
|
|
hidden_states = hidden_states + residual
|
|
|
|
return hidden_states
|
|
|
|
|
|
class Ernie4_5_MTPForCausalLM(ModelForCasualLM):
|
|
"""
|
|
Ernie4_5_MTPForCausalLM
|
|
"""
|
|
|
|
def __init__(self, fd_config: FDConfig):
|
|
"""
|
|
Args:
|
|
fd_config (FDConfig): Configurations for the LLM model.
|
|
"""
|
|
super(Ernie4_5_MTPForCausalLM, self).__init__(fd_config)
|
|
self.fd_config = fd_config
|
|
self.model = Ernie4_5_MTPModel(fd_config=fd_config)
|
|
|
|
self.ori_vocab_size = fd_config.model_config.ori_vocab_size
|
|
|
|
self.lm_head = fd_config.speculative_config.sharing_model.lm_head
|
|
self.tie_word_embeddings = fd_config.model_config.tie_word_embeddings
|
|
|
|
@classmethod
|
|
def name(self):
|
|
"""
|
|
"""
|
|
return "Ernie4_5_MTPForCausalLM"
|
|
|
|
@paddle.no_grad()
|
|
def set_state_dict(self, state_dict: Dict[str, Union[np.ndarray,
|
|
paddle.Tensor]]):
|
|
"""
|
|
Load model parameters from a given state dictionary.
|
|
|
|
Args:
|
|
state_dict (dict[str, np.ndarray | paddle.Tensor]):
|
|
A dictionary containing model parameters, where keys are parameter names
|
|
and values are NumPy arrays or PaddlePaddle tensors.
|
|
"""
|
|
self.model.load_state_dict(state_dict)
|
|
# if self.tie_word_embeddings:
|
|
# self.lm_head.out_linear.weight.set_value(
|
|
# self.model.embeddings.word_embeddings.weight.transpose([1, 0]))
|
|
# else:
|
|
# self.lm_head.load_state_dict(state_dict)
|
|
|
|
def compute_logits(self, hidden_states: paddle.Tensor):
|
|
"""
|
|
compute logits
|
|
"""
|
|
logits = self.lm_head(hidden_states)
|
|
logits = paddle.cast(logits, paddle.float32)
|
|
logits[:, self.ori_vocab_size:] = -float("inf")
|
|
|
|
return logits
|
|
|
|
def empty_input_forward(self):
|
|
"""
|
|
empty_input_forward
|
|
"""
|
|
fake_hidden_states = paddle.empty(
|
|
shape=[0, self.fd_config.model_config.hidden_size],
|
|
dtype=paddle.get_default_dtype(),
|
|
)
|
|
for i in range(self.fd_config.moe_config.moe_layer_start_index,
|
|
self.fd_config.model_config.num_layers):
|
|
self.model.hidden_layers[i].mlp.fused_moe(fake_hidden_states)
|
|
|
|
def forward(
|
|
self,
|
|
ids_remove_padding: paddle.Tensor,
|
|
previous_hidden_states: paddle.Tensor,
|
|
forward_meta: ForwardMeta,
|
|
):
|
|
"""
|
|
forward
|
|
"""
|
|
hidden_states = self.model(ids_remove_padding, previous_hidden_states,
|
|
forward_meta)
|
|
|
|
return hidden_states
|