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* fix mtp eh_proj layer * fix mtp update_cfg function * fix stringdoc * simplify class name
418 lines
15 KiB
Python
418 lines
15 KiB
Python
"""
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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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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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from __future__ import annotations
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from functools import partial
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from typing import Dict, Union
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import numpy as np
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import paddle
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from paddle import nn
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from paddleformers.transformers import PretrainedModel
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from paddleformers.utils.log import logger
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from fastdeploy.config import FDConfig, ModelConfig
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from fastdeploy.model_executor.layers.mtp_linear import ParallelEHProjection
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from fastdeploy.model_executor.layers.normalization import RMSNorm
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from fastdeploy.model_executor.models.ernie4_5_moe import Ernie4_5_DecoderLayer
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from fastdeploy.model_executor.models.model_base import ModelForCasualLM
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from fastdeploy.worker.forward_meta import ForwardMeta
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class Ernie4_5_MTPPretrainedModel(PretrainedModel):
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"""
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Ernie4_5_MTPPretrainedModel
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"""
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config_class = FDConfig
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def _init_weight(self, layer):
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"""
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_init_weight
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"""
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return None
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@classmethod
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def _get_tensor_parallel_mappings(cls, config: ModelConfig, is_split=True):
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"""
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get_tensor_parallel_mappings
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"""
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logger.info("erine inference model _get_tensor_parallel_mappings")
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from paddleformers.transformers.conversion_utils import \
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split_or_merge_func
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fn = split_or_merge_func(
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is_split=is_split,
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tensor_parallel_degree=config.tensor_parallel_degree,
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tensor_parallel_rank=config.tensor_parallel_rank,
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num_attention_heads=config.num_attention_heads,
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)
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def gqa_qkv_split_func(
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weight,
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tensor_parallel_degree,
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tensor_parallel_rank,
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num_attention_heads,
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num_key_value_heads,
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head_dim,
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):
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def get_shape(tensor):
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return (tensor.get_shape()
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if hasattr(tensor, "get_shape") else tensor.shape)
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def slice_tensor(tensor, start, end):
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shape = get_shape(tensor)
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if len(shape) == 1:
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return tensor[start:end]
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else:
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return tensor[..., start:end]
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q_end = num_attention_heads * head_dim
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k_end = q_end + num_key_value_heads * head_dim
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v_end = k_end + num_key_value_heads * head_dim
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q = slice_tensor(weight, 0, q_end)
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k = slice_tensor(weight, q_end, k_end)
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v = slice_tensor(weight, k_end, v_end)
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def split_tensor(tensor, degree):
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shape = get_shape(tensor)
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size = shape[-1]
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block_size = size // degree
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if hasattr(tensor, "get_shape"):
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return [
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slice_tensor(tensor, i * block_size,
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(i + 1) * block_size)
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for i in range(degree)
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]
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else:
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return np.split(tensor, degree, axis=-1)
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q_list = split_tensor(q, tensor_parallel_degree)
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k_list = split_tensor(k, tensor_parallel_degree)
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v_list = split_tensor(v, tensor_parallel_degree)
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if tensor_parallel_rank is None:
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return [
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np.concatenate([q_i, k_i, v_i], axis=-1)
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for q_i, k_i, v_i in zip(q_list, k_list, v_list)
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]
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else:
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return np.concatenate(
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[
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q_list[tensor_parallel_rank],
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k_list[tensor_parallel_rank],
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v_list[tensor_parallel_rank],
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],
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axis=-1,
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)
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def gqa_qkv_merge_func(weight_list, num_attention_heads,
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num_key_value_heads, head_dim):
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tensor_parallel_degree = len(weight_list)
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num_attention_heads = num_attention_heads // tensor_parallel_degree
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num_key_value_heads = num_key_value_heads // tensor_parallel_degree
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is_paddle_tensor = not isinstance(weight_list[0], np.ndarray)
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def get_shape(tensor):
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return (tensor.get_shape()
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if hasattr(tensor, "get_shape") else tensor.shape)
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def slice_tensor(tensor, start, end):
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if len(get_shape(tensor)) == 1:
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return tensor[start:end]
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else:
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return tensor[..., start:end]
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q_list, k_list, v_list = [], [], []
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for weight in weight_list:
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q_end = num_attention_heads * head_dim
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k_end = q_end + num_key_value_heads * head_dim
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v_end = k_end + num_key_value_heads * head_dim
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q = slice_tensor(weight, 0, q_end)
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k = slice_tensor(weight, q_end, k_end)
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v = slice_tensor(weight, k_end, v_end)
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q_list.append(q)
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k_list.append(k)
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v_list.append(v)
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merged = q_list + k_list + v_list
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if is_paddle_tensor:
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tensor = paddle.concat(merged, axis=-1)
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if tensor.place.is_gpu_place():
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tensor = tensor._copy_to(paddle.CUDAPinnedPlace(), False)
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return tensor
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else:
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return np.concatenate(merged, axis=-1)
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if (config.num_key_value_heads is not None
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and config.num_key_value_heads != config.num_attention_heads):
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if is_split:
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qkv_fn = partial(
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gqa_qkv_split_func,
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tensor_parallel_degree=config.tensor_parallel_degree,
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tensor_parallel_rank=config.tensor_parallel_rank,
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num_attention_heads=config.num_attention_heads,
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num_key_value_heads=config.num_key_value_heads,
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head_dim=config.hidden_size // config.num_attention_heads,
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)
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else:
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qkv_fn = partial(
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gqa_qkv_merge_func,
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num_attention_heads=config.num_attention_heads,
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num_key_value_heads=config.num_key_value_heads,
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head_dim=config.hidden_size // config.num_attention_heads,
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)
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else:
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qkv_fn = partial(fn, is_column=True)
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def get_tensor_parallel_split_mappings(num_layers, moe_num_experts,
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moe_layer_start_index):
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"""
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get tensor from parallel-split-mappings
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"""
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final_actions = {}
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base_model_prefix = "ernie.mtp_block"
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base_actions = {}
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base_actions["ernie.mtp_linear_proj.0.weight"] = partial(
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fn, is_column=True)
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base_actions[
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f"{base_model_prefix}.0.self_attn.qkv_proj.weight"] = qkv_fn
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base_actions[
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f"{base_model_prefix}.0.self_attn.o_proj.weight"] = partial(
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fn, is_column=False)
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base_actions[
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f"{base_model_prefix}.0.mlp.up_gate_proj.weight"] = partial(
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fn, is_column=True, is_naive_2fuse=True)
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base_actions[f"{base_model_prefix}.0.mlp.down_proj.weight"] = (
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partial(fn, is_column=False))
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for expert_idx in range(moe_num_experts):
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base_actions[
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f"{base_model_prefix}.{moe_layer_start_index}"
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f".mlp.experts.{expert_idx}.up_gate_proj.weight"] = partial(
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fn, is_column=True, is_naive_2fuse=True)
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base_actions[
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f"{base_model_prefix}.{moe_layer_start_index}"
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f".mlp.experts.{expert_idx}.down_proj.weight"] = partial(
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fn, is_column=False)
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for key, action in base_actions.items():
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if (f"{base_model_prefix}.0.mlp.up_gate_proj.weight" in key or
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f"{base_model_prefix}.0.mlp.down_proj.weight" in key):
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for i in range(moe_layer_start_index):
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final_actions[key.replace("0.", f"{i}.")] = action
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elif f"{moe_layer_start_index}.mlp.experts." in key:
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for i in range(moe_layer_start_index, num_layers):
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final_actions[key.replace(f"{moe_layer_start_index}.",
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f"{i}.")] = action
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elif f"{base_model_prefix}.0." in key:
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for i in range(num_layers):
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final_actions[key.replace("0.", f"{i}.")] = action
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final_actions[key] = action
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return final_actions
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moe_num_experts = 0
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mappings = get_tensor_parallel_split_mappings(
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config.num_layers,
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moe_num_experts,
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config.moe_layer_start_index,
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)
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return mappings
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class Ernie4_5_MTPModel(nn.Layer):
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"""
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Ernie4_5_MTPModel
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"""
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def __init__(
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self,
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fd_config: FDConfig = None,
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):
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"""
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Initializer for the Ernie4_5_MTPModel class.
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Args:
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"""
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super().__init__()
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self.num_layers = fd_config.model_config.num_layers
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self.embeddings = fd_config.speculative_config.sharing_model.model.embeddings
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self.hidden_layers = nn.LayerList([
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Ernie4_5_DecoderLayer(
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fd_config=fd_config,
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prefix=f"{fd_config.model_config.prefix_name}.{i}")
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for i in range(self.num_layers)
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])
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self.enorm = RMSNorm(
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fd_config,
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hidden_size=fd_config.model_config.hidden_size,
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eps=1e-5,
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prefix="ernie.mtp_emb_norm.0",
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)
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self.hnorm = RMSNorm(
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fd_config,
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hidden_size=fd_config.model_config.hidden_size,
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eps=1e-5,
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prefix="ernie.mtp_hidden_norm.0",
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)
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self.eh_proj = ParallelEHProjection(
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fd_config=fd_config,
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num_embeddings=fd_config.model_config.hidden_size,
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embedding_dim=fd_config.model_config.hidden_size * 2,
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prefix="ernie.mtp_linear_proj.0",
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)
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def load_state_dict(self, state_dict):
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"""
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Load model parameters from a given state dictionary.
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Args:
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state_dict (dict[str, np.ndarray | paddle.Tensor]):
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A dictionary containing model parameters, where keys are parameter names
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and values are NumPy arrays or PaddlePaddle tensors.
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"""
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# self.embeddings.load_state_dict(state_dict)
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self.enorm.load_state_dict(state_dict)
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self.hnorm.load_state_dict(state_dict)
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self.eh_proj.load_state_dict(state_dict)
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for i in range(self.num_layers):
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logger.info(f"Start load layer {i}")
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self.hidden_layers[i].load_state_dict(state_dict)
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def forward(
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self,
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ids_remove_padding: paddle.Tensor,
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previous_hidden_states: paddle.Tensor,
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forward_meta: ForwardMeta,
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):
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"""
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forward
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"""
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inputs_embedding = self.embeddings(
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ids_remove_padding=ids_remove_padding)
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inputs_embedding = paddle.concat(
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[self.enorm(inputs_embedding),
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self.hnorm(previous_hidden_states)],
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axis=-1)
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hidden_states = self.eh_proj(inputs_embedding)
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residual = None
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for i in range(self.num_layers):
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hidden_states, residual = self.hidden_layers[i](forward_meta,
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hidden_states,
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residual)
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hidden_states = hidden_states + residual
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return hidden_states
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class Ernie4_5_MTPForCausalLM(ModelForCasualLM):
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"""
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Ernie4_5_MTPForCausalLM
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"""
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def __init__(self, fd_config: FDConfig):
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"""
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Args:
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fd_config (FDConfig): Configurations for the LLM model.
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"""
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super(Ernie4_5_MTPForCausalLM, self).__init__(fd_config)
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self.fd_config = fd_config
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self.model = Ernie4_5_MTPModel(fd_config=fd_config)
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self.ori_vocab_size = fd_config.model_config.ori_vocab_size
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self.lm_head = fd_config.speculative_config.sharing_model.lm_head
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self.tie_word_embeddings = fd_config.model_config.tie_word_embeddings
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@classmethod
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def name(self):
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"""
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"""
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return "Ernie4_5_MTPForCausalLM"
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@paddle.no_grad()
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def set_state_dict(self, state_dict: Dict[str, Union[np.ndarray,
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paddle.Tensor]]):
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"""
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Load model parameters from a given state dictionary.
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Args:
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state_dict (dict[str, np.ndarray | paddle.Tensor]):
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A dictionary containing model parameters, where keys are parameter names
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and values are NumPy arrays or PaddlePaddle tensors.
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"""
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self.model.load_state_dict(state_dict)
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# if self.tie_word_embeddings:
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# self.lm_head.out_linear.weight.set_value(
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# self.model.embeddings.word_embeddings.weight.transpose([1, 0]))
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# else:
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# self.lm_head.load_state_dict(state_dict)
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def compute_logits(self, hidden_states: paddle.Tensor):
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"""
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compute logits
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"""
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logits = self.lm_head(hidden_states)
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logits = paddle.cast(logits, paddle.float32)
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logits[:, self.ori_vocab_size:] = -float("inf")
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return logits
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def empty_input_forward(self):
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"""
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empty_input_forward
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"""
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fake_hidden_states = paddle.empty(
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shape=[0, self.fd_config.model_config.hidden_size],
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dtype=paddle.get_default_dtype(),
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)
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for i in range(self.fd_config.moe_config.moe_layer_start_index,
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self.fd_config.model_config.num_layers):
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self.model.hidden_layers[i].mlp.fused_moe(fake_hidden_states)
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def forward(
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self,
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ids_remove_padding: paddle.Tensor,
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previous_hidden_states: paddle.Tensor,
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forward_meta: ForwardMeta,
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):
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"""
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forward
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"""
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hidden_states = self.model(ids_remove_padding, previous_hidden_states,
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forward_meta)
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return hidden_states
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