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533 lines
18 KiB
Python
533 lines
18 KiB
Python
"""
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# Copyright (c) 2023 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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import enum
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import hashlib
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import json
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import os
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import random
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import re
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import struct
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from functools import partial
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from typing import NamedTuple, Optional
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import numpy as np
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import paddle
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import paddle.distributed as dist
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from paddle.common_ops_import import convert_dtype
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from paddle.distributed import fleet
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from paddleformers.transformers.model_utils import _add_variant
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from paddleformers.transformers.utils import paddleformers_load
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from paddleformers.utils.env import (PADDLE_WEIGHTS_INDEX_NAME,
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SAFE_MASTER_WEIGHTS_INDEX_NAME,
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SAFE_PEFT_WEIGHTS_INDEX_NAME,
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SAFE_WEIGHTS_INDEX_NAME)
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from paddleformers.utils.log import logger
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from tqdm import tqdm
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MAX_BSZ = 512
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MAX_DRAFT_TOKENS = 6
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class LayerIdPlaceholder(str, enum.Enum):
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"""LayerIdPlaceholder"""
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LAYER_ID = "layer_id"
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FFN_LAYER_ID = "ffn_layer_id"
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MOE_LAYER_ID = "moe_layer_id"
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EXPERT_ID = "export_id"
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class WeightMeta(NamedTuple):
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"""
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#tensor split parameters
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# weight_name: weight name
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# is_column: whether to split by columns
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# extra: optional flags like "is_naive_2fuse", "is_gqa", "is_naive_3fuse"
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"""
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weight_name: str
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is_column: bool
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extra: Optional[str] = None
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class UniqueIDGenerator:
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"""
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The generator for the export model id
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"""
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def __init__(self):
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pass
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def generate_unique_id(self, state_dict):
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"""
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Generate the model id from the timestamp
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"""
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keys = state_dict.keys()
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sorted_keys = sorted(keys)
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first_key = sorted_keys[0]
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first_parameter = state_dict[first_key].cast("float32")
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# 假设模型参数是唯一的，通过第一个key来获取md5sum
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model_md5 = hashlib.md5(str(
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first_parameter.sum()).encode("utf-8")).hexdigest()
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unique_id = f"{model_md5}-{random.randint(10000, 99999)}"
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return unique_id
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def load_sharded_checkpoint(folder, variant=None, return_numpy=False):
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"""
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This load is performed efficiently: each checkpoint shard is loaded one by one in RAM and deleted after being
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loaded in the model.
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Args:
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folder (`str` or `os.PathLike`): A path to a folder containing the sharded checkpoint.
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variant (`str`): The model variant.
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"""
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# Load the index
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pdparams_file = os.path.join(folder,
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_add_variant("model_state.pdparams", variant))
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lora_pdparams_file = os.path.join(
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folder, _add_variant("lora_model_state.pdparams", variant))
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safetensors_file = os.path.join(folder,
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_add_variant("model.safetensors", variant))
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if os.path.isfile(pdparams_file):
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return paddle.load(pdparams_file, return_numpy=return_numpy)
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if os.path.isfile(lora_pdparams_file):
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return paddle.load(lora_pdparams_file, return_numpy=return_numpy)
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if os.path.isfile(safetensors_file):
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try:
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from paddleformers.utils.safetensors import \
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fast_load_file as safe_load_file
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except ImportError:
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from safetensors.numpy import load_file as safe_load_file
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state_dict = safe_load_file(safetensors_file)
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if not return_numpy:
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for key in list(state_dict.keys()):
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if isinstance(state_dict[key], np.ndarray):
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state_dict[key] = paddle.Tensor(state_dict.pop(key),
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zero_copy=True)
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return state_dict
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index_file = os.path.join(folder,
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_add_variant(PADDLE_WEIGHTS_INDEX_NAME, variant))
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safe_index_file = os.path.join(
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folder, _add_variant(SAFE_WEIGHTS_INDEX_NAME, variant))
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safe_master_file = os.path.join(
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folder, _add_variant(SAFE_MASTER_WEIGHTS_INDEX_NAME, variant))
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safe_peft_file = os.path.join(
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folder, _add_variant(SAFE_PEFT_WEIGHTS_INDEX_NAME, variant))
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index_present = os.path.isfile(index_file)
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safe_index_present = os.path.isfile(safe_index_file)
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safe_master_present = os.path.isfile(safe_master_file)
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safe_peft_present = os.path.isfile(safe_peft_file)
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load_safe = False
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load_index = None
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if safe_index_present:
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load_safe = True # load safe due to preference
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load_index = safe_index_file
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elif safe_master_present:
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load_safe = True
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load_index = safe_master_file
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elif index_present:
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load_index = index_file
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elif safe_peft_present:
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load_safe = True
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load_index = safe_peft_file
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else:
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raise ValueError(
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f"Could not find {index_file} or {safe_index_file} or {safe_peft_file}"
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)
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if load_safe:
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try:
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from paddleformers.utils.safetensors import \
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fast_load_file as safe_load_file
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except ImportError:
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from safetensors.numpy import load_file as safe_load_file
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with open(load_index, "r", encoding="utf-8") as f:
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index = json.load(f)
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shard_files = list(set(index["weight_map"].values()))
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loader = (safe_load_file if load_safe else partial(
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paddleformers_load, map_location="np" if return_numpy else "cpu"))
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ret = {}
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for shard_file in tqdm(shard_files):
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state_dict = loader(os.path.join(folder, shard_file))
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ret.update(state_dict)
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if not return_numpy:
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for key in list(ret.keys()):
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if isinstance(ret[key], np.ndarray):
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ret[key] = paddle.Tensor(ret.pop(key), zero_copy=True)
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return ret
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def convert_ndarray_dtype(np_array: np.ndarray,
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target_dtype: str) -> np.ndarray:
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"""convert ndarray
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Args:
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np_array (np.ndarray): numpy ndarray instance
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target_dtype (str): the target dtype
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Returns:
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np.ndarray: converted numpy ndarray instance
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"""
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source_dtype = convert_dtype(np_array.dtype)
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if source_dtype == "uint16" and target_dtype == "bfloat16" and paddle.is_compiled_with_custom_device(
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"iluvatar_gpu"):
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return np_array.view(dtype=target_dtype)
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if source_dtype == "uint16" or target_dtype == "bfloat16":
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if paddle.is_compiled_with_xpu():
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# xpu not support bf16.
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tensor = paddle.to_tensor(np_array, place=paddle.CPUPlace())
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else:
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tensor = paddle.to_tensor(np_array)
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tensor = paddle.cast(tensor, target_dtype)
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return tensor.numpy()
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# TODO(wj-Mcat): device_guard will slow the converting
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# with device_guard("cpu"):
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# tensor = paddle.to_tensor(np_array)
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# tensor = paddle.cast(tensor, target_dtype)
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# return tensor.numpy()
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if target_dtype == "bfloat16":
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target_dtype = "uint16"
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return np_array.astype(target_dtype)
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def set_seed(seed: int):
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"""
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set random seed for all random modules
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"""
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paddle.seed(seed)
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random.seed(seed)
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np.random.seed(seed)
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def pad_batch_data(insts, pad_id=0, return_seq_len=False, pad_style="right"):
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"""Pad the instances to the max sequence length in batch."""
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# pad to max input len i bsz
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max_len = max(map(len, insts))
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# pad to max input len
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# max_len = args.max_len
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if pad_style == "left":
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inst_data = np.array([[pad_id] * (max_len - len(inst)) + list(inst)
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for inst in insts])
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else:
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inst_data = np.array(
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[list(inst) + [pad_id] * (max_len - len(inst)) for inst in insts])
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if return_seq_len:
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seq_len = np.array([len(inst) for inst in insts])
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return inst_data.astype("int64").reshape([-1, max_len]), seq_len
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else:
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return inst_data.astype("int64").reshape([-1, max_len])
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def load_prefix_weights(
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prefix_path: str,
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inference: bool = False,
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batch_size: int = 1,
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dtype: str = "bfloat16",
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) -> np.ndarray | list[paddle.Tensor]:
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"""load prefix weight by path
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Args:
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prefix_path (str): the path of prefix weight
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"""
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past_key_values = paddle.to_tensor(
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np.load(f"{prefix_path}/pre_caches.npy")).unsqueeze(2)
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if batch_size > 1:
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past_key_values = paddle.concat([past_key_values] * batch_size, axis=2)
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# .chatglm static model require one tensor, otherwise list of tensor
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past_key_values = past_key_values.astype(dtype)
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if inference:
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return past_key_values.numpy()
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return past_key_values
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def init_distributed_env() -> tuple[int, int]:
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"""init distributed envs, and only support mp in ErnieBotModel
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Returns:
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tuple[int, int]: tensor_parallel_degree, tensor_parallel_rank
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"""
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tensor_parallel_degree = dist.get_world_size()
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tensor_parallel_rank = 0
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if tensor_parallel_degree > 1:
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strategy = fleet.DistributedStrategy()
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strategy.hybrid_configs = {
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"dp_degree": 1,
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"mp_degree": tensor_parallel_degree,
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"pp_degree": 1,
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"sharding_degree": 1,
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}
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fleet.init(is_collective=True, strategy=strategy)
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hcg = fleet.get_hybrid_communicate_group()
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tensor_parallel_rank = hcg.get_model_parallel_rank()
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return tensor_parallel_degree, tensor_parallel_rank
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def w4a8_weight_convert(state_dict):
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"""W4A8 权重转换函数
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Args:
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state_dict (dict): state_dict of model
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"""
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def w4_weight_squash(value, name, w4a8_weight_bites_name_map):
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weight_dq = value
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# W8表象下的W4权重的absmax值为112，使用正负112进行权重类型判断
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if weight_dq.max() == 112 or weight_dq.min() == -112:
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weight_dq = weight_dq.cast("int8")
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np_weight_dq = np.array(weight_dq, dtype="int8").view("uint8")
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np_weight_dq_left_div_16 = (np_weight_dq / 16).astype("int8")
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# weight_q = (weight_dq/16).cast('int8')
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weight_q = paddle.to_tensor(np_weight_dq_left_div_16, dtype="int8")
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logger.debug(f"int4 weight:{name}")
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w4a8_weight_bites_name_map[name] = 4
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return weight_q.cast("int8")
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elif weight_dq.max() == 127 or weight_dq.min() == -128:
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logger.debug(f"int8 weight:{name}")
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w4a8_weight_bites_name_map[name] = 8
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return weight_dq.cast("int8")
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else:
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logger.debug(f"fp16/bf16/float weight:{name}")
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return weight_dq
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w4a8_weight_bites_name_map = {}
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for name, value in state_dict.items():
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if value.dtype == "uint16":
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weight_q = w4_weight_squash(
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paddle.to_tensor(value).cast("float32"),
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name,
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w4a8_weight_bites_name_map,
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)
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state_dict[name] = weight_q.numpy(
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) if weight_q is not None else value
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del weight_q
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w4a8_weight_bites_layers_map = {}
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w4a8_weight_bites_layers_map["qkv_gemm_bits_map"] = []
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w4a8_weight_bites_layers_map["out_gemm_bits_map"] = []
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w4a8_weight_bites_layers_map["ffn1_gemm_bits_map"] = []
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w4a8_weight_bites_layers_map["ffn2_gemm_bits_map"] = []
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for name_keys, gemm_bits in w4a8_weight_bites_name_map.items():
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if "qkv_proj" in name_keys:
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w4a8_weight_bites_layers_map["qkv_gemm_bits_map"].append(gemm_bits)
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elif "out_proj" in name_keys:
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w4a8_weight_bites_layers_map["out_gemm_bits_map"].append(gemm_bits)
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elif "linear1" in name_keys:
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w4a8_weight_bites_layers_map["ffn1_gemm_bits_map"].append(
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gemm_bits)
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elif "linear2" in name_keys:
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w4a8_weight_bites_layers_map["ffn2_gemm_bits_map"].append(
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gemm_bits)
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logger.debug(
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f"w4a8_weight_bites_layers_map:{w4a8_weight_bites_layers_map}")
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return state_dict, w4a8_weight_bites_layers_map
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def _vocab_size_with_padding(vocab_size, div_unit, mp_degree):
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padded_size = vocab_size
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multiple = div_unit * mp_degree
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while (padded_size % multiple) != 0:
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padded_size += 1
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# logger.warning(
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# " > padded vocab (size: {}) with {} dummy tokens "
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# "(new size: {})".format(vocab_size, padded_size - vocab_size, padded_size)
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# )
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return padded_size
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def save_test_case(cases: list[list[dict]], file: str):
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"""save test to result file
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Args:
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cases (list[list[dict]]): the content of case
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file (str): the path of saved file
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"""
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with open(file, "w+", encoding="utf-8") as f:
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for case in cases:
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raw = json.dumps(case, ensure_ascii=False)
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f.write(raw + "\n")
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def infer_save_test_case(cases: list[list[dict]], file: str):
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"""save test to result file
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Args:
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cases (list[list[dict]]): the content of case
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file (str): the path of saved file
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"""
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with open(file, "a+", encoding="utf-8") as f:
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for case in cases:
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raw = json.dumps(case, ensure_ascii=False)
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f.write(raw + "\n")
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def deserialize_from_file(fp):
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"""
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deserialize a binary file into an array
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"""
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x_type = fp.read(1)
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x_type_out = struct.unpack("c", x_type)[0]
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# data
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data_list = []
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if x_type_out == b"0":
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data = fp.read(4)
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data_out = struct.unpack("f", data)[0]
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while data:
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data_out = struct.unpack("f", data)[0]
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data_list.append(data_out)
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data = fp.read(4)
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elif x_type_out == b"1":
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data = fp.read(8)
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while data:
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data_out = struct.unpack("l", data)[0]
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data_list.append(data_out)
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data = fp.read(8)
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elif x_type_out == b"2":
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data = fp.read(4)
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while data:
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data_out = struct.unpack("i", data)[0]
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data_list.append(data_out)
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data = fp.read(4)
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else:
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print("type error")
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data_arr = np.array(data_list)
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return data_arr
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def calculate_effective_tokens(training_args, train_dataset, max_seq_len):
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"""
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Calculate the effective tokens during training.
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"""
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total_effective_tokens = 0
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try:
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data_parallel_degree = training_args.data_parallel_degree
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except Exception:
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data_parallel_degree = 1
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if training_args.sharding_parallel_degree > 1:
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sharding_parallel_degree = training_args.sharding_parallel_degree
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else:
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sharding_parallel_degree = 1
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total_batch = (training_args.max_steps *
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training_args.per_device_train_batch_size *
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training_args.gradient_accumulation_steps *
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sharding_parallel_degree * data_parallel_degree)
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for i, data in enumerate(train_dataset):
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if i == total_batch:
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break
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for dd in data:
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total_effective_tokens += len(dd.token_ids)
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total_tokens = total_batch * max_seq_len
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return total_effective_tokens, total_tokens
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def parser_quant_type(quant_type):
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"""
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Parse the quantization type string and return the corresponding quantization types for weights,
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activations, and custom.
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Args:
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quant_type (str): The quantization type string. It can be one of the following formats:
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- "weight_only_int8" or "wint8": Only weights are quantized to int8.
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- "weight_only_int4" or "wint4": Only weights are quantized to int4.
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- A custom string in the format of "wxaybzcfp8", where 'x', 'y', 'z' are the quantization bitwidths
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for weights, activations, and custom respectively,
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and 'a', 'b', 'c' are the prefixes indicating the quantization types
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(e.g., 'fp8' for floating-point 8-bit).
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If a prefix is missing, the default quantization type will be used.
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Returns:
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tuple: A tuple of three strings representing the quantization types for weights, activations,
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and custom respectively.
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If the input is "weight_only_int8" or "wint8", returns ("int8", default_type, default_type).
|
||
If the input is "weight_only_int4" or "wint4", returns ("int4", default_type, default_type).
|
||
For custom strings, returns the parsed quantization types based on the input format.
|
||
|
||
Raises:
|
||
AssertionError: If the custom quantization type string format is incorrect.
|
||
"""
|
||
default_type = paddle.get_default_dtype()
|
||
if quant_type == "default" or quant_type is None:
|
||
return default_type, default_type, default_type
|
||
conver_dict = {
|
||
"8": "int8",
|
||
"4": "int4",
|
||
"16": paddle.get_default_dtype(),
|
||
"fp8": "float8_e4m3fn",
|
||
"fp16": "float16",
|
||
"bf16": "bfloat16",
|
||
"fp32": "float32"
|
||
}
|
||
cache_type = default_type
|
||
if "c8" in quant_type:
|
||
cache_type = "int8"
|
||
elif "cfp8" in quant_type:
|
||
cache_type = "fp8"
|
||
elif "c4" in quant_type:
|
||
cache_type = "int4"
|
||
|
||
if "weight_only_int8" in quant_type or "wint8" in quant_type:
|
||
return "int8", default_type, cache_type
|
||
elif "weight_only_int4" in quant_type or "wint4" in quant_type:
|
||
return "int4", default_type, cache_type
|
||
else:
|
||
# split quant type, eg. w4afp8c8 -> ['w', '4', 'a', 'fp8', 'c', '8']
|
||
pattern = f"({'|'.join(map(re.escape, ['w', 'a', 'c']))})"
|
||
splited_type = re.split(pattern, quant_type)
|
||
splited_type = [tmp_type for tmp_type in splited_type if tmp_type]
|
||
assert (len(splited_type) % 2 == 0 and len(splited_type)
|
||
<= 6), f"Quant type[{quant_type}] format error."
|
||
|
||
quant_type_list = []
|
||
if "w" in splited_type:
|
||
w_idx = splited_type.index("w")
|
||
quant_type_list.append(conver_dict[splited_type[w_idx + 1]])
|
||
else:
|
||
quant_type_list.append(default_type)
|
||
if "a" in splited_type:
|
||
a_idx = splited_type.index("a")
|
||
quant_type_list.append(conver_dict[splited_type[a_idx + 1]])
|
||
else:
|
||
quant_type_list.append(default_type)
|
||
if "c" in splited_type:
|
||
c_idx = splited_type.index("c")
|
||
quant_type_list.append(conver_dict[splited_type[c_idx + 1]])
|
||
else:
|
||
quant_type_list.append(default_type)
|
||
|
||
return quant_type_list[0], quant_type_list[1], quant_type_list[2]
|