Refine model of qwen and add runner.
This commit is contained in:
Colin
parent
7c047f0b32
commit
9d28280cb1
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@ -1,5 +0,0 @@
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{
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"framework": "pytorch",
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"task": "chat",
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"allow_remote": true
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}
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@ -1,55 +0,0 @@
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from torch.utils import cpp_extension
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import pathlib
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import os
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import subprocess
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def _get_cuda_bare_metal_version(cuda_dir):
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raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"],
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universal_newlines=True)
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output = raw_output.split()
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release_idx = output.index("release") + 1
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release = output[release_idx].split(".")
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bare_metal_major = release[0]
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bare_metal_minor = release[1][0]
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return raw_output, bare_metal_major, bare_metal_minor
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def _create_build_dir(buildpath):
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try:
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os.mkdir(buildpath)
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except OSError:
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if not os.path.isdir(buildpath):
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print(f"Creation of the build directory {buildpath} failed")
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# Check if cuda 11 is installed for compute capability 8.0
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cc_flag = []
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_, bare_metal_major, bare_metal_minor = _get_cuda_bare_metal_version(cpp_extension.CUDA_HOME)
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if int(bare_metal_major) >= 11:
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cc_flag.append('-gencode')
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cc_flag.append('arch=compute_80,code=sm_80')
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if int(bare_metal_minor) >= 7:
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cc_flag.append('-gencode')
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cc_flag.append('arch=compute_90,code=sm_90')
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# Build path
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srcpath = pathlib.Path(__file__).parent.absolute()
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buildpath = srcpath / 'build'
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_create_build_dir(buildpath)
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def _cpp_extention_load_helper(name, sources, extra_cuda_flags):
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return cpp_extension.load(
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name=name,
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sources=sources,
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build_directory=buildpath,
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extra_cflags=['-O3', ],
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extra_cuda_cflags=['-O3',
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'-gencode', 'arch=compute_70,code=sm_70',
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'--use_fast_math'] + extra_cuda_flags + cc_flag,
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verbose=1
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)
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extra_flags = []
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cache_autogptq_cuda_256_sources = ["./cache_autogptq_cuda_256.cpp",
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"./cache_autogptq_cuda_kernel_256.cu"]
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cache_autogptq_cuda_256 = _cpp_extention_load_helper("cache_autogptq_cuda_256", cache_autogptq_cuda_256_sources, extra_flags)
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@ -5,6 +5,7 @@ from transformers.generation import GenerationConfig
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from transformers import AutoConfig
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from modeling_qwen import QWenLMHeadModel
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from modeling_qwen import QwenRunner
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seed = 4321
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torch.manual_seed(seed)
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@ -35,8 +36,10 @@ model = model.eval()
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# model_dir, trust_remote_code=True
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# )
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runner = QwenRunner(model)
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# 第一轮对话
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response, history, decode_tokens = model.chat(tokenizer, "东南亚国家日本的首都是什么市", "", history=None)
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response, history, decode_tokens = runner.Chat(tokenizer, "东南亚国家日本的首都是什么市", "")
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print(decode_tokens)
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# <|im_start|>system
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# You are a helpful assistant.<|im_end|>
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@ -46,7 +49,8 @@ print(decode_tokens)
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# 日本的首都东京。<|im_end|><|endoftext|>
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# 第二轮对话
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response, history, decode_tokens = model.chat(tokenizer, "给我讲一个年轻人奋斗创业最终取得成功的故事。", "", history=None)
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response, history, decode_tokens = runner.Chat(tokenizer, "给我讲一个年轻人奋斗创业最终取得成功的故事。", "")
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print(decode_tokens)
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if decode_tokens.split("\n")[-2] != """这个故事告诉我们，只要我们有决心和毅力，就一定能够克服困难，实现我们的梦想。<|im_end|>""":
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403
qwen/finetune.py
403
qwen/finetune.py
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@ -1,403 +0,0 @@
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# This code is based on the revised code from fastchat based on tatsu-lab/stanford_alpaca.
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from dataclasses import dataclass, field
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import json
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import math
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import logging
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import os
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from typing import Dict, Optional, List
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import torch
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from torch.utils.data import Dataset
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from deepspeed import zero
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from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
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import transformers
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from transformers import Trainer, GPTQConfig, deepspeed
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from transformers.trainer_pt_utils import LabelSmoother
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from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
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from accelerate.utils import DistributedType
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from modelscope import snapshot_download
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from modeling_qwen import QWenLMHeadModel
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IGNORE_TOKEN_ID = LabelSmoother.ignore_index
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@dataclass
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class ModelArguments:
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model_name_or_path: Optional[str] = field(default="qwen/Qwen-1_8B-Chat")
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@dataclass
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class DataArguments:
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data_path: str = field(
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default=None, metadata={"help": "Path to the training data."}
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)
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eval_data_path: str = field(
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default=None, metadata={"help": "Path to the evaluation data."}
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)
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lazy_preprocess: bool = False
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@dataclass
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class TrainingArguments(transformers.TrainingArguments):
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cache_dir: Optional[str] = field(default=None)
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optim: str = field(default="adamw_torch")
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model_max_length: int = field(
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default=8192,
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metadata={
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"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
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},
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)
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use_lora: bool = False
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@dataclass
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class LoraArguments:
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lora_r: int = 64
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lora_alpha: int = 16
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lora_dropout: float = 0.05
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lora_target_modules: List[str] = field(
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default_factory=lambda: ["c_attn", "c_proj", "w1", "w2"]
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)
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lora_weight_path: str = ""
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lora_bias: str = "none"
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q_lora: bool = False
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def maybe_zero_3(param):
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if hasattr(param, "ds_id"):
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assert param.ds_status == ZeroParamStatus.NOT_AVAILABLE
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with zero.GatheredParameters([param]):
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param = param.data.detach().cpu().clone()
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else:
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param = param.detach().cpu().clone()
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return param
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# Borrowed from peft.utils.get_peft_model_state_dict
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def get_peft_state_maybe_zero_3(named_params, bias):
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if bias == "none":
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to_return = {k: t for k, t in named_params if "lora_" in k}
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elif bias == "all":
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to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k}
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elif bias == "lora_only":
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to_return = {}
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maybe_lora_bias = {}
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lora_bias_names = set()
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for k, t in named_params:
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if "lora_" in k:
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to_return[k] = t
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bias_name = k.split("lora_")[0] + "bias"
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lora_bias_names.add(bias_name)
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elif "bias" in k:
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maybe_lora_bias[k] = t
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for k, t in maybe_lora_bias:
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if bias_name in lora_bias_names:
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to_return[bias_name] = t
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else:
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raise NotImplementedError
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to_return = {k: maybe_zero_3(v) for k, v in to_return.items()}
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return to_return
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local_rank = None
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def rank0_print(*args):
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if local_rank == 0:
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print(*args)
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def safe_save_model_for_hf_trainer(
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trainer: transformers.Trainer, output_dir: str, bias="none"
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):
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"""Collects the state dict and dump to disk."""
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# check if zero3 mode enabled
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if deepspeed.is_deepspeed_zero3_enabled():
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state_dict = trainer.model_wrapped._zero3_consolidated_16bit_state_dict()
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else:
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if trainer.args.use_lora:
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state_dict = get_peft_state_maybe_zero_3(
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trainer.model.named_parameters(), bias
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)
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else:
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state_dict = trainer.model.state_dict()
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if trainer.args.should_save and trainer.args.local_rank == 0:
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trainer._save(output_dir, state_dict=state_dict)
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def preprocess(
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sources,
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tokenizer: transformers.PreTrainedTokenizer,
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max_len: int,
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system_message: str = "You are a helpful assistant.",
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) -> Dict:
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roles = {"user": "<|im_start|>user", "assistant": "<|im_start|>assistant"}
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im_start = tokenizer.im_start_id
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im_end = tokenizer.im_end_id
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nl_tokens = tokenizer("\n").input_ids
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_system = tokenizer("system").input_ids + nl_tokens
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_user = tokenizer("user").input_ids + nl_tokens
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_assistant = tokenizer("assistant").input_ids + nl_tokens
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# Apply prompt templates
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input_ids, targets = [], []
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for i, source in enumerate(sources):
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if roles[source[0]["from"]] != roles["user"]:
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source = source[1:]
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input_id, target = [], []
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system = (
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[im_start]
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+ _system
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+ tokenizer(system_message).input_ids
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+ [im_end]
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+ nl_tokens
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)
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input_id += system
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target += (
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[im_start] + [IGNORE_TOKEN_ID] * (len(system) - 3) + [im_end] + nl_tokens
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)
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assert len(input_id) == len(target)
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for j, sentence in enumerate(source):
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role = roles[sentence["from"]]
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_input_id = (
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tokenizer(role).input_ids
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+ nl_tokens
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+ tokenizer(sentence["value"]).input_ids
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+ [im_end]
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+ nl_tokens
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)
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input_id += _input_id
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if role == "<|im_start|>user":
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_target = (
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[im_start]
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+ [IGNORE_TOKEN_ID] * (len(_input_id) - 3)
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+ [im_end]
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+ nl_tokens
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)
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elif role == "<|im_start|>assistant":
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_target = (
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[im_start]
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+ [IGNORE_TOKEN_ID] * len(tokenizer(role).input_ids)
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+ _input_id[len(tokenizer(role).input_ids) + 1 : -2]
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+ [im_end]
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+ nl_tokens
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)
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else:
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raise NotImplementedError
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target += _target
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assert len(input_id) == len(target)
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input_id += [tokenizer.pad_token_id] * (max_len - len(input_id))
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target += [IGNORE_TOKEN_ID] * (max_len - len(target))
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input_ids.append(input_id[:max_len])
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targets.append(target[:max_len])
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input_ids = torch.tensor(input_ids, dtype=torch.int)
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targets = torch.tensor(targets, dtype=torch.int)
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return dict(
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input_ids=input_ids,
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labels=targets,
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attention_mask=input_ids.ne(tokenizer.pad_token_id),
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)
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class SupervisedDataset(Dataset):
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"""Dataset for supervised fine-tuning."""
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def __init__(
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self, raw_data, tokenizer: transformers.PreTrainedTokenizer, max_len: int
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):
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super(SupervisedDataset, self).__init__()
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rank0_print("Formatting inputs...")
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sources = [example["conversations"] for example in raw_data]
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data_dict = preprocess(sources, tokenizer, max_len)
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self.input_ids = data_dict["input_ids"]
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self.labels = data_dict["labels"]
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self.attention_mask = data_dict["attention_mask"]
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def __len__(self):
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return len(self.input_ids)
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def __getitem__(self, i) -> Dict[str, torch.Tensor]:
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return dict(
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input_ids=self.input_ids[i],
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labels=self.labels[i],
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attention_mask=self.attention_mask[i],
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)
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class LazySupervisedDataset(Dataset):
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"""Dataset for supervised fine-tuning."""
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def __init__(
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self, raw_data, tokenizer: transformers.PreTrainedTokenizer, max_len: int
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):
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super(LazySupervisedDataset, self).__init__()
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self.tokenizer = tokenizer
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self.max_len = max_len
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rank0_print("Formatting inputs...Skip in lazy mode")
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self.tokenizer = tokenizer
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self.raw_data = raw_data
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self.cached_data_dict = {}
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def __len__(self):
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return len(self.raw_data)
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def __getitem__(self, i) -> Dict[str, torch.Tensor]:
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if i in self.cached_data_dict:
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return self.cached_data_dict[i]
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ret = preprocess(
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[self.raw_data[i]["conversations"]], self.tokenizer, self.max_len
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)
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ret = dict(
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input_ids=ret["input_ids"][0],
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labels=ret["labels"][0],
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attention_mask=ret["attention_mask"][0],
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)
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self.cached_data_dict[i] = ret
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return ret
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def make_supervised_data_module(
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tokenizer: transformers.PreTrainedTokenizer,
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data_args,
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max_len,
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) -> Dict:
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"""Make dataset and collator for supervised fine-tuning."""
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dataset_cls = (
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LazySupervisedDataset if data_args.lazy_preprocess else SupervisedDataset
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)
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rank0_print("Loading data...")
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train_json = json.load(open(data_args.data_path, "r"))
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train_dataset = dataset_cls(train_json, tokenizer=tokenizer, max_len=max_len)
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if data_args.eval_data_path:
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eval_json = json.load(open(data_args.eval_data_path, "r"))
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eval_dataset = dataset_cls(eval_json, tokenizer=tokenizer, max_len=max_len)
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else:
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eval_dataset = None
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return dict(train_dataset=train_dataset, eval_dataset=eval_dataset)
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def train():
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global local_rank
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parser = transformers.HfArgumentParser(
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(ModelArguments, DataArguments, TrainingArguments, LoraArguments)
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)
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(
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model_args,
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data_args,
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training_args,
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lora_args,
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) = parser.parse_args_into_dataclasses()
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# This serves for single-gpu qlora.
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if (
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getattr(training_args, "deepspeed", None)
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and int(os.environ.get("WORLD_SIZE", 1)) == 1
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):
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training_args.distributed_state.distributed_type = DistributedType.DEEPSPEED
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local_rank = training_args.local_rank
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device_map = "auto"
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world_size = int(os.environ.get("WORLD_SIZE", 1))
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ddp = world_size != 1
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if lora_args.q_lora:
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device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)} if ddp else "auto"
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if len(training_args.fsdp) > 0 or deepspeed.is_deepspeed_zero3_enabled():
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logging.warning("FSDP or ZeRO3 are incompatible with QLoRA.")
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model_dir = snapshot_download(model_args.model_name_or_path)
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# Set RoPE scaling factor
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config = transformers.AutoConfig.from_pretrained(
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model_dir,
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cache_dir=training_args.cache_dir,
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trust_remote_code=True,
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)
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config.use_cache = False
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# Load model and tokenizer
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model = QWenLMHeadModel(config)
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model = model.from_pretrained(
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model_dir,
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config=config,
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cache_dir=training_args.cache_dir,
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device_map=device_map,
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trust_remote_code=True,
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quantization_config=GPTQConfig(bits=4, disable_exllama=True)
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if training_args.use_lora and lora_args.q_lora
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else None,
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)
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tokenizer = transformers.AutoTokenizer.from_pretrained(
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model_dir,
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cache_dir=training_args.cache_dir,
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model_max_length=training_args.model_max_length,
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padding_side="right",
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use_fast=False,
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trust_remote_code=True,
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)
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tokenizer.pad_token_id = tokenizer.eod_id
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if training_args.use_lora:
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if lora_args.q_lora or "chat" in model_dir.lower():
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modules_to_save = None
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else:
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modules_to_save = ["wte", "lm_head"]
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lora_config = LoraConfig(
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r=lora_args.lora_r,
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lora_alpha=lora_args.lora_alpha,
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target_modules=lora_args.lora_target_modules,
|
||||
lora_dropout=lora_args.lora_dropout,
|
||||
bias=lora_args.lora_bias,
|
||||
task_type="CAUSAL_LM",
|
||||
modules_to_save=modules_to_save, # This argument serves for adding new tokens.
|
||||
)
|
||||
if lora_args.q_lora:
|
||||
model = prepare_model_for_kbit_training(
|
||||
model, use_gradient_checkpointing=training_args.gradient_checkpointing
|
||||
)
|
||||
|
||||
model = get_peft_model(model, lora_config)
|
||||
|
||||
# Print peft trainable params
|
||||
model.print_trainable_parameters()
|
||||
|
||||
if training_args.gradient_checkpointing:
|
||||
model.enable_input_require_grads()
|
||||
|
||||
# Load data
|
||||
data_module = make_supervised_data_module(
|
||||
tokenizer=tokenizer, data_args=data_args, max_len=training_args.model_max_length
|
||||
)
|
||||
|
||||
# Start trainner
|
||||
trainer = Trainer(
|
||||
model=model, tokenizer=tokenizer, args=training_args, **data_module
|
||||
)
|
||||
|
||||
trainer.train()
|
||||
trainer.save_state()
|
||||
|
||||
safe_save_model_for_hf_trainer(
|
||||
trainer=trainer, output_dir=training_args.output_dir, bias=lora_args.lora_bias
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
train()
|
||||
|
|
@ -1,65 +0,0 @@
|
|||
#!/bin/bash
|
||||
export CUDA_DEVICE_MAX_CONNECTIONS=1
|
||||
|
||||
MODEL="qwen/Qwen-1_8B-Chat" # Set the path if you do not want to load from huggingface directly
|
||||
# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
|
||||
# See the section for finetuning in README for more information.
|
||||
DATA="data.json"
|
||||
|
||||
function usage() {
|
||||
echo '
|
||||
Usage: bash finetune/finetune_lora_single_gpu.sh [-m MODEL_PATH] [-d DATA_PATH]
|
||||
'
|
||||
}
|
||||
|
||||
while [[ "$1" != "" ]]; do
|
||||
case $1 in
|
||||
-m | --model )
|
||||
shift
|
||||
MODEL=$1
|
||||
;;
|
||||
-d | --data )
|
||||
shift
|
||||
DATA=$1
|
||||
;;
|
||||
-h | --help )
|
||||
usage
|
||||
exit 0
|
||||
;;
|
||||
* )
|
||||
echo "Unknown argument ${1}"
|
||||
exit 1
|
||||
;;
|
||||
esac
|
||||
shift
|
||||
done
|
||||
|
||||
export CUDA_VISIBLE_DEVICES=0
|
||||
|
||||
python finetune.py \
|
||||
--model_name_or_path $MODEL \
|
||||
--data_path $DATA \
|
||||
--bf16 False \
|
||||
--output_dir output_qwen \
|
||||
--num_train_epochs 5 \
|
||||
--per_device_train_batch_size 2 \
|
||||
--per_device_eval_batch_size 1 \
|
||||
--gradient_accumulation_steps 8 \
|
||||
--evaluation_strategy "no" \
|
||||
--save_strategy "steps" \
|
||||
--save_steps 1000 \
|
||||
--save_total_limit 10 \
|
||||
--learning_rate 3e-4 \
|
||||
--weight_decay 0.1 \
|
||||
--adam_beta2 0.95 \
|
||||
--warmup_ratio 0.01 \
|
||||
--lr_scheduler_type "cosine" \
|
||||
--logging_steps 1 \
|
||||
--report_to "none" \
|
||||
--model_max_length 512 \
|
||||
--lazy_preprocess True \
|
||||
--gradient_checkpointing \
|
||||
--use_lora
|
||||
|
||||
# If you use fp16 instead of bf16, you should use deepspeed
|
||||
# --fp16 True --deepspeed finetune/ds_config_zero2.json
|
||||
|
|
@ -16,10 +16,7 @@ from torch import nn
|
|||
from safetensors.torch import load_file as safe_load_file
|
||||
from safetensors.torch import save_file as safe_save_file
|
||||
|
||||
from transformers.generation.utils import GenerateOutput
|
||||
from configuration_qwen import QWenConfig
|
||||
from qwen_generation_utils import (
|
||||
HistoryType,
|
||||
make_context,
|
||||
decode_tokens,
|
||||
)
|
||||
|
|
@ -137,7 +134,6 @@ class QWenLMHeadModel(nn.Module):
|
|||
def __init__(self, config):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
|
||||
self.transformer = QWenModel(config)
|
||||
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||||
|
||||
|
|
@ -186,65 +182,42 @@ class QWenLMHeadModel(nn.Module):
|
|||
print(f"All model checkpoint weights were used when initializing {cls.__class__.__name__}.\n")
|
||||
return cls
|
||||
|
||||
|
||||
class QwenRunner:
|
||||
def __init__(self, qwen):
|
||||
self.qwen = qwen
|
||||
|
||||
@torch.no_grad()
|
||||
def chat(
|
||||
def Chat(
|
||||
self,
|
||||
tokenizer,
|
||||
query: str,
|
||||
query_assistant: str,
|
||||
history: Optional[HistoryType],
|
||||
system: str = "You are a helpful assistant.",
|
||||
**kwargs,
|
||||
) -> Tuple[str, HistoryType]:
|
||||
if history is None:
|
||||
history = []
|
||||
else:
|
||||
history=[],
|
||||
):
|
||||
qwen = self.qwen
|
||||
history = copy.deepcopy(history)
|
||||
|
||||
raw_text, context_tokens = make_context(tokenizer, query, query_assistant, history=history, system=system)
|
||||
input_ids = torch.tensor([context_tokens]).to(next(self.parameters()).device)
|
||||
outputs = self.generate(
|
||||
input_ids,
|
||||
tokenizer=tokenizer,
|
||||
**kwargs,
|
||||
)
|
||||
decoded, response, end_reason = decode_tokens(
|
||||
outputs[0],
|
||||
tokenizer,
|
||||
raw_text_len=len(raw_text),
|
||||
context_length=len(context_tokens),
|
||||
errors="replace",
|
||||
)
|
||||
history.append((query, response))
|
||||
return response, history, decoded
|
||||
input_ids = torch.tensor([context_tokens]).to(next(qwen.parameters()).device)
|
||||
eos_token_id_tensor = torch.tensor([qwen.config.eos_token_id]).to(input_ids.device)
|
||||
pad_token_id = qwen.config.pad_token_id
|
||||
|
||||
def generate(
|
||||
self,
|
||||
input_ids: Optional[torch.Tensor] = None,
|
||||
tokenizer=None,
|
||||
) -> Union[GenerateOutput, torch.LongTensor]:
|
||||
pad_token_id = self.config.pad_token_id
|
||||
eos_token_id_tensor = torch.tensor([self.config.eos_token_id]).to(input_ids.device)
|
||||
|
||||
# keep track of which sequences are already finished
|
||||
unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
|
||||
|
||||
this_peer_finished = False
|
||||
# auto-regressive generation
|
||||
while True:
|
||||
# forward pass to get next token
|
||||
outputs = forwardQWen(self, input_ids)
|
||||
outputs = self.forwardQWen(input_ids)
|
||||
next_token_scores = outputs[:, -1, :]
|
||||
|
||||
# repetition_penalty
|
||||
penalty = self.config.repetition_penalty
|
||||
penalty = qwen.config.repetition_penalty
|
||||
score = torch.gather(next_token_scores, 1, input_ids)
|
||||
# if score < 0 then repetition penalty has to be multiplied to reduce the token probabilities
|
||||
score = torch.where(score < 0, score * penalty, score / penalty)
|
||||
next_token_scores = next_token_scores.scatter_(1, input_ids, score)
|
||||
|
||||
# top_p
|
||||
top_p = self.config.top_p
|
||||
top_p = qwen.config.top_p
|
||||
filter_value = -float("Inf")
|
||||
min_tokens_to_keep = 1
|
||||
sorted_logits, sorted_indices = torch.sort(next_token_scores, descending=False)
|
||||
|
|
@ -262,33 +235,29 @@ class QWenLMHeadModel(nn.Module):
|
|||
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
|
||||
|
||||
next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
|
||||
|
||||
# update generated ids, model inputs, and length for next step
|
||||
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
|
||||
|
||||
unfinished_sequences = unfinished_sequences.mul(
|
||||
next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
|
||||
)
|
||||
|
||||
# decoded, response, end_reason = decode_tokens(
|
||||
# next_tokens,
|
||||
# tokenizer,
|
||||
# raw_text_len=0,
|
||||
# context_length=0,
|
||||
# errors="replace",
|
||||
# )
|
||||
# print(decoded)
|
||||
|
||||
# stop when each sentence is finished
|
||||
if unfinished_sequences.max() == 0:
|
||||
this_peer_finished = True
|
||||
|
||||
if this_peer_finished:
|
||||
break
|
||||
return input_ids
|
||||
|
||||
decoded, response, end_reason = decode_tokens(
|
||||
input_ids[0],
|
||||
tokenizer,
|
||||
raw_text_len=len(raw_text),
|
||||
context_length=len(context_tokens),
|
||||
errors="replace",
|
||||
)
|
||||
history.append((query, response))
|
||||
return response, history, decoded
|
||||
|
||||
def forwardAttention(
|
||||
self,
|
||||
attention,
|
||||
hidden_states: Optional[Tuple[torch.FloatTensor]],
|
||||
rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
|
||||
|
|
@ -340,15 +309,15 @@ def forwardAttention(
|
|||
|
||||
return attn_output
|
||||
|
||||
|
||||
def forwardQWenBlock(
|
||||
self,
|
||||
block,
|
||||
hidden_states: Optional[Tuple[torch.FloatTensor]],
|
||||
rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
|
||||
):
|
||||
layernorm_output = block.ln_1(hidden_states)
|
||||
|
||||
attn_outputs = forwardAttention(block.attn, layernorm_output, rotary_pos_emb_list)
|
||||
attn_outputs = self.forwardAttention(block.attn, layernorm_output, rotary_pos_emb_list)
|
||||
attn_output = attn_outputs[0]
|
||||
layernorm_input = attn_output + hidden_states
|
||||
|
||||
|
|
@ -361,13 +330,12 @@ def forwardQWenBlock(
|
|||
hidden_states = layernorm_input + mlp_output
|
||||
return hidden_states
|
||||
|
||||
|
||||
def forwardQWen(
|
||||
qwen,
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
):
|
||||
transfm = qwen.transformer
|
||||
transfm = self.qwen.transformer
|
||||
input_shape = input_ids.size()
|
||||
input_ids = input_ids.view(-1, input_shape[-1])
|
||||
hidden_states = transfm.wte(input_ids)
|
||||
|
|
@ -381,12 +349,12 @@ def forwardQWen(
|
|||
output_shape = input_shape + (hidden_states.size(-1),)
|
||||
|
||||
for block in transfm.h:
|
||||
hidden_states = forwardQWenBlock(block, hidden_states, rotary_pos_emb_list=rotary_pos_emb_list)
|
||||
hidden_states = self.forwardQWenBlock(block, hidden_states, rotary_pos_emb_list=rotary_pos_emb_list)
|
||||
|
||||
hidden_states = transfm.ln_f(hidden_states)
|
||||
hidden_states = hidden_states.view(output_shape)
|
||||
|
||||
lm_logits = qwen.lm_head(hidden_states)
|
||||
lm_logits = self.qwen.lm_head(hidden_states)
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
|
|
|
|||
|
|
@ -0,0 +1,29 @@
|
|||
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
class People(ABC):
|
||||
# @abstractmethod
|
||||
def walk(self):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def eat(self):
|
||||
pass
|
||||
|
||||
def auto(self):
|
||||
self.walk()
|
||||
self.eat()
|
||||
|
||||
class kid1(People):
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def walk(self):
|
||||
print('走路')
|
||||
|
||||
def eat(self):
|
||||
print('吃饭')
|
||||
|
||||
if __name__ == '__main__':
|
||||
k = kid1()
|
||||
k.auto()
|
||||
Loading…
Reference in New Issue