1.0

README.md

@@ -102,10 +102,73 @@ ChatGLM-6B 是清华大学开源的开源的、支持中英双语的对话语言
 运行如下命令：
 
     python cli_demo.py
-    
+
 程序会在命令行中进行交互式的对话，在命令行中输入指示并回车即可生成回复，输入 clear 可以清空对话历史，输入 stop 终止程序。
 
+## 重新 pretrain
+
+由于当前的 [GLM-130B](https://github.com/THUDM/GLM-130B#news) 与 ChatGLM 的模型结构非常类似，所以对于有训练 GLM-130B 的用户来说，可以通过修改 ChatGLM 的 config.json 使用堆参数的方式将参数量达到130B。该项目为了满足用户对 ChatGLM 重新 pretrain 的需求，继续添加了 simple-pretrain 目录，旨在提供一种改动最小的 pretrain 示例。pretrain步骤如下：
+
+1. 将 simple-pretrain/ptuning 下的文件移到本 ptuning 目录下，替换相关文件
+
+2. 将 modeling_chatglm.py 移到[ ChatGLM 模型](https://huggingface.co/THUDM/chatglm-6b)所在目录替换原始 modeling_chatglm.py 
+
+3. 在本 ptuning 目录下：
+
+   ```
+   bash ds_pretrain.sh
+   ```
+
+说明：convert.py 可以将原始的txt数据转换成 chatglm 可用的 json 形式的数据集格式。该示例使用指环王1书籍作为预训练数据集。
+
+### 实验设置
+
+```
+LR=1e-5
+
+MASTER_PORT=$(shuf -n 1 -i 10000-65535)
+
+HIP_VISIBLE_DEVICES=0,1,2,3 deepspeed --num_gpus=4 --master_port $MASTER_PORT main.py \
+    --deepspeed deepspeed.json \
+    --do_train \
+    --train_file The-Lord-of-the-Rings-1.json \
+    --prompt_column prompt \
+    --response_column response \
+    --overwrite_cache \
+    --model_name_or_path THUDM/chatglm-6b \
+    --output_dir ./output/pretrain \
+    --overwrite_output_dir \
+    --max_source_length 8 \
+    --max_target_length 128 \
+    --per_device_train_batch_size 16 \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 4 \
+    --predict_with_generate \
+    --max_steps 2000 \
+    --logging_steps 5 \
+    --save_steps 1000 \
+    --learning_rate $LR \
+    --fp16
+```
+
+### 训练loss收敛情况
+
+由于该示例预训练数据集较小，loss会降的至较低水平到0.1左右。
+
+![img](http://hpczentao.sugon.com/file-read-106608.jpeg)
+
+## 强化学习(RLHF)微调方案
+
+目前在 DCU 上 ChatGLM 使用强化学习微调有两种方案：
+
+- 使用 Lora，只更新低秩适配层，可以直接参考项目：https://github.com/hiyouga/ChatGLM-Efficient-Tuning/blob/main/examples/covid_doctor.md
+- 使用 DeepSpeed-Chat 方案全参微调，目前已经适配完成，欢迎尝试：https://github.com/yuguo-Jack/ChatGLM-6B-in-DeepSpeed-Chat
+
+## 源码仓库及问题反馈
+
+https://developer.hpccube.com/codes/modelzoo/chatglm
+
 ## 参考
-[THUDM/ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B/tree/main)
 
+[THUDM/ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B/tree/main)
 

model.properties

+# 模型名称
+modelName=ChatGLM-6B
+# 模型描述
+modelDescription=基于Pytorch框架的ChatGLM-6B
+# 应用场景(多个标签以英文逗号分割)
+appScenario=训练,推理,train,inference,nlp,智能聊天助手
+# 框架类型(多个标签以英文逗号分割)
+frameType=Pytorch

simple-pretrain/convert.py

+import json
+
+def convert_txt_to_json(txt_file):
+    json_data = []
+    with open(txt_file, 'r', encoding='gb18030') as file:
+        for line in file:
+            line = line.strip()
+            if line:
+                prompt = line[0]
+                response = line[1:]
+                json_entry = {'prompt': prompt, 'response': response}
+                json_data.append(json_entry)
+    
+    return json_data
+
+txt_file = 'The-Lord-of-the-Rings-1.txt'  # 替换成你的文本文件路径
+json_data = convert_txt_to_json(txt_file)
+
+# 将JSON数据写入文件
+json_file = 'The-Lord-of-the-Rings-1.json'  # 输出的JSON文件路径
+with open(json_file, 'w', encoding='utf-8') as file:
+    for entry in json_data:
+        json.dump(entry, file, ensure_ascii=False)
+        file.write('\n')
+

simple-pretrain/ptuning/deepspeed.json

+{
+  "train_micro_batch_size_per_gpu": "auto",
+  "gradient_accumulation_steps": "auto",
+  "zero_allow_untested_optimizer": true,
+  "fp16": {
+    "enabled": "auto",
+    "loss_scale": 0,
+    "initial_scale_power": 16,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "zero_optimization": {
+    "stage": 3,
+    "offload_optimizer": {
+      "device": "cpu",
+      "pin_memory": true
+    },
+    "offload_param": {
+      "device": "cpu",
+      "pin_memory": true
+    },
+    "allgather_partitions": true,
+    "allgather_bucket_size": 5e8,
+    "overlap_comm": true,
+    "reduce_scatter": true,
+    "reduce_bucket_size": 5e8,
+    "contiguous_gradients" : true,
+    "stage3_gather_16bit_weights_on_model_save": true
+  }
+}

simple-pretrain/ptuning/ds_pretrain.sh

+LR=1e-5
+
+MASTER_PORT=$(shuf -n 1 -i 10000-65535)
+
+HIP_VISIBLE_DEVICES=0,1,2,3 deepspeed --num_gpus=4 --master_port $MASTER_PORT main.py \
+    --deepspeed deepspeed.json \
+    --do_train \
+    --train_file The-Lord-of-the-Rings-1.json \
+    --prompt_column prompt \
+    --response_column response \
+    --overwrite_cache \
+    --model_name_or_path THUDM/chatglm-6b \
+    --output_dir ./output/pretrain \
+    --overwrite_output_dir \
+    --max_source_length 8 \
+    --max_target_length 128 \
+    --per_device_train_batch_size 16 \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 4 \
+    --predict_with_generate \
+    --max_steps 2000 \
+    --logging_steps 5 \
+    --save_steps 1000 \
+    --learning_rate $LR \
+    --fp16
+

simple-pretrain/ptuning/main.py

+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fine-tuning the library models for sequence to sequence.
+"""
+# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
+
+import logging
+import os
+import sys
+import json
+
+import numpy as np
+from datasets import load_dataset
+import jieba 
+from rouge_chinese import Rouge
+from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
+import torch
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoModel,
+    AutoTokenizer,
+    DataCollatorForSeq2Seq,
+    HfArgumentParser,
+    Seq2SeqTrainingArguments,
+    set_seed,
+)
+from trainer_seq2seq import Seq2SeqTrainer
+
+from arguments import ModelArguments, DataTrainingArguments
+
+logger = logging.getLogger(__name__)
+
+def main():
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+
+    if training_args.should_log:
+        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
+        transformers.utils.logging.set_verbosity_info()
+
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    # datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # Load dataset
+    data_files = {}
+    if data_args.train_file is not None:
+        data_files["train"] = data_args.train_file
+        extension = data_args.train_file.split(".")[-1]
+    if data_args.validation_file is not None:
+        data_files["validation"] = data_args.validation_file
+        extension = data_args.validation_file.split(".")[-1]
+    if data_args.test_file is not None:
+        data_files["test"] = data_args.test_file
+        extension = data_args.test_file.split(".")[-1]
+
+    raw_datasets = load_dataset(
+        extension,
+        data_files=data_files,
+        cache_dir=model_args.cache_dir,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    # Load pretrained model and tokenizer
+    config = AutoConfig.from_pretrained(model_args.model_name_or_path, trust_remote_code=True)
+    config.pre_seq_len = model_args.pre_seq_len
+    config.prefix_projection = model_args.prefix_projection
+
+    tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, trust_remote_code=True)
+
+    if model_args.ptuning_checkpoint is not None:
+        # Evaluation
+        # Loading extra state dict of prefix encoder
+        model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True)
+        prefix_state_dict = torch.load(os.path.join(model_args.ptuning_checkpoint, "pytorch_model.bin"))
+        new_prefix_state_dict = {}
+        for k, v in prefix_state_dict.items():
+            if k.startswith("transformer.prefix_encoder."):
+                new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
+        model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
+    else:
+        # model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True)
+        model = AutoModel.from_config(config=config, trust_remote_code=True)
+
+    if model_args.quantization_bit is not None:
+        print(f"Quantized to {model_args.quantization_bit} bit")
+        model = model.quantize(model_args.quantization_bit)
+    if model_args.pre_seq_len is not None:
+        # P-tuning v2
+        model = model.half()
+        model.transformer.prefix_encoder.float()
+    else:
+        # Finetune
+        model = model.float()
+
+    prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
+
+    # Preprocessing the datasets.
+    # We need to tokenize inputs and targets.
+    if training_args.do_train:
+        column_names = raw_datasets["train"].column_names
+    elif training_args.do_eval:
+        column_names = raw_datasets["validation"].column_names
+    elif training_args.do_predict:
+        column_names = raw_datasets["test"].column_names
+    else:
+        logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
+        return
+
+    # Get the column names for input/target.
+    prompt_column = data_args.prompt_column
+    response_column = data_args.response_column
+    history_column = data_args.history_column
+    
+    # Temporarily set max_target_length for training.
+    max_target_length = data_args.max_target_length
+
+    def preprocess_function_eval(examples):
+        inputs, targets = [], []
+        for i in range(len(examples[prompt_column])):
+            if examples[prompt_column][i] and examples[response_column][i]:
+                query = examples[prompt_column][i]
+                if history_column is None or len(examples[history_column][i]) == 0:
+                    prompt = query
+                else:
+                    prompt = ""
+                    history = examples[history_column][i]
+                    for turn_idx, (old_query, response) in enumerate(history):
+                        prompt += "[Round {}]\n问：{}\n答：{}\n".format(turn_idx, old_query, response)
+                    prompt += "[Round {}]\n问：{}\n答：".format(len(history), query)
+                inputs.append(prompt)
+                targets.append(examples[response_column][i])
+
+        inputs = [prefix + inp for inp in inputs]
+        model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, truncation=True, padding=True)
+        labels = tokenizer(text_target=targets, max_length=max_target_length, truncation=True)
+
+        if data_args.ignore_pad_token_for_loss:
+            labels["input_ids"] = [
+                [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
+            ]
+        model_inputs["labels"] = labels["input_ids"]
+
+        return model_inputs
+
+    def preprocess_function_train(examples):
+        max_seq_length = data_args.max_source_length + data_args.max_target_length
+
+        model_inputs = {
+            "input_ids": [],
+            "labels": [],
+        }
+        for i in range(len(examples[prompt_column])):
+            if examples[prompt_column][i] and examples[response_column][i]:
+                query, answer = examples[prompt_column][i], examples[response_column][i]
+
+                if history_column is None:
+                    prompt = query
+                else:
+                    prompt = ""
+                    history = examples[history_column][i]
+                    for turn_idx, (old_query, response) in enumerate(history):
+                        prompt += "[Round {}]\n问：{}\n答：{}\n".format(turn_idx, old_query, response)
+                    prompt += "[Round {}]\n问：{}\n答：".format(len(history), query)
+
+                prompt = prefix + prompt
+                a_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
+                b_ids = tokenizer.encode(text=answer, add_special_tokens=False)
+
+                if len(a_ids) > data_args.max_source_length - 1:
+                    a_ids = a_ids[: data_args.max_source_length - 1]
+
+                if len(b_ids) > data_args.max_target_length - 2:
+                    b_ids = b_ids[: data_args.max_target_length - 2]
+
+                input_ids = tokenizer.build_inputs_with_special_tokens(a_ids, b_ids)
+
+                context_length = input_ids.index(tokenizer.bos_token_id)
+                mask_position = context_length - 1
+                labels = [-100] * context_length + input_ids[mask_position+1:]
+                
+                pad_len = max_seq_length - len(input_ids)
+                input_ids = input_ids + [tokenizer.pad_token_id] * pad_len
+                labels = labels + [tokenizer.pad_token_id] * pad_len
+                if data_args.ignore_pad_token_for_loss:
+                    labels = [(l if l != tokenizer.pad_token_id else -100) for l in labels]
+
+                model_inputs["input_ids"].append(input_ids)
+                model_inputs["labels"].append(labels)
+
+        return model_inputs
+    
+    def print_dataset_example(example):
+        print("input_ids",example["input_ids"])
+        print("inputs", tokenizer.decode(example["input_ids"]))
+        print("label_ids", example["labels"])
+        print("labels", tokenizer.decode(example["labels"]))
+
+    if training_args.do_train:
+        if "train" not in raw_datasets:
+            raise ValueError("--do_train requires a train dataset")
+        train_dataset = raw_datasets["train"]
+        if data_args.max_train_samples is not None:
+            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
+            train_dataset = train_dataset.select(range(max_train_samples))
+        with training_args.main_process_first(desc="train dataset map pre-processing"):
+            train_dataset = train_dataset.map(
+                preprocess_function_train,
+                batched=True,
+                num_proc=data_args.preprocessing_num_workers,
+                remove_columns=column_names,
+                load_from_cache_file=not data_args.overwrite_cache,
+                desc="Running tokenizer on train dataset",
+            )
+        print_dataset_example(train_dataset[0])
+
+    if training_args.do_eval:
+        max_target_length = data_args.val_max_target_length
+        if "validation" not in raw_datasets:
+            raise ValueError("--do_eval requires a validation dataset")
+        eval_dataset = raw_datasets["validation"]
+        if data_args.max_eval_samples is not None:
+            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
+            eval_dataset = eval_dataset.select(range(max_eval_samples))
+        with training_args.main_process_first(desc="validation dataset map pre-processing"):
+            eval_dataset = eval_dataset.map(
+                preprocess_function_eval,
+                batched=True,
+                num_proc=data_args.preprocessing_num_workers,
+                remove_columns=column_names,
+                load_from_cache_file=not data_args.overwrite_cache,
+                desc="Running tokenizer on validation dataset",
+            )
+        print_dataset_example(eval_dataset[0])
+
+    if training_args.do_predict:
+        max_target_length = data_args.val_max_target_length
+        if "test" not in raw_datasets:
+            raise ValueError("--do_predict requires a test dataset")
+        predict_dataset = raw_datasets["test"]
+        if data_args.max_predict_samples is not None:
+            max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
+            predict_dataset = predict_dataset.select(range(max_predict_samples))
+        with training_args.main_process_first(desc="prediction dataset map pre-processing"):
+            predict_dataset = predict_dataset.map(
+                preprocess_function_eval,
+                batched=True,
+                num_proc=data_args.preprocessing_num_workers,
+                remove_columns=column_names,
+                load_from_cache_file=not data_args.overwrite_cache,
+                desc="Running tokenizer on prediction dataset",
+            )
+        print_dataset_example(predict_dataset[0])
+
+    # Data collator
+    label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
+    data_collator = DataCollatorForSeq2Seq(
+        tokenizer,
+        model=model,
+        label_pad_token_id=label_pad_token_id,
+        pad_to_multiple_of=None,
+        padding=False
+    )
+
+    # Metric
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        if isinstance(preds, tuple):
+            preds = preds[0]
+        decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
+        if data_args.ignore_pad_token_for_loss:
+            # Replace -100 in the labels as we can't decode them.
+            labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+        decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+        score_dict = {
+            "rouge-1": [],
+            "rouge-2": [],
+            "rouge-l": [],
+            "bleu-4": []
+        }
+        for pred, label in zip(decoded_preds, decoded_labels):
+            hypothesis = list(jieba.cut(pred))
+            reference = list(jieba.cut(label))
+            rouge = Rouge()
+            scores = rouge.get_scores(' '.join(hypothesis) , ' '.join(reference))
+            result = scores[0]
+            
+            for k, v in result.items():
+                score_dict[k].append(round(v["f"] * 100, 4))
+            bleu_score = sentence_bleu([list(label)], list(pred), smoothing_function=SmoothingFunction().method3)
+            score_dict["bleu-4"].append(round(bleu_score * 100, 4))
+
+        for k, v in score_dict.items():
+            score_dict[k] = float(np.mean(v))
+        return score_dict
+
+    # Override the decoding parameters of Seq2SeqTrainer
+    training_args.generation_max_length = (
+        training_args.generation_max_length
+        if training_args.generation_max_length is not None
+        else data_args.val_max_target_length
+    )
+    training_args.generation_num_beams = (
+        data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
+    )
+    # Initialize our Trainer
+    trainer = Seq2SeqTrainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset if training_args.do_train else None,
+        eval_dataset=eval_dataset if training_args.do_eval else None,
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics if training_args.predict_with_generate else None,
+        save_prefixencoder=model_args.pre_seq_len is not None
+    )
+
+    # Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        # elif last_checkpoint is not None:
+        #     checkpoint = last_checkpoint
+        model.gradient_checkpointing_enable()
+        model.enable_input_require_grads()
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        # trainer.save_model()  # Saves the tokenizer too for easy upload
+
+        metrics = train_result.metrics
+        max_train_samples = (
+            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
+        )
+        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
+
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # Evaluation
+    results = {}
+    max_seq_length = data_args.max_source_length + data_args.max_target_length + 1
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate(metric_key_prefix="eval", do_sample=True, top_p=0.7, max_length=max_seq_length, temperature=0.95)
+        max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
+        metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    if training_args.do_predict:
+        logger.info("*** Predict ***")
+        predict_results = trainer.predict(predict_dataset, metric_key_prefix="predict", max_length=max_seq_length, do_sample=True, top_p=0.7, temperature=0.95)
+        metrics = predict_results.metrics
+        max_predict_samples = (
+            data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
+        )
+        metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
+
+        trainer.log_metrics("predict", metrics)
+        trainer.save_metrics("predict", metrics)
+
+        if trainer.is_world_process_zero():
+            if training_args.predict_with_generate:
+                predictions = tokenizer.batch_decode(
+                    predict_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
+                )
+                predictions = [pred.strip() for pred in predictions]
+                labels = tokenizer.batch_decode(
+                    predict_results.label_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
+                )
+                labels = [label.strip() for label in labels]
+                output_prediction_file = os.path.join(training_args.output_dir, "generated_predictions.txt")
+                with open(output_prediction_file, "w", encoding="utf-8") as writer:
+                    for p, l in zip(predictions, labels):
+                        res = json.dumps({"labels": l, "predict": p}, ensure_ascii=False)
+                        writer.write(f"{res}\n")
+    return results
+
+
+def _mp_fn(index):
+    # For xla_spawn (TPUs)
+    main()
+
+
+if __name__ == "__main__":
+    main()