883 lines
50 KiB
Plaintext
883 lines
50 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "bb9102c3-5b8d-4295-8f29-113b35ec5679",
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"metadata": {},
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"source": [
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"# 一、LLM 预训练"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8557a6a6-294a-49c3-a8f6-e58bc3bf443d",
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"metadata": {},
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"source": [
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"1.1 初始化 LLM"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "25f1fad8-772c-474e-a43e-77623106485d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Qwen2Config {\n",
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" \"_name_or_path\": \"autodl-tmp/qwen-1.5b\",\n",
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" \"architectures\": [\n",
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" \"Qwen2ForCausalLM\"\n",
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" ],\n",
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" \"attention_dropout\": 0.0,\n",
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" \"bos_token_id\": 151643,\n",
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" \"eos_token_id\": 151643,\n",
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" \"hidden_act\": \"silu\",\n",
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" \"hidden_size\": 1536,\n",
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" \"initializer_range\": 0.02,\n",
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" \"intermediate_size\": 8960,\n",
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" \"max_position_embeddings\": 131072,\n",
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" \"max_window_layers\": 28,\n",
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" \"model_type\": \"qwen2\",\n",
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" \"num_attention_heads\": 12,\n",
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" \"num_hidden_layers\": 28,\n",
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" \"num_key_value_heads\": 2,\n",
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" \"rms_norm_eps\": 1e-06,\n",
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" \"rope_theta\": 1000000.0,\n",
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" \"sliding_window\": null,\n",
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" \"tie_word_embeddings\": true,\n",
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" \"torch_dtype\": \"bfloat16\",\n",
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" \"transformers_version\": \"4.44.2\",\n",
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" \"use_cache\": true,\n",
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" \"use_mrope\": false,\n",
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" \"use_sliding_window\": false,\n",
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" \"vocab_size\": 151936\n",
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"}"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 加载定义好的模型参数-此处以 Qwen-2.5-1.5B 为例\n",
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"# 使用 transforemrs 的 Config 类进行加载\n",
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"from transformers import AutoConfig\n",
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"\n",
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"model_path = \"autodl-tmp/qwen-1.5b\"\n",
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"config = AutoConfig.from_pretrained(model_path)\n",
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"config"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "82b075a1-4fe9-4abb-b5b4-769d1c1a7156",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training new model from scratch - Total size=1472.20M params\n"
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]
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}
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],
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"source": [
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"# 使用该配置生成一个定义好的模型\n",
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"from transformers import AutoModelForCausalLM\n",
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"\n",
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"model = AutoModelForCausalLM.from_config(config,trust_remote_code=True)\n",
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"model.to(\"cuda\")\n",
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"n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values())\n",
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"print(f\"Training new model from scratch - Total size={n_params/2**20:.2f}M params\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "e05ea707-23db-4e67-8b7d-e57d019887dd",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Qwen2ForCausalLM(\n",
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" (model): Qwen2Model(\n",
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" (embed_tokens): Embedding(151936, 1536)\n",
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" (layers): ModuleList(\n",
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" (0-27): 28 x Qwen2DecoderLayer(\n",
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" (self_attn): Qwen2SdpaAttention(\n",
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" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
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" (k_proj): Linear(in_features=1536, out_features=256, bias=True)\n",
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" (v_proj): Linear(in_features=1536, out_features=256, bias=True)\n",
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" (o_proj): Linear(in_features=1536, out_features=1536, bias=False)\n",
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" (rotary_emb): Qwen2RotaryEmbedding()\n",
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" )\n",
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" (mlp): Qwen2MLP(\n",
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" (gate_proj): Linear(in_features=1536, out_features=8960, bias=False)\n",
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" (up_proj): Linear(in_features=1536, out_features=8960, bias=False)\n",
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" (down_proj): Linear(in_features=8960, out_features=1536, bias=False)\n",
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" (act_fn): SiLU()\n",
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" )\n",
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" (input_layernorm): Qwen2RMSNorm((1536,), eps=1e-06)\n",
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" (post_attention_layernorm): Qwen2RMSNorm((1536,), eps=1e-06)\n",
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" )\n",
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" )\n",
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" (norm): Qwen2RMSNorm((1536,), eps=1e-06)\n",
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" )\n",
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" (lm_head): Linear(in_features=1536, out_features=151936, bias=False)\n",
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")"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 看一下模型\n",
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"model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "3408137b-eb50-4119-be1c-7a4ff951ab24",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Qwen2TokenizerFast(name_or_path='autodl-tmp/qwen-1.5b', vocab_size=151643, model_max_length=131072, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'eos_token': '<|endoftext|>', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|im_start|>', '<|im_end|>', '<|object_ref_start|>', '<|object_ref_end|>', '<|box_start|>', '<|box_end|>', '<|quad_start|>', '<|quad_end|>', '<|vision_start|>', '<|vision_end|>', '<|vision_pad|>', '<|image_pad|>', '<|video_pad|>']}, clean_up_tokenization_spaces=False), added_tokens_decoder={\n",
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"\t151643: AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
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"\t151644: AddedToken(\"<|im_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
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"\t151645: AddedToken(\"<|im_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
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"\t151646: AddedToken(\"<|object_ref_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
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"\t151647: AddedToken(\"<|object_ref_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
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"\t151648: AddedToken(\"<|box_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
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"\t151649: AddedToken(\"<|box_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
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"\t151650: AddedToken(\"<|quad_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
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"\t151651: AddedToken(\"<|quad_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
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"\t151652: AddedToken(\"<|vision_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
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"\t151653: AddedToken(\"<|vision_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
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"\t151654: AddedToken(\"<|vision_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
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"\t151655: AddedToken(\"<|image_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
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"\t151656: AddedToken(\"<|video_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
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"\t151657: AddedToken(\"<tool_call>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
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"\t151658: AddedToken(\"</tool_call>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
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"\t151659: AddedToken(\"<|fim_prefix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
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"\t151660: AddedToken(\"<|fim_middle|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
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"\t151661: AddedToken(\"<|fim_suffix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
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"\t151662: AddedToken(\"<|fim_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
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"\t151663: AddedToken(\"<|repo_name|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
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"\t151664: AddedToken(\"<|file_sep|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
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"}"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 加载一个预训练好的 tokenizer\n",
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"from transformers import AutoTokenizer\n",
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"\n",
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"tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
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"tokenizer"
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]
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},
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{
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"cell_type": "markdown",
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"id": "221a0fe2-a244-4e73-b82c-6da255d710dd",
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"metadata": {},
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"source": [
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"1.2 预训练数据准备"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "936261a6-94cf-4cf3-842c-d3f1fde47a71",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "66ae9baa159b424ea5f5bc8d05b9b567",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Generating train split: 0 examples [00:00, ? examples/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# 加载预训练数据\n",
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"from datasets import load_dataset\n",
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"\n",
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"ds = load_dataset('json', data_files='autodl-tmp/dataset/pretrain_data/mobvoi_seq_monkey_general_open_corpus_small.jsonl')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "068edbb9-cb3c-49b1-aaf9-67b97ddfc58c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'text': '在查处虚开增值税专用发票案件中，常常涉及进项留抵税额和税款损失的认定和处理。在计算税款损失时，要不要将进项留抵税额包括在内？\\n对此，实务中存在意见分歧。\\n有人主张归并，即计算税款损失时包括进项留抵税额；\\n有人主张剥离，即计算税款损失时剔除进项留抵税额。分析这个问题，需要确定进项留抵税额与税款损失之间是什么关系。\\n理清这二者之间的关系，首先需要了解增值税的概念和其抵扣机制。增值税是以商品（货物、服务等）在流转过程中产生的增值额作为计税依据而征收的一种流转税。为避免重复征税，在增值税中存在抵扣链条机制。\\n一般而言，交易上游企业缴纳的税额，交易下游企业可以对相应的税额进行抵扣。\\n对增值税一般纳税人来说，其购进货物、服务等取得增值税专用发票，发票上的税额是进项税额。\\n其出售货物、服务等，向购买方开具增值税专用发票，发票的税额是销项税额。\\n一般情况下，销项税额减去进项税额的金额是应纳税额，企业根据应纳税额按期申报纳税。\\n其次需要了解进项留抵税额的概念及产生原因。\\n在计算销项税额和进项税额的差额时，有时会出现负数，即当期进项税额大于当期销项税额。这个差额在当期未实现抵扣，为进项留抵税额，在以后纳税人有销项税额时再进行抵扣。\\n企业产生进项留抵税额的主要原因是其进项税额和销项税额时间上的不一致。\\n例如，企业前期集中采购货物和服务，投资大，销项税率低于进项税率等。\\n从税款抵扣的角度看，进项留抵税额只是购进的这部分进项税额参与到增值税应纳税额的计算过程中，但是其对应的进项税额抵扣还未真正实现，一般要等到其未来有相应的销项税额时，才能真正实现进项税额抵扣。\\n可见，进项留抵税额处于不确定状态，能否抵扣受到很多因素影响，例如企业经营中断，没有销项税额，这时进项留抵税额就无法实现抵扣。但如果企业按照税收政策规定申请进项留抵退税，进项税额抵扣就随之实现。\\n最后需要了解税款损失的概念。\\n税款损失，通常是指因虚开增值税专用发票，导致国家税款被骗或者流失的金额。关于税款损失，实务中有多种表述。\\n例如，北京大学法学院教授陈兴良曾谈到虚开行为本身不会造成国家税款损失，只有利用发票抵扣时才会造成国家税款损失。刘兵等编著的《虚开增值税专用发票案例司法观点和案例解析》一书中提到：“给国家税款造成损失的数额，实际上就是被骗取的国家税款在侦查终结以前无法追回的部分。”\\n赵清海与王家欣合著的《增值税专用发票虚开的判定与预防》一书中提到：“司法实践中，受票方用虚开的增值税专用发票予以抵扣的税款，从而导致受票方应纳税额的减少是法院所认定的国家税款流失的金额。”\\n从这些表述可见，税款损失应该是实际造成的损失，不应包括不确定的部分——进项留抵税额，进项留抵税额与税款损失之间不能直接画等号。\\n综上分析，进项留抵税额，只是使国家税款处于可能被抵扣的状态，还没有真正造成国家税款流失，一般情况下应将其从税款损失中剥离，特殊条件下将其归并入税款损失。\\n例如，当纳税人造假按照税收政策规定申请进项留抵税额退税后，有关税款损失将会从危险状态转化成危害结果，这时候要将有关进项留抵税额并入税款损失。\\n所以，在虚开增值税专用发票案件中，一般情况下，如果以纳税人的进项税额作为税款损失的计算基数，在对其进行行政处罚或刑事处罚时，应把进项留抵税额从税款损失中剔除，但纳税人申请进项留抵退税的除外。这样处理，把处罚与危害结果相对应，体现行政处罚法的过罚相当原则和刑法的罚当其罪原则。'}"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ds[\"train\"][0]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "ef372a1f-e82f-4f5d-8495-f21f06b35635",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['text']\n"
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]
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}
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],
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"source": [
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"# 查看特征\n",
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"column_names = list(ds[\"train\"].features)\n",
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"print(column_names)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "1fa637f5-3b23-4a33-b19b-4c90d1815c39",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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||
"model_id": "316489431b9e494eb8358a0d0048096f",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Running tokenizer on dataset (num_proc=10): 0%| | 0/100001 [00:00<?, ? examples/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# 对数据集进行 tokenize\n",
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"\n",
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"def tokenize_function(examples):\n",
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" # 使用预先加载的 tokenizer 进行分词\n",
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" output = tokenizer([item for item in examples[\"text\"]])\n",
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" return output\n",
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"\n",
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"# 批量处理\n",
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"tokenized_datasets = ds.map(\n",
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" tokenize_function,\n",
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" batched=True,\n",
|
||
" num_proc=10,\n",
|
||
" remove_columns=column_names,\n",
|
||
" load_from_cache_file=True,\n",
|
||
" desc=\"Running tokenizer on dataset\",\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "ec30197e-fe7f-4f0d-903c-663146421f58",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"DatasetDict({\n",
|
||
" train: Dataset({\n",
|
||
" features: ['input_ids', 'attention_mask'],\n",
|
||
" num_rows: 100001\n",
|
||
" })\n",
|
||
"})"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"tokenized_datasets"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "9ec5431b-e3cf-44e0-9260-479d984253e4",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 预训练一般将文本拼接成固定长度的文本段\n",
|
||
"from itertools import chain\n",
|
||
"\n",
|
||
"# 这里我们取块长为 2048\n",
|
||
"block_size = 2048\n",
|
||
"\n",
|
||
"def group_texts(examples):\n",
|
||
" # 将文本段拼接起来\n",
|
||
" concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}\n",
|
||
" # 计算拼起来的整体长度\n",
|
||
" total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
|
||
" # 如果长度太长，进行分块\n",
|
||
" if total_length >= block_size:\n",
|
||
" total_length = (total_length // block_size) * block_size\n",
|
||
" # Split by chunks of max_len.\n",
|
||
" result = {\n",
|
||
" k: [t[i : i + block_size] for i in range(0, total_length, block_size)]\n",
|
||
" for k, t in concatenated_examples.items()\n",
|
||
" }\n",
|
||
" # print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')) \n",
|
||
" print(\"group texts input examples length%d after_group size%d\"%(len(examples['input_ids']),len(result[\"input_ids\"])))\n",
|
||
" result[\"labels\"] = result[\"input_ids\"].copy()\n",
|
||
" return result"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "38428a53-6ba6-429f-8c4b-0985579e726b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "ae53ab8aaa0043418c2b7eb86f3d462b",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Grouping texts in chunks of 2048 (num_proc=10): 0%| | 0/100001 [00:00<?, ? examples/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"group texts input examples length10001 after_group size2752\n",
|
||
"group texts input examples length10000 after_group size2817\n",
|
||
"group texts input examples length10000 after_group size2820\n",
|
||
"group texts input examples length10000 after_group size2817\n",
|
||
"group texts input examples length10000 after_group size2787\n",
|
||
"group texts input examples length10000 after_group size2797\n",
|
||
"group texts input examples length10000 after_group size2800\n",
|
||
"group texts input examples length10000 after_group size2835\n",
|
||
"group texts input examples length10000 after_group size2778\n",
|
||
"group texts input examples length10000 after_group size2825\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 批量处理\n",
|
||
"lm_datasets = tokenized_datasets.map(\n",
|
||
" group_texts,\n",
|
||
" batched=True,\n",
|
||
" num_proc=10,\n",
|
||
" load_from_cache_file=True,\n",
|
||
" desc=f\"Grouping texts in chunks of {block_size}\",\n",
|
||
" batch_size = 40000,\n",
|
||
")\n",
|
||
"train_dataset = lm_datasets[\"train\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9b0dd8c8-fb1f-4af9-8af4-21285ba389c0",
|
||
"metadata": {},
|
||
"source": [
|
||
"1.3 使用 Trainer"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "e3e1a85e-fc28-4154-870e-f6a09f108059",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from transformers import TrainingArguments\n",
|
||
"# 配置训练参数\n",
|
||
"\n",
|
||
"training_args = TrainingArguments(\n",
|
||
" output_dir=\"autodl-tmp/output/pretrain\",\n",
|
||
" per_device_train_batch_size=4,\n",
|
||
" gradient_accumulation_steps=4,\n",
|
||
" logging_steps=10,\n",
|
||
" num_train_epochs=1,\n",
|
||
" save_steps=100, \n",
|
||
" learning_rate=1e-4,\n",
|
||
" save_on_each_node=True,\n",
|
||
" gradient_checkpointing=True\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "62a97e46-ff06-4278-b318-e3e4da1b93d7",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/root/miniconda3/lib/python3.10/site-packages/torchdata/datapipes/__init__.py:18: UserWarning: \n",
|
||
"################################################################################\n",
|
||
"WARNING!\n",
|
||
"The 'datapipes', 'dataloader2' modules are deprecated and will be removed in a\n",
|
||
"future torchdata release! Please see https://github.com/pytorch/data/issues/1196\n",
|
||
"to learn more and leave feedback.\n",
|
||
"################################################################################\n",
|
||
"\n",
|
||
" deprecation_warning()\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from transformers import Trainer, default_data_collator\n",
|
||
"from torchdata.datapipes.iter import IterableWrapper\n",
|
||
"\n",
|
||
"# 训练器\n",
|
||
"trainer = Trainer(\n",
|
||
" model=model,\n",
|
||
" args=training_args,\n",
|
||
" train_dataset= IterableWrapper(train_dataset),\n",
|
||
" eval_dataset= None,\n",
|
||
" tokenizer=tokenizer,\n",
|
||
" # 默认为 MLM 的 collator，使用 CLM 的 collater\n",
|
||
" data_collator=default_data_collator\n",
|
||
")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "a929b11a-99f5-45fc-9f9a-05c0163204c3",
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"start train\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n",
|
||
"/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
|
||
" return fn(*args, **kwargs)\n",
|
||
"/root/miniconda3/lib/python3.10/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
|
||
" with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"\n",
|
||
" <div>\n",
|
||
" \n",
|
||
" <progress value='101' max='1751' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
||
" [ 101/1751 29:31 < 8:12:11, 0.06 it/s, Epoch 0.06/1]\n",
|
||
" </div>\n",
|
||
" <table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: left;\">\n",
|
||
" <th>Step</th>\n",
|
||
" <th>Training Loss</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <td>10</td>\n",
|
||
" <td>10.987700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>20</td>\n",
|
||
" <td>9.160700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>30</td>\n",
|
||
" <td>8.352700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>40</td>\n",
|
||
" <td>8.159800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>50</td>\n",
|
||
" <td>8.042500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>60</td>\n",
|
||
" <td>8.014400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>70</td>\n",
|
||
" <td>7.986700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>80</td>\n",
|
||
" <td>7.951800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>90</td>\n",
|
||
" <td>7.875500</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table><p>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"ename": "RuntimeError",
|
||
"evalue": "[enforce fail at inline_container.cc:603] . unexpected pos 6546708864 vs 6546708760",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
|
||
"File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/serialization.py:652\u001b[0m, in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m 651\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _open_zipfile_writer(f) \u001b[38;5;28;01mas\u001b[39;00m opened_zipfile:\n\u001b[0;32m--> 652\u001b[0m \u001b[43m_save\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopened_zipfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpickle_module\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpickle_protocol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_disable_byteorder_record\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 653\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n",
|
||
"File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/serialization.py:886\u001b[0m, in \u001b[0;36m_save\u001b[0;34m(obj, zip_file, pickle_module, pickle_protocol, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m 885\u001b[0m num_bytes \u001b[38;5;241m=\u001b[39m storage\u001b[38;5;241m.\u001b[39mnbytes()\n\u001b[0;32m--> 886\u001b[0m \u001b[43mzip_file\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_record\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstorage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_bytes\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[0;31mRuntimeError\u001b[0m: [enforce fail at inline_container.cc:778] . PytorchStreamWriter failed writing file data/401: file write failed",
|
||
"\nDuring handling of the above exception, another exception occurred:\n",
|
||
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
|
||
"Cell \u001b[0;32mIn[15], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstart train\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m train_result \u001b[38;5;241m=\u001b[39m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/transformers/trainer.py:1938\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1936\u001b[0m hf_hub_utils\u001b[38;5;241m.\u001b[39menable_progress_bars()\n\u001b[1;32m 1937\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1938\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner_training_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1939\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1940\u001b[0m \u001b[43m \u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1941\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1942\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1943\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/transformers/trainer.py:2356\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 2353\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mepoch \u001b[38;5;241m=\u001b[39m epoch \u001b[38;5;241m+\u001b[39m (step \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m steps_skipped) \u001b[38;5;241m/\u001b[39m steps_in_epoch\n\u001b[1;32m 2354\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_handler\u001b[38;5;241m.\u001b[39mon_step_end(args, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol)\n\u001b[0;32m-> 2356\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_log_save_evaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtr_loss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_norm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2357\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2358\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_handler\u001b[38;5;241m.\u001b[39mon_substep_end(args, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol)\n",
|
||
"File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/transformers/trainer.py:2807\u001b[0m, in \u001b[0;36mTrainer._maybe_log_save_evaluate\u001b[0;34m(self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 2804\u001b[0m metrics \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_evaluate(trial, ignore_keys_for_eval)\n\u001b[1;32m 2806\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol\u001b[38;5;241m.\u001b[39mshould_save:\n\u001b[0;32m-> 2807\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_save_checkpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetrics\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetrics\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2808\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_handler\u001b[38;5;241m.\u001b[39mon_save(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol)\n",
|
||
"File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/transformers/trainer.py:2890\u001b[0m, in \u001b[0;36mTrainer._save_checkpoint\u001b[0;34m(self, model, trial, metrics)\u001b[0m\n\u001b[1;32m 2886\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_model(output_dir, _internal_call\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 2888\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39msave_only_model:\n\u001b[1;32m 2889\u001b[0m \u001b[38;5;66;03m# Save optimizer and scheduler\u001b[39;00m\n\u001b[0;32m-> 2890\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_save_optimizer_and_scheduler\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2891\u001b[0m \u001b[38;5;66;03m# Save RNG state\u001b[39;00m\n\u001b[1;32m 2892\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_save_rng_state(output_dir)\n",
|
||
"File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/transformers/trainer.py:3006\u001b[0m, in \u001b[0;36mTrainer._save_optimizer_and_scheduler\u001b[0;34m(self, output_dir)\u001b[0m\n\u001b[1;32m 3001\u001b[0m save_fsdp_optimizer(\n\u001b[1;32m 3002\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccelerator\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfsdp_plugin, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccelerator, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptimizer, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel, output_dir\n\u001b[1;32m 3003\u001b[0m )\n\u001b[1;32m 3004\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mshould_save:\n\u001b[1;32m 3005\u001b[0m \u001b[38;5;66;03m# deepspeed.save_checkpoint above saves model/optim/sched\u001b[39;00m\n\u001b[0;32m-> 3006\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstate_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mOPTIMIZER_NAME\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3008\u001b[0m \u001b[38;5;66;03m# Save SCHEDULER & SCALER\u001b[39;00m\n\u001b[1;32m 3009\u001b[0m is_deepspeed_custom_scheduler \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_deepspeed_enabled \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[1;32m 3010\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlr_scheduler, DeepSpeedSchedulerWrapper\n\u001b[1;32m 3011\u001b[0m )\n",
|
||
"File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/serialization.py:651\u001b[0m, in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m 648\u001b[0m _check_save_filelike(f)\n\u001b[1;32m 650\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _use_new_zipfile_serialization:\n\u001b[0;32m--> 651\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _open_zipfile_writer(f) \u001b[38;5;28;01mas\u001b[39;00m opened_zipfile:\n\u001b[1;32m 652\u001b[0m _save(obj, opened_zipfile, pickle_module, pickle_protocol, _disable_byteorder_record)\n\u001b[1;32m 653\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n",
|
||
"File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/serialization.py:499\u001b[0m, in \u001b[0;36m_open_zipfile_writer_file.__exit__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 498\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 499\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfile_like\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_end_of_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 500\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfile_stream \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 501\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfile_stream\u001b[38;5;241m.\u001b[39mclose()\n",
|
||
"\u001b[0;31mRuntimeError\u001b[0m: [enforce fail at inline_container.cc:603] . unexpected pos 6546708864 vs 6546708760"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print('start train')\n",
|
||
"train_result = trainer.train()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a1ed2cd9-7169-4376-a26c-053918074761",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 二、模型 SFT"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1bb6e02b-c04c-45a4-b36c-904f9fedf61e",
|
||
"metadata": {},
|
||
"source": [
|
||
"2.1 处理指令数据"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "0d7cd012-fa2d-4c21-b6a5-c3830d12f59b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'conversations': [{'from': 'human',\n",
|
||
" 'value': '针对健身房的新手，设计一套适合他们的健身器械使用指南，包括安全应用、正确姿势等方面。'},\n",
|
||
" {'from': 'assistant',\n",
|
||
" 'value': '健身器械使用指南\\n1. 开始前，请先进行热身运动。这会帮助你的身体适应运动，并减少受伤的风险。\\n2. 在使用健身器械前，确保你已经了解了其使用方法。请阅读说明书或咨询教练以获得正确的使用技巧。\\n3. 谨防过度使用或过度挑战你的身体。 如果你觉得有些动作太难或太重，请添加锻炼计划，以逐步提高动作难度。\\n4. 使用合适的装备。 确保你拥有合适的运动鞋和舒适的运动服。 不要在裸露的脚或短裤上进行重量训练。\\n5. 在健身器械上使用安全装置。 这些通常用于保护你的身体免受不当操作造成的损伤。 例如，重量训练中，你需要使用杠铃和负重时，一定要使用卡子来防止重量滑落。\\n6. 注意正确的姿势。 如果你的姿势是错误的，那么你的身体很容易被伤害到，你也可能无法获得最佳的锻炼效果。 至关重要的是，保持直立的身体，保持头部和颈部的稳定，并使用合适的重量。\\n7. 保持合理的呼吸方式。 无论何时进行训练，都必须保持正常呼吸。 当你需要用力时，呼气； 当你放松时，吸气。\\n8. 安全存放器械。 在使用健身器械后，你需要把它们归还给适当的位置，以便其他人可以使用它们。\\n总之，健身器械的正确使用是关键之一，如果不健康和不安全，它们将无法帮助您达到您所需的健康成果。 选择适当的训练计划，并为训练提供足够的时间，以备逐渐适应新方法。 对于任何问题，请向教练咨询。'}],\n",
|
||
" 'id': '66182880'}"
|
||
]
|
||
},
|
||
"execution_count": 1,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import json\n",
|
||
"\n",
|
||
"with open(\"autodl-tmp/dataset/sft_data/BelleGroup/train_3.5M_CN.json\") as f:\n",
|
||
" lst = [json.loads(line) for line in f.readlines()]\n",
|
||
"\n",
|
||
"lst[0]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "2fc8c599-89e9-4c35-a011-d2e52a1a4d9c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Qwen2TokenizerFast(name_or_path='autodl-tmp/qwen-1.5b', vocab_size=151643, model_max_length=131072, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'eos_token': '<|endoftext|>', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|im_start|>', '<|im_end|>', '<|object_ref_start|>', '<|object_ref_end|>', '<|box_start|>', '<|box_end|>', '<|quad_start|>', '<|quad_end|>', '<|vision_start|>', '<|vision_end|>', '<|vision_pad|>', '<|image_pad|>', '<|video_pad|>']}, clean_up_tokenization_spaces=False), added_tokens_decoder={\n",
|
||
"\t151643: AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
|
||
"\t151644: AddedToken(\"<|im_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
|
||
"\t151645: AddedToken(\"<|im_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
|
||
"\t151646: AddedToken(\"<|object_ref_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
|
||
"\t151647: AddedToken(\"<|object_ref_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
|
||
"\t151648: AddedToken(\"<|box_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
|
||
"\t151649: AddedToken(\"<|box_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
|
||
"\t151650: AddedToken(\"<|quad_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
|
||
"\t151651: AddedToken(\"<|quad_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
|
||
"\t151652: AddedToken(\"<|vision_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
|
||
"\t151653: AddedToken(\"<|vision_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
|
||
"\t151654: AddedToken(\"<|vision_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
|
||
"\t151655: AddedToken(\"<|image_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
|
||
"\t151656: AddedToken(\"<|video_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
|
||
"\t151657: AddedToken(\"<tool_call>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
|
||
"\t151658: AddedToken(\"</tool_call>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
|
||
"\t151659: AddedToken(\"<|fim_prefix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
|
||
"\t151660: AddedToken(\"<|fim_middle|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
|
||
"\t151661: AddedToken(\"<|fim_suffix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
|
||
"\t151662: AddedToken(\"<|fim_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
|
||
"\t151663: AddedToken(\"<|repo_name|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
|
||
"\t151664: AddedToken(\"<|file_sep|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n",
|
||
"}"
|
||
]
|
||
},
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 加载一个预训练好的 tokenizer\n",
|
||
"from transformers import AutoTokenizer\n",
|
||
"\n",
|
||
"model_path = \"autodl-tmp/qwen-1.5b\"\n",
|
||
"tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
|
||
"tokenizer"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "46730b29-41c0-4295-81f2-913d069b4669",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import torch\n",
|
||
"from tqdm import tqdm\n",
|
||
"\n",
|
||
"# 指令文本处理\n",
|
||
"# 参考：https://github.com/QwenLM/Qwen/blob/main/finetune.py\n",
|
||
"def preprocess(sources, tokenizer, max_len, system_message: str = \"You are a helpful assistant.\"):\n",
|
||
" # prompt 模板\n",
|
||
" roles = {\"human\": \"<|im_start|>human\", \"assistant\": \"<|im_start|>assistant\"}\n",
|
||
"\n",
|
||
" # 不同的 tokenizer 需要特别定义\n",
|
||
" # BOS\n",
|
||
" im_start = tokenizer(\"<|im_start|>\").input_ids\n",
|
||
" # EOS\n",
|
||
" im_end = tokenizer(\"<|im_end|>\").input_ids\n",
|
||
" # PAD\n",
|
||
" IGNORE_TOKEN_ID = tokenizer.pad_token_id\n",
|
||
" # 换行符\n",
|
||
" nl_tokens = tokenizer('\\n').input_ids\n",
|
||
" # 角色标识符\n",
|
||
" _system = tokenizer('system').input_ids + nl_tokens\n",
|
||
" _user = tokenizer('human').input_ids + nl_tokens\n",
|
||
" _assistant = tokenizer('assistant').input_ids + nl_tokens\n",
|
||
"\n",
|
||
" # 拼接多轮对话\n",
|
||
" input_ids, targets = [], []\n",
|
||
" for i in tqdm(range(len(sources))):\n",
|
||
" source = sources[i]\n",
|
||
" # 从 user 开始\n",
|
||
" if source[0][\"from\"] != \"human\":\n",
|
||
" source = source[1:]\n",
|
||
" # 分别是输入和输出\n",
|
||
" input_id, target = [], []\n",
|
||
" # system: 【BOS】system\\nYou are a helpful assistant.【EOS】\\n\n",
|
||
" system = im_start + _system + tokenizer(system_message).input_ids + im_end + nl_tokens\n",
|
||
" input_id += system\n",
|
||
" # system 不需要拟合\n",
|
||
" target += im_start + [IGNORE_TOKEN_ID] * (len(system)-3) + im_end + nl_tokens\n",
|
||
" assert len(input_id) == len(target)\n",
|
||
" # 依次拼接\n",
|
||
" for j, sentence in enumerate(source):\n",
|
||
" role = roles[sentence[\"from\"]]\n",
|
||
" # user：<|im_start|>human\\ninstruction【EOS】\\n\n",
|
||
" # assistant：<|im_start|>assistant\\nresponse【EOS】\\n\n",
|
||
" _input_id = tokenizer(role).input_ids + nl_tokens + \\\n",
|
||
" tokenizer(sentence[\"value\"]).input_ids + im_end + nl_tokens\n",
|
||
" input_id += _input_id\n",
|
||
" if role == '<|im_start|>human':\n",
|
||
" # user 不需要拟合\n",
|
||
" _target = im_start + [IGNORE_TOKEN_ID] * (len(_input_id)-3) + im_end + nl_tokens\n",
|
||
" elif role == '<|im_start|>assistant':\n",
|
||
" # assistant 需要拟合\n",
|
||
" _target = im_start + [IGNORE_TOKEN_ID] * len(tokenizer(role).input_ids) + \\\n",
|
||
" _input_id[len(tokenizer(role).input_ids)+1:-2] + im_end + nl_tokens\n",
|
||
" else:\n",
|
||
" print(role)\n",
|
||
" raise NotImplementedError\n",
|
||
" target += _target\n",
|
||
" assert len(input_id) == len(target)\n",
|
||
" # 最后进行 PAD\n",
|
||
" input_id += [tokenizer.pad_token_id] * (max_len - len(input_id))\n",
|
||
" target += [IGNORE_TOKEN_ID] * (max_len - len(target))\n",
|
||
" input_ids.append(input_id[:max_len])\n",
|
||
" targets.append(target[:max_len])\n",
|
||
" # print(input_ids)\n",
|
||
" input_ids = torch.tensor(input_ids)\n",
|
||
" targets = torch.tensor(targets)\n",
|
||
"\n",
|
||
" return dict(\n",
|
||
" input_ids=input_ids,\n",
|
||
" labels=targets,\n",
|
||
" attention_mask=input_ids.ne(tokenizer.pad_token_id),\n",
|
||
" )\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"id": "7b3576cb-04d7-448a-9bd1-07cb7b344e6d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'input_ids': tensor([[151644, 8948, 198, ..., 151643, 151643, 151643],\n",
|
||
" [151644, 8948, 198, ..., 151643, 151643, 151643]]),\n",
|
||
" 'labels': tensor([[151644, 151643, 151643, ..., 151643, 151643, 151643],\n",
|
||
" [151644, 151643, 151643, ..., 151643, 151643, 151643]]),\n",
|
||
" 'attention_mask': tensor([[ True, True, True, ..., False, False, False],\n",
|
||
" [ True, True, True, ..., False, False, False]])}"
|
||
]
|
||
},
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 测试一下\n",
|
||
"preprocess([lst[0][\"conversations\"],lst[1][\"conversations\"]], tokenizer, 1024)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "63e01dcf-4de4-4470-97dd-3317ef1aa00b",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 自定义一个 Dataset\n",
|
||
"from torch.utils.data import Dataset\n",
|
||
"from typing import Dict\n",
|
||
"\n",
|
||
"class SupervisedDataset(Dataset):\n",
|
||
"\n",
|
||
" def __init__(self, raw_data, tokenizer, max_len: int):\n",
|
||
" super(SupervisedDataset, self).__init__()\n",
|
||
" # 加载并预处理数据\n",
|
||
" sources = [example[\"conversations\"] for example in raw_data[:10000]]\n",
|
||
" data_dict = preprocess(sources, tokenizer, max_len)\n",
|
||
"\n",
|
||
" self.input_ids = data_dict[\"input_ids\"]\n",
|
||
" self.labels = data_dict[\"labels\"]\n",
|
||
" self.attention_mask = data_dict[\"attention_mask\"]\n",
|
||
"\n",
|
||
" def __len__(self):\n",
|
||
" return len(self.input_ids)\n",
|
||
"\n",
|
||
" def __getitem__(self, i) -> Dict[str, torch.Tensor]:\n",
|
||
" return dict(\n",
|
||
" input_ids=self.input_ids[i],\n",
|
||
" labels=self.labels[i],\n",
|
||
" attention_mask=self.attention_mask[i],\n",
|
||
" )"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "934316d3-098f-4889-9cb0-d234a630b194",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|██████████| 10000/10000 [00:08<00:00, 1235.98it/s]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"train_ds = SupervisedDataset(lst, tokenizer=tokenizer, max_len=2048)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.10.8"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|