add autoawq example

finetune/finetune.py

@@ -6,8 +6,7 @@ from typing import Dict, Optional
 import torch
 import transformers
 from torch.utils.data import Dataset
-from transformers import (AutoModelForCausalLM, AutoTokenizer, Trainer,
-                          TrainingArguments)
+from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
 
 
 @dataclass
@@ -42,21 +41,25 @@ class TrainingArguments(transformers.TrainingArguments):
 
 class SupervisedDataset(Dataset):
     """Dataset for supervised fine-tuning."""
-    
+
     def __init__(
         self,
         data_path,
         tokenizer,
         model_max_length=4096,
-        user_tokens='<用户>',
-        assistant_tokens='<AI>',
+        user_tokens="<用户>",
+        assistant_tokens="<AI>",
     ):
         super(SupervisedDataset, self).__init__()
         self.data = json.load(open(data_path))
         self.tokenizer = tokenizer
         self.model_max_length = model_max_length
-        self.user_tokens = self.tokenizer.encode(user_tokens) #针对不同模型，都可以对应到<用户>的id
-        self.assistant_tokens = self.tokenizer.encode(assistant_tokens) #针对不同模型，都可以对应到<AI>的id
+        self.user_tokens = self.tokenizer.encode(
+            user_tokens
+        )  # 针对不同模型，都可以对应到<用户>的id
+        self.assistant_tokens = self.tokenizer.encode(
+            assistant_tokens
+        )  # 针对不同模型，都可以对应到<AI>的id
         self.ignore_index = -100
         item = self.preprocessing(self.data[0])
         print("input:", self.tokenizer.decode(item["input_ids"]))
@@ -86,10 +89,9 @@ class SupervisedDataset(Dataset):
                 ] * len(content_ids)
             else:
                 input_ids += self.assistant_tokens + content_ids
-                label_ids += (
-                    [self.ignore_index] * len(self.assistant_tokens)
-                    + content_ids
-                )
+                label_ids += [self.ignore_index] * len(
+                    self.assistant_tokens
+                ) + content_ids
 
         input_ids.append(self.tokenizer.eos_token_id)
         label_ids.append(self.tokenizer.eos_token_id)
@@ -171,7 +173,7 @@ if __name__ == "__main__":
         max_length=training_args.model_max_length,
         use_lora=training_args.use_lora,
         bf16=training_args.bf16,
-        fp16=training_args.fp16
+        fp16=training_args.fp16,
     )
 
     train_dataset = SupervisedDataset(
@@ -195,4 +197,4 @@ if __name__ == "__main__":
 
     trainer.train()
     # save the incremental PEFT weights, more details can be found in https://huggingface.co/blog/peft
-    # model.save_pretrained("output_dir") 
+    # model.save_pretrained("output_dir")

quantize/awq_quantize.py

@@ -0,0 +1,40 @@
+
+
+from datasets import load_dataset
+from awq import AutoAWQForCausalLM
+from transformers import AutoTokenizer
+import os
+
+model_path = '/root/ld/ld_model_pretrained/MiniCPM-1B-sft-bf16' # model_path or model_id
+quant_path = '/root/ld/ld_project/pull_request/MiniCPM/quantize/awq_cpm_1b_4bit' # quant_save_path
+quant_data_path='/root/ld/ld_project/pull_request/MiniCPM/quantize/quantize_data/alpaca'
+quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" } #"w_bit":4 or 8
+quant_samples=512 #how many samples to use for calibration
+
+# Load model
+model = AutoAWQForCausalLM.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True,device_map={"": "cuda:0"})
+
+# Define data loading methods
+def load_alpaca(quant_data_path):
+    data = load_dataset(quant_data_path, split="train") #Set the absolute path to alpaca or huggingface id
+
+    # concatenate data
+    def concatenate_data(x):
+        return {"text": '<s><用户>'+x['instruction'] + '<AI>' + x['input'] + '\n' + x['output']}
+    
+    concatenated = data.map(concatenate_data)[:quant_samples]
+    return [text for text in concatenated["text"]]
+
+def load_wikitext():
+    data = load_dataset('wikitext', 'wikitext-2-raw-v1', split="train")
+    return [text for text in data["text"] if text.strip() != '' and len(text.split(' ')) > 20][:quant_samples]
+
+# Quantize
+model.quantize(tokenizer, quant_config=quant_config, calib_data=load_alpaca(quant_data_path=quant_data_path))
+
+# Save quantized model
+model.save_quantized(quant_path)
+tokenizer.save_pretrained(quant_path)
+
+print(f'Model is quantized and saved at "{quant_path}"')

quantize/quantize_data/alpaca/downloads/69e71f4ebbd3d7f8f822d46b759228b3fb785d2c74d87f14126cf8da4f403610.json

@@ -0,0 +1 @@
+{"url": "hf://datasets/tatsu-lab/alpaca@dce01c9b08f87459cf36a430d809084718273017/data/train-00000-of-00001-a09b74b3ef9c3b56.parquet", "etag": null}

quantize/quantize_data/alpaca/tatsu-lab___alpaca/default/0.0.0/dce01c9b08f87459cf36a430d809084718273017/dataset_info.json

@@ -0,0 +1 @@
+{"description": "", "citation": "", "homepage": "", "license": "", "features": {"instruction": {"dtype": "string", "_type": "Value"}, "input": {"dtype": "string", "_type": "Value"}, "output": {"dtype": "string", "_type": "Value"}, "text": {"dtype": "string", "_type": "Value"}}, "builder_name": "parquet", "dataset_name": "alpaca", "config_name": "default", "version": {"version_str": "0.0.0", "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 46208623, "num_examples": 52002, "dataset_name": "alpaca"}}, "download_checksums": {"hf://datasets/tatsu-lab/alpaca@dce01c9b08f87459cf36a430d809084718273017/data/train-00000-of-00001-a09b74b3ef9c3b56.parquet": {"num_bytes": 24246638, "checksum": null}}, "download_size": 24246638, "dataset_size": 46208623, "size_in_bytes": 70455261}

quantize/quantize_data/wikitext/downloads/300e51c2d3e98b6cf3739090e25ea220d1a568457b1b2d97857e1cf71da48434.json

@@ -0,0 +1 @@
+{"url": "hf://datasets/wikitext@b08601e04326c79dfdd32d625aee71d232d685c3/wikitext-2-raw-v1/validation-00000-of-00001.parquet", "etag": null}

quantize/quantize_data/wikitext/downloads/5e548f01091b2f01906ea7be35b8a1a6aa74842629dc550b7a02ca0821f1002d.json

@@ -0,0 +1 @@
+{"url": "hf://datasets/wikitext@b08601e04326c79dfdd32d625aee71d232d685c3/wikitext-2-raw-v1/test-00000-of-00001.parquet", "etag": null}

quantize/quantize_data/wikitext/downloads/e7f32d67ecf57307f5bc9834102f2f01ad03802eea0fdfc86172a9ec98676280.json

@@ -0,0 +1 @@
+{"url": "hf://datasets/wikitext@b08601e04326c79dfdd32d625aee71d232d685c3/wikitext-2-raw-v1/train-00000-of-00001.parquet", "etag": null}

quantize/quantize_data/wikitext/wikitext/wikitext-2-raw-v1/0.0.0/b08601e04326c79dfdd32d625aee71d232d685c3/dataset_info.json

@@ -0,0 +1 @@
+{"description": "", "citation": "", "homepage": "", "license": "", "features": {"text": {"dtype": "string", "_type": "Value"}}, "builder_name": "parquet", "dataset_name": "wikitext", "config_name": "wikitext-2-raw-v1", "version": {"version_str": "0.0.0", "major": 0, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1305088, "num_examples": 4358, "dataset_name": "wikitext"}, "train": {"name": "train", "num_bytes": 11061717, "num_examples": 36718, "dataset_name": "wikitext"}, "validation": {"name": "validation", "num_bytes": 1159288, "num_examples": 3760, "dataset_name": "wikitext"}}, "download_checksums": {"hf://datasets/wikitext@b08601e04326c79dfdd32d625aee71d232d685c3/wikitext-2-raw-v1/test-00000-of-00001.parquet": {"num_bytes": 732610, "checksum": null}, "hf://datasets/wikitext@b08601e04326c79dfdd32d625aee71d232d685c3/wikitext-2-raw-v1/train-00000-of-00001.parquet": {"num_bytes": 6357543, "checksum": null}, "hf://datasets/wikitext@b08601e04326c79dfdd32d625aee71d232d685c3/wikitext-2-raw-v1/validation-00000-of-00001.parquet": {"num_bytes": 657209, "checksum": null}}, "download_size": 7747362, "dataset_size": 13526093, "size_in_bytes": 21273455}

quantize/quantize_eval.py

@@ -0,0 +1,113 @@
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from awq import AutoAWQForCausalLM
+from auto_gptq import AutoGPTQForCausalLM
+import GPUtil
+import argparse
+
+parser = argparse.ArgumentParser(description="========量化困惑度测试========")
+parser.add_argument(
+    "--model_path",  
+    type=str,  
+    default='/root/ld/ld_model_pretrained/miniCPM-bf16',  
+    help="未量化前的模型路径。"  
+)
+parser.add_argument(
+    "--awq_path",  
+    type=str,  
+    default='/root/ld/ld_project/pull_request/MiniCPM/quantize/awq_cpm_2b_4bit',  
+    help="awq量化后的模型保存路径。"  
+)
+#we will support gptq later
+parser.add_argument(
+    "--gptq_path",  
+    type=str,  
+    default='/root/ld/ld_project/AutoGPTQ/examples/quantization/minicpm_2b_4bit',  
+    help="gptq量化后的模型保存路径。"  
+)
+parser.add_argument(
+    "--data_path",  
+    type=str,  
+    default='/root/ld/ld_project/pull_request/MiniCPM/quantize/quantize_data/wikitext',  
+    help="可以是以后的量化数据集，示例中默认为wiki_text"  
+)
+
+def get_device():
+    if torch.backends.mps.is_available():
+        return "mps"
+    elif torch.cuda.is_available():
+        return "cuda:0"
+    else:
+        return "cpu"
+
+def evaluate_perplexity(model, tokenizer,data_path):
+    def _perplexity(nlls, n_samples, seqlen):
+        return torch.exp(torch.stack(nlls).sum() / (n_samples * seqlen))
+
+    data = load_dataset(data_path,  split="test")
+    data = tokenizer("\n\n".join(data["text"]), return_tensors="pt")
+    data = data.input_ids.to('cuda:0')
+
+    seqlen = 2048
+    model = model.eval()
+    n_samples = data.numel() // seqlen
+
+    nlls = []
+
+    with tqdm(range(n_samples), desc="Perplexity -") as progress_bar:
+        for i in progress_bar:
+            start_index = i * seqlen
+            end_index = (i + 1) * seqlen
+            batch = data[:, start_index:end_index].to('cuda:0')
+            with torch.no_grad():
+                logits = model(batch).logits
+            shift_logits = logits[:, :-1, :].contiguous().float()
+            shift_labels = data[:, start_index:end_index][:, 1:]
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
+            )
+            neg_log_likelihood = loss.float() * seqlen
+            nlls.append(neg_log_likelihood)
+
+            curr_ppl = _perplexity(nlls, i + 1, seqlen)
+            progress_bar.set_description(f"Perplexity {curr_ppl:.3f}")
+
+    ppl = _perplexity(nlls, n_samples, seqlen)
+
+    return ppl.item()
+
+if __name__ == "__main__":
+    
+    args = parser.parse_args()
+
+    if args.model_path:
+        model = AutoModelForCausalLM.from_pretrained(args.model_path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True)
+        tokenizer = AutoTokenizer.from_pretrained(args.model_path)
+        print("pretrained model：",args.model_path.split('/')[-1])
+        gpu_usage = GPUtil.getGPUs()[0].memoryUsed 
+        print(f"gpu usage: {round(gpu_usage/1024,2)}GB")
+        evaluate_perplexity(model, tokenizer, args.data_path)
+        del model
+
+    if args.awq_path:
+        model = AutoAWQForCausalLM.from_quantized(args.awq_path, fuse_layers=True,device_map={"":'cuda:0'})
+        tokenizer = AutoTokenizer.from_pretrained(args.awq_path)
+        print("awq model：",args.awq_path.split('/')[-1])
+        gpu_usage = GPUtil.getGPUs()[0].memoryUsed 
+        print(f"gpu usage: {round(gpu_usage/1024,2)}GB")
+        evaluate_perplexity(model, tokenizer, args.data_path)
+        del model
+
+#we will support the autogptq  later
+    if args.gptq_path:
+        tokenizer = AutoTokenizer.from_pretrained(args.gptq_path, use_fast=True)
+        model = AutoGPTQForCausalLM.from_quantized(args.gptq_path, device="cuda:0",trust_remote_code=True)
+        print("gptq model：",args.gptq_path.split('/')[-1])
+        gpu_usage = GPUtil.getGPUs()[0].memoryUsed 
+        print(f"gpu usage: {round(gpu_usage/1024,2)}GB")
+        evaluate_perplexity(model, tokenizer, args.data_path)
+