File size: 6,191 Bytes
f396208
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
# Copyright (c) 2024, SliceX AI, Inc.

from elm.model import *
from elm.utils import batchify
from transformers import AutoTokenizer
import json


def load_elm_model_and_tokenizer(local_path, 
                                 model_config_dict,
                                 device="cuda",
                                 load_partial=True,
                                 get_num_layers_from_ckpt=True):
    """Load ELM model and tokenizer from local checkpoint."""
    model_args = ModelArgs(**model_config_dict)
    model = load_elm_model_from_ckpt(local_path, device=device, model_args=model_args, load_partial=load_partial, get_num_layers_from_ckpt=get_num_layers_from_ckpt)

    tokenizer = AutoTokenizer.from_pretrained(local_path)
    tokenizer.padding_side = "left"
    tokenizer.truncation_side = "left"
    return model, tokenizer


def generate_elm_response_given_model(prompts, model, tokenizer, 
                          device="cuda",
                          max_ctx_word_len=1024,
                          max_ctx_token_len=0,
                          max_new_tokens=500,
                          temperature=0.8, # set to 0 for greedy decoding
                          top_k=200,
                          return_tok_cnt=False,
                          return_gen_only=False,
                          early_stop_on_eos=False):
    """Generate responses from ELM model given an input list of prompts ([str])."""
    if max_ctx_token_len > 0:
        inputs = tokenizer(prompts, return_tensors="pt", padding=True, truncation=True, max_length=max_ctx_token_len).to(device)
    else:
        prompts = [" ".join(p.split(" ")[-max_ctx_word_len:]) for p in prompts]
        inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(device)
    
    results = []
    
    input_tok_cnt = torch.numel(inputs.input_ids)

    model.eval()

    out_tok_cnt = 0
    with torch.no_grad():
        temperature = temperature
        top_k = top_k

        outputs = model.generate(inputs.input_ids, max_new_tokens, temperature=temperature, top_k=top_k,
                                 return_gen_only=return_gen_only)

        if return_tok_cnt:
            out_tok_cnt += torch.numel(outputs)

        if early_stop_on_eos:
            mod_outputs = []
            for i in range(len(outputs)):
                curr_out = outputs[i]

                eos_loc_id = -1
                for j in range(len(outputs[i])):
                    tok_id = outputs[i][j]
                    if tok_id == tokenizer.eos_token_id:
                        eos_loc_id = j
                        break
                if eos_loc_id >= 0:
                    curr_out = outputs[i][:eos_loc_id]
                mod_outputs.append(curr_out)
            outputs = mod_outputs
        detokenized_output = tokenizer.batch_decode(outputs, skip_special_tokens=False)

        results = detokenized_output

    if return_tok_cnt:
        return results, (input_tok_cnt, out_tok_cnt)

    return results

def load_elm_model_given_path(elm_model_path, elm_model_config={}, device=None):
    if not device:
        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(f"Setting device to {device}")
    model_config_dict = {
            "hidden_size": elm_model_config.get("hidden_size", 2048),
            "max_inp_len": elm_model_config.get("max_inp_len", 2048),
            "num_attention_heads": elm_model_config.get("num_attention_heads", 32),
            "num_layers": elm_model_config.get("num_layers", 48),
            "bits": elm_model_config.get("bits", 256),
            "vocab_size": elm_model_config.get("vocab_size", 50304),
            "dropout": elm_model_config.get("dropout", 0.1),
            "use_rotary_embeddings": elm_model_config.get("use_rotary_embeddings", True)
        }
        
    model, tokenizer = load_elm_model_and_tokenizer(local_path=elm_model_path, model_config_dict=model_config_dict, device=device, load_partial=True)
    return {"model": model, "tokenizer": tokenizer}

def generate_elm_responses(elm_model_path, 
                           prompts,
                           device=None, 
                           elm_model_config={},
                           eval_batch_size=1,
                           verbose=True,
                           model_info=None):


    if not device:
        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(f"Setting device to {device}")

    if not model_info:
        model_info = load_elm_model_given_path(elm_model_path, elm_model_config=elm_model_config, device=device)
    
    model, tokenizer = model_info["model"], model_info["tokenizer"]

    #prompts = [prompt if "[INST]" in prompt else f"[INST]{prompt}[/INST]" for prompt in prompts]
    max_new_tokens = 128
    if "classification" in elm_model_path or "detection" in elm_model_path:
        max_new_tokens = 12
    result = []
    for prompt_batch in batchify(prompts, eval_batch_size):
        responses, _ = generate_elm_response_given_model(prompt_batch,
                                                            model, 
                                                            tokenizer, 
                                                            device=device,
                                                            max_ctx_word_len=1024,
                                                            max_ctx_token_len=512,
                                                            max_new_tokens=max_new_tokens,
                                                            return_tok_cnt=True, 
                                                            return_gen_only=False, 
                                                            temperature=0.0, 
                                                            early_stop_on_eos=True)
    
        for prompt, response in zip(prompt_batch, responses):
            response = response.split("[/INST]")[-1].strip()
            result.append(response)
            if verbose:
                print(json.dumps({"prompt": prompt, "response": response}, indent=4))
                print("\n***\n")
    return result