File size: 6,885 Bytes
f520676
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
import inspect
import re
from pathlib import Path

import accelerate
import torch
import transformers
from accelerate.utils import is_xpu_available
from gptq_for_llama import llama_inference_offload
from gptq_for_llama.modelutils import find_layers
from gptq_for_llama.quant import make_quant
from transformers import AutoConfig, AutoModelForCausalLM

import modules.shared as shared
from modules.logging_colors import logger


# This function is a replacement for the load_quant function in the
# GPTQ-for_LLaMa repository. It supports more models and branches.
def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=None, kernel_switch_threshold=128, eval=True):
    exclude_layers = exclude_layers or ['lm_head']

    def noop(*args, **kwargs):
        pass

    config = AutoConfig.from_pretrained(model, trust_remote_code=shared.args.trust_remote_code)
    torch.nn.init.kaiming_uniform_ = noop
    torch.nn.init.uniform_ = noop
    torch.nn.init.normal_ = noop

    torch.set_default_dtype(torch.half)
    transformers.modeling_utils._init_weights = False
    torch.set_default_dtype(torch.half)
    model = AutoModelForCausalLM.from_config(config, trust_remote_code=shared.args.trust_remote_code)
    torch.set_default_dtype(torch.float)
    if eval:
        model = model.eval()

    layers = find_layers(model)
    for name in exclude_layers:
        if name in layers:
            del layers[name]

    gptq_args = inspect.getfullargspec(make_quant).args

    make_quant_kwargs = {
        'module': model,
        'names': layers,
        'bits': wbits,
    }
    if 'groupsize' in gptq_args:
        make_quant_kwargs['groupsize'] = groupsize
    if 'faster' in gptq_args:
        make_quant_kwargs['faster'] = faster_kernel
    if 'kernel_switch_threshold' in gptq_args:
        make_quant_kwargs['kernel_switch_threshold'] = kernel_switch_threshold

    make_quant(**make_quant_kwargs)

    del layers
    if checkpoint.endswith('.safetensors'):
        from safetensors.torch import load_file as safe_load
        model.load_state_dict(safe_load(checkpoint), strict=False)
    else:
        model.load_state_dict(torch.load(checkpoint, weights_only=True), strict=False)

    model.seqlen = 2048
    return model


# Used to locate the .pt/.safetensors quantized file
def find_quantized_model_file(model_name):
    if shared.args.checkpoint:
        return Path(shared.args.checkpoint)

    path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
    pt_path = None
    priority_name_list = [
        Path(f'{shared.args.model_dir}/{model_name}{hyphen}{shared.args.wbits}bit{group}{ext}')
        for group in ([f'-{shared.args.groupsize}g', ''] if shared.args.groupsize > 0 else [''])
        for ext in ['.safetensors', '.pt']
        for hyphen in ['-', f'/{model_name}-', '/']
    ]

    for path in priority_name_list:
        if path.exists():
            pt_path = path
            break

    # If the model hasn't been found with a well-behaved name, pick the last .pt
    # or the last .safetensors found in its folder as a last resort
    if not pt_path:
        for ext in ['.pt', '.safetensors']:
            found = list(path_to_model.glob(f"*{ext}"))
            if len(found) > 0:
                if len(found) > 1:
                    logger.warning(f'More than one {ext} model has been found. The last one will be selected. It could be wrong.')

                pt_path = found[-1]
                break

    return pt_path


# The function that loads the model in modules/models.py
def load_quantized(model_name):
    if shared.args.model_type is None:
        logger.error("The model could not be loaded because its type could not be inferred from its name.")
        logger.error("Please specify the type manually using the --model_type argument.")
        return None

    # Select the appropriate load_quant function
    model_type = shared.args.model_type.lower()
    if shared.args.pre_layer and model_type == 'llama':
        load_quant = llama_inference_offload.load_quant
    elif model_type in ('llama', 'opt', 'gptj'):
        if shared.args.pre_layer:
            logger.warning("Ignoring --pre_layer because it only works for llama model type.")

        load_quant = _load_quant
    else:
        logger.error("Unknown pre-quantized model type specified. Only 'llama', 'opt' and 'gptj' are supported")
        exit()

    # Find the quantized model weights file (.pt/.safetensors)
    path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
    pt_path = find_quantized_model_file(model_name)
    if not pt_path:
        logger.error("Could not find the quantized model in .pt or .safetensors format. Exiting.")
        exit()
    else:
        logger.info(f"Found the following quantized model: {pt_path}")

    # qwopqwop200's offload
    if model_type == 'llama' and shared.args.pre_layer:
        if len(shared.args.pre_layer) == 1:
            pre_layer = shared.args.pre_layer[0]
        else:
            pre_layer = shared.args.pre_layer

        model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, pre_layer)
    else:
        threshold = False if model_type == 'gptj' else 128
        model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, kernel_switch_threshold=threshold)

        # accelerate offload (doesn't work properly)
        if shared.args.gpu_memory or torch.cuda.device_count() > 1 or (is_xpu_available() and torch.xpu.device_count() > 1):
            if shared.args.gpu_memory:
                memory_map = list(map(lambda x: x.strip(), shared.args.gpu_memory))
                max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB'
                max_memory = {}
                for i in range(len(memory_map)):
                    max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i]

                max_memory['cpu'] = f'{max_cpu_memory}GiB' if not re.match('.*ib$', max_cpu_memory.lower()) else max_cpu_memory
            else:
                max_memory = accelerate.utils.get_balanced_memory(model)

            device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"])
            logger.info("Using the following device map for the quantized model:", device_map)
            # https://huggingface.co/docs/accelerate/package_reference/big_modeling#accelerate.dispatch_model
            model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True)

        # No offload
        elif not shared.args.cpu:
            if is_xpu_available():
                model = model.to(torch.device("xpu:0"))
            else:
                model = model.to(torch.device('cuda:0'))

    return model