mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-09-20 18:45:09 +02:00
132 lines
5.1 KiB
Python
132 lines
5.1 KiB
Python
import re
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import sys
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from pathlib import Path
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import accelerate
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import torch
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import transformers
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from transformers import AutoConfig, AutoModelForCausalLM
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import modules.shared as shared
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sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa")))
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import llama_inference_offload
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from quant import make_quant
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from modelutils import find_layers
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def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=['lm_head']):
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config = AutoConfig.from_pretrained(model)
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def noop(*args, **kwargs):
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pass
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torch.nn.init.kaiming_uniform_ = noop
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torch.nn.init.uniform_ = noop
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torch.nn.init.normal_ = noop
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torch.set_default_dtype(torch.half)
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transformers.modeling_utils._init_weights = False
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torch.set_default_dtype(torch.half)
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model = AutoModelForCausalLM.from_config(config)
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torch.set_default_dtype(torch.float)
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model = model.eval()
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layers = find_layers(model)
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for name in exclude_layers:
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if name in layers:
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del layers[name]
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make_quant(model, layers, wbits, groupsize, faster=faster_kernel)
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del layers
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print('Loading model ...')
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if checkpoint.endswith('.safetensors'):
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from safetensors.torch import load_file as safe_load
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model.load_state_dict(safe_load(checkpoint))
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else:
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model.load_state_dict(torch.load(checkpoint))
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model.seqlen = 2048
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print('Done.')
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return model
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def load_quantized(model_name):
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if not shared.args.model_type:
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# Try to determine model type from model name
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if model_name.lower().startswith(('llama', 'alpaca')):
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model_type = 'llama'
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elif model_name.lower().startswith(('opt', 'galactica')):
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model_type = 'opt'
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elif model_name.lower().startswith(('gpt-j', 'pygmalion-6b')):
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model_type = 'gptj'
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else:
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print("Can't determine model type from model name. Please specify it manually using --model_type "
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"argument")
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exit()
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else:
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model_type = shared.args.model_type.lower()
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if model_type == 'llama' and shared.args.pre_layer:
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oad_quant = llama_inference_offload.load_quant
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elif model_type in ('llama', 'opt', 'gptj'):
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load_quant = _load_quant
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else:
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print("Unknown pre-quantized model type specified. Only 'llama', 'opt' and 'gptj' are supported")
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exit()
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# Now we are going to try to locate the quantized model file.
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path_to_model = Path(f'models/{model_name}')
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found_pts = list(path_to_model.glob("*.pt"))
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found_safetensors = list(path_to_model.glob("*.safetensors"))
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pt_path = None
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if len(found_pts) == 1:
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pt_path = found_pts[0]
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elif len(found_safetensors) == 1:
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pt_path = found_safetensors[0]
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else:
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if path_to_model.name.lower().startswith('llama-7b'):
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pt_model = f'llama-7b-{shared.args.wbits}bit'
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elif path_to_model.name.lower().startswith('llama-13b'):
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pt_model = f'llama-13b-{shared.args.wbits}bit'
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elif path_to_model.name.lower().startswith('llama-30b'):
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pt_model = f'llama-30b-{shared.args.wbits}bit'
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elif path_to_model.name.lower().startswith('llama-65b'):
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pt_model = f'llama-65b-{shared.args.wbits}bit'
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else:
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pt_model = f'{model_name}-{shared.args.wbits}bit'
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# Try to find the .safetensors or .pt both in models/ and in the subfolder
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for path in [Path(p+ext) for ext in ['.safetensors', '.pt'] for p in [f"models/{pt_model}", f"{path_to_model}/{pt_model}"]]:
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if path.exists():
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print(f"Found {path}")
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pt_path = path
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break
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if not pt_path:
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print("Could not find the quantized model in .pt or .safetensors format, exiting...")
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exit()
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# qwopqwop200's offload
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if shared.args.pre_layer:
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model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, shared.args.pre_layer)
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else:
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model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize)
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# accelerate offload (doesn't work properly)
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if shared.args.gpu_memory:
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memory_map = list(map(lambda x : x.strip(), shared.args.gpu_memory))
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max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB'
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max_memory = {}
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for i in range(len(memory_map)):
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max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i]
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max_memory['cpu'] = max_cpu_memory
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device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"])
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print("Using the following device map for the 4-bit model:", device_map)
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# https://huggingface.co/docs/accelerate/package_reference/big_modeling#accelerate.dispatch_model
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model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True)
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# No offload
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elif not shared.args.cpu:
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model = model.to(torch.device('cuda:0'))
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return model
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