mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-30 14:10:14 +01:00
208 lines
7.7 KiB
Python
208 lines
7.7 KiB
Python
import inspect
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import logging
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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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try:
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import llama_inference_offload
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except ImportError:
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logging.error('Failed to load GPTQ-for-LLaMa')
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logging.error('See https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md')
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sys.exit(-1)
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try:
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from modelutils import find_layers
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except ImportError:
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from utils import find_layers
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try:
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from quant import make_quant
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is_triton = False
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except ImportError:
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import quant
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is_triton = True
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# This function is a replacement for the load_quant function in the
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# GPTQ-for_LLaMa repository. It supports more models and branches.
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def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=None, kernel_switch_threshold=128, eval=True):
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exclude_layers = exclude_layers or ['lm_head']
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def noop(*args, **kwargs):
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pass
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config = AutoConfig.from_pretrained(model, trust_remote_code=shared.args.trust_remote_code)
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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, trust_remote_code=shared.args.trust_remote_code)
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torch.set_default_dtype(torch.float)
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if eval:
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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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if not is_triton:
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gptq_args = inspect.getfullargspec(make_quant).args
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make_quant_kwargs = {
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'module': model,
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'names': layers,
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'bits': wbits,
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}
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if 'groupsize' in gptq_args:
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make_quant_kwargs['groupsize'] = groupsize
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if 'faster' in gptq_args:
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make_quant_kwargs['faster'] = faster_kernel
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if 'kernel_switch_threshold' in gptq_args:
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make_quant_kwargs['kernel_switch_threshold'] = kernel_switch_threshold
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make_quant(**make_quant_kwargs)
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else:
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quant.make_quant_linear(model, layers, wbits, groupsize)
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del layers
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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), strict=False)
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else:
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model.load_state_dict(torch.load(checkpoint), strict=False)
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if is_triton:
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if shared.args.quant_attn:
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quant.make_quant_attn(model)
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if eval and shared.args.fused_mlp:
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quant.make_fused_mlp(model)
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if shared.args.warmup_autotune:
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quant.autotune_warmup_linear(model, transpose=not eval)
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if eval and shared.args.fused_mlp:
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quant.autotune_warmup_fused(model)
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model.seqlen = 2048
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return model
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# Used to locate the .pt/.safetensors quantized file
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def find_quantized_model_file(model_name):
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if shared.args.checkpoint:
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return Path(shared.args.checkpoint)
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path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
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pt_path = None
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priority_name_list = [
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Path(f'{shared.args.model_dir}/{model_name}{hyphen}{shared.args.wbits}bit{group}{ext}')
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for group in ([f'-{shared.args.groupsize}g', ''] if shared.args.groupsize > 0 else [''])
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for ext in ['.safetensors', '.pt']
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for hyphen in ['-', f'/{model_name}-', '/']
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]
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for path in priority_name_list:
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if path.exists():
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pt_path = path
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break
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# If the model hasn't been found with a well-behaved name, pick the last .pt
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# or the last .safetensors found in its folder as a last resort
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if not pt_path:
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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) > 0:
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if len(found_pts) > 1:
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logging.warning('More than one .pt model has been found. The last one will be selected. It could be wrong.')
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pt_path = found_pts[-1]
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elif len(found_safetensors) > 0:
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if len(found_safetensors) > 1:
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logging.warning('More than one .safetensors model has been found. The last one will be selected. It could be wrong.')
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pt_path = found_safetensors[-1]
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return pt_path
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# The function that loads the model in modules/models.py
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def load_quantized(model_name):
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if shared.args.model_type is None:
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logging.error("The model could not be loaded because its type could not be inferred from its name.")
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logging.error("Please specify the type manually using the --model_type argument.")
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return
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# Select the appropriate load_quant function
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model_type = shared.args.model_type.lower()
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if shared.args.pre_layer and model_type == 'llama':
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load_quant = llama_inference_offload.load_quant
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elif model_type in ('llama', 'opt', 'gptj'):
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if shared.args.pre_layer:
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logging.warning("Ignoring --pre_layer because it only works for llama model type.")
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load_quant = _load_quant
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else:
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logging.error("Unknown pre-quantized model type specified. Only 'llama', 'opt' and 'gptj' are supported")
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exit()
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# Find the quantized model weights file (.pt/.safetensors)
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path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
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pt_path = find_quantized_model_file(model_name)
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if not pt_path:
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logging.error("Could not find the quantized model in .pt or .safetensors format, exiting...")
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exit()
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else:
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logging.info(f"Found the following quantized model: {pt_path}")
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# qwopqwop200's offload
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if model_type == 'llama' and shared.args.pre_layer:
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if len(shared.args.pre_layer) == 1:
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pre_layer = shared.args.pre_layer[0]
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else:
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pre_layer = shared.args.pre_layer
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model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, pre_layer)
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else:
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threshold = False if model_type == 'gptj' else 128
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model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, kernel_switch_threshold=threshold)
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# accelerate offload (doesn't work properly)
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if shared.args.gpu_memory or torch.cuda.device_count() > 1:
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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$', memory_map[i].lower()) else memory_map[i]
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max_memory['cpu'] = f'{max_cpu_memory}GiB' if not re.match('.*ib$', max_cpu_memory.lower()) else max_cpu_memory
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else:
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max_memory = accelerate.utils.get_balanced_memory(model)
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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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logging.info("Using the following device map for the quantized 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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