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https://github.com/oobabooga/text-generation-webui.git
synced 2024-12-23 21:18:00 +01:00
Refactor models.py (#2113)
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oobabooga
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@ -9,7 +9,8 @@ As far as I know, DeepSpeed is only available for Linux at the moment.
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1. Install DeepSpeed:
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```
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pip install deepspeed
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conda install -c conda-forge mpi4py mpich
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pip install -U deepspeed
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```
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2. Start the web UI replacing `python` with `deepspeed --num_gpus=1` and adding the `--deepspeed` flag. Example:
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@ -20,9 +20,6 @@ from modules import llama_attn_hijack
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transformers.logging.set_verbosity_error()
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if shared.args.flexgen:
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from flexgen.flex_opt import CompressionConfig, ExecutionEnv, OptLM, Policy
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local_rank = None
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if shared.args.deepspeed:
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import deepspeed
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@ -40,6 +37,8 @@ if shared.args.deepspeed:
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dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration
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# Some models require special treatment in various parts of the code.
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# This function detects those models
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def find_model_type(model_name):
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model_name_lower = model_name.lower()
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if 'rwkv-' in model_name_lower:
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@ -72,7 +71,60 @@ def load_model(model_name):
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t0 = time.time()
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shared.model_type = find_model_type(model_name)
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trust_remote_code = shared.args.trust_remote_code
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if shared.args.wbits > 0:
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load_func = GPTQ_loader
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elif shared.model_type == 'llamacpp':
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load_func = llamacpp_loader
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elif shared.model_type == 'rwkv':
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load_func = RWKV_loader
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elif shared.args.flexgen:
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load_func = flexgen_loader
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else:
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load_func = huggingface_loader
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output = load_func(model_name)
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if type(output) is tuple:
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model, tokenizer = output
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else:
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model = output
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tokenizer = load_tokenizer(model_name, model)
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# Hijack attention with xformers
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if any((shared.args.xformers, shared.args.sdp_attention)):
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llama_attn_hijack.hijack_llama_attention()
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logging.info(f"Loaded the model in {(time.time()-t0):.2f} seconds.\n")
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return model, tokenizer
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def load_tokenizer(model_name, model):
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if shared.model_type == 'gpt4chan' and Path(f"{shared.args.model_dir}/gpt-j-6B/").exists():
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tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/gpt-j-6B/"))
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elif type(model) is transformers.LlamaForCausalLM:
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# Try to load an universal LLaMA tokenizer
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if shared.model_type not in ['llava', 'oasst']:
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for p in [Path(f"{shared.args.model_dir}/llama-tokenizer/"), Path(f"{shared.args.model_dir}/oobabooga_llama-tokenizer/")]:
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if p.exists():
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logging.info(f"Loading the universal LLaMA tokenizer from {p}...")
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tokenizer = LlamaTokenizer.from_pretrained(p, clean_up_tokenization_spaces=True)
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return tokenizer
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# Otherwise, load it from the model folder and hope that these
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# are not outdated tokenizer files.
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tokenizer = LlamaTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}/"), clean_up_tokenization_spaces=True)
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try:
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tokenizer.eos_token_id = 2
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tokenizer.bos_token_id = 1
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tokenizer.pad_token_id = 0
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except:
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pass
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else:
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tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}/"), trust_remote_code=shared.args.trust_remote_code)
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return tokenizer
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def huggingface_loader(model_name):
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if shared.model_type == 'chatglm':
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LoaderClass = AutoModel
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elif shared.model_type == 'HF_seq2seq':
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@ -81,37 +133,14 @@ def load_model(model_name):
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LoaderClass = AutoModelForCausalLM
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# Load the model in simple 16-bit mode by default
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if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.wbits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.model_type in ['rwkv', 'llamacpp']]):
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model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16, trust_remote_code=trust_remote_code)
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if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.auto_devices, shared.args.disk, shared.args.deepspeed, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None]):
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model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16, trust_remote_code=shared.args.trust_remote_code)
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if torch.has_mps:
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device = torch.device('mps')
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model = model.to(device)
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else:
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model = model.cuda()
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# FlexGen
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elif shared.args.flexgen:
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# Initialize environment
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env = ExecutionEnv.create(shared.args.disk_cache_dir)
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# Offloading policy
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policy = Policy(1, 1,
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shared.args.percent[0], shared.args.percent[1],
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shared.args.percent[2], shared.args.percent[3],
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shared.args.percent[4], shared.args.percent[5],
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overlap=True, sep_layer=True, pin_weight=shared.args.pin_weight,
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cpu_cache_compute=False, attn_sparsity=1.0,
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compress_weight=shared.args.compress_weight,
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comp_weight_config=CompressionConfig(
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num_bits=4, group_size=64,
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group_dim=0, symmetric=False),
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compress_cache=False,
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comp_cache_config=CompressionConfig(
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num_bits=4, group_size=64,
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group_dim=2, symmetric=False))
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model = OptLM(f"facebook/{model_name}", env, shared.args.model_dir, policy)
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# DeepSpeed ZeRO-3
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elif shared.args.deepspeed:
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model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}"), torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
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@ -119,50 +148,11 @@ def load_model(model_name):
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model.module.eval() # Inference
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logging.info(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}")
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# RMKV model (not on HuggingFace)
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elif shared.model_type == 'rwkv':
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from modules.RWKV import RWKVModel, RWKVTokenizer
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model = RWKVModel.from_pretrained(Path(f'{shared.args.model_dir}/{model_name}'), dtype="fp32" if shared.args.cpu else "bf16" if shared.args.bf16 else "fp16", device="cpu" if shared.args.cpu else "cuda")
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tokenizer = RWKVTokenizer.from_pretrained(Path(shared.args.model_dir))
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return model, tokenizer
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# llamacpp model
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elif shared.model_type == 'llamacpp':
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from modules.llamacpp_model import LlamaCppModel
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path = Path(f'{shared.args.model_dir}/{model_name}')
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if path.is_file():
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model_file = path
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else:
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model_file = list(Path(f'{shared.args.model_dir}/{model_name}').glob('*ggml*.bin'))[0]
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logging.info(f"llama.cpp weights detected: {model_file}\n")
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model, tokenizer = LlamaCppModel.from_pretrained(model_file)
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return model, tokenizer
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# Quantized model
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elif shared.args.wbits > 0:
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# Monkey patch
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if shared.args.monkey_patch:
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logging.warning("Applying the monkey patch for using LoRAs in 4-bit mode. It may cause undefined behavior outside its intended scope.")
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from modules.monkey_patch_gptq_lora import load_model_llama
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model, _ = load_model_llama(model_name)
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# No monkey patch
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else:
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from modules.GPTQ_loader import load_quantized
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model = load_quantized(model_name)
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# Custom
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else:
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params = {
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"low_cpu_mem_usage": True,
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"trust_remote_code": trust_remote_code
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"trust_remote_code": shared.args.trust_remote_code
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}
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if not any((shared.args.cpu, torch.cuda.is_available(), torch.has_mps)):
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@ -188,9 +178,9 @@ def load_model(model_name):
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checkpoint = Path(f'{shared.args.model_dir}/{model_name}')
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if shared.args.load_in_8bit and params.get('max_memory', None) is not None and params['device_map'] == 'auto':
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config = AutoConfig.from_pretrained(checkpoint, trust_remote_code=trust_remote_code)
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config = AutoConfig.from_pretrained(checkpoint, trust_remote_code=shared.args.trust_remote_code)
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with init_empty_weights():
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model = LoaderClass.from_config(config, trust_remote_code=trust_remote_code)
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model = LoaderClass.from_config(config, trust_remote_code=shared.args.trust_remote_code)
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model.tie_weights()
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params['device_map'] = infer_auto_device_map(
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@ -202,44 +192,77 @@ def load_model(model_name):
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model = LoaderClass.from_pretrained(checkpoint, **params)
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# Hijack attention with xformers
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if any((shared.args.xformers, shared.args.sdp_attention)):
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llama_attn_hijack.hijack_llama_attention()
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return model
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# Loading the tokenizer
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if shared.model_type == 'gpt4chan' and Path(f"{shared.args.model_dir}/gpt-j-6B/").exists():
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tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/gpt-j-6B/"))
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elif type(model) is transformers.LlamaForCausalLM:
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tokenizer = None
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# Try to load an universal LLaMA tokenizer
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if shared.model_type not in ['llava', 'oasst']:
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for p in [Path(f"{shared.args.model_dir}/llama-tokenizer/"), Path(f"{shared.args.model_dir}/oobabooga_llama-tokenizer/")]:
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if p.exists():
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logging.info(f"Loading the universal LLaMA tokenizer from {p}...")
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tokenizer = LlamaTokenizer.from_pretrained(p, clean_up_tokenization_spaces=True)
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break
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def flexgen_loader(model_name):
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from flexgen.flex_opt import CompressionConfig, ExecutionEnv, OptLM, Policy
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# Otherwise, load it from the model folder and hope that these
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# are not outdated tokenizer files.
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if tokenizer is None:
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tokenizer = LlamaTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}/"), clean_up_tokenization_spaces=True)
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try:
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tokenizer.eos_token_id = 2
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tokenizer.bos_token_id = 1
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tokenizer.pad_token_id = 0
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except:
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pass
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else:
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tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}/"), trust_remote_code=trust_remote_code)
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# Initialize environment
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env = ExecutionEnv.create(shared.args.disk_cache_dir)
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logging.info(f"Loaded the model in {(time.time()-t0):.2f} seconds.\n")
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# Offloading policy
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policy = Policy(1, 1,
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shared.args.percent[0], shared.args.percent[1],
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shared.args.percent[2], shared.args.percent[3],
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shared.args.percent[4], shared.args.percent[5],
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overlap=True, sep_layer=True, pin_weight=shared.args.pin_weight,
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cpu_cache_compute=False, attn_sparsity=1.0,
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compress_weight=shared.args.compress_weight,
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comp_weight_config=CompressionConfig(
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num_bits=4, group_size=64,
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group_dim=0, symmetric=False),
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compress_cache=False,
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comp_cache_config=CompressionConfig(
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num_bits=4, group_size=64,
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group_dim=2, symmetric=False))
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model = OptLM(f"facebook/{model_name}", env, shared.args.model_dir, policy)
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return model
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def RWKV_loader(model_name):
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from modules.RWKV import RWKVModel, RWKVTokenizer
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model = RWKVModel.from_pretrained(Path(f'{shared.args.model_dir}/{model_name}'), dtype="fp32" if shared.args.cpu else "bf16" if shared.args.bf16 else "fp16", device="cpu" if shared.args.cpu else "cuda")
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tokenizer = RWKVTokenizer.from_pretrained(Path(shared.args.model_dir))
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return model, tokenizer
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def llamacpp_loader(model_name):
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from modules.llamacpp_model import LlamaCppModel
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path = Path(f'{shared.args.model_dir}/{model_name}')
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if path.is_file():
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model_file = path
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else:
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model_file = list(Path(f'{shared.args.model_dir}/{model_name}').glob('*ggml*.bin'))[0]
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logging.info(f"llama.cpp weights detected: {model_file}\n")
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model, tokenizer = LlamaCppModel.from_pretrained(model_file)
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return model, tokenizer
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def GPTQ_loader(model_name):
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# Monkey patch
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if shared.args.monkey_patch:
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logging.warning("Applying the monkey patch for using LoRAs in 4-bit mode. It may cause undefined behavior outside its intended scope.")
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from modules.monkey_patch_gptq_lora import load_model_llama
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model, _ = load_model_llama(model_name)
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# No monkey patch
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else:
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from modules.GPTQ_loader import load_quantized
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model = load_quantized(model_name)
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return model
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def get_max_memory_dict():
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max_memory = {}
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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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for i in range(len(memory_map)):
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@ -456,8 +456,8 @@ def create_settings_menus(default_preset):
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shared.gradio['truncation_length'] = gr.Slider(value=shared.settings['truncation_length'], minimum=shared.settings['truncation_length_min'], maximum=shared.settings['truncation_length_max'], step=1, label='Truncate the prompt up to this length', info='The leftmost tokens are removed if the prompt exceeds this length. Most models require this to be at most 2048.')
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shared.gradio['custom_stopping_strings'] = gr.Textbox(lines=1, value=shared.settings["custom_stopping_strings"] or None, label='Custom stopping strings', info='In addition to the defaults. Written between "" and separated by commas. For instance: "\\nYour Assistant:", "\\nThe assistant:"')
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with gr.Column():
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shared.gradio['add_bos_token'] = gr.Checkbox(value=shared.settings['add_bos_token'], label='Add the bos_token to the beginning of prompts', info='Disabling this can make the replies more creative.')
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shared.gradio['ban_eos_token'] = gr.Checkbox(value=shared.settings['ban_eos_token'], label='Ban the eos_token', info='Forces the model to never end the generation prematurely.')
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shared.gradio['add_bos_token'] = gr.Checkbox(value=shared.settings['add_bos_token'], label='Add the bos_token to the beginning of prompts', info='Disabling this can make the replies more creative.')
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shared.gradio['skip_special_tokens'] = gr.Checkbox(value=shared.settings['skip_special_tokens'], label='Skip special tokens', info='Some specific models need this unset.')
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shared.gradio['stream'] = gr.Checkbox(value=not shared.args.no_stream, label='Activate text streaming')
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