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https://github.com/oobabooga/text-generation-webui.git
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Remove flexgen support
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oobabooga
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75c2dd38cf
@ -1,63 +0,0 @@
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'''
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Converts a transformers model to a format compatible with flexgen.
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'''
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import argparse
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import os
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from pathlib import Path
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import numpy as np
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import torch
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from tqdm import tqdm
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from transformers import AutoModelForCausalLM, AutoTokenizer
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parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54))
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parser.add_argument('MODEL', type=str, default=None, nargs='?', help="Path to the input model.")
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args = parser.parse_args()
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def disable_torch_init():
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"""
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Disable the redundant torch default initialization to accelerate model creation.
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"""
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import torch
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global torch_linear_init_backup
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global torch_layer_norm_init_backup
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torch_linear_init_backup = torch.nn.Linear.reset_parameters
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setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
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torch_layer_norm_init_backup = torch.nn.LayerNorm.reset_parameters
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setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
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def restore_torch_init():
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"""Rollback the change made by disable_torch_init."""
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import torch
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setattr(torch.nn.Linear, "reset_parameters", torch_linear_init_backup)
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setattr(torch.nn.LayerNorm, "reset_parameters", torch_layer_norm_init_backup)
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if __name__ == '__main__':
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path = Path(args.MODEL)
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model_name = path.name
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print(f"Loading {model_name}...")
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# disable_torch_init()
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model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
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# restore_torch_init()
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tokenizer = AutoTokenizer.from_pretrained(path)
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out_folder = Path(f"models/{model_name}-np")
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if not Path(out_folder).exists():
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os.mkdir(out_folder)
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print(f"Saving the converted model to {out_folder}...")
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for name, param in tqdm(list(model.model.named_parameters())):
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name = name.replace("decoder.final_layer_norm", "decoder.layer_norm")
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param_path = os.path.join(out_folder, name)
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with open(param_path, "wb") as f:
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np.save(f, param.cpu().detach().numpy())
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@ -1,64 +0,0 @@
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>FlexGen is a high-throughput generation engine for running large language models with limited GPU memory (e.g., a 16GB T4 GPU or a 24GB RTX3090 gaming card!).
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https://github.com/FMInference/FlexGen
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## Installation
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No additional installation steps are necessary. FlexGen is in the `requirements.txt` file for this project.
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## Converting a model
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FlexGen only works with the OPT model, and it needs to be converted to numpy format before starting the web UI:
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```
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python convert-to-flexgen.py models/opt-1.3b/
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```
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The output will be saved to `models/opt-1.3b-np/`.
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## Usage
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The basic command is the following:
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```
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python server.py --model opt-1.3b --loader flexgen
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```
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For large models, the RAM usage may be too high and your computer may freeze. If that happens, you can try this:
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```
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python server.py --model opt-1.3b --loader flexgen --compress-weight
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```
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With this second command, I was able to run both OPT-6.7b and OPT-13B with **2GB VRAM**, and the speed was good in both cases.
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You can also manually set the offload strategy with
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```
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python server.py --model opt-1.3b --loader flexgen --percent 0 100 100 0 100 0
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```
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where the six numbers after `--percent` are:
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```
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the percentage of weight on GPU
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the percentage of weight on CPU
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the percentage of attention cache on GPU
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the percentage of attention cache on CPU
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the percentage of activations on GPU
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the percentage of activations on CPU
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```
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You should typically only change the first two numbers. If their sum is less than 100, the remaining layers will be offloaded to the disk, by default into the `text-generation-webui/cache` folder.
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## Performance
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In my experiments with OPT-30B using a RTX 3090 on Linux, I have obtained these results:
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* `--loader flexgen --compress-weight --percent 0 100 100 0 100 0`: 0.99 seconds per token.
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* `--loader flexgen --compress-weight --percent 100 0 100 0 100 0`: 0.765 seconds per token.
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## Limitations
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* Only works with the OPT models.
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* Only two generation parameters are available: `temperature` and `do_sample`.
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@ -56,7 +56,6 @@ def load_model(model_name, loader=None):
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'GPTQ-for-LLaMa': GPTQ_loader,
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'llama.cpp': llamacpp_loader,
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'llamacpp_HF': llamacpp_HF_loader,
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'FlexGen': flexgen_loader,
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'RWKV': RWKV_loader,
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'ExLlama': ExLlama_loader,
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'ExLlama_HF': ExLlama_HF_loader
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@ -221,32 +220,6 @@ def huggingface_loader(model_name):
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return model
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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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# 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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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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@ -30,8 +30,6 @@ def infer_loader(model_name):
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loader = 'llama.cpp'
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elif re.match('.*rwkv.*\.pth', model_name.lower()):
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loader = 'RWKV'
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elif shared.args.flexgen:
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loader = 'FlexGen'
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else:
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loader = 'Transformers'
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@ -95,7 +95,7 @@ parser.add_argument('--extensions', type=str, nargs="+", help='The list of exten
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parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
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# Model loader
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parser.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv, flexgen')
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parser.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv')
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# Accelerate/transformers
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parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text. Warning: Training on CPU is extremely slow.')
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@ -156,7 +156,6 @@ parser.add_argument('--gpu-split', type=str, help="Comma-separated list of VRAM
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parser.add_argument('--max_seq_len', type=int, default=2048, help="Maximum sequence length.")
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# FlexGen
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parser.add_argument('--flexgen', action='store_true', help='DEPRECATED')
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parser.add_argument('--percent', type=int, nargs="+", default=[0, 100, 100, 0, 100, 0], help='FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0).')
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parser.add_argument("--compress-weight", action="store_true", help="FlexGen: activate weight compression.")
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parser.add_argument("--pin-weight", type=str2bool, nargs="?", const=True, default=True, help="FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%%).")
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@ -202,9 +201,6 @@ if args.autogptq:
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if args.gptq_for_llama:
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logger.warning('--gptq-for-llama has been deprecated and will be removed soon. Use --loader gptq-for-llama instead.')
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args.loader = 'gptq-for-llama'
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if args.flexgen:
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logger.warning('--flexgen has been deprecated and will be removed soon. Use --loader flexgen instead.')
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args.loader = 'FlexGen'
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# Security warnings
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if args.trust_remote_code:
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@ -53,8 +53,6 @@ def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_lengt
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if shared.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel', 'ExllamaModel'] or shared.args.cpu:
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return input_ids
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elif shared.args.flexgen:
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return input_ids.numpy()
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elif shared.args.deepspeed:
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return input_ids.to(device=local_rank)
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elif torch.backends.mps.is_available():
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@ -182,8 +180,6 @@ def _generate_reply(question, state, stopping_strings=None, is_chat=False):
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if shared.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel', 'ExllamaModel']:
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generate_func = generate_reply_custom
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elif shared.args.flexgen:
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generate_func = generate_reply_flexgen
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else:
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generate_func = generate_reply_HF
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@ -339,66 +335,3 @@ def generate_reply_custom(question, original_question, seed, state, stopping_str
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new_tokens = len(encode(original_question + reply)[0]) - original_tokens
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print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
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return
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def generate_reply_flexgen(question, original_question, seed, state, stopping_strings=None, is_chat=False):
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generate_params = {}
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for k in ['max_new_tokens', 'do_sample', 'temperature']:
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generate_params[k] = state[k]
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if state['stream']:
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generate_params['max_new_tokens'] = 8
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# Encode the input
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input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))
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output = input_ids[0]
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# Find the eos tokens
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eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
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if not state['ban_eos_token']:
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generate_params['stop'] = eos_token_ids[-1]
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# Add the encoded tokens to generate_params
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question, input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, input_ids, None)
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original_input_ids = input_ids
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generate_params.update({'inputs': input_ids})
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if inputs_embeds is not None:
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generate_params.update({'inputs_embeds': inputs_embeds})
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t0 = time.time()
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try:
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if not is_chat:
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yield ''
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# Generate the entire reply at once.
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if not state['stream']:
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with torch.no_grad():
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output = shared.model.generate(**generate_params)[0]
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yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat)
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# Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
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else:
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for i in range(state['max_new_tokens'] // 8 + 1):
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if shared.stop_everything:
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break
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clear_torch_cache()
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with torch.no_grad():
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output = shared.model.generate(**generate_params)[0]
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if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)):
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break
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yield get_reply_from_output_ids(output, original_input_ids, original_question, state)
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input_ids = np.reshape(output, (1, output.shape[0]))
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generate_params.update({'inputs': input_ids})
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except Exception:
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traceback.print_exc()
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finally:
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t1 = time.time()
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original_tokens = len(original_input_ids[0])
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new_tokens = len(output) - (original_tokens if not shared.is_seq2seq else 0)
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print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
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return
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@ -71,9 +71,6 @@ def natural_keys(text):
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def get_available_models():
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if shared.args.flexgen:
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return sorted([re.sub('-np$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if item.name.endswith('-np')], key=natural_keys)
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else:
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return sorted([re.sub('.pth$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json', '.yaml'))], key=natural_keys)
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@ -321,7 +321,7 @@ def create_settings_menus(default_preset):
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with gr.Row():
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with gr.Column():
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with gr.Row():
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shared.gradio['preset_menu'] = gr.Dropdown(choices=utils.get_available_presets(), value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset', elem_classes='slim-dropdown')
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shared.gradio['preset_menu'] = gr.Dropdown(choices=utils.get_available_presets(), value=default_preset, label='Generation parameters preset', elem_classes='slim-dropdown')
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ui.create_refresh_button(shared.gradio['preset_menu'], lambda: None, lambda: {'choices': utils.get_available_presets()}, 'refresh-button')
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shared.gradio['save_preset'] = gr.Button('💾', elem_classes='refresh-button')
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shared.gradio['delete_preset'] = gr.Button('🗑️', elem_classes='refresh-button')
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