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https://github.com/oobabooga/text-generation-webui.git
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Move towards HF LLaMA implementation
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oobabooga
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@ -1,96 +0,0 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# This software may be used and distributed according to the terms of the GNU General Public License version 3.
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import json
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import os
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import sys
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import time
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from pathlib import Path
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from typing import Tuple
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import fire
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import torch
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from fairscale.nn.model_parallel.initialize import initialize_model_parallel
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from llama import LLaMA, ModelArgs, Tokenizer, Transformer
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os.environ['RANK'] = '0'
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os.environ['WORLD_SIZE'] = '1'
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os.environ['MP'] = '1'
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os.environ['MASTER_ADDR'] = '127.0.0.1'
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os.environ['MASTER_PORT'] = '2223'
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def setup_model_parallel() -> Tuple[int, int]:
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local_rank = int(os.environ.get("LOCAL_RANK", -1))
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world_size = int(os.environ.get("WORLD_SIZE", -1))
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torch.distributed.init_process_group("gloo")
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initialize_model_parallel(world_size)
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torch.cuda.set_device(local_rank)
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# seed must be the same in all processes
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torch.manual_seed(1)
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return local_rank, world_size
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def load(
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ckpt_dir: str,
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tokenizer_path: str,
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local_rank: int,
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world_size: int,
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max_seq_len: int,
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max_batch_size: int,
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) -> LLaMA:
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start_time = time.time()
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checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
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assert world_size == len(
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checkpoints
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), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {world_size}"
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ckpt_path = checkpoints[local_rank]
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print("Loading")
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checkpoint = torch.load(ckpt_path, map_location="cpu")
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with open(Path(ckpt_dir) / "params.json", "r") as f:
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params = json.loads(f.read())
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model_args: ModelArgs = ModelArgs(
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max_seq_len=max_seq_len, max_batch_size=max_batch_size, **params
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)
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tokenizer = Tokenizer(model_path=tokenizer_path)
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model_args.vocab_size = tokenizer.n_words
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torch.set_default_tensor_type(torch.cuda.HalfTensor)
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model = Transformer(model_args)
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torch.set_default_tensor_type(torch.FloatTensor)
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model.load_state_dict(checkpoint, strict=False)
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generator = LLaMA(model, tokenizer)
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print(f"Loaded in {time.time() - start_time:.2f} seconds")
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return generator
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class LLaMAModel:
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def __init__(self):
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pass
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@classmethod
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def from_pretrained(self, path, max_seq_len=2048, max_batch_size=1):
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tokenizer_path = path / "tokenizer.model"
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path = os.path.abspath(path)
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tokenizer_path = os.path.abspath(tokenizer_path)
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local_rank, world_size = setup_model_parallel()
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if local_rank > 0:
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sys.stdout = open(os.devnull, "w")
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generator = load(
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path, tokenizer_path, local_rank, world_size, max_seq_len, max_batch_size
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)
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result = self()
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result.pipeline = generator
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return result
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def generate(self, prompt, token_count=512, temperature=0.8, top_p=0.95):
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results = self.pipeline.generate(
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[prompt], max_gen_len=token_count, temperature=temperature, top_p=top_p
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)
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return results[0]
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@ -1,125 +0,0 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# This software may be used and distributed according to the terms of the GNU General Public License version 3.
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from typing import Tuple
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import os
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import sys
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import torch
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import fire
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import time
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import json
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from pathlib import Path
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from fairscale.nn.model_parallel.initialize import initialize_model_parallel
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from repositories.llama_int8.llama import ModelArgs, Transformer, Tokenizer, LLaMA
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def setup_model_parallel() -> Tuple[int, int]:
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local_rank = int(os.environ.get("LOCAL_RANK", -1))
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world_size = int(os.environ.get("WORLD_SIZE", -1))
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torch.distributed.init_process_group("nccl")
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initialize_model_parallel(world_size)
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torch.cuda.set_device(local_rank)
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# seed must be the same in all processes
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torch.manual_seed(1)
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return local_rank, world_size
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def load(
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ckpt_dir: str,
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tokenizer_path: str,
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max_seq_len: int,
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max_batch_size: int,
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) -> LLaMA:
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start_time = time.time()
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checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
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with open(Path(ckpt_dir) / "params.json", "r") as f:
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params = json.loads(f.read())
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model_args: ModelArgs = ModelArgs(
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max_seq_len=max_seq_len, max_batch_size=max_batch_size, **params
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)
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tokenizer = Tokenizer(model_path=tokenizer_path)
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model_args.vocab_size = tokenizer.n_words
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# torch.set_default_tensor_type(torch.cuda.HalfTensor)
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torch.set_default_tensor_type(torch.HalfTensor)
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print("Creating transformer")
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model = Transformer(model_args)
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print("Transformer created")
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key_to_dim = {
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"w1": 0,
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"w2": -1,
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"w3": 0,
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"wo": -1,
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"wq": 0,
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"wk": 0,
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"wv": 0,
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"output": 0,
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"tok_embeddings": -1,
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"ffn_norm": None,
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"attention_norm": None,
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"norm": None,
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"rope": None,
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}
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# ?
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torch.set_default_tensor_type(torch.FloatTensor)
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# load the state dict incrementally, to avoid memory problems
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for i, ckpt in enumerate(checkpoints):
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print(f"Loading checkpoint {i}")
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checkpoint = torch.load(ckpt, map_location="cpu")
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for parameter_name, parameter in model.named_parameters():
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short_name = parameter_name.split(".")[-2]
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if key_to_dim[short_name] is None and i == 0:
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parameter.data = checkpoint[parameter_name]
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elif key_to_dim[short_name] == 0:
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size = checkpoint[parameter_name].size(0)
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parameter.data[size * i : size * (i + 1), :] = checkpoint[
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parameter_name
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]
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elif key_to_dim[short_name] == -1:
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size = checkpoint[parameter_name].size(-1)
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parameter.data[:, size * i : size * (i + 1)] = checkpoint[
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parameter_name
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]
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del checkpoint
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# model.load_state_dict(checkpoint, strict=False)
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model.quantize()
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generator = LLaMA(model, tokenizer)
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print(f"Loaded in {time.time() - start_time:.2f} seconds")
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return generator
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class LLaMAModel_8bit:
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def __init__(self):
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pass
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@classmethod
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def from_pretrained(self, path, max_seq_len=2048, max_batch_size=1):
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tokenizer_path = path / "tokenizer.model"
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path = os.path.abspath(path)
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tokenizer_path = os.path.abspath(tokenizer_path)
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generator = load(path, tokenizer_path, max_seq_len, max_batch_size)
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result = self()
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result.pipeline = generator
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return result
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def generate(self, prompt, token_count=512, temperature=0.8, top_p=0.95):
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results = self.pipeline.generate(
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[prompt], max_gen_len=token_count, temperature=temperature, top_p=top_p
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)
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return results[0]
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@ -39,10 +39,9 @@ def load_model(model_name):
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t0 = time.time()
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shared.is_RWKV = model_name.lower().startswith('rwkv-')
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shared.is_LLaMA = model_name.lower().startswith('llama-')
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# Default settings
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if not (shared.args.cpu or shared.args.load_in_8bit or shared.args.auto_devices or shared.args.disk or shared.args.gpu_memory is not None or shared.args.cpu_memory is not None or shared.args.deepspeed or shared.args.flexgen or shared.is_RWKV or shared.is_LLaMA):
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if not (shared.args.cpu or shared.args.load_in_8bit or shared.args.auto_devices or shared.args.disk or shared.args.gpu_memory is not None or shared.args.cpu_memory is not None or shared.args.deepspeed or shared.args.flexgen or shared.is_RWKV):
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if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
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model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
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else:
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@ -86,23 +85,6 @@ def load_model(model_name):
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return model, None
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# LLaMA model (not on HuggingFace)
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elif shared.is_LLaMA:
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if shared.args.load_in_8bit:
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import modules.LLaMA_8bit
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from modules.LLaMA_8bit import LLaMAModel_8bit
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model = LLaMAModel_8bit.from_pretrained(Path(f'models/{model_name}'))
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return model, None
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else:
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import modules.LLaMA
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from modules.LLaMA import LLaMAModel
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model = LLaMAModel.from_pretrained(Path(f'models/{model_name}'))
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return model, None
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# Custom
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else:
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command = "AutoModelForCausalLM.from_pretrained"
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@ -6,7 +6,6 @@ model_name = ""
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soft_prompt_tensor = None
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soft_prompt = False
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is_RWKV = False
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is_LLaMA = False
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# Chat variables
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history = {'internal': [], 'visible': []}
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@ -43,7 +42,6 @@ settings = {
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'default': 'NovelAI-Sphinx Moth',
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'pygmalion-*': 'Pygmalion',
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'RWKV-*': 'Naive',
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'llama-*': 'Naive',
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'(rosey|chip|joi)_.*_instruct.*': 'Instruct Joi (Contrastive Search)'
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},
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'prompts': {
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@ -24,7 +24,7 @@ def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
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# These models do not have explicit tokenizers for now, so
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# we return an estimate for the number of tokens
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if shared.is_RWKV or shared.is_LLaMA:
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if shared.is_RWKV:
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return np.zeros((1, len(prompt)//4))
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input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=get_max_prompt_length(tokens_to_generate), add_special_tokens=add_special_tokens)
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@ -90,7 +90,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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# These models are not part of Hugging Face, so we handle them
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# separately and terminate the function call earlier
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if shared.is_RWKV or shared.is_LLaMA:
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if shared.is_RWKV:
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if shared.args.no_stream:
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reply = shared.model.generate(question, token_count=max_new_tokens, temperature=temperature, top_p=top_p)
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t1 = time.time()
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@ -5,4 +5,4 @@ gradio==3.18.0
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numpy
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rwkv==0.0.6
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safetensors==0.2.8
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git+https://github.com/huggingface/transformers
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git+https://github.com/oobabooga/transformers@llama_push
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