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This commit is contained in:
LiangSong 2023-04-07 23:20:20 +08:00
parent f4ba4b6ff2
commit c67d365db3
3 changed files with 15 additions and 124 deletions
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23
chat_server.py
View File

@ -32,13 +32,15 @@ raw_model = LlamaForCausalLM(
)
)
ckpt = torch.load(
"data/saved_ckpt/instruction_tuning_math_code_multiturn/36001.pt", map_location="cpu"
"data/saved_ckpt/instruction_tuning_math_code_multiturn/36001.pt",
map_location="cpu",
)
raw_model.load_state_dict(ckpt)
raw_model.eval()
model = raw_model.cuda()
print("ready")
def parse_codeblock(text):
lines = text.split("\n")
for i, line in enumerate(lines):
@ -46,12 +48,13 @@ def parse_codeblock(text):
if line != "```":
lines[i] = f'<pre><code class="{lines[i][3:]}">'
else:
lines[i] = '</code></pre>'
lines[i] = "</code></pre>"
else:
if i > 0:
lines[i] = "<br/>" + line.replace("<", "&lt;").replace(">", "&gt;")
return "".join(lines)
with gr.Blocks() as demo:
gr.Markdown(
"""
@ -75,15 +78,17 @@ with gr.Blocks() as demo:
for prompt, completion in history:
round += 1
if completion is None:
inputs = 'user:{}\nsystem:'.format(prompt)
inputs = tokenizer(inputs, return_tensors=True, add_special_tokens=False)
context.append(inputs['input_ids'])
inputs = "user:{}\nsystem:".format(prompt)
inputs = tokenizer(
inputs, return_tensors=True, add_special_tokens=False
)
context.append(inputs["input_ids"])
else:
inputs = 'user:{}\nsystem:{}'.format(prompt, completion)
inputs = "user:{}\nsystem:{}".format(prompt, completion)
inputs = tokenizer(inputs, return_tensors=True, add_special_tokens=True)
context.append(inputs['input_ids'])
context.append(inputs["input_ids"])
context = torch.cat(context, dim=-1)
context = context[:, -1024: ]
context = context[:, -1024:]
inputs_len = context.shape[1]
context = context.cuda()
pred = model.generate(input_ids=context, max_new_tokens=512, do_sample=True)
@ -99,7 +104,7 @@ with gr.Blocks() as demo:
)
clear.click(lambda: None, None, chatbot, queue=False)
gr.Markdown(
"""
"""
当前体验服务生成的所有内容都是由人工智能模型生成，我们对其生成内容的准确性、完整性和功能性不做任何保证，并且其生成的内容不代表我们的态度或观点。
联系方式: sl12160010@gmail.com 对于该项目有任何意见和建议都欢迎联系我.

2
dataset/tokenizer.py
View File

@ -169,7 +169,7 @@ class Tokenizer:
flag = True
break
if flag:
ids = ids[: j]
ids = ids[:j]
else:
ids = ids
out.append(ids)

114
speed_test.py
View File

@ -1,114 +0,0 @@
# import time
# import torch
# from colossalai.nn.optimizer import HybridAdam
# from deepspeed.ops.adam import FusedAdam
# from transformers import LlamaForCausalLM, LlamaConfig
# import lightning.pytorch as pl
# # define the LightningModule
# class LitAutoEncoder(pl.LightningModule):
# def __init__(self):
# super().__init__()
# def training_step(self, inputs, batch_idx):
# # training_step defines the train loop.
# # it is independent of forward
# # print(inputs.shape)
# out = self.model(input_ids=inputs, labels=inputs)
# loss = out.loss
# return loss
# def configure_optimizers(self):
# optimizer = HybridAdam(self.parameters(), lr=1e-5)
# return optimizer
# def configure_sharded_model(self):
# self.model = LlamaForCausalLM(
# LlamaConfig(
# vocab_size=32000,
# initializer_range=0.001,
# pad_token_id=0,
# rms_norm_eps=1e-5,
# hidden_dropout_prob=0.1,
# attention_dropout_prob=0.1,
# use_stable_embedding=False,
# shared_input_output_embedding=False,
# )
# )
# # init the autoencoder
# autoencoder = LitAutoEncoder()
# trainer = pl.Trainer(limit_train_batches=500, max_epochs=1, accelerator='gpu', devices=8, strategy="colossalai", precision=16)
# class FakeSet(torch.utils.data.Dataset):
# def __getitem__(self, idx):
# return torch.randint(0, 32000, (2048, ))
# def __len__(self):
# return 10000
# train_loader = torch.utils.data.DataLoader(FakeSet(), batch_size=1)
# trainer.fit(model=autoencoder, train_dataloaders=train_loader)
# import time
# import torch
# from accelerate import Accelerator
# from deepspeed.ops.adam import FusedAdam
# from transformers import LlamaForCausalLM, LlamaConfig
# accelerator = Accelerator()
# raw_model = LlamaForCausalLM(
# LlamaConfig(
# vocab_size=32000,
# initializer_range=0.001,
# pad_token_id=0,
# rms_norm_eps=1e-5,
# hidden_dropout_prob=0.1,
# attention_dropout_prob=0.1,
# use_stable_embedding=False,
# shared_input_output_embedding=False,
# )
# )
# optimizer = FusedAdam(raw_model.parameters(), lr=1e-5)
# import random
# import sentencepiece as spm
# from dataset.tokenizer import Tokenizer
# from dataset.data_iter import create_shard_kwargs, DataIter
# from torch.utils.data import DataLoader
# max_length = 2048
# tokenizer_model_path = 'configs/10w_vocab_wudao5_pile10.model'
# sp_model = spm.SentencePieceProcessor(model_file=tokenizer_model_path)
# tokenizer = Tokenizer(sp_model)
# paths = create_shard_kwargs(['1*'])
# random.shuffle(paths)
# data_set = DataIter(
# paths
# )
# train_loader = DataLoader(
# data_set,
# batch_size=1
# )
# model, optimizer, train_loader = accelerator.prepare(raw_model, optimizer, train_loader)
# inputs = torch.randint(0, 32000, (1, 2048), device=accelerator.device)
# for i in range(10):
# optimizer.zero_grad()
# out = model(input_ids=inputs, labels=inputs)
# loss = out.loss
# accelerator.backward(loss)
# optimizer.step()
# start_time = time.time()
# for i in range(500):
# optimizer.zero_grad()
# out = model(input_ids=inputs, labels=inputs)
# loss = out.loss
# accelerator.backward(loss)
# optimizer.step()
# end_time = time.time()
# accelerator.print(end_time - start_time)