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Simplify deepspeed implementation (#40)

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oobabooga 2023-02-02 12:15:44 -03:00
parent d6b2d68527
commit 2583bc5840
1 changed files with 18 additions and 26 deletions
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44
server.py
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@ -83,10 +83,7 @@ if args.deepspeed:
from modules.deepspeed_parameters import generate_ds_config from modules.deepspeed_parameters import generate_ds_config
# Distributed setup # Distributed setup
if args.local_rank is not None: local_rank = args.local_rank if args.local_rank is not None else int(os.getenv("LOCAL_RANK", "0"))
local_rank = args.local_rank
else:
local_rank = int(os.getenv("LOCAL_RANK", "0"))
world_size = int(os.getenv("WORLD_SIZE", "1")) world_size = int(os.getenv("WORLD_SIZE", "1"))
torch.cuda.set_device(local_rank) torch.cuda.set_device(local_rank)
deepspeed.init_distributed() deepspeed.init_distributed()
@ -109,15 +106,8 @@ def load_model(model_name):
# DeepSpeed ZeRO-3 # DeepSpeed ZeRO-3
elif args.deepspeed: elif args.deepspeed:
if args.bf16: model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), torch_dtype=torch.bfloat16 if args.bf16 else torch.float16)
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), torch_dtype=torch.bfloat16) model = deepspeed.initialize(model=model, config_params=ds_config, model_parameters=None, optimizer=None, lr_scheduler=None)[0]
else:
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), torch_dtype=torch.float16)
model = deepspeed.initialize(model=model,
config_params=ds_config,
model_parameters=None,
optimizer=None,
lr_scheduler=None)[0]
model.module.eval() # Inference model.module.eval() # Inference
print(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}") print(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}")
@ -183,7 +173,11 @@ def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
else: else:
torch.cuda.empty_cache() torch.cuda.empty_cache()
input_ids = tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=2048-tokens_to_generate, add_special_tokens=add_special_tokens).cuda() input_ids = tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=2048-tokens_to_generate, add_special_tokens=add_special_tokens).cuda()
return input_ids
if not args.deepspeed:
return input_ids
else:
return input_ids.to(device=local_rank)
def decode(output_ids): def decode(output_ids):
reply = tokenizer.decode(output_ids, skip_special_tokens=True) reply = tokenizer.decode(output_ids, skip_special_tokens=True)
@ -226,10 +220,8 @@ def generate_reply(question, tokens, inference_settings, selected_model, eos_tok
cuda = "" if args.cpu else ".cuda()" cuda = "" if args.cpu else ".cuda()"
n = tokenizer.eos_token_id if eos_token is None else tokenizer.encode(eos_token, return_tensors='pt')[0][-1] n = tokenizer.eos_token_id if eos_token is None else tokenizer.encode(eos_token, return_tensors='pt')[0][-1]
if args.deepspeed: input_ids = encode(question, tokens)
input_ids = encode(question, tokens).to(device=local_rank)
else:
input_ids = encode(question, tokens)
if stopping_string is not None: if stopping_string is not None:
# The stopping_criteria code below was copied from # The stopping_criteria code below was copied from
# https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py # https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py
@ -246,11 +238,11 @@ def generate_reply(question, tokens, inference_settings, selected_model, eos_tok
# Generate the entire reply at once # Generate the entire reply at once
if args.no_stream: if args.no_stream:
t0 = time.time() t0 = time.time()
if args.deepspeed: with torch.no_grad():
with torch.no_grad(): if not args.deepspeed:
output = eval(f"model.generate(input_ids, eos_token_id={n}, stopping_criteria=stopping_criteria_list, {preset}){cuda}")
else:
output = eval(f"model.generate(input_ids, synced_gpus=True, eos_token_id={n}, stopping_criteria=stopping_criteria_list, {preset})") output = eval(f"model.generate(input_ids, synced_gpus=True, eos_token_id={n}, stopping_criteria=stopping_criteria_list, {preset})")
else:
output = eval(f"model.generate(input_ids, eos_token_id={n}, stopping_criteria=stopping_criteria_list, {preset}){cuda}")
reply = decode(output[0]) reply = decode(output[0])
t1 = time.time() t1 = time.time()
print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output[0])-len(input_ids[0]))/(t1-t0):.2f} it/s)") print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output[0])-len(input_ids[0]))/(t1-t0):.2f} it/s)")
@ -263,11 +255,11 @@ def generate_reply(question, tokens, inference_settings, selected_model, eos_tok
yield formatted_outputs(original_question, model_name) yield formatted_outputs(original_question, model_name)
preset = preset.replace('max_new_tokens=tokens', 'max_new_tokens=8') preset = preset.replace('max_new_tokens=tokens', 'max_new_tokens=8')
for i in tqdm(range(tokens//8+1)): for i in tqdm(range(tokens//8+1)):
if args.deepspeed: with torch.no_grad():
with torch.no_grad(): if not args.deepspeed:
output = eval(f"model.generate(input_ids, eos_token_id={n}, stopping_criteria=stopping_criteria_list, {preset}){cuda}")
else:
output = eval(f"model.generate(input_ids, synced_gpus=True, eos_token_id={n}, stopping_criteria=stopping_criteria_list, {preset})") output = eval(f"model.generate(input_ids, synced_gpus=True, eos_token_id={n}, stopping_criteria=stopping_criteria_list, {preset})")
else:
output = eval(f"model.generate(input_ids, eos_token_id={n}, stopping_criteria=stopping_criteria_list, {preset}){cuda}")
reply = decode(output[0]) reply = decode(output[0])
if not (args.chat or args.cai_chat): if not (args.chat or args.cai_chat):
reply = original_question + apply_extensions(reply[len(question):], "output") reply = original_question + apply_extensions(reply[len(question):], "output")