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fix pylint

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This commit is contained in:
FangYin Cheng 2023-07-13 18:11:41 +08:00
parent 189ac995ec
commit accce56d49
2 changed files with 23 additions and 18 deletions
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2
pilot/configs/config.py
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@ -28,7 +28,7 @@ class Config(metaclass=Singleton):
self.skip_reprompt = False self.skip_reprompt = False
self.temperature = float(os.getenv("TEMPERATURE", 0.7)) self.temperature = float(os.getenv("TEMPERATURE", 0.7))
self.NUM_GPUS = int(os.getenv("NUM_GPUS",1)) self.NUM_GPUS = int(os.getenv("NUM_GPUS", 1))
self.execute_local_commands = ( self.execute_local_commands = (
os.getenv("EXECUTE_LOCAL_COMMANDS", "False") == "True" os.getenv("EXECUTE_LOCAL_COMMANDS", "False") == "True"

39
pilot/model/adapter.py
View File

@ -73,6 +73,7 @@ class VicunaLLMAdapater(BaseLLMAdaper):
) )
return model, tokenizer return model, tokenizer
def auto_configure_device_map(num_gpus): def auto_configure_device_map(num_gpus):
"""handling multi gpu calls""" """handling multi gpu calls"""
# transformer.word_embeddings occupying 1 floors # transformer.word_embeddings occupying 1 floors
@ -81,18 +82,18 @@ def auto_configure_device_map(num_gpus):
# Allocate a total of 30 layers to number On gpus cards # Allocate a total of 30 layers to number On gpus cards
num_trans_layers = 28 num_trans_layers = 28
per_gpu_layers = 30 / num_gpus per_gpu_layers = 30 / num_gpus
#Bugfix: call torch.embedding in Linux and the incoming weight and input are not on the same device, resulting in a RuntimeError # Bugfix: call torch.embedding in Linux and the incoming weight and input are not on the same device, resulting in a RuntimeError
#Under Windows, model. device will be set to transformer. word_ Embeddings. device # Under Windows, model. device will be set to transformer. word_ Embeddings. device
#Under Linux, model. device will be set to lm_ Head.device # Under Linux, model. device will be set to lm_ Head.device
#When calling chat or stream_ During chat, input_ IDS will be placed on model. device # When calling chat or stream_ During chat, input_ IDS will be placed on model. device
#If transformer. word_ If embeddings. device and model. device are different, it will cause a RuntimeError # If transformer. word_ If embeddings. device and model. device are different, it will cause a RuntimeError
#Therefore, here we will transform. word_ Embeddings, transformer. final_ Layernorm, lm_ Put all the heads on the first card # Therefore, here we will transform. word_ Embeddings, transformer. final_ Layernorm, lm_ Put all the heads on the first card
device_map = { device_map = {
'transformer.embedding.word_embeddings': 0, "transformer.embedding.word_embeddings": 0,
'transformer.encoder.final_layernorm': 0, "transformer.encoder.final_layernorm": 0,
'transformer.output_layer': 0, "transformer.output_layer": 0,
'transformer.rotary_pos_emb': 0, "transformer.rotary_pos_emb": 0,
'lm_head': 0 "lm_head": 0,
} }
used = 2 used = 2
@ -102,7 +103,7 @@ def auto_configure_device_map(num_gpus):
gpu_target += 1 gpu_target += 1
used = 0 used = 0
assert gpu_target < num_gpus assert gpu_target < num_gpus
device_map[f'transformer.encoder.layers.{i}'] = gpu_target device_map[f"transformer.encoder.layers.{i}"] = gpu_target
used += 1 used += 1
return device_map return device_map
@ -114,7 +115,13 @@ class ChatGLMAdapater(BaseLLMAdaper):
def match(self, model_path: str): def match(self, model_path: str):
return "chatglm" in model_path return "chatglm" in model_path
def loader(self, model_path: str, from_pretrained_kwargs: dict, device_map=None, num_gpus=CFG.NUM_GPUS): def loader(
self,
model_path: str,
from_pretrained_kwargs: dict,
device_map=None,
num_gpus=CFG.NUM_GPUS,
):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
if DEVICE != "cuda": if DEVICE != "cuda":
@ -125,10 +132,8 @@ class ChatGLMAdapater(BaseLLMAdaper):
else: else:
model = ( model = (
AutoModel.from_pretrained( AutoModel.from_pretrained(
model_path, trust_remote_code=True, model_path, trust_remote_code=True, **from_pretrained_kwargs
**from_pretrained_kwargs ).half()
)
.half()
# .cuda() # .cuda()
) )
from accelerate import dispatch_model from accelerate import dispatch_model