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Builder torch_dtype FIX #1181

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Builder torch_dtype FIX #1181

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17 changes: 10 additions & 7 deletions llava/model/builder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@

def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", device="cuda", use_flash_attn=False, **kwargs):
kwargs = {"device_map": device_map, **kwargs}
kwargs["device_map"] = kwargs["device_map"] if kwargs["device_map"] is not None else "auto"

if device != "cuda":
kwargs['device_map'] = {"": device}
Expand All @@ -39,8 +40,10 @@ def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, l
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4'
)
else:
kwargs['torch_dtype'] = torch.float16

kwargs['torch_dtype'] = kwargs.get('torch_dtype',torch.float16)

torch_dtype_to_use = kwargs['torch_dtype']

if use_flash_attn:
kwargs['attn_implementation'] = 'flash_attention_2'
Expand Down Expand Up @@ -99,7 +102,7 @@ def load_from_hf(repo_id, filename, subfolder=None):
model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)

mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
mm_projector_weights = {k: v.to(torch_dtype_to_use) for k, v in mm_projector_weights.items()}
model.load_state_dict(mm_projector_weights, strict=False)
else:
if 'mpt' in model_name.lower():
Expand Down Expand Up @@ -130,8 +133,8 @@ def load_from_hf(repo_id, filename, subfolder=None):
model = PeftModel.from_pretrained(model, model_path)
print(f"Merging weights")
model = model.merge_and_unload()
print('Convert to FP16...')
model.to(torch.float16)
print('Convert to specified dtype...')
model.to(torch_dtype_to_use)
else:
use_fast = False
if 'mpt' in model_name.lower():
Expand All @@ -156,12 +159,12 @@ def load_from_hf(repo_id, filename, subfolder=None):
if not vision_tower.is_loaded:
vision_tower.load_model(device_map=device_map)
if device_map != 'auto':
vision_tower.to(device=device_map, dtype=torch.float16)
vision_tower.to(device=device_map, dtype=torch_dtype_to_use)
image_processor = vision_tower.image_processor

if hasattr(model.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
else:
context_len = 2048

return tokenizer, model, image_processor, context_len