phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-06-28 10:54:19 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Avoid using nearly double the memory needed (#30)

Browse source
This commit is contained in:
Ashwin Bharambe 2024-08-14 17:44:36 -07:00 • committed by GitHub
parent b311dcd143
commit 00f0e6d92b
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
1 changed files with 9 additions and 22 deletions
Download patch file Download diff file Expand all files Collapse all files

23
llama_toolchain/inference/meta_reference/generation.py
View file

@ -106,8 +106,6 @@ class Llama:
with open(Path(ckpt_dir) / "params.json", "r") as f: with open(Path(ckpt_dir) / "params.json", "r") as f:
params = json.loads(f.read()) params = json.loads(f.read())
# TODO(ashwin): this block is so we can load internal checkpoints without additional
# fuss. the final code should _not_ have this blurb
if "model" in params: if "model" in params:
params = params["model"] params = params["model"]
@ -130,34 +128,23 @@ class Llama:
) )
if fp8: if fp8:
from .quantization.loader import convert_to_quantized_model
# load on CPU in bf16 so that fp8 conversion does not find an # load on CPU in bf16 so that fp8 conversion does not find an
# unexpected (fp32, e.g.) datatype # unexpected (fp32, e.g.) datatype
torch.set_default_tensor_type(torch.BFloat16Tensor) torch.set_default_tensor_type(torch.BFloat16Tensor)
model = Transformer(model_args) model = Transformer(model_args)
if fp8:
# load on CPU first since if we are doing fp8, we probably don't
# have enough memory on GPU for bf16
model.load_state_dict(state_dict, strict=False) model.load_state_dict(state_dict, strict=False)
model = convert_to_quantized_model(model, config)
else:
if torch.cuda.is_bf16_supported(): if torch.cuda.is_bf16_supported():
torch.set_default_tensor_type(torch.cuda.BFloat16Tensor) torch.set_default_tensor_type(torch.cuda.BFloat16Tensor)
else: else:
torch.set_default_tensor_type(torch.cuda.HalfTensor) torch.set_default_tensor_type(torch.cuda.HalfTensor)
model = Transformer(model_args)
if not fp8:
model.load_state_dict(state_dict, strict=False) model.load_state_dict(state_dict, strict=False)
if config.quantization:
from .quantization.loader import convert_to_quantized_model
model = convert_to_quantized_model(model, config)
else:
model = model.to("cuda")
print(f"Loaded in {time.time() - start_time:.2f} seconds") print(f"Loaded in {time.time() - start_time:.2f} seconds")
return Llama(model, tokenizer, model_args) return Llama(model, tokenizer, model_args)
def __init__(self, model: Transformer, tokenizer: Tokenizer, args: ModelArgs): def __init__(self, model: Transformer, tokenizer: Tokenizer, args: ModelArgs):