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fix gptq of llama #11749

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Aug 9, 2024
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fix gptq of llama #11749

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e942222
fix gptq of llama
rnwang04 Aug 9, 2024
aa1ea6a
small fix
rnwang04 Aug 9, 2024
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9 changes: 3 additions & 6 deletions python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GPTQ/generate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,13 +47,10 @@
load_in_4bit=True,
torch_dtype=torch.float,
trust_remote_code=True,).to("xpu")

# Load tokenizer
if "qwen" in model_path.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
else:
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

# Generate predicted tokens
with torch.inference_mode():
prompt = LLAMA2_PROMPT_FORMAT.format(prompt=args.prompt)
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29 changes: 17 additions & 12 deletions python/llm/src/ipex_llm/transformers/models/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,17 +19,21 @@


def merge_linear(linears: List[torch.nn.Linear]) -> torch.nn.Linear:
new_weight = torch.cat(list(linear.weight.data for linear in linears), dim=0)
if linears[0].bias is not None:
new_linear = torch.nn.Linear(0, 0, bias=True)
new_bias = torch.cat(list(linear.bias.data for linear in linears), dim=0)
new_linear.bias = torch.nn.Parameter(new_bias, requires_grad=False)
if hasattr(linears[0], "weight"):
# For GPTQ model, it might be qweight
new_weight = torch.cat(list(linear.weight.data for linear in linears), dim=0)
if linears[0].bias is not None:
new_linear = torch.nn.Linear(0, 0, bias=True)
new_bias = torch.cat(list(linear.bias.data for linear in linears), dim=0)
new_linear.bias = torch.nn.Parameter(new_bias, requires_grad=False)
else:
new_linear = torch.nn.Linear(0, 0, bias=False)
new_linear.weight = torch.nn.Parameter(new_weight, requires_grad=False)
new_linear.in_features = new_weight.size(1)
new_linear.out_features = new_weight.size(0)
return new_linear
else:
new_linear = torch.nn.Linear(0, 0, bias=False)
new_linear.weight = torch.nn.Parameter(new_weight, requires_grad=False)
new_linear.in_features = new_weight.size(1)
new_linear.out_features = new_weight.size(0)
return new_linear
return None


def merge_qkv_base(module: torch.nn.Module, attention_class):
Expand All @@ -39,8 +43,9 @@ def merge_qkv_base(module: torch.nn.Module, attention_class):
module.k_proj,
module.v_proj,
])
module.qkv_proj = qkv_proj
del module.q_proj, module.k_proj, module.v_proj
if qkv_proj is not None:
module.qkv_proj = qkv_proj
del module.q_proj, module.k_proj, module.v_proj


def fuse_mlp_base(module: torch.nn.Module, act: int, x: torch.Tensor):
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