Resolve messages formatting issues (#13095)
Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
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6 changed files with 8 additions and 8 deletions
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@ -470,7 +470,7 @@ if __name__ == "__main__":
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if args.gpus:
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invalidInputError(len(args.gpus.split(",")) > args.num_gpus, f"Larger --num-gpus "
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"({args.num_gpus}) than --gpus {args.gpus}!")
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f"({args.num_gpus}) than --gpus {args.gpus}!")
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os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
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gptq_config = GptqConfig(
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@ -672,7 +672,7 @@ class _BaseAutoModelClass:
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else:
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invalidInputError(False,
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f'`torch_dtype` can be either `torch.dtype` or `"auto"`,'
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'but received {torch_dtype}')
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f'but received {torch_dtype}')
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dtype_orig = model_class._set_default_torch_dtype(torch_dtype)
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# Pretrained Model
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@ -217,7 +217,7 @@ class _BaseAutoModelClass:
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max_prompt_len < max_context_len,
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(
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f"max_prompt_len ({max_prompt_len}) should be less"
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" than max_context_len ({max_context_len})"
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f" than max_context_len ({max_context_len})"
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),
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)
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optimize_kwargs = {
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@ -553,7 +553,7 @@ class _BaseAutoModelClass:
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invalidInputError(
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False,
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f'`torch_dtype` can be either `torch.dtype` or `"auto"`,'
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"but received {torch_dtype}",
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f"but received {torch_dtype}",
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)
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dtype_orig = model_class._set_default_torch_dtype(torch_dtype)
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@ -588,7 +588,7 @@ class _BaseAutoModelClass:
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max_prompt_len < max_context_len,
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(
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f"max_prompt_len ({max_prompt_len}) should be less"
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" than max_context_len ({max_context_len})"
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f" than max_context_len ({max_context_len})"
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),
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)
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from ipex_llm.transformers.npu_models.convert_mp import optimize_llm_pre
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@ -127,7 +127,7 @@ def phi3_attention_forward(
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invalidInputError(
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False,
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
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" but is {attention_mask.size()}"
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f" but is {attention_mask.size()}"
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)
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attn_weights = attn_weights + attention_mask
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@ -92,7 +92,7 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike]):
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except Exception as e:
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invalidInputError(False,
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f"Unable to load weights"
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"from pytorch checkpoint file for '{checkpoint_file}' "
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f"from pytorch checkpoint file for '{checkpoint_file}' "
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f"at '{checkpoint_file}'. ")
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@ -112,7 +112,7 @@ def _load(pickle_fp, map_location, picklemoudle, pickle_file='data.pkl', zip_fil
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data = fp.read(size)
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return torch.frombuffer(bytearray(data), dtype=dtype)
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description = f'storage data_type={data_type} ' \
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'path-in-zip={filename} path={self.zip_file.filename}'
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f'path-in-zip={filename} path={self.zip_file.filename}'
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return LazyStorage(load=load, kind=pid[1], description=description)
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@staticmethod
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