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[Bugfix] Adding Rope Scaling to Handle Both rope_type and type in vLLM Neuron Code #2

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[Bugfix] Adding Rope Scaling to Handle Both rope_type and type in vLLM Neuron Code #2

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40 changes: 26 additions & 14 deletions vllm/config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1284,20 +1284,32 @@ def _get_and_verify_max_len(
derived_max_model_len = default_max_len

rope_scaling = getattr(hf_config, "rope_scaling", None)
if rope_scaling is not None and rope_scaling["type"] != "su":
if disable_sliding_window:
# TODO(robertgshaw): Find a model that supports rope_scaling
# with sliding window to see if this case should be allowed.
raise NotImplementedError(
"Disabling sliding window is not supported for models "
"with rope_scaling. Please raise an issue so we can "
"investigate.")
assert "factor" in rope_scaling
scaling_factor = rope_scaling["factor"]
if rope_scaling["type"] == "yarn":
derived_max_model_len = rope_scaling[
"original_max_position_embeddings"]
derived_max_model_len *= scaling_factor
if rope_scaling is not None:
if "type" in rope_scaling:
rope_type = rope_scaling["type"]
elif "rope_type" in rope_scaling:
rope_type = rope_scaling["rope_type"]
else:
raise ValueError(
"rope_scaling must have a 'type' or 'rope_type' key.")

# The correct one should be "longrope", kept "su" here
# to be backward compatible
if rope_type not in ("su", "longrope", "llama3"):
if disable_sliding_window:
# TODO(robertgshaw): Find a model that supports rope_scaling
# with sliding window to see if this case should be allowed.
raise NotImplementedError(
"Disabling sliding window is not supported for models "
"with rope_scaling. Please raise an issue so we can "
"investigate.")

assert "factor" in rope_scaling
scaling_factor = rope_scaling["factor"]
if rope_type == "yarn":
derived_max_model_len = rope_scaling[
"original_max_position_embeddings"]
derived_max_model_len *= scaling_factor

# If the user specified a max length, make sure it is smaller than the
# derived length from the HF model config.
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