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display available cached versions in TGI server error message of Neuron backend #3063

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display available cached versions in TGI server error message of Neuron backend #3063

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20 changes: 18 additions & 2 deletions backends/neuron/server/text_generation_server/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -107,10 +107,26 @@ def fetch_model(
if not is_cached(model_id, neuron_config):
hub_cache_url = "https://huggingface.co/aws-neuron/optimum-neuron-cache"
neuron_export_url = "https://huggingface.co/docs/optimum-neuron/main/en/guides/export_model#exporting-neuron-models-using-neuronx-tgi"
entries = get_hub_cached_entries(model_id, "inference")
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This method is already called by is_cached: I'd rather avoid having two consecutive calls to the hub.
The is_cached method is never called anywhere else, so maybe you can change its signature to something like has_compatible_entry(neuron_config, entries). That way you can first fetch the entries, check if one is compatible, and otherwise just loop over incompatible entries just like you do.

available_configs = ""
if entries:
config_list = []
for entry in entries:
config = (
f"batch_size={entry['batch_size']}, "
f"sequence_length={entry['sequence_length']}, "
f"num_cores={entry['num_cores']}, "
f"auto_cast_type={entry['auto_cast_type']}"
)
config_list.append(config)
available_configs = "\nAvailable cached configurations for this model:\n- " + "\n- ".join(config_list)
else:
available_configs = "\nNo cached versions are currently available for that model with any configuration."
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It looks quite redundant with the first line of the error message. Do we really need to say something more specific here ?

error_msg = (
f"No cached version found for {model_id} with {neuron_config}."
f"You can start a discussion to request it on {hub_cache_url}"
f"Alternatively, you can export your own neuron model as explained in {neuron_export_url}"
f"{available_configs}"
f"\nYou can start a discussion to request it on {hub_cache_url}"
f"\nAlternatively, you can export your own neuron model as explained in {neuron_export_url}"
)
raise ValueError(error_msg)
logger.warning(
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