huggingface/text-generation-inference
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Cyril Vallez 2024-12-13 14:02:45 +00:00
parent 715b2d19ed
commit e93ab925f9
1 changed files with 12 additions and 16 deletions
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28
server/text_generation_server/models/__init__.py
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@ -12,7 +12,7 @@ import os
from loguru import logger
from transformers.configuration_utils import PretrainedConfig
from transformers.models.auto import modeling_auto
from transformers.models.auto import modeling_auto, modeling_task
from huggingface_hub import hf_hub_download, HfApi
from typing import Optional, List, Dict
from pathlib import Path
@ -375,30 +375,26 @@ def get_model(
)
model_type = config_dict.get("model_type", None)
# transformers_causal_lm_class = CausalLM
transformers_causal_lm_class = TransformersFlashCausalLM
if (
not USE_CUSTOM_MODELING
and model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
):
transformers_causal_lm_class = CausalLM
if not USE_CUSTOM_MODELING:
logger.info(
"TGI's flash enabled models could either not be loaded or are disabled, using Transformers fallback."
)
transformers_model_class = getattr(
transformers, modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES[model_type]
)
try:
transformers_model_class = getattr(transformers, modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES[model_type])
except KeyError:
transformers_model_class = modeling_task.AutoForCausalLM
if (
transformers_model_class._supports_flash_attn_2
and transformers_model_class._supports_cache_class
):
if transformers_model_class._supports_flash_attn_2:
logger.info(
f"Transformers' {model_type} implementation supports custom cache and flash/paged attention. Using TransformersFlashCausalLM with ragged tensors (single dimension for batch and sequence length)."
f"Transformers' {model_type} implementation supports ragged tensors format (single dimension for "
"batch and sequence length). All TGI's batching/caching optimizations are enabled."
)
transformers_causal_lm_class = TransformersFlashCausalLM
else:
logger.info(
f"Transformers' {model_type} implementation supports custom cache and flash/paged attention. Using TransformersCausalLM with classic tensors with padding (two dimensions for batch size and sequence length)."
f"Transformers' {model_type} implementation does not supports ragged tensors format. Will use classic "
"format with padding (two dimensions for batch size and sequence length). This is expected to be slow."
)
quantization_config = config_dict.get("quantization_config", None)