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Move call to adapt_transformers_to_gaudi earlier in the code (#133)

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regisss 2024-04-26 11:07:27 +02:00 committed by GitHub
parent ae6215fcea
commit 37aabf8571
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2 changed files with 3 additions and 3 deletions
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3
server/text_generation_server/models/__init__.py
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@ -10,6 +10,8 @@ from text_generation_server.models.causal_lm import CausalLM
from text_generation_server.models.bloom import BLOOM from text_generation_server.models.bloom import BLOOM
from text_generation_server.models.santacoder import SantaCoder from text_generation_server.models.santacoder import SantaCoder
from optimum.habana.transformers.modeling_utils import adapt_transformers_to_gaudi
# Disable gradients # Disable gradients
torch.set_grad_enabled(False) torch.set_grad_enabled(False)
@ -20,6 +22,7 @@ def get_model(
revision: Optional[str], revision: Optional[str],
dtype: Optional[torch.dtype] = None, dtype: Optional[torch.dtype] = None,
) -> Model: ) -> Model:
adapt_transformers_to_gaudi()
config = AutoConfig.from_pretrained(model_id, revision=revision) config = AutoConfig.from_pretrained(model_id, revision=revision)
model_type = config.model_type model_type = config.model_type

3
server/text_generation_server/models/causal_lm.py
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@ -16,7 +16,6 @@ from opentelemetry import trace
import text_generation_server.habana_quantization_env as hq_env import text_generation_server.habana_quantization_env as hq_env
import habana_frameworks.torch as htorch import habana_frameworks.torch as htorch
from habana_frameworks.torch.hpu import wrap_in_hpu_graph from habana_frameworks.torch.hpu import wrap_in_hpu_graph
from optimum.habana.transformers.modeling_utils import adapt_transformers_to_gaudi
from optimum.habana.utils import HabanaProfile from optimum.habana.utils import HabanaProfile
from optimum.habana.transformers.generation import MODELS_OPTIMIZED_WITH_STATIC_SHAPES from optimum.habana.transformers.generation import MODELS_OPTIMIZED_WITH_STATIC_SHAPES
from optimum.habana.checkpoint_utils import ( from optimum.habana.checkpoint_utils import (
@ -572,8 +571,6 @@ class CausalLM(Model):
revision: Optional[str] = None, revision: Optional[str] = None,
dtype: Optional[torch.dtype] = None, dtype: Optional[torch.dtype] = None,
): ):
adapt_transformers_to_gaudi()
# Create tokenizer # Create tokenizer
tokenizer = AutoTokenizer.from_pretrained( tokenizer = AutoTokenizer.from_pretrained(
model_id, model_id,