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Clean npu dtype branch (#11515)

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* clean branch

* create_npu_kernels
This commit is contained in:
Zhao Changmin 2024-07-05 15:45:26 +08:00 committed by GitHub
parent 14ce058004
commit f7e957aaf9
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2 changed files with 27 additions and 51 deletions
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5
python/llm/src/ipex_llm/transformers/model.py
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@ -332,6 +332,11 @@ class _BaseAutoModelClass:
else: else:
kwargs["pretraining_tp"] = 1 kwargs["pretraining_tp"] = 1
q_k = load_in_low_bit if load_in_low_bit else "sym_int4" q_k = load_in_low_bit if load_in_low_bit else "sym_int4"
invalidInputError(q_k not in ["sym_int4_rtn", "sym_int8_rtn"],
f"The dtype {q_k} is specified for NPU"
"and cannot be used on CPU and GPU")
imatrix_file = kwargs.pop("imatrix", None) imatrix_file = kwargs.pop("imatrix", None)
if q_k in ["gguf_iq2_xxs", "gguf_iq2_xs", "gguf_iq1_s"]: if q_k in ["gguf_iq2_xxs", "gguf_iq2_xs", "gguf_iq1_s"]:
invalidInputError(imatrix_file is not None, invalidInputError(imatrix_file is not None,

73
python/llm/src/ipex_llm/transformers/npu_model.py
View file

@ -25,8 +25,6 @@ from unittest.mock import patch
from transformers.dynamic_module_utils import get_imports from transformers.dynamic_module_utils import get_imports
from transformers.configuration_utils import PretrainedConfig from transformers.configuration_utils import PretrainedConfig
import intel_npu_acceleration_library as npu_lib
from ipex_llm.utils.common.log4Error import invalidInputError from ipex_llm.utils.common.log4Error import invalidInputError
from ipex_llm.transformers.utils import logger from ipex_llm.transformers.utils import logger
from ipex_llm.transformers.npu_models.convert import optimize_llm from ipex_llm.transformers.npu_models.convert import optimize_llm
@ -90,23 +88,12 @@ class _BaseAutoModelClass:
warnings.warn("`torch_dtype` will be ignored, `torch.float` will be used") warnings.warn("`torch_dtype` will be ignored, `torch.float` will be used")
kwargs['torch_dtype'] = torch.float kwargs['torch_dtype'] = torch.float
low_bit = kwargs.pop('load_in_low_bit', 'fp32') low_bit = kwargs.pop('load_in_low_bit', 'sym_int4')
try: qtype_map = {
# for intel_npu_acceleration_library >= 1.1.0 'sym_int4': "sym_int4_rtn",
from intel_npu_acceleration_library.dtypes import int8, int4 'sym_int8': "sym_int8_rtn",
qtype_map = { }
'sym_int4': "sym_int4_rtn",
'sym_int8': "sym_int8_rtn",
'fp16': torch.half,
'fp32': torch.float,
}
except ImportError as _e:
# for intel_npu_acceleration_library < 1.1.0
qtype_map = {
'sym_int8': torch.int8,
'fp16': torch.half,
'fp32': torch.float,
}
invalidInputError(low_bit in qtype_map.keys(), invalidInputError(low_bit in qtype_map.keys(),
f"unsupported low_bit: {low_bit}, " f"unsupported low_bit: {low_bit}, "
f"only {list(qtype_map.keys())} are supported") f"only {list(qtype_map.keys())} are supported")
@ -143,22 +130,15 @@ class _BaseAutoModelClass:
model.config.update({"bigdl_lcmu_enabled": False}) model.config.update({"bigdl_lcmu_enabled": False})
logger.info(f"Converting model, it may takes up to several minutes ...") logger.info(f"Converting model, it may takes up to several minutes ...")
try:
# for intel_npu_acceleration_library >= 1.1.0 from intel_npu_acceleration_library.compiler import create_npu_kernels
from intel_npu_acceleration_library.quantization import quantize_model with torch.no_grad():
from intel_npu_acceleration_library.compiler import create_npu_kernels optimize_llm(model)
with torch.no_grad(): cls.load_convert(qtype, model, 'cpu', *args, **kwargs)
optimize_llm(model) create_npu_kernels(model)
if qtype in ["sym_int8_rtn", "sym_int4_rtn"]:
cls.load_convert(qtype, model, 'cpu', *args, **kwargs) model = model.eval()
else:
if not qtype.is_floating_point:
model = quantize_model(model, qtype)
create_npu_kernels(model)
model = model.eval()
except ImportError as _e:
# for intel_npu_acceleration_library < 1.1.0
model = npu_lib.compile(model, qtype, False)
logger.info(f"Finish to convert model") logger.info(f"Finish to convert model")
model.config.update({"bigdl_transformers_low_bit": qtype}) model.config.update({"bigdl_transformers_low_bit": qtype})
@ -313,22 +293,13 @@ class _BaseAutoModelClass:
# Loading args may differ based on their usage # Loading args may differ based on their usage
quant_device = "meta" if bigdl_lcmu_enabled else "cpu" quant_device = "meta" if bigdl_lcmu_enabled else "cpu"
logger.info(f"Converting model, it may takes up to several minutes ...") logger.info(f"Converting model, it may takes up to several minutes ...")
try: from intel_npu_acceleration_library.compiler import create_npu_kernels
# for intel_npu_acceleration_library >= 1.1.0 with torch.no_grad():
from intel_npu_acceleration_library.quantization import quantize_model optimize_llm(model)
from intel_npu_acceleration_library.compiler import create_npu_kernels cls.load_convert(qtype, model, quant_device, *model_args, **kwargs)
with torch.no_grad(): create_npu_kernels(model)
optimize_llm(model)
if qtype in ["sym_int8_rtn", "sym_int4_rtn"]: model = model.eval()
cls.load_convert(qtype, model, quant_device, *model_args, **kwargs)
else:
if not qtype.is_floating_point:
model = quantize_model(model, qtype)
create_npu_kernels(model)
model = model.eval()
except ImportError as _e:
# for intel_npu_acceleration_library < 1.1.0
model = npu_lib.compile(model, qtype, False)
if is_sharded: if is_sharded:
loaded_state_dict_keys = sharded_metadata["all_checkpoint_keys"] loaded_state_dict_keys = sharded_metadata["all_checkpoint_keys"]