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small fix and remove ununsed code about ipex (#12671)

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Yishuo Wang 2025-01-08 17:39:04 +08:00 committed by GitHub
parent c11f5f0fcd
commit a22a8c21bb
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4 changed files with 4 additions and 28 deletions
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13
python/llm/src/ipex_llm/transformers/convert.py
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@ -847,18 +847,9 @@ def replace_with_low_bit_linear_for_module(model, qtype, module_name=None,
mp_group=mp_group, mp_group=mp_group,
) )
device = module.weight.data.device device = module.weight.data.device
from ipex_llm.transformers.utils import get_ipex_version new_linear._parameters['weight'] = nn.Parameter(module.weight)
if get_ipex_version() < "2.1.10+xpu":
new_linear._parameters['weight'] = nn.Parameter(module.weight)
else:
# only from 2.1, ipex provides matmul_bias_out
# so we need to transpose weight
new_weight = module.weight.transpose(0, 1).contiguous()
new_linear._parameters['weight'] = nn.Parameter(new_weight)
new_linear.weight_type = 2
if module.bias is not None: if module.bias is not None:
new_linear._parameters['bias'] = nn.Parameter(module.bias.data)\ new_linear._parameters['bias'] = nn.Parameter(module.bias.data).to(device)
.to(device)
elif qtype == ggml_tensor_qtype["bf16"]: elif qtype == ggml_tensor_qtype["bf16"]:
module.to(torch.bfloat16) module.to(torch.bfloat16)
new_linear = BF16Linear( new_linear = BF16Linear(

3
python/llm/src/ipex_llm/transformers/low_bit_linear.py
View file

@ -51,8 +51,7 @@ from torch import Tensor, device, dtype, nn
from operator import mul from operator import mul
from functools import reduce from functools import reduce
from ipex_llm.transformers.xpu_customize_fwd import custom_fwd, custom_bwd from ipex_llm.transformers.xpu_customize_fwd import custom_fwd, custom_bwd
from ipex_llm.transformers.utils import get_autocast_dtype, get_xpu_device_name, \ from ipex_llm.transformers.utils import get_autocast_dtype, get_xpu_device_name
get_ipex_version
from ipex_llm.transformers.convert import is_deepspeed_available, get_use_vllm from ipex_llm.transformers.convert import is_deepspeed_available, get_use_vllm
T = TypeVar("T", bound="torch.nn.Module") T = TypeVar("T", bound="torch.nn.Module")

2
python/llm/src/ipex_llm/transformers/models/utils.py
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@ -19,7 +19,7 @@ import torch
import warnings import warnings
from ipex_llm.utils.common import invalidInputError from ipex_llm.utils.common import invalidInputError
from ipex_llm.ggml.quantize import ggml_tensor_qtype from ipex_llm.ggml.quantize import ggml_tensor_qtype
from ipex_llm.transformers.utils import get_ipex_version, get_xpu_device_name from ipex_llm.transformers.utils import get_xpu_device_name
from ipex_llm.transformers.low_bit_linear import SYM_INT4, SYM_INT8, FP8E5, IQ2_XXS, FP4, FP8E4,\ from ipex_llm.transformers.low_bit_linear import SYM_INT4, SYM_INT8, FP8E5, IQ2_XXS, FP4, FP8E4,\
FP6, ASYM_INT4 FP6, ASYM_INT4

14
python/llm/src/ipex_llm/transformers/utils.py
View file

@ -154,20 +154,6 @@ def get_autocast_dtype(x):
f"Device {x.device} is not supported.") f"Device {x.device} is not supported.")
_ipex_version = None
def get_ipex_version():
global _ipex_version
if _ipex_version is not None:
return _ipex_version
import intel_extension_for_pytorch as ipex
_ipex_version = ipex.__version__
return _ipex_version
def get_xpu_device_name(device: torch.device): def get_xpu_device_name(device: torch.device):
if device.type != "xpu": if device.type != "xpu":
return device.type return device.type