ayo/ipex-llm
1
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 1

Fix vllm condition (#11169)

Browse source 
* add use-vllm

* done

* fix style

* fix done
This commit is contained in:
Guancheng Fu 2024-05-30 15:23:17 +08:00 committed by GitHub
parent dcbf4d3d0a
commit 50ee004ac7
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
2 changed files with 21 additions and 23 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

23
python/llm/src/ipex_llm/transformers/convert.py
View file

@ -53,6 +53,7 @@ import subprocess
import sys import sys
_IS_VLLM_AVAILABLE = None _IS_VLLM_AVAILABLE = None
_USE_VLLM = False
def is_auto_gptq_available(): def is_auto_gptq_available():
@ -76,6 +77,10 @@ def is_vllm_available():
return _IS_VLLM_AVAILABLE return _IS_VLLM_AVAILABLE
def get_use_vllm():
return _USE_VLLM
def is_torch_distributed_initialized(): def is_torch_distributed_initialized():
return torch.distributed.is_initialized() return torch.distributed.is_initialized()
@ -119,14 +124,15 @@ def is_gptq_linear(module):
def is_linear_module(module): def is_linear_module(module):
global _USE_VLLM
in_features = None in_features = None
out_features = None out_features = None
mp_group = None mp_group = None
is_awq = is_auto_awq_available() and isinstance(module, WQLinear_GEMM) is_awq = is_auto_awq_available() and isinstance(module, WQLinear_GEMM)
if is_vllm_available(): if is_vllm_available():
# TODO: add tensor parallel feature later # Only convert vllm modules
from vllm.model_executor.layers.linear import ( from vllm.model_executor.layers.linear import (
ColumnParallelLinear, RowParallelLinear, QKVParallelLinear, MergedColumnParallelLinear ColumnParallelLinear, RowParallelLinear, QKVParallelLinear, MergedColumnParallelLinear
) )
@ -148,16 +154,9 @@ def is_linear_module(module):
in_features = module.input_size_per_partition in_features = module.input_size_per_partition
elif isinstance(module, ColumnParallelLinear) and tp_size >= 2: elif isinstance(module, ColumnParallelLinear) and tp_size >= 2:
out_features = module.output_size_per_partition out_features = module.output_size_per_partition
else: _USE_VLLM = True
# Also check for Linear module return result, (in_features, out_features, mp_group)
if isinstance(module, nn.Linear) or is_awq: if is_gptq_linear(module):
in_features = module.in_features
out_features = module.out_features
mp_group = None
result = True
else:
result = False
elif is_gptq_linear(module):
in_features = module.infeatures in_features = module.infeatures
out_features = module.outfeatures out_features = module.outfeatures
mp_group = None mp_group = None

21
python/llm/src/ipex_llm/transformers/low_bit_linear.py
View file

@ -53,7 +53,7 @@ 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_type, \ from ipex_llm.transformers.utils import get_autocast_dtype, get_xpu_device_type, \
get_ipex_version get_ipex_version
from ipex_llm.transformers.convert import is_deepspeed_available, is_vllm_available 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")
@ -737,12 +737,11 @@ class LowBitLinear(nn.Linear):
torch.xpu.empty_cache() torch.xpu.empty_cache()
result = result.view(new_shape) result = result.view(new_shape)
if self.mp_group is not None: if self.mp_group is not None:
# FIXME: the user may install both vllm and deepspeed if get_use_vllm():
if is_deepspeed_available(): torch.distributed.all_reduce(result, group=self.mp_group)
elif is_deepspeed_available():
from deepspeed import comm as dist from deepspeed import comm as dist
dist.inference_all_reduce(result, group=self.mp_group) dist.inference_all_reduce(result, group=self.mp_group)
elif is_vllm_available():
torch.distributed.all_reduce(result, group=self.mp_group)
else: else:
invalidInputError(False, "mp_group is not None, but no supported backend found") invalidInputError(False, "mp_group is not None, but no supported backend found")
if self.bias is not None: if self.bias is not None:
@ -822,11 +821,11 @@ class FP16Linear(nn.Linear):
self.weight_type = 2 self.weight_type = 2
result = torch.ops.torch_ipex.matmul_bias_out(x, self.weight, self.bias) result = torch.ops.torch_ipex.matmul_bias_out(x, self.weight, self.bias)
if self.mp_group is not None: if self.mp_group is not None:
if is_deepspeed_available(): if get_use_vllm():
torch.distributed.all_reduce(result, group=self.mp_group)
elif is_deepspeed_available():
from deepspeed import comm as dist from deepspeed import comm as dist
dist.inference_all_reduce(result, group=self.mp_group) dist.inference_all_reduce(result, group=self.mp_group)
elif is_vllm_available():
torch.distributed.all_reduce(result, group=self.mp_group)
else: else:
invalidInputError(False, "mp_group is not None, but no supported backend found") invalidInputError(False, "mp_group is not None, but no supported backend found")
return result return result
@ -859,11 +858,11 @@ class FP16Linear(nn.Linear):
new_shape = x_shape[:-1] + (self.out_len,) new_shape = x_shape[:-1] + (self.out_len,)
result = result.view(new_shape) result = result.view(new_shape)
if self.mp_group is not None: if self.mp_group is not None:
if is_deepspeed_available(): if get_use_vllm():
torch.distributed.all_reduce(result, group=self.mp_group)
elif is_deepspeed_available():
from deepspeed import comm as dist from deepspeed import comm as dist
dist.inference_all_reduce(result, group=self.mp_group) dist.inference_all_reduce(result, group=self.mp_group)
elif is_vllm_available():
torch.distributed.all_reduce(result, group=self.mp_group)
else: else:
invalidInputError(False, "mp_group is not None, but no supported backend found") invalidInputError(False, "mp_group is not None, but no supported backend found")
if self.bias is not None: if self.bias is not None: