mirror of
https://github.com/deepspeedai/DeepSpeed.git
synced 2025-10-20 15:33:51 +08:00
4f803852ac
Set the default value of op_builder/xxx.py/is_compatible()/verbose to False for quite warning. Add verbose judgement before op_builder/xxx.py/is_compatible()/self.warning(...). Otherwise the verbose arg will not work. --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com>
78 lines
2.8 KiB
Python
Executable File
78 lines
2.8 KiB
Python
Executable File
# Copyright (c) Microsoft Corporation.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
# DeepSpeed Team
|
|
|
|
from .builder import CUDAOpBuilder, installed_cuda_version
|
|
|
|
|
|
class InferenceBuilder(CUDAOpBuilder):
|
|
BUILD_VAR = "DS_BUILD_TRANSFORMER_INFERENCE"
|
|
NAME = "transformer_inference"
|
|
|
|
def __init__(self, name=None):
|
|
name = self.NAME if name is None else name
|
|
super().__init__(name=name)
|
|
|
|
def absolute_name(self):
|
|
return f'deepspeed.ops.transformer.inference.{self.NAME}_op'
|
|
|
|
def is_compatible(self, verbose=False):
|
|
try:
|
|
import torch
|
|
except ImportError:
|
|
if verbose:
|
|
self.warning("Please install torch if trying to pre-compile inference kernels")
|
|
return False
|
|
|
|
cuda_okay = True
|
|
if not self.is_rocm_pytorch() and torch.cuda.is_available():
|
|
sys_cuda_major, _ = installed_cuda_version()
|
|
torch_cuda_major = int(torch.version.cuda.split('.')[0])
|
|
cuda_capability = torch.cuda.get_device_properties(0).major
|
|
if cuda_capability < 6:
|
|
if verbose:
|
|
self.warning("NVIDIA Inference is only supported on Pascal and newer architectures")
|
|
cuda_okay = False
|
|
if cuda_capability >= 8:
|
|
if torch_cuda_major < 11 or sys_cuda_major < 11:
|
|
if verbose:
|
|
self.warning("On Ampere and higher architectures please use CUDA 11+")
|
|
cuda_okay = False
|
|
return super().is_compatible(verbose) and cuda_okay
|
|
|
|
def filter_ccs(self, ccs):
|
|
ccs_retained = []
|
|
ccs_pruned = []
|
|
for cc in ccs:
|
|
if int(cc[0]) >= 6:
|
|
ccs_retained.append(cc)
|
|
else:
|
|
ccs_pruned.append(cc)
|
|
if len(ccs_pruned) > 0:
|
|
self.warning(f"Filtered compute capabilities {ccs_pruned}")
|
|
return ccs_retained
|
|
|
|
def sources(self):
|
|
return [
|
|
'csrc/transformer/inference/csrc/pt_binding.cpp',
|
|
'csrc/transformer/inference/csrc/gelu.cu',
|
|
'csrc/transformer/inference/csrc/relu.cu',
|
|
'csrc/transformer/inference/csrc/layer_norm.cu',
|
|
'csrc/transformer/inference/csrc/rms_norm.cu',
|
|
'csrc/transformer/inference/csrc/softmax.cu',
|
|
'csrc/transformer/inference/csrc/dequantize.cu',
|
|
'csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu',
|
|
'csrc/transformer/inference/csrc/transform.cu',
|
|
'csrc/transformer/inference/csrc/pointwise_ops.cu',
|
|
]
|
|
|
|
def extra_ldflags(self):
|
|
if not self.is_rocm_pytorch():
|
|
return ['-lcurand']
|
|
else:
|
|
return []
|
|
|
|
def include_paths(self):
|
|
return ['csrc/transformer/inference/includes', 'csrc/includes']
|