wehub-resource-sync
91 lines
3.6 KiB
Python
Executable File
91 lines
3.6 KiB
Python
Executable File
# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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import os
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from .builder import CUDAOpBuilder, installed_cuda_version
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class InferenceBuilder(CUDAOpBuilder):
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BUILD_VAR = "DS_BUILD_TRANSFORMER_INFERENCE"
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NAME = "transformer_inference"
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def __init__(self, name=None):
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name = self.NAME if name is None else name
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super().__init__(name=name)
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def absolute_name(self):
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return f'deepspeed.ops.transformer.inference.{self.NAME}_op'
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def is_compatible(self, verbose=False):
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try:
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import torch
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except ImportError:
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if verbose:
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self.warning("Please install torch if trying to pre-compile inference kernels")
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return False
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cuda_okay = True
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if not os.environ.get("DS_IGNORE_CUDA_DETECTION"):
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if not self.is_rocm_pytorch() and torch.cuda.is_available():
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sys_cuda_major, _ = installed_cuda_version()
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torch_cuda_major = int(torch.version.cuda.split('.')[0])
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cuda_capability = self.cuda_capability_major()
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if cuda_capability is not None and cuda_capability < 6:
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if verbose:
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self.warning("NVIDIA Inference is only supported on Pascal and newer architectures")
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cuda_okay = False
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if cuda_capability is not None and cuda_capability >= 8:
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if torch_cuda_major < 11 or sys_cuda_major < 11:
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if verbose:
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self.warning("On Ampere and higher architectures please use CUDA 11+")
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cuda_okay = False
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return super().is_compatible(verbose) and cuda_okay
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def filter_ccs(self, ccs):
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ccs_retained = []
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ccs_pruned = []
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for cc in [cc.split('.') for cc in ccs]:
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if int(cc[0]) >= 6:
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ccs_retained.append(cc)
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else:
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ccs_pruned.append(cc)
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if len(ccs_pruned) > 0:
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self.warning(f"Filtered compute capabilities {ccs_pruned}")
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return ccs_retained
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def sources(self):
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return [
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'csrc/transformer/inference/csrc/pt_binding.cpp',
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'csrc/transformer/inference/csrc/gelu.cu',
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'csrc/transformer/inference/csrc/relu.cu',
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'csrc/transformer/inference/csrc/layer_norm.cu',
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'csrc/transformer/inference/csrc/rms_norm.cu',
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'csrc/transformer/inference/csrc/softmax.cu',
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'csrc/transformer/inference/csrc/dequantize.cu',
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'csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu',
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'csrc/transformer/inference/csrc/transform.cu',
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'csrc/transformer/inference/csrc/pointwise_ops.cu',
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]
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def extra_ldflags(self):
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if not self.is_rocm_pytorch():
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return ['-lcurand']
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else:
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return []
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def include_paths(self):
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return ['csrc/transformer/inference/includes', 'csrc/includes']
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def nvcc_args(self):
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args = super().nvcc_args()
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"""BF16 is supported on AMD, but including `cuda_bf16.h` (`<hip/hip_bfloat16.h>` after hipification)
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in host-only translation units (*.cpp files) fails because GPU-specific builtins are pulled in with the BF16 type.
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This cannot be avoided via forward declarations for this transformer_inference extension,
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since `pt_binding.cpp` code explicitly requires the BF16 header, so disable it for now.
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"""
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if self.is_rocm_pytorch():
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self.enable_bf16 = False
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return args
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