mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-16 21:00:14 +00:00
433 lines
17 KiB
Python
433 lines
17 KiB
Python
# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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import concurrent.futures
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import fnmatch
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import functools
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import os
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import shutil
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import subprocess
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from logger import get_logger
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log = get_logger("amd_hipify")
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def path_in_repo(path):
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repo_root = os.path.relpath(os.path.join(os.path.dirname(__file__), "../.."))
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return os.path.join(repo_root, path)
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contrib_ops_path = path_in_repo("onnxruntime/contrib_ops")
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providers_path = path_in_repo("onnxruntime/core/providers")
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training_ops_path = path_in_repo("orttraining/orttraining/training_ops")
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def is_excluded(f, excluded_patterns):
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return any([fnmatch.fnmatch(f, pat) for pat in excluded_patterns])
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contrib_ops_excluded_files = [
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"bert/attention.cc",
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"bert/attention.h",
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"bert/attention_impl.cu",
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"bert/attention_softmax.h",
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"bert/embed_layer_norm.cc",
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"bert/embed_layer_norm.h",
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"bert/embed_layer_norm_impl.cu",
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"bert/embed_layer_norm_impl.h",
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"bert/fast_gelu_impl.cu",
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"bert/fast_gelu_impl.h",
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"bert/fast_gelu.cc",
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"bert/fast_gelu.h",
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"bert/skip_layer_norm.cc",
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"bert/skip_layer_norm.h",
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"bert/skip_layer_norm_impl.cu",
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"bert/skip_layer_norm_impl.h",
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"bert/tensorrt_fused_multihead_attention/*",
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"bert/transformer_common.h",
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"bert/transformer_common.cc",
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"math/complex_mul.cc",
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"math/complex_mul.h",
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"math/complex_mul_impl.cu",
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"math/complex_mul_impl.h",
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"math/cufft_plan_cache.h",
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"math/fft_ops.cc",
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"math/fft_ops.h",
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"math/fft_ops_impl.cu",
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"math/fft_ops_impl.h",
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"quantization/attention_quantization.cc",
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"quantization/attention_quantization.h",
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"quantization/attention_quantization_impl.cu",
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"quantization/attention_quantization_impl.cuh",
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"quantization/quantize_dequantize_linear.cc",
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"quantization/qordered_ops/qordered_common.cuh",
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"quantization/qordered_ops/qordered_layer_norm.h",
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"quantization/qordered_ops/qordered_layer_norm.cc",
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"quantization/qordered_ops/qordered_layer_norm_impl.h",
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"quantization/qordered_ops/qordered_layer_norm_impl.cu",
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"quantization/qordered_ops/qordered_matmul.h",
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"quantization/qordered_ops/qordered_matmul.cc",
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"quantization/qordered_ops/qordered_matmul_utils.h",
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"quantization/qordered_ops/qordered_matmul_utils.cc",
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"quantization/qordered_ops/qordered_unary_ops.h",
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"quantization/qordered_ops/qordered_unary_ops.cc",
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"quantization/qordered_ops/qordered_unary_ops_impl.h",
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"quantization/qordered_ops/qordered_unary_ops_impl.cu",
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"tensor/crop.cc",
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"tensor/crop.h",
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"tensor/crop_impl.cu",
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"tensor/crop_impl.h",
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"tensor/dynamicslice.cc",
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"tensor/image_scaler.cc",
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"tensor/image_scaler.h",
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"tensor/image_scaler_impl.cu",
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"tensor/image_scaler_impl.h",
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"transformers/beam_search.cc",
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"transformers/beam_search.h",
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"transformers/generation_device_helper.cc",
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"transformers/generation_device_helper.h",
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"transformers/beam_search_impl.cu",
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"transformers/beam_search_impl.h",
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"transformers/greedy_search.cc",
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"transformers/greedy_search.h",
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"transformers/dump_cuda_tensor.cc",
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"transformers/dump_cuda_tensor.h",
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"conv_transpose_with_dynamic_pads.cc",
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"conv_transpose_with_dynamic_pads.h",
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"cuda_contrib_kernels.cc",
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"cuda_contrib_kernels.h",
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"inverse.cc",
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"fused_conv.cc",
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]
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provider_excluded_files = [
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"atomic/common.cuh",
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"controlflow/if.cc",
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"controlflow/if.h",
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"controlflow/loop.cc",
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"controlflow/loop.h",
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"controlflow/scan.cc",
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"controlflow/scan.h",
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"cu_inc/common.cuh",
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"math/einsum_utils/einsum_auxiliary_ops.cc",
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"math/einsum_utils/einsum_auxiliary_ops.h",
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"math/einsum_utils/einsum_auxiliary_ops_diagonal.cu",
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"math/einsum_utils/einsum_auxiliary_ops_diagonal.h",
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"math/einsum.cc",
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"math/einsum.h",
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"math/gemm.cc",
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"math/matmul.cc",
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"math/softmax_impl.cu",
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"math/softmax_warpwise_impl.cuh",
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"math/softmax_common.cc",
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"math/softmax.cc",
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"nn/conv.cc",
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"nn/conv.h",
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"nn/conv_transpose.cc",
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"nn/conv_transpose.h",
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"reduction/reduction_ops.cc",
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"rnn/cudnn_rnn_base.cc",
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"rnn/cudnn_rnn_base.h",
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"rnn/gru.cc",
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"rnn/gru.h",
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"rnn/lstm.cc",
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"rnn/lstm.h",
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"rnn/rnn.cc",
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"rnn/rnn.h",
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"rnn/rnn_impl.cu",
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"rnn/rnn_impl.h",
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"shared_inc/cuda_call.h",
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"shared_inc/fpgeneric.h",
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"cuda_allocator.cc",
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"cuda_allocator.h",
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"cuda_call.cc",
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"cuda_common.cc",
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"cuda_common.h",
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"cuda_execution_provider_info.cc",
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"cuda_execution_provider_info.h",
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"cuda_execution_provider.cc",
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"cuda_execution_provider.h",
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"cuda_memory_check.cc",
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"cuda_memory_check.h",
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"cuda_fence.cc",
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"cuda_fence.h",
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"cuda_fwd.h",
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"cuda_kernel.h",
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"cuda_pch.cc",
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"cuda_pch.h",
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"cuda_profiler.cc",
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"cuda_profiler.h",
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"cuda_provider_factory.cc",
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"cuda_provider_factory.h",
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"cuda_utils.cu",
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"cudnn_common.cc",
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"cudnn_common.h",
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"fpgeneric.cu",
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"gpu_data_transfer.cc",
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"gpu_data_transfer.h",
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"integer_gemm.cc",
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"symbols.txt",
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]
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training_ops_excluded_files = [
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"activation/gelu_grad_impl_common.cuh", # uses custom tanh
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"collective/adasum_kernels.cc",
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"collective/adasum_kernels.h",
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"math/div_grad.cc", # miopen API differs from cudnn, no double type support
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"nn/batch_norm_grad.cc", # no double type support
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"nn/batch_norm_grad.h", # miopen API differs from cudnn
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"nn/batch_norm_internal.cc", # miopen API differs from cudnn, no double type support
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"nn/batch_norm_internal.h", # miopen API differs from cudnn, no double type support
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"nn/conv_grad.cc",
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"nn/conv_grad.h",
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"reduction/reduction_all.cc", # deterministic = true, ignore ctx setting
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"reduction/reduction_ops.cc", # no double type support
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"cuda_training_kernels.cc",
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"cuda_training_kernels.h",
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]
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@functools.lru_cache(maxsize=1)
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def get_hipify_path():
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# prefer the hipify-perl in PATH
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HIPIFY_PERL = shutil.which("hipify-perl")
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# if not found, attempt hard-coded location 1
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if HIPIFY_PERL is None:
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print("hipify-perl not found, trying default location 1")
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hipify_path = "/opt/rocm/hip/bin/hipify-perl"
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HIPIFY_PERL = hipify_path if os.access(hipify_path, os.X_OK) else None
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# if not found, attempt hard-coded location 2
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if HIPIFY_PERL is None:
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print("hipify-perl not found, trying default location 2")
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hipify_path = "/opt/rocm/bin/hipify-perl"
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HIPIFY_PERL = hipify_path if os.access(hipify_path, os.X_OK) else None
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# fail
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if HIPIFY_PERL is None:
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raise RuntimeError("Could not locate hipify-perl script")
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return HIPIFY_PERL
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def hipify(src_file_path, dst_file_path):
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dst_file_path = dst_file_path.replace("cuda", "rocm")
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dir_name = os.path.dirname(dst_file_path)
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if not os.path.exists(dir_name):
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os.makedirs(dir_name, exist_ok=True)
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# Run hipify-perl first, capture output
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s = subprocess.run(
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[get_hipify_path(), "-roc", src_file_path], stdout=subprocess.PIPE, universal_newlines=True, check=False
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).stdout
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# Additional exact-match replacements.
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# Order matters for all of the following replacements, reglardless of appearing in logical sections.
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s = s.replace("kCudaExecutionProvider", "kRocmExecutionProvider")
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s = s.replace("CUDAStreamType", "HIPStreamType")
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s = s.replace("kCudaStreamDefault", "kHipStreamDefault")
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s = s.replace("kCudaStreamCopyIn", "kHipStreamCopyIn")
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s = s.replace("kCudaStreamCopyOut", "kHipStreamCopyOut")
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s = s.replace("kTotalCudaStreams", "kTotalHipStreams")
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# We want rocblas interfaces, not hipblas. Also force some hipify replacements back to rocblas from hipblas.
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s = s.replace("CublasHandle", "RocblasHandle")
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s = s.replace("cublas_handle", "rocblas_handle")
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s = s.replace("hipblasHandle_t", "rocblas_handle")
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s = s.replace("hipblasDatatype_t", "rocblas_datatype")
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s = s.replace("HIPBLAS_STATUS_SUCCESS", "rocblas_status_success")
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s = s.replace("hipblasStatus_t", "rocblas_status")
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s = s.replace("hipblasCreate", "rocblas_create_handle")
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s = s.replace("hipblasDestroy", "rocblas_destroy_handle")
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s = s.replace("hipblasSetStream", "rocblas_set_stream")
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s = s.replace("HIPBLAS_OP_T", "rocblas_operation_transpose")
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s = s.replace("HIPBLAS_OP_N", "rocblas_operation_none")
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s = s.replace("RegisterCudaContribKernels", "RegisterRocmContribKernels")
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s = s.replace("cudaEvent", "hipEvent")
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s = s.replace("CreateCudaAllocator", "CreateRocmAllocator")
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s = s.replace("CudaErrString", "RocmErrString")
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s = s.replace("CudaAsyncBuffer", "RocmAsyncBuffer")
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s = s.replace("CudaKernel", "RocmKernel")
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s = s.replace("ToCudaType", "ToHipType")
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s = s.replace("CudaT", "HipT")
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s = s.replace("CUDA_LONG", "HIP_LONG")
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s = s.replace("CUDA_RETURN_IF_ERROR", "HIP_RETURN_IF_ERROR")
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s = s.replace("CUDA_KERNEL_ASSERT", "HIP_KERNEL_ASSERT")
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s = s.replace("CUDA_CALL", "HIP_CALL")
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s = s.replace("SliceCuda", "SliceRocm")
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s = s.replace("thrust::cuda", "thrust::hip")
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s = s.replace("CudaCall", "RocmCall")
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s = s.replace("cuda", "rocm")
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# s = s.replace('Cuda', 'Rocm')
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s = s.replace("CUDA", "ROCM")
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s = s.replace("GPU_WARP_SIZE = 32", "GPU_WARP_SIZE = 64")
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s = s.replace("std::exp", "expf")
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s = s.replace("std::log", "logf")
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s = s.replace(
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"#include <cub/device/device_radix_sort.cuh>",
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"#include <hipcub/hipcub.hpp>\n#include <hipcub/backend/rocprim/device/device_radix_sort.hpp>",
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)
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s = s.replace(
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'#include "cub/device/device_radix_sort.cuh"',
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"#include <hipcub/hipcub.hpp>\n#include <hipcub/backend/rocprim/device/device_radix_sort.hpp>",
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)
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s = s.replace(
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"#include <cub/device/device_reduce.cuh>", "#include <hipcub/backend/rocprim/device/device_reduce.hpp>"
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)
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s = s.replace(
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"#include <cub/device/device_run_length_encode.cuh>",
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"#include <hipcub/backend/rocprim/device/device_run_length_encode.hpp>",
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)
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s = s.replace("#include <cub/device/device_scan.cuh>", "#include <hipcub/backend/rocprim/device/device_scan.hpp>")
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s = s.replace(
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"#include <cub/iterator/counting_input_iterator.cuh>",
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"#include <hipcub/backend/rocprim/iterator/counting_input_iterator.hpp>",
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)
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s = s.replace(
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"#include <cub/iterator/discard_output_iterator.cuh>",
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"#include <hipcub/backend/rocprim/iterator/discard_output_iterator.hpp>",
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)
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s = s.replace("#include <cub/util_allocator.cuh>", "#include <hipcub/util_allocator.hpp>")
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s = s.replace('#include "cub/util_allocator.cuh"', "#include <hipcub/util_allocator.hpp>")
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s = s.replace("#include <cub/util_type.cuh>", "#include <hipcub/backend/rocprim/util_type.hpp>")
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s = s.replace('#include "cub/util_type.cuh"', "#include <hipcub/backend/rocprim/util_type.hpp>")
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s = s.replace("#include <cub/device/device_partition.cuh>", "#include <hipcub/device/device_partition.hpp>")
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s = s.replace("#include <math_constants.h>", "#include <limits>")
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s = s.replace("#include <library_types.h>", "") # Doesn't exist
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s = s.replace("typedef half MappedType", "typedef __half MappedType")
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# CUBLAS -> HIPBLAS
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# Note: We do not use the hipblas marshalling interfaces; use rocblas instead.
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# s = s.replace('CUBLAS', 'HIPBLAS')
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# s = s.replace('Cublas', 'Hipblas')
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# s = s.replace('cublas', 'hipblas')
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# CUBLAS -> ROCBLAS
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s = s.replace("CUBLAS", "ROCBLAS")
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s = s.replace("Cublas", "Rocblas")
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s = s.replace("cublas", "rocblas")
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# Undefined ROCMRT constants -> std::numeric_limits
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s = s.replace("ROCMRT_INF_F", "std::numeric_limits<float>::infinity()")
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# HIPBLAS -> rocblas
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s = s.replace("HIPBLAS_R_16F", "rocblas_datatype_f16_r")
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s = s.replace("HIPBLAS_R_32F", "rocblas_datatype_f32_r")
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s = s.replace("ROCBLAS_GEMM_DEFAULT_TENSOR_OP", "rocblas_gemm_algo_standard")
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s = s.replace("ROCBLAS_TENSOR_OP_MATH", "0 /* CUBLAS_TENSOR_OP_MATH is deprecated */")
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# compatible layer
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s = s.replace("rocblas_gemm_strided_batched_ex", "_compat_rocblas_gemm_strided_batched_ex")
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s = s.replace("RocblasMathModeSetter", "CompatRocblasMathModeSetter")
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# CURAND -> HIPRAND
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s = s.replace("CURAND", "HIPRAND")
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s = s.replace("Curand", "Hiprand")
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s = s.replace("curand", "hiprand")
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# NCCL -> RCCL
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# s = s.replace('NCCL_CALL', 'RCCL_CALL')
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s = s.replace("#include <nccl.h>", "#include <rccl.h>")
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# CUDNN -> MIOpen
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s = s.replace("CUDNN", "MIOPEN")
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s = s.replace("Cudnn", "Miopen")
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s = s.replace("cudnn", "miopen")
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# hipify seems to have a bug for MIOpen, cudnn.h -> hipDNN.h, cudnn -> hipdnn
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s = s.replace("#include <hipDNN.h>", "#include <miopen/miopen.h>")
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s = s.replace("hipdnn", "miopen")
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s = s.replace("HIPDNN_STATUS_SUCCESS", "miopenStatusSuccess")
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s = s.replace("HIPDNN", "MIOPEN")
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s = s.replace("MIOPEN_BATCHNORM_SPATIAL", "miopenBNSpatial")
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s = s.replace("MIOPEN_BATCHNORM_PER_ACTIVATION", "miopenBNPerActivation")
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s = s.replace("MIOPEN_LRN_CROSS_CHANNEL", "miopenLRNCrossChannel")
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s = s.replace("MIOPEN_POOLING_MAX", "miopenPoolingMax")
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s = s.replace("MIOPEN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING", "miopenPoolingAverageInclusive")
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s = s.replace("MIOPEN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING", "miopenPoolingAverage")
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# CUSPARSE -> HIPSPARSE
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s = s.replace("CUSPARSE", "HIPSPARSE")
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# CUFFT -> HIPFFT
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s = s.replace("CUFFT", "HIPFFT")
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# Undo where above hipify steps went too far.
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s = s.replace("id, ROCM", "id, CUDA") # cuda_execution_provider.cc
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s = s.replace("ROCM error executing", "HIP error executing")
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s = s.replace("ROCM_PINNED", "CUDA_PINNED")
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s = s.replace("rocm_err", "hip_err")
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s = s.replace("RegisterHipTrainingKernels", "RegisterRocmTrainingKernels")
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s = s.replace("ROCM_VERSION", "CUDA_VERSION") # semantically different meanings, cannot hipify
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s = s.replace("__ROCM_ARCH__", "__CUDA_ARCH__") # semantically different meanings, cannot hipify
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# "std::log" above incorrectly changed "std::logic_error" to "logfic_error"
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s = s.replace("logfic_error", "std::logic_error")
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# Deletions
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s = s.replace('#include "device_atomic_functions.h"', "") # HIP atomics in main hip header already
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do_write = True
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if os.path.exists(dst_file_path):
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with open(dst_file_path, "r", encoding="utf-8") as fout_old:
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do_write = fout_old.read() != s
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if do_write:
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with open(dst_file_path, "w") as f:
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f.write(s)
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return 'Hipified: "{}" -> "{}"'.format(src_file_path, dst_file_path)
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else:
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return 'Repeated: "{}" -> "{}"'.format(src_file_path, dst_file_path)
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def list_files(prefix, path):
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all_files = []
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curr_path = os.path.join(prefix, path)
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for root, dirs, files in os.walk(curr_path):
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for file in files:
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full_path = os.path.join(root, file)
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all_files.append(os.path.relpath(full_path, curr_path))
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return all_files
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def amd_hipify(config_build_dir):
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# determine hipify script path now to avoid doing so concurrently in the thread pool
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print("Using %s" % get_hipify_path())
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with concurrent.futures.ThreadPoolExecutor() as executor:
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cuda_path = os.path.join(contrib_ops_path, "cuda")
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rocm_path = os.path.join(config_build_dir, "amdgpu", contrib_ops_path, "rocm")
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contrib_files = list_files(cuda_path, "")
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contrib_results = [
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executor.submit(hipify, os.path.join(cuda_path, f), os.path.join(rocm_path, f))
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for f in contrib_files
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if not is_excluded(f, contrib_ops_excluded_files)
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]
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cuda_path = os.path.join(providers_path, "cuda")
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rocm_path = os.path.join(config_build_dir, "amdgpu", providers_path, "rocm")
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provider_files = list_files(cuda_path, "")
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provider_results = [
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executor.submit(hipify, os.path.join(cuda_path, f), os.path.join(rocm_path, f))
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for f in provider_files
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if not is_excluded(f, provider_excluded_files)
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]
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cuda_path = os.path.join(training_ops_path, "cuda")
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rocm_path = os.path.join(config_build_dir, "amdgpu", training_ops_path, "rocm")
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training_files = list_files(cuda_path, "")
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training_results = [
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executor.submit(hipify, os.path.join(cuda_path, f), os.path.join(rocm_path, f))
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for f in training_files
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if not is_excluded(f, training_ops_excluded_files)
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]
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# explicitly wait so that hipify warnings finish printing before logging the hipify statements
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concurrent.futures.wait(contrib_results)
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concurrent.futures.wait(provider_results)
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concurrent.futures.wait(training_results)
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for result in contrib_results:
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log.debug(result.result())
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for result in provider_results:
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log.debug(result.result())
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for result in training_results:
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log.debug(result.result())
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if __name__ == "__main__":
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import sys
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amd_hipify(sys.argv[1])
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