mirror of
https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-04-03 03:00:17 -04:00
cec6eda6b4
* Optimize device enumeration overhead and log details on long operations. * Various fixes to add `@functools.cache` to what should be one time, expensive, device enumeration and setup activities. Cuts several seconds off of initialization on my machine. * Add detailed tracing to actual invocations if they exceed a certain timeout or have an exception. * Add detailed tracing to loading status. * By default detail logging is only printed if an operation takes an excessive amount of time. All logging/timing can be printed by setting the variable `$env:SHARK_DETAIL_TRACE = "1"` * Remove cache from unhashable functions
138 lines
4.2 KiB
Python
138 lines
4.2 KiB
Python
# Copyright 2020 The Nod Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# All the iree_gpu related functionalities go here.
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import functools
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import iree.runtime as ireert
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import ctypes
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from shark.parser import shark_args
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# Get the default gpu args given the architecture.
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@functools.cache
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def get_iree_gpu_args():
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ireert.flags.FUNCTION_INPUT_VALIDATION = False
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ireert.flags.parse_flags("--cuda_allow_inline_execution")
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# TODO: Give the user_interface to pass the sm_arch.
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sm_arch = get_cuda_sm_cc()
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if (
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sm_arch
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in ["sm_70", "sm_72", "sm_75", "sm_80", "sm_84", "sm_86", "sm_89"]
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) and (shark_args.enable_tf32 == True):
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return [
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f"--iree-hal-cuda-llvm-target-arch={sm_arch}",
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]
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else:
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return []
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# Get the default gpu args given the architecture.
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@functools.cache
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def get_iree_rocm_args():
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ireert.flags.FUNCTION_INPUT_VALIDATION = False
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# get arch from rocminfo.
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import re
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import subprocess
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rocm_arch = re.match(
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r".*(gfx\w+)",
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subprocess.check_output(
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"rocminfo | grep -i 'gfx'", shell=True, text=True
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),
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).group(1)
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print(f"Found rocm arch {rocm_arch}...")
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return [
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f"--iree-rocm-target-chip={rocm_arch}",
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"--iree-rocm-link-bc=true",
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"--iree-rocm-bc-dir=/opt/rocm/amdgcn/bitcode",
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]
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# Some constants taken from cuda.h
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CUDA_SUCCESS = 0
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CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT = 16
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CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR = 39
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CU_DEVICE_ATTRIBUTE_CLOCK_RATE = 13
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CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE = 36
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@functools.cache
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def get_cuda_sm_cc():
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libnames = ("libcuda.so", "libcuda.dylib", "nvcuda.dll")
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for libname in libnames:
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try:
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cuda = ctypes.CDLL(libname)
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except OSError:
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continue
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else:
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break
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else:
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raise OSError("could not load any of: " + " ".join(libnames))
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nGpus = ctypes.c_int()
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name = b" " * 100
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cc_major = ctypes.c_int()
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cc_minor = ctypes.c_int()
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result = ctypes.c_int()
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device = ctypes.c_int()
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context = ctypes.c_void_p()
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error_str = ctypes.c_char_p()
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result = cuda.cuInit(0)
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if result != CUDA_SUCCESS:
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cuda.cuGetErrorString(result, ctypes.byref(error_str))
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print(
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"cuInit failed with error code %d: %s"
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% (result, error_str.value.decode())
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)
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return 1
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result = cuda.cuDeviceGetCount(ctypes.byref(nGpus))
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if result != CUDA_SUCCESS:
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cuda.cuGetErrorString(result, ctypes.byref(error_str))
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print(
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"cuDeviceGetCount failed with error code %d: %s"
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% (result, error_str.value.decode())
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)
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return 1
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print("Found %d device(s)." % nGpus.value)
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for i in range(nGpus.value):
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result = cuda.cuDeviceGet(ctypes.byref(device), i)
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if result != CUDA_SUCCESS:
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cuda.cuGetErrorString(result, ctypes.byref(error_str))
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print(
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"cuDeviceGet failed with error code %d: %s"
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% (result, error_str.value.decode())
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)
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return 1
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print("Device: %d" % i)
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if (
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cuda.cuDeviceGetName(ctypes.c_char_p(name), len(name), device)
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== CUDA_SUCCESS
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):
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print(" Name: %s" % (name.split(b"\0", 1)[0].decode()))
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if (
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cuda.cuDeviceComputeCapability(
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ctypes.byref(cc_major), ctypes.byref(cc_minor), device
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)
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== CUDA_SUCCESS
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):
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print(
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" Compute Capability: %d.%d"
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% (cc_major.value, cc_minor.value)
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)
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sm = f"sm_{cc_major.value}{cc_minor.value}"
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return sm
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