Files
AMD-SHARK-Studio/shark/iree_utils/gpu_utils.py
Ean Garvey 9c8cbaf498 Add support for ROCM (Windows) in Studio + compile utils (#1770)
* WIP: MSVC ROCM support for SHARK Studio

* Make get_iree_rocm_args platform-agnostic.

* Update stable_args.py

* Update rocm arg handling in SD utils

* Guard quantization imports.

Co-authored-by: jam https://github.com/jammm
2023-08-25 20:56:05 -07:00

169 lines
5.3 KiB
Python

# Copyright 2020 The Nod Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# All the iree_gpu related functionalities go here.
import functools
import iree.runtime as ireert
import ctypes
import sys
from shark.parser import shark_args
# Get the default gpu args given the architecture.
@functools.cache
def get_iree_gpu_args():
ireert.flags.FUNCTION_INPUT_VALIDATION = False
ireert.flags.parse_flags("--cuda_allow_inline_execution")
# TODO: Give the user_interface to pass the sm_arch.
sm_arch = get_cuda_sm_cc()
if (
sm_arch
in ["sm_70", "sm_72", "sm_75", "sm_80", "sm_84", "sm_86", "sm_89"]
) and (shark_args.enable_tf32 == True):
return [
f"--iree-hal-cuda-llvm-target-arch={sm_arch}",
]
else:
return []
# Get the default gpu args given the architecture.
@functools.cache
def get_iree_rocm_args():
ireert.flags.FUNCTION_INPUT_VALIDATION = False
# get arch from hipinfo.
import os
import re
import subprocess
if sys.platform == "win32":
if "HIP_PATH" in os.environ:
rocm_path = os.environ["HIP_PATH"]
print(f"Found a ROCm installation at {rocm_path}.")
else:
print("Failed to find ROCM_PATH. Defaulting to C:\\AMD\\ROCM\\5.5")
rocm_path = "C:\\AMD\\ROCM\\5.5"
else:
if "ROCM_PATH" in os.environ:
rocm_path = os.environ["ROCM_PATH"]
print(f"Found a ROCm installation at {rocm_path}.")
else:
print("Failed to find ROCM_PATH. Defaulting to /opt/rocm")
rocm_path = "/opt/rocm/"
try:
if sys.platform == "win32":
rocm_arch = re.search(
r"gfx\d{3,}",
subprocess.check_output("hipinfo", shell=True, text=True),
).group(0)
else:
rocm_arch = re.match(
r".*(gfx\w+)",
subprocess.check_output(
"rocminfo | grep -i 'gfx'", shell=True, text=True
),
).group(1)
print(f"Found rocm arch {rocm_arch}...")
except:
print(
"Failed to find ROCm architecture from hipinfo / rocminfo. Defaulting to gfx1100."
)
rocm_arch = "gfx1100"
bc_path = os.path.join(rocm_path, "amdgcn", "bitcode")
return [
f"--iree-rocm-target-chip={rocm_arch}",
"--iree-rocm-link-bc=true",
f"--iree-rocm-bc-dir={bc_path}",
]
# Some constants taken from cuda.h
CUDA_SUCCESS = 0
CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT = 16
CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR = 39
CU_DEVICE_ATTRIBUTE_CLOCK_RATE = 13
CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE = 36
@functools.cache
def get_cuda_sm_cc():
libnames = ("libcuda.so", "libcuda.dylib", "nvcuda.dll")
for libname in libnames:
try:
cuda = ctypes.CDLL(libname)
except OSError:
continue
else:
break
else:
raise OSError("could not load any of: " + " ".join(libnames))
nGpus = ctypes.c_int()
name = b" " * 100
cc_major = ctypes.c_int()
cc_minor = ctypes.c_int()
result = ctypes.c_int()
device = ctypes.c_int()
context = ctypes.c_void_p()
error_str = ctypes.c_char_p()
result = cuda.cuInit(0)
if result != CUDA_SUCCESS:
cuda.cuGetErrorString(result, ctypes.byref(error_str))
print(
"cuInit failed with error code %d: %s"
% (result, error_str.value.decode())
)
return 1
result = cuda.cuDeviceGetCount(ctypes.byref(nGpus))
if result != CUDA_SUCCESS:
cuda.cuGetErrorString(result, ctypes.byref(error_str))
print(
"cuDeviceGetCount failed with error code %d: %s"
% (result, error_str.value.decode())
)
return 1
print("Found %d device(s)." % nGpus.value)
for i in range(nGpus.value):
result = cuda.cuDeviceGet(ctypes.byref(device), i)
if result != CUDA_SUCCESS:
cuda.cuGetErrorString(result, ctypes.byref(error_str))
print(
"cuDeviceGet failed with error code %d: %s"
% (result, error_str.value.decode())
)
return 1
print("Device: %d" % i)
if (
cuda.cuDeviceGetName(ctypes.c_char_p(name), len(name), device)
== CUDA_SUCCESS
):
print(" Name: %s" % (name.split(b"\0", 1)[0].decode()))
if (
cuda.cuDeviceComputeCapability(
ctypes.byref(cc_major), ctypes.byref(cc_minor), device
)
== CUDA_SUCCESS
):
print(
" Compute Capability: %d.%d"
% (cc_major.value, cc_minor.value)
)
sm = f"sm_{cc_major.value}{cc_minor.value}"
return sm