mirror of
https://github.com/nod-ai/SHARK-Studio.git
synced 2026-01-08 21:38:04 -05:00
* Shark Studio SDXL support, HIP driver support, simpler device info, small fixes * Fixups to llm API/UI and ignore user config files. * Small fixes for unifying pipelines. * Update requirements.txt for iree-turbine (#2130) * Fix Llama2 on CPU (#2133) * Filesystem cleanup and custom model fixes (#2127) * Fix some formatting issues * Remove IREE pin (fixes exe issue) (#2126) * Update find links for IREE packages (#2136) * Shark Studio SDXL support, HIP driver support, simpler device info, small fixes * Abstract out SD pipelines from Studio Webui (WIP) * Switch from pin to minimum torch version and fix index url * Fix device parsing. * Fix linux setup * Fix custom weights. --------- Co-authored-by: saienduri <77521230+saienduri@users.noreply.github.com> Co-authored-by: gpetters-amd <159576198+gpetters-amd@users.noreply.github.com> Co-authored-by: gpetters94 <gpetters@protonmail.com>
210 lines
6.8 KiB
Python
210 lines
6.8 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 subprocess import CalledProcessError
|
|
from shark.parser import shark_args
|
|
from shark.iree_utils._common import run_cmd
|
|
|
|
# TODO: refactor to rocm and cuda utils
|
|
|
|
|
|
# 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 []
|
|
|
|
|
|
def check_rocm_device_arch_in_args(extra_args):
|
|
# Check if the target arch flag for rocm device present in extra_args
|
|
for flag in extra_args:
|
|
if "iree-rocm-target-chip" in flag:
|
|
flag_arch = flag.split("=")[1]
|
|
return flag_arch
|
|
return None
|
|
|
|
|
|
def get_rocm_device_arch(device_num=0, extra_args=[], hip_driver=False):
|
|
# ROCM Device Arch selection:
|
|
# 1 : User given device arch using `--iree-rocm-target-chip` flag
|
|
# 2 : Device arch from `iree-run-module --dump_devices=rocm` for device on index <device_num>
|
|
# 3 : default arch : gfx1100
|
|
|
|
arch_in_flag = check_rocm_device_arch_in_args(extra_args)
|
|
if arch_in_flag is not None:
|
|
print(
|
|
f"User Specified rocm target device arch from flag : {arch_in_flag} will be used"
|
|
)
|
|
return arch_in_flag
|
|
|
|
arch_in_device_dump = None
|
|
|
|
# get rocm arch from iree dump devices
|
|
def get_devices_info_from_dump(dump, driver):
|
|
from os import linesep
|
|
|
|
if driver == "hip":
|
|
dump_clean = list(
|
|
filter(
|
|
lambda s: "AMD" in s,
|
|
dump.split(linesep),
|
|
)
|
|
)
|
|
else:
|
|
dump_clean = list(
|
|
filter(
|
|
lambda s: f"--device={driver}" in s or "gpu-arch-name:" in s,
|
|
dump.split(linesep),
|
|
)
|
|
)
|
|
arch_pairs = [
|
|
(
|
|
dump_clean[i].split("=")[1].strip(),
|
|
dump_clean[i + 1].split(":")[1].strip(),
|
|
)
|
|
for i in range(0, len(dump_clean), 2)
|
|
]
|
|
return arch_pairs
|
|
|
|
dump_device_info = None
|
|
driver = "hip" if hip_driver else "rocm"
|
|
try:
|
|
dump_device_info = run_cmd(
|
|
"iree-run-module --dump_devices=" + driver, raise_err=True
|
|
)
|
|
except Exception as e:
|
|
print("could not execute `iree-run-module --dump_devices=" + driver + "`")
|
|
|
|
if dump_device_info is not None:
|
|
device_num = 0 if device_num is None else device_num
|
|
device_arch_pairs = get_devices_info_from_dump(dump_device_info[0], driver)
|
|
if len(device_arch_pairs) > device_num: # can find arch in the list
|
|
arch_in_device_dump = device_arch_pairs[device_num][1]
|
|
|
|
if arch_in_device_dump is not None:
|
|
print(f"Found ROCm device arch : {arch_in_device_dump}")
|
|
return arch_in_device_dump
|
|
|
|
default_rocm_arch = "gfx1100"
|
|
print(
|
|
"Did not find ROCm architecture from `--iree-rocm-target-chip` flag"
|
|
"\n or from `iree-run-module --dump_devices` command."
|
|
f"\nUsing {default_rocm_arch} as ROCm arch for compilation."
|
|
)
|
|
return default_rocm_arch
|
|
|
|
|
|
# Get the default gpu args given the architecture.
|
|
def get_iree_rocm_args(device_num=0, extra_args=[], hip_driver=False):
|
|
ireert.flags.FUNCTION_INPUT_VALIDATION = False
|
|
rocm_flags = []
|
|
if check_rocm_device_arch_in_args(extra_args) is None:
|
|
rocm_arch = get_rocm_device_arch(device_num, extra_args, hip_driver=hip_driver)
|
|
rocm_flags.append(f"--iree-rocm-target-chip={rocm_arch}")
|
|
|
|
return rocm_flags
|
|
|
|
# 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
|