mirror of
https://github.com/smyalygames/FiniteVolumeGPU_HIP.git
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296 lines
12 KiB
Python
296 lines
12 KiB
Python
# -*- coding: utf-8 -*-
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"""
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This python module implements Cuda context handling
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Copyright (C) 2018 SINTEF ICT
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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"""
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import os
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import numpy as np
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import time
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import re
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import io
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import hashlib
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import logging
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import gc
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#import pycuda.compiler as cuda_compiler
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#import pycuda.gpuarray
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#import pycuda.driver as cuda
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from hip import hip,hiprtc
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from hip import rccl
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from GPUSimulators import Autotuner, Common
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"""
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Class which keeps track of the CUDA context and some helper functions
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"""
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class CudaContext(object):
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def hip_check(call_result):
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err = call_result[0]
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result = call_result[1:]
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if len(result) == 1:
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result = result[0]
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if isinstance(err, hip.hipError_t) and err != hip.hipError_t.hipSuccess:
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raise RuntimeError(str(err))
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elif (
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isinstance(err, hiprtc.hiprtcResult)
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and err != hiprtc.hiprtcResult.HIPRTC_SUCCESS
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):
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raise RuntimeError(str(err))
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return result
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def __init__(self, device=None, context_flags=None, use_cache=True, autotuning=True):
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"""
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Create a new CUDA context
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Set device to an id or pci_bus_id to select a specific GPU
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Set context_flags to cuda.ctx_flags.SCHED_BLOCKING_SYNC for a blocking context
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"""
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self.use_cache = use_cache
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self.logger = logging.getLogger(__name__)
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self.modules = {}
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self.module_path = os.path.dirname(os.path.realpath(__file__))
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#Initialize cuda (must be first call to PyCUDA)
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##cuda.init(flags=0)
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##self.logger.info("PyCUDA version %s", str(pycuda.VERSION_TEXT))
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#Print some info about CUDA
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##self.logger.info("CUDA version %s", str(cuda.get_version()))
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#self.logger.info("Driver version %s", str(cuda.get_driver_version()))
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self.logger.info("Driver version %s", str(hip_check(hip.hipDriverGetVersion())))
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if device is None:
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device = 0
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self.cuda_device = hip.Device(device)
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#self.logger.info("Using device %d/%d '%s' (%s) GPU", device, cuda.Device.count(), self.cuda_device.name(), self.cuda_device.pci_bus_id())
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self.logger.info("Using device %d/%d '%s' (%s) GPU", device, hip_check(hip.hipGetDeviceCount()))
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#self.logger.debug(" => compute capability: %s", str(self.cuda_device.compute_capability()))
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self.logger.debug(" => compute capability: %s", str(self.hip.hipDeviceComputeCapability(device)))
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# Create the CUDA context
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#In HIP there is no need to specify a scheduling policy (it is abstracted). Here the HIP runtime system manages the workload to fit a specifc target architecture
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#if context_flags is None:
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# context_flags=cuda.ctx_flags.SCHED_AUTO
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#self.cuda_context = self.cuda_device.make_context(flags=context_flags)
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#free, total = cuda.mem_get_info()
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total = hip_check(hip.hipDeviceTotalMem(device))
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#self.logger.debug(" => memory: %d / %d MB available", int(free/(1024*1024)), int(total/(1024*1024)))
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self.logger.debug(" => memory: %d / %d MB available", int(total/(1024*1024)))
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#self.logger.info("Created context handle <%s>", str(self.cuda_context.handle))
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#Create cache dir for cubin files
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self.cache_path = os.path.join(self.module_path, "cuda_cache")
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if (self.use_cache):
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if not os.path.isdir(self.cache_path):
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os.mkdir(self.cache_path)
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self.logger.info("Using CUDA cache dir %s", self.cache_path)
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self.autotuner = None
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if (autotuning):
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self.logger.info("Autotuning enabled. It may take several minutes to run the code the first time: have patience")
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self.autotuner = Autotuner.Autotuner()
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# def __del__(self, *args):
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# self.logger.info("Cleaning up CUDA context handle <%s>", str(self.cuda_context.handle))
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# Loop over all contexts in stack, and remove "this"
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# other_contexts = []
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# while (cuda.Context.get_current() != None):
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# context = cuda.Context.get_current()
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# if (context.handle != self.cuda_context.handle):
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# self.logger.debug("<%s> Popping <%s> (*not* ours)", str(self.cuda_context.handle), str(context.handle))
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# other_contexts = [context] + other_contexts
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# cuda.Context.pop()
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# else:
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# self.logger.debug("<%s> Popping <%s> (ours)", str(self.cuda_context.handle), str(context.handle))
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# cuda.Context.pop()
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# Add all the contexts we popped that were not our own
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# for context in other_contexts:
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# self.logger.debug("<%s> Pushing <%s>", str(self.cuda_context.handle), str(context.handle))
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# cuda.Context.push(context)
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# self.logger.debug("<%s> Detaching", str(self.cuda_context.handle))
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# self.cuda_context.detach()
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# def __str__(self):
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# return "CudaContext id " + str(self.cuda_context.handle)
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def hash_kernel(kernel_filename, include_dirs):
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# Generate a kernel ID for our caches
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num_includes = 0
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max_includes = 100
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kernel_hasher = hashlib.md5()
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logger = logging.getLogger(__name__)
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# Loop over file and includes, and check if something has changed
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files = [kernel_filename]
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while len(files):
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if (num_includes > max_includes):
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raise("Maximum number of includes reached - circular include in {:}?".format(kernel_filename))
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filename = files.pop()
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#logger.debug("Hashing %s", filename)
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modified = os.path.getmtime(filename)
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# Open the file
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with io.open(filename, "r") as file:
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# Search for #inclue <something> and also hash the file
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file_str = file.read()
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kernel_hasher.update(file_str.encode('utf-8'))
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kernel_hasher.update(str(modified).encode('utf-8'))
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#Find all includes
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includes = re.findall('^\W*#include\W+(.+?)\W*$', file_str, re.M)
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# Loop over everything that looks like an include
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for include_file in includes:
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#Search through include directories for the file
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file_path = os.path.dirname(filename)
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for include_path in [file_path] + include_dirs:
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# If we find it, add it to list of files to check
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temp_path = os.path.join(include_path, include_file)
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if (os.path.isfile(temp_path)):
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files = files + [temp_path]
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num_includes = num_includes + 1 #For circular includes...
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break
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return kernel_hasher.hexdigest()
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"""
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Reads a text file and creates an OpenCL kernel from that
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"""
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def get_module(self, kernel_filename,
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include_dirs=[], \
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defines={}, \
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compile_args={'no_extern_c', True}, jit_compile_args={}):
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"""
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Helper function to print compilation output
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"""
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def cuda_compile_message_handler(compile_success_bool, info_str, error_str):
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self.logger.debug("Compilation returned %s", str(compile_success_bool))
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if info_str:
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self.logger.debug("Info: %s", info_str)
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if error_str:
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self.logger.debug("Error: %s", error_str)
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kernel_filename = os.path.normpath(kernel_filename)
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kernel_path = os.path.abspath(os.path.join(self.module_path, kernel_filename))
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#self.logger.debug("Getting %s", kernel_filename)
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# Create a hash of the kernel options
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options_hasher = hashlib.md5()
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options_hasher.update(str(defines).encode('utf-8') + str(compile_args).encode('utf-8'));
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options_hash = options_hasher.hexdigest()
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# Create hash of kernel souce
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source_hash = CudaContext.hash_kernel( \
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kernel_path, \
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include_dirs=[self.module_path] + include_dirs)
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# Create final hash
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root, ext = os.path.splitext(kernel_filename)
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kernel_hash = root \
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+ "_" + source_hash \
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+ "_" + options_hash \
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+ ext
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cached_kernel_filename = os.path.join(self.cache_path, kernel_hash)
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# If we have the kernel in our hashmap, return it
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if (kernel_hash in self.modules.keys()):
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self.logger.debug("Found kernel %s cached in hashmap (%s)", kernel_filename, kernel_hash)
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return self.modules[kernel_hash]
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# If we have it on disk, return it
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elif (self.use_cache and os.path.isfile(cached_kernel_filename)):
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self.logger.debug("Found kernel %s cached on disk (%s)", kernel_filename, kernel_hash)
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with io.open(cached_kernel_filename, "rb") as file:
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file_str = file.read()
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module = cuda.module_from_buffer(file_str, message_handler=cuda_compile_message_handler, **jit_compile_args)
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self.modules[kernel_hash] = module
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return module
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# Otherwise, compile it from source
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else:
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self.logger.debug("Compiling %s (%s)", kernel_filename, kernel_hash)
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#Create kernel string
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kernel_string = ""
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for key, value in defines.items():
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kernel_string += "#define {:s} {:s}\n".format(str(key), str(value))
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kernel_string += '#include "{:s}"'.format(os.path.join(self.module_path, kernel_filename))
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if (self.use_cache):
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cached_kernel_dir = os.path.dirname(cached_kernel_filename)
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if not os.path.isdir(cached_kernel_dir):
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os.mkdir(cached_kernel_dir)
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with io.open(cached_kernel_filename + ".txt", "w") as file:
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file.write(kernel_string)
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with Common.Timer("compiler") as timer:
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import warnings
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", message="The CUDA compiler succeeded, but said the following:\nkernel.cu", category=UserWarning)
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cubin = cuda_compiler.compile(kernel_string, include_dirs=include_dirs, cache_dir=False, **compile_args)
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module = cuda.module_from_buffer(cubin, message_handler=cuda_compile_message_handler, **jit_compile_args)
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if (self.use_cache):
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with io.open(cached_kernel_filename, "wb") as file:
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file.write(cubin)
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self.modules[kernel_hash] = module
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return module
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"""
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Clears the kernel cache (useful for debugging & development)
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"""
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def clear_kernel_cache(self):
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self.logger.debug("Clearing cache")
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self.modules = {}
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gc.collect()
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"""
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Synchronizes all streams etc
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"""
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def synchronize(self):
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self.cuda_context.synchronize()
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