from __future__ import print_function from contextlib import contextmanager import sys import threading import numpy as np from numba import six from numba.six import reraise from .cudadrv.devicearray import to_device, auto_device from .kernelapi import Dim3, FakeCUDAModule, swapped_cuda_module from ..errors import normalize_kernel_dimensions from ..args import wrap_arg, ArgHint """ Global variable to keep track of the current "kernel context", i.e the FakeCUDAModule. We only support one kernel launch at a time. No support for concurrent kernel launch. """ _kernel_context = None @contextmanager def _push_kernel_context(mod): """ Push the current kernel context. """ global _kernel_context assert _kernel_context is None, "conrrent simulated kernel not supported" _kernel_context = mod try: yield finally: _kernel_context = None def _get_kernel_context(): """ Get the current kernel context. This is usually done by a device function. """ return _kernel_context class FakeCUDAKernel(object): ''' Wraps a @cuda.jit-ed function. ''' def __init__(self, fn, device, fastmath=False, extensions=[]): self.fn = fn self._device = device self._fastmath = fastmath self.extensions = list(extensions) # defensive copy # Initial configuration: 1 block, 1 thread, stream 0, no dynamic shared # memory. self[1, 1, 0, 0] def __call__(self, *args): if self._device: with swapped_cuda_module(self.fn, _get_kernel_context()): return self.fn(*args) fake_cuda_module = FakeCUDAModule(self.grid_dim, self.block_dim, self.dynshared_size) with _push_kernel_context(fake_cuda_module): # fake_args substitutes all numpy arrays for FakeCUDAArrays # because they implement some semantics differently retr = [] def fake_arg(arg): # map the arguments using any extension you've registered _, arg = six.moves.reduce( lambda ty_val, extension: extension.prepare_args( *ty_val, stream=0, retr=retr), self.extensions, (None, arg) ) if isinstance(arg, np.ndarray) and arg.ndim > 0: return wrap_arg(arg).to_device(retr) elif isinstance(arg, ArgHint): return arg.to_device(retr) else: return arg fake_args = [fake_arg(arg) for arg in args] with swapped_cuda_module(self.fn, fake_cuda_module): # Execute one block at a time for grid_point in np.ndindex(*self.grid_dim): bm = BlockManager(self.fn, self.grid_dim, self.block_dim) bm.run(grid_point, *fake_args) for wb in retr: wb() def __getitem__(self, configuration): self.grid_dim, self.block_dim = \ normalize_kernel_dimensions(*configuration[:2]) if len(configuration) == 4: self.dynshared_size = configuration[3] return self def bind(self): pass def specialize(self, *args): return self def forall(self, ntasks, tpb=0, stream=0, sharedmem=0): return self[ntasks, 1, stream, sharedmem] @property def ptx(self): ''' Required in order to proceed through some tests, but serves no functional purpose. ''' res = '.const' res += '\n.local' if self._fastmath: res += '\ndiv.full.ftz.f32' return res # Thread emulation class BlockThread(threading.Thread): ''' Manages the execution of a function for a single CUDA thread. ''' def __init__(self, f, manager, blockIdx, threadIdx): super(BlockThread, self).__init__(target=f) self.syncthreads_event = threading.Event() self.syncthreads_blocked = False self._manager = manager self.blockIdx = Dim3(*blockIdx) self.threadIdx = Dim3(*threadIdx) self.exception = None self.daemon = True self.abort = False blockDim = Dim3(*self._manager._block_dim) self.thread_id = self.threadIdx.x + blockDim.x * (self.threadIdx.y + blockDim.y * self.threadIdx.z) def run(self): try: super(BlockThread, self).run() except Exception as e: tid = 'tid=%s' % list(self.threadIdx) ctaid = 'ctaid=%s' % list(self.blockIdx) if str(e) == '': msg = '%s %s' % (tid, ctaid) else: msg = '%s %s: %s' % (tid, ctaid, e) tb = sys.exc_info()[2] self.exception = (type(e), type(e)(msg), tb) def syncthreads(self): if self.abort: raise RuntimeError("abort flag set on syncthreads call") self.syncthreads_blocked = True self.syncthreads_event.wait() self.syncthreads_event.clear() if self.abort: raise RuntimeError("abort flag set on syncthreads clear") def syncthreads_count(self, value): self._manager.block_state[self.threadIdx.x, self.threadIdx.y, self.threadIdx.z] = value self.syncthreads() count = np.count_nonzero(self._manager.block_state) self.syncthreads() return count def syncthreads_and(self, value): self._manager.block_state[self.threadIdx.x, self.threadIdx.y, self.threadIdx.z] = value self.syncthreads() test = np.all(self._manager.block_state) self.syncthreads() return 1 if test else 0 def syncthreads_or(self, value): self._manager.block_state[self.threadIdx.x, self.threadIdx.y, self.threadIdx.z] = value self.syncthreads() test = np.any(self._manager.block_state) self.syncthreads() return 1 if test else 0 def __str__(self): return 'Thread <<<%s, %s>>>' % (self.blockIdx, self.threadIdx) class BlockManager(object): ''' Manages the execution of a thread block. When run() is called, all threads are started. Each thread executes until it hits syncthreads(), at which point it sets its own syncthreads_blocked to True so that the BlockManager knows it is blocked. It then waits on its syncthreads_event. The BlockManager polls threads to determine if they are blocked in syncthreads(). If it finds a blocked thread, it adds it to the set of blocked threads. When all threads are blocked, it unblocks all the threads. The thread are unblocked by setting their syncthreads_blocked back to False and setting their syncthreads_event. The polling continues until no threads are alive, when execution is complete. ''' def __init__(self, f, grid_dim, block_dim): self._grid_dim = grid_dim self._block_dim = block_dim self._f = f self.block_state = np.zeros(block_dim, dtype=np.bool) def run(self, grid_point, *args): # Create all threads threads = set() livethreads = set() blockedthreads = set() for block_point in np.ndindex(*self._block_dim): def target(): self._f(*args) t = BlockThread(target, self, grid_point, block_point) t.start() threads.add(t) livethreads.add(t) # Potential optimisations: # 1. Continue the while loop immediately after finding a blocked thread # 2. Don't poll already-blocked threads while livethreads: for t in livethreads: if t.syncthreads_blocked: blockedthreads.add(t) elif t.exception: # Abort all other simulator threads on exception, # do *not* join immediately to facilitate debugging. for t_other in threads: t_other.abort = True t_other.syncthreads_blocked = False t_other.syncthreads_event.set() reraise(*(t.exception)) if livethreads == blockedthreads: for t in blockedthreads: t.syncthreads_blocked = False t.syncthreads_event.set() blockedthreads = set() livethreads = set([ t for t in livethreads if t.is_alive() ]) # Final check for exceptions in case any were set prior to thread # finishing, before we could check it for t in threads: if t.exception: reraise(*(t.exception))