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"""Interface to runtime cuda kernel compile module."""
from __future__ import absolute_import
from array import array
import re
import ctypes
import numpy as np
from .base import _LIB, mx_uint, c_array, c_array_buf, c_str_array, check_call
from .base import c_str, CudaModuleHandle, CudaKernelHandle, numeric_types, string_types
from .ndarray import _DTYPE_NP_TO_MX, _DTYPE_MX_TO_NP, NDArray
_DTYPE_CPP_TO_NP = {
'float': np.float32,
'double': np.float64,
'__half': np.float16,
'uint8_t': np.uint8,
'int': np.int32,
'int32_t': np.int32,
'int8_t': np.int8,
'char': np.int8,
'int64_t': np.int64,
}
[docs]class CudaModule(object):
r"""Compile and run CUDA code from Python.
In CUDA 7.5, you need to prepend your kernel definitions
with 'extern "C"' to avoid name mangling::
source = r'''
extern "C" __global__ void axpy(const float *x, float *y, float alpha) {
int i = threadIdx.x + blockIdx.x * blockDim.x;
y[i] += alpha * x[i];
}
'''
module = mx.rtc.CudaModule(source)
func = module.get_kernel("axpy", "const float *x, float *y, float alpha")
x = mx.nd.ones((10,), ctx=mx.gpu(0))
y = mx.nd.zeros((10,), ctx=mx.gpu(0))
func.launch([x, y, 3.0], mx.gpu(0), (1, 1, 1), (10, 1, 1))
print(y)
Starting from CUDA 8.0, you can instead export functions by name.
This also allows you to use templates::
source = r'''
template
__global__ void axpy(const DType *x, DType *y, DType alpha) {
int i = threadIdx.x + blockIdx.x * blockDim.x;
y[i] += alpha * x[i];
}
'''
module = mx.rtc.CudaModule(source, exports=['axpy', 'axpy'])
func32 = module.get_kernel("axpy", "const float *x, float *y, float alpha")
x = mx.nd.ones((10,), dtype='float32', ctx=mx.gpu(0))
y = mx.nd.zeros((10,), dtype='float32', ctx=mx.gpu(0))
func32.launch([x, y, 3.0], mx.gpu(0), (1, 1, 1), (10, 1, 1))
print(y)
func64 = module.get_kernel("axpy", "const double *x, double *y, double alpha")
x = mx.nd.ones((10,), dtype='float64', ctx=mx.gpu(0))
y = mx.nd.zeros((10,), dtype='float64', ctx=mx.gpu(0))
func32.launch([x, y, 3.0], mx.gpu(0), (1, 1, 1), (10, 1, 1))
print(y)
Parameters
----------
source : str
Complete source code.
options : tuple of str
Compiler flags. For example, use "-I/usr/local/cuda/include" to
add cuda headers to include path.
exports : tuple of str
Export kernel names.
"""
def __init__(self, source, options=(), exports=()):
if isinstance(options, string_types):
options = (options,)
if isinstance(exports, string_types):
exports = (exports,)
self.handle = CudaModuleHandle()
check_call(_LIB.MXRtcCudaModuleCreate(
c_str(source),
len(options),
c_str_array(options),
len(exports),
c_str_array(exports),
ctypes.byref(self.handle)))
def __del__(self):
check_call(_LIB.MXRtcCudaModuleFree(self.handle))
[docs] def get_kernel(self, name, signature):
r"""Get CUDA kernel from compiled module.
Parameters
----------
name : str
String name of the kernel.
signature : str
Function signature for the kernel. For example, if a kernel is
declared as::
extern "C" __global__ void axpy(const float *x, double *y, int alpha)
Then its signature should be::
const float *x, double *y, int alpha
or::
const float *, double *, int
Note that `*` in signature marks an argument as array and
`const` marks an argument as constant (input) array.
Returns
-------
CudaKernel
CUDA kernels that can be launched on GPUs.
"""
hdl = CudaKernelHandle()
is_ndarray = []
is_const = []
dtypes = []
pattern = re.compile(r"""^\s*(const)?\s*([\w_]+)\s*(\*)?\s*([\w_]+)?\s*$""")
args = re.sub(r"\s+", " ", signature).split(",")
for arg in args:
match = pattern.match(arg)
if not match or match.groups()[1] == 'const':
raise ValueError(
'Invalid function prototype "%s". Must be in the '
'form of "(const) type (*) (name)"'%arg)
is_const.append(bool(match.groups()[0]))
dtype = match.groups()[1]
is_ndarray.append(bool(match.groups()[2]))
if dtype not in _DTYPE_CPP_TO_NP:
raise TypeError(
"Unsupported kernel argument type %s. Supported types are: %s."%(
arg, ','.join(_DTYPE_CPP_TO_NP.keys())))
dtypes.append(_DTYPE_NP_TO_MX[_DTYPE_CPP_TO_NP[dtype]])
check_call(_LIB.MXRtcCudaKernelCreate(
self.handle,
c_str(name),
len(dtypes),
c_array_buf(ctypes.c_int, array('i', is_ndarray)),
c_array_buf(ctypes.c_int, array('i', is_const)),
c_array_buf(ctypes.c_int, array('i', dtypes)),
ctypes.byref(hdl)))
return CudaKernel(hdl, name, is_ndarray, dtypes)
[docs]class CudaKernel(object):
"""Constructs CUDA kernel. Should be created by `CudaModule.get_kernel`,
not intended to be used by users."""
def __init__(self, handle, name, is_ndarray, dtypes):
self.handle = handle
self._name = name
self._is_ndarray = is_ndarray
self._dtypes = [_DTYPE_MX_TO_NP[i] for i in dtypes]
def __del__(self):
check_call(_LIB.MXRtcCudaKernelFree(self.handle))
[docs] def launch(self, args, ctx, grid_dims, block_dims, shared_mem=0):
"""Launch cuda kernel.
Parameters
----------
args : tuple of NDArray or numbers
List of arguments for kernel. NDArrays are expected for pointer
types (e.g. `float*`, `double*`) while numbers are expected for
non-pointer types (e.g. `int`, `float`).
ctx : Context
The context to launch kernel on. Must be GPU context.
grid_dims : tuple of 3 integers
Grid dimensions for CUDA kernel.
block_dims : tuple of 3 integers
Block dimensions for CUDA kernel.
shared_mem : integer, optional
Size of dynamically allocated shared memory. Defaults to 0.
"""
assert ctx.device_type == 'gpu', "Cuda kernel can only be launched on GPU"
assert len(grid_dims) == 3, "grid_dims must be a tuple of 3 integers"
assert len(block_dims) == 3, "grid_dims must be a tuple of 3 integers"
assert len(args) == len(self._dtypes), \
"CudaKernel(%s) expects %d arguments but got %d"%(
self._name, len(self._dtypes), len(args))
void_args = []
ref_holder = []
for i, (arg, is_nd, dtype) in enumerate(zip(args, self._is_ndarray, self._dtypes)):
if is_nd:
assert isinstance(arg, NDArray), \
"The %d-th argument is expected to be a NDArray but got %s"%(
i, type(arg))
void_args.append(arg.handle)
else:
assert isinstance(arg, numeric_types), \
"The %d-th argument is expected to be a number, but got %s"%(
i, type(arg))
ref_holder.append(np.array(arg, dtype=dtype))
void_args.append(ref_holder[-1].ctypes.data_as(ctypes.c_void_p))
check_call(_LIB.MXRtcCudaKernelCall(
self.handle,
ctx.device_id,
c_array(ctypes.c_void_p, void_args),
mx_uint(grid_dims[0]), mx_uint(grid_dims[1]), mx_uint(grid_dims[2]),
mx_uint(block_dims[0]), mx_uint(block_dims[1]), mx_uint(block_dims[2]),
mx_uint(shared_mem)))
