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pytensor
提交 e5ba1b08 authored 作者: Frédéric Bastien's avatar Frédéric Bastien 提交者： GitHub
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Merge pull request #6012 from abergeron/fix_offset

Fix offset problems in the new backend.
上级 8dcc5fc6 a762b617
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正在显示 包含 173 行增加 和 173 行删除
theano/gpuarray/basic_ops.py
浏览文件 @ e5ba1b08
......@@ -1630,7 +1630,9 @@ class GpuEye(GpuKernelBase, Op):
def gpu_kernels(self, node, name):
code = """
KERNEL void eye(GLOBAL_MEM %(ctype)s *a, ga_size n, ga_size m, ga_ssize k) {
KERNEL void eye(GLOBAL_MEM %(ctype)s *a, ga_size a_off,
ga_size n, ga_size m, ga_ssize k) {
a = (GLOBAL_MEM %(ctype)s *)(((char *)a) + a_off);
ga_ssize coff = max(k, (ga_ssize) 0);
ga_ssize roff = -min(k, (ga_ssize) 0);
ga_size nb = (ga_size) min(n - roff, m - coff);
......@@ -1641,7 +1643,8 @@ KERNEL void eye(GLOBAL_MEM %(ctype)s *a, ga_size n, ga_size m, ga_ssize k) {
name=name, write_a=write_w(self.dtype))
return [Kernel(
code=code, name="eye",
params=[gpuarray.GpuArray, gpuarray.SIZE, gpuarray.SIZE, gpuarray.SSIZE],
params=[gpuarray.GpuArray, gpuarray.SIZE, gpuarray.SIZE,
gpuarray.SIZE, gpuarray.SSIZE],
flags=Kernel.get_flags(self.dtype),
objvar='k_eye_' + name)]
......@@ -1685,7 +1688,8 @@ KERNEL void eye(GLOBAL_MEM %(ctype)s *a, ga_size n, ga_size m, ga_ssize k) {
col_off = (size_t) (k > 0?k:0);
row_off = (size_t) (k < 0?-k:0);
if (row_off < dims[0] && col_off < dims[1]) {
err = eye_call(1, &gs, &ls, 0, %(z)s->ga.data, dims[0], dims[1], k);
err = eye_call(1, &gs, &ls, 0, %(z)s->ga.data, %(z)s->ga.offset,
dims[0], dims[1], k);
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
"gpuarray error: kEye: %%s. n%%lu, m=%%lu.",
......@@ -1702,4 +1706,4 @@ KERNEL void eye(GLOBAL_MEM %(ctype)s *a, ga_size n, ga_size m, ga_ssize k) {
return s
def c_code_cache_version(self):
return (7,)
return (8,)
theano/gpuarray/blas.py
浏览文件 @ e5ba1b08
......@@ -552,8 +552,8 @@ class BaseGpuCorrMM(CGpuKernelBase):
return [os.path.dirname(__file__)]
def c_code_cache_version(self):
# Raise this whenever modifying the code below.
return (7,)
# Raise this whenever modifying the C code (including the file).
return (8,)
def c_code_helper(self, bottom, weights, top, direction, sub, height=None, width=None):
"""
......
theano/gpuarray/extra_ops.py
浏览文件 @ e5ba1b08
......@@ -35,7 +35,7 @@ class GpuCumOp(GpuKernelBase, Op):
return hash(self.axis) ^ hash(self.mode)
def c_code_cache_version(self):
return (5,)
return (6,)
def c_headers(self):
return ['<numpy_compat.h>', '<gpuarray/types.h>', '<gpuarray_helper.h>']
......@@ -69,11 +69,9 @@ class GpuCumOp(GpuKernelBase, Op):
code = """
KERNEL void %(kname)s(float* input, ga_size input_offset,
float* output, ga_size output_offset,
ga_ssize inputStrides_x,
ga_ssize inputStrides_y,
ga_ssize inputStrides_z,
ga_ssize outputStrides_x, ga_ssize outputStrides_y,
ga_ssize outputStrides_z, const int offsetY, const int offsetZ,
ga_ssize inputStrides_x, ga_ssize inputStrides_y, ga_ssize inputStrides_z,
ga_ssize outputStrides_x, ga_ssize outputStrides_y, ga_ssize outputStrides_z,
const int offsetY, const int offsetZ,
const int beforeLastElementIdx, const int lastElementIdx){
input = (float *)(((char *)input) + input_offset);
output = (float *)(((char *)output) + output_offset);
......@@ -216,6 +214,7 @@ class GpuCumOp(GpuKernelBase, Op):
output = (float *)(((char *)output) + output_offset);
blockSum = (float *)(((char *)blockSum) + blockSum_offset);
int globalThreadID = (blockIdx.x + 1) * blockDim.x + threadIdx.x;
// Check if current has data to process.
......@@ -397,23 +396,8 @@ class GpuCumOp(GpuKernelBase, Op):
size_t dimGrid[3] = {dimGridX, localDimGridY, localDimGridZ};
size_t dimBlock[3] = {dimBlockX, 1, 1}; // One cum op per block.
size_t sharedBytes = (2*dimBlockX) * sizeof(float);
void* kernel_params[] = {(void*) input->ga.data,
(void*) &(input->ga.offset),
(void*) output->ga.data,
(void*) &(output->ga.offset),
(void*) &nbElementsPerCumOp,
(void*) &inputStrides_x,
(void*) &inputStrides_y,
(void*) &inputStrides_z,
(void*) &outputStrides_x,
(void*) &outputStrides_y,
(void*) &outputStrides_z,
(void*) &offsetY,
(void*) &offsetZ,
(void*) deviceBlockSum->ga.data,
(void*) &(deviceBlockSum->ga.offset)
};
int err = GpuKernel_call(&k_blockCumOp_%(nodename)s, 3, dimGrid, dimBlock, sharedBytes, kernel_params);
int err = k_blockCumOp_call(3, dimGrid, dimBlock, sharedBytes, input->ga.data, input->ga.offset, output->ga.data, output->ga.offset, nbElementsPerCumOp, inputStrides_x, inputStrides_y, inputStrides_z, outputStrides_x, outputStrides_y, outputStrides_z, offsetY, offsetZ, deviceBlockSum->ga.data, deviceBlockSum->ga.offset);
if (err != GA_NO_ERROR){
PyErr_SetString(PyExc_RuntimeError, "blockCumOp call failed");
return -1;
......@@ -429,18 +413,8 @@ class GpuCumOp(GpuKernelBase, Op):
// report partial cum ops of previous blocks to subsequents ones.
size_t dimGrid[3] = {dimGridX, localDimGridY, localDimGridZ};
size_t dimBlock[3] = {dimBlockX, 1, 1};
void* kernel_params[] = {(void*) output->ga.data,
(void*) &(output->ga.offset),
(void*) deviceBlockSum->ga.data,
(void*) &(deviceBlockSum->ga.offset),
(void*) &nbElementsPerCumOp,
(void*) &outputStrides_x,
(void*) &outputStrides_y,
(void*) &outputStrides_z,
(void*) &offsetY,
(void*) &offsetZ
};
int err = GpuKernel_call(&k_finalCumOp_%(nodename)s, 3, dimGrid, dimBlock, sharedBytes, kernel_params);
int err = k_finalCumOp_call(3, dimGrid, dimBlock, sharedBytes, output->ga.data, output->ga.offset, deviceBlockSum->ga.data, deviceBlockSum->ga.offset, nbElementsPerCumOp, outputStrides_x, outputStrides_y, outputStrides_z, offsetY, offsetZ);
if (err != GA_NO_ERROR){
PyErr_SetString(PyExc_RuntimeError, "finalCumOp call failed");
return -1;
......@@ -450,24 +424,8 @@ class GpuCumOp(GpuKernelBase, Op):
if (shape[axis] != nbElementsPerCumOp){
size_t dimGrid[3] = {1, localDimGridY, localDimGridZ};
size_t dimBlock[3] = {1, 1, 1};
size_t tmp0 = shape[axis]-2;
size_t tmp1 = shape[axis]-1;
void* kernel_params[] = {(void*) input->ga.data,
(void*) &(input->ga.offset),
(void*) output->ga.data,
(void*) &(output->ga.offset),
(void*) &inputStrides_x,
(void*) &inputStrides_y,
(void*) &inputStrides_z,
(void*) &outputStrides_x,
(void*) &outputStrides_y,
(void*) &outputStrides_z,
(void*) &offsetY,
(void*) &offsetZ,
(void*) &(tmp0),
(void*) &(tmp1)
};
int err = GpuKernel_call(&k_cumadd_%(nodename)s, 3, dimGrid, dimBlock, sharedBytes, kernel_params);
int err = k_cumadd_call(3, dimGrid, dimBlock, sharedBytes, input->ga.data, input->ga.offset, output->ga.data, output->ga.offset, inputStrides_x, inputStrides_y, inputStrides_z, outputStrides_x, outputStrides_y, outputStrides_z, offsetY, offsetZ, shape[axis] - 2, shape[axis] - 1);
if (err != GA_NO_ERROR){
PyErr_SetString(PyExc_RuntimeError, "cumadd call failed");
return -1;
......
theano/gpuarray/multinomial.py
浏览文件 @ e5ba1b08
......@@ -71,16 +71,22 @@ class GPUAMultinomialFromUniform(GpuKernelBase, Op):
KERNEL void k_multi_warp_multinomial(
const ga_size nb_multi,
const ga_size nb_outcomes,
GLOBAL_MEM %(in_ctype)s * global_pvals,
GLOBAL_MEM %(in_ctype)s *global_pvals,
const ga_size global_pvals_offset,
const ga_ssize pvals_row_stride,
const ga_ssize pvals_col_stride,
GLOBAL_MEM %(in_ctype)s * global_unis,
GLOBAL_MEM %(in_ctype)s *global_unis,
const ga_size global_unis_offset,
const ga_ssize unis_stride,
GLOBAL_MEM %(out_ctype)s * global_outs,
GLOBAL_MEM %(out_ctype)s *global_outs,
const ga_size global_outs_offset,
const ga_ssize outs_row_stride,
const ga_ssize outs_col_stride
)
{
global_pvals = (GLOBAL_MEM %(in_ctype)s *)(((char *)global_pvals) + global_pvals_offset);
global_unis = (GLOBAL_MEM %(in_ctype)s *)(((char *)global_unis) + global_unis_offset);
global_outs = (GLOBAL_MEM %(out_ctype)s *)(((char *)global_outs) + global_outs_offset);
// each thread takes care of one multinomial draw
int n = LDIM_0*GID_0 + LID_0;
if (n < nb_multi)
......@@ -113,11 +119,14 @@ KERNEL void k_multi_warp_multinomial(
params=[pygpu.gpuarray.SIZE,
pygpu.gpuarray.SIZE,
pygpu.gpuarray.GpuArray,
pygpu.gpuarray.SIZE,
pygpu.gpuarray.SSIZE,
pygpu.gpuarray.SSIZE,
pygpu.gpuarray.GpuArray,
pygpu.gpuarray.SIZE,
pygpu.gpuarray.SSIZE,
pygpu.gpuarray.GpuArray,
pygpu.gpuarray.SIZE,
pygpu.gpuarray.SSIZE,
pygpu.gpuarray.SSIZE],
flags=Kernel.get_flags(node.outputs[0].dtype),
......@@ -193,27 +202,8 @@ KERNEL void k_multi_warp_multinomial(
assert(nb_blocks*nb_threads >= nb_multi);
void *args[10];
ssize_t strides[5] = {
PyGpuArray_STRIDES(pvals)[0]/gpuarray_get_elsize(%(in_typecode)s),
PyGpuArray_STRIDES(pvals)[1]/gpuarray_get_elsize(%(in_typecode)s),
PyGpuArray_STRIDES(unis)[0]/gpuarray_get_elsize(%(in_typecode)s),
PyGpuArray_STRIDES(out)[0]/gpuarray_get_elsize(%(out_typecode)s),
PyGpuArray_STRIDES(out)[1]/gpuarray_get_elsize(%(out_typecode)s)
};
int err;
args[0] = (void*)&PyGpuArray_DIMS(out)[1];
args[1] = (void*)&PyGpuArray_DIMS(out)[0];
args[2] = pvals->ga.data; //PyGpuArray_DEV_DATA(pvals);
args[3] = (void*)&strides[0];
args[4] = (void*)&strides[1];
args[5] = unis->ga.data; //PyGpuArray_DEV_DATA(unis);
args[6] = (void*)&strides[2];
args[7] = out->ga.data; //PyGpuArray_DEV_DATA(out);
args[8] = (void*)&strides[3];
args[9] = (void*)&strides[4];
err = GpuKernel_call(&%(kname)s, 1, &nb_blocks, &nb_threads, 0, args);
int err = k_multi_warp_multinomial_call(1, &nb_blocks, &nb_threads, 0, PyGpuArray_DIMS(out)[1], PyGpuArray_DIMS(out)[0], pvals->ga.data, pvals->ga.offset, PyGpuArray_STRIDES(pvals)[0]/gpuarray_get_elsize(%(in_typecode)s), PyGpuArray_STRIDES(pvals)[1]/gpuarray_get_elsize(%(in_typecode)s), unis->ga.data, unis->ga.offset, PyGpuArray_STRIDES(unis)[0]/gpuarray_get_elsize(%(in_typecode)s), out->ga.data, out->ga.offset, PyGpuArray_STRIDES(out)[0]/gpuarray_get_elsize(%(out_typecode)s), PyGpuArray_STRIDES(out)[1]/gpuarray_get_elsize(%(out_typecode)s));
if (err != GA_NO_ERROR) {
PyErr_Format(
PyExc_RuntimeError,
......@@ -230,7 +220,7 @@ KERNEL void k_multi_warp_multinomial(
return s
def c_code_cache_version(self):
return (3,)
return (4,)
class GPUAChoiceFromUniform(GpuKernelBase, Op):
......@@ -295,15 +285,21 @@ KERNEL void k_multi_warp_multinomial_wor(
const ga_size nb_outcomes,
const ga_size n_samples,
GLOBAL_MEM float * global_pvals_copy,
const ga_size global_pvals_offset,
const ga_ssize pvals_row_stride,
const ga_ssize pvals_col_stride,
GLOBAL_MEM float * global_unis,
const ga_size global_unis_offset,
const ga_ssize unis_stride,
GLOBAL_MEM ga_long * global_outs,
const ga_size global_outs_offset,
const ga_ssize outs_row_stride,
const ga_ssize outs_col_stride
)
{
global_pvals_copy = (GLOBAL_MEM float *)(((char *)global_pvals_copy) + global_pvals_offset);
global_unis = (GLOBAL_MEM float *)(((char *)global_unis) + global_unis_offset);
global_outs = (GLOBAL_MEM ga_long *)(((char *)global_outs) + global_outs_offset);
// each thread takes care of one multinomial-wor n_samples-draw
int n = LDIM_0*GID_0 + LID_0;
......@@ -344,11 +340,14 @@ KERNEL void k_multi_warp_multinomial_wor(
pygpu.gpuarray.SIZE,
pygpu.gpuarray.SIZE,
pygpu.gpuarray.GpuArray,
pygpu.gpuarray.SIZE,
pygpu.gpuarray.SSIZE,
pygpu.gpuarray.SSIZE,
pygpu.gpuarray.GpuArray,
pygpu.gpuarray.SIZE,
pygpu.gpuarray.SSIZE,
pygpu.gpuarray.GpuArray,
pygpu.gpuarray.SIZE,
pygpu.gpuarray.SSIZE,
pygpu.gpuarray.SSIZE
],
......@@ -438,28 +437,7 @@ KERNEL void k_multi_warp_multinomial_wor(
assert(nb_blocks*nb_threads >= nb_multi);
void *args[11];
ssize_t strides[5] = {
PyGpuArray_STRIDES(pvals)[0]/sizeof(float),
PyGpuArray_STRIDES(pvals)[1]/sizeof(float),
PyGpuArray_STRIDES(unis)[0]/sizeof(float),
PyGpuArray_STRIDES(out)[0]/8,
PyGpuArray_STRIDES(out)[1]/8
};
int err;
args[0] = (void*)&PyGpuArray_DIMS(pvals)[0];
args[1] = (void*)&PyGpuArray_DIMS(pvals)[1];
args[2] = (void*)&n_samples;
args[3] = pvals_copy->ga.data; //PyGpuArray_DEV_DATA(pvals);
args[4] = (void*)&strides[0];
args[5] = (void*)&strides[1];
args[6] = unis->ga.data; //PyGpuArray_DEV_DATA(unis);
args[7] = (void*)&strides[2];
args[8] = out->ga.data; //PyGpuArray_DEV_DATA(out);
args[9] = (void*)&strides[3];
args[10] = (void*)&strides[4];
err = GpuKernel_call(&%(kname)s, 1, &nb_blocks, &nb_threads, 0, args);
int err = k_multi_warp_multinomial_wor_call(1, &nb_blocks, &nb_threads, 0, PyGpuArray_DIMS(pvals)[0], PyGpuArray_DIMS(pvals)[1], n_samples, pvals_copy->ga.data, pvals_copy->ga.offset, PyGpuArray_STRIDES(pvals)[0]/sizeof(float), PyGpuArray_STRIDES(pvals)[1]/sizeof(float), unis->ga.data, unis->ga.offset, PyGpuArray_STRIDES(unis)[0]/sizeof(float), out->ga.data, out->ga.offset, PyGpuArray_STRIDES(out)[0]/8, PyGpuArray_STRIDES(out)[1]/8);
if (err != GA_NO_ERROR) {
PyErr_Format(
PyExc_RuntimeError,
......@@ -477,7 +455,7 @@ KERNEL void k_multi_warp_multinomial_wor(
return s
def c_code_cache_version(self):
return (7,)
return (8,)
@register_opt('fast_compile')
......
theano/gpuarray/pool.c
浏览文件 @ e5ba1b08
#section kernels
#kernel max_pool2d_kernel : size, size, size, size, size, size, size, *, size, size, size, size, size, size, * :
#kernel max_pool2d_kernel : size, size, size, size, size, size, size, *, size, size, size, size, size, size, size, *, size :
// (borrowed from Caffe: https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cu)
KERNEL void max_pool2d_kernel(const ga_size nthreads,
const ga_size num, const ga_size channels, const ga_size pooled_height,
const ga_size pooled_width, const ga_size height, const ga_size width,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size kernel_h, const ga_size kernel_w,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size x_off, const ga_size kernel_h, const ga_size kernel_w,
const ga_size stride_h, const ga_size stride_w, const ga_size pad_h, const ga_size pad_w,
GLOBAL_MEM DTYPE_OUTPUT_0 *z)
GLOBAL_MEM DTYPE_OUTPUT_0 *z, const ga_size z_off)
{
x = (GLOBAL_MEM DTYPE_INPUT_0 *)(((char *)x) + x_off);
z = (GLOBAL_MEM DTYPE_OUTPUT_0 *)(((char *)z) + z_off);
// grid stride looping
for (ga_size index = GID_0 * LDIM_0 + LID_0;
index < nthreads;
......@@ -41,18 +43,20 @@ KERNEL void max_pool2d_kernel(const ga_size nthreads,
}
}
#kernel max_pool3d_kernel : size, size, size, size, size, size, size, size, size, *, size, size, size, size, size, size, size, size, size, * :
#kernel max_pool3d_kernel : size, size, size, size, size, size, size, size, size, *, size, size, size, size, size, size, size, size, size, size, *, size :
// (adopted from Caffe: https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cu)
KERNEL void max_pool3d_kernel(const ga_size nthreads,
const ga_size num, const ga_size channels, const ga_size pooled_depth,
const ga_size pooled_height, const ga_size pooled_width,
const ga_size depth, const ga_size height, const ga_size width,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size kernel_d, const ga_size kernel_h,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size x_off, const ga_size kernel_d, const ga_size kernel_h,
const ga_size kernel_w, const ga_size stride_d, const ga_size stride_h,
const ga_size stride_w, const ga_size pad_d, const ga_size pad_h, const ga_size pad_w,
GLOBAL_MEM DTYPE_OUTPUT_0 *z)
GLOBAL_MEM DTYPE_OUTPUT_0 *z, const ga_size z_off)
{
x = (GLOBAL_MEM DTYPE_INPUT_0 *)(((char *)x) + x_off);
z = (GLOBAL_MEM DTYPE_OUTPUT_0 *)(((char *)z) + z_off);
// grid stride looping
for (ga_size index = GID_0 * LDIM_0 + LID_0;
index < nthreads;
......@@ -90,17 +94,19 @@ KERNEL void max_pool3d_kernel(const ga_size nthreads,
}
}
#kernel ave_pool2d_kernel : size, size, size, size, size, size, size, *, size, size, size, size, size, size, size, size, * :
#kernel ave_pool2d_kernel : size, size, size, size, size, size, size, *, size, size, size, size, size, size, size, size, size, *, size:
// (adopted from Caffe: https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cu)
KERNEL void ave_pool2d_kernel(const ga_size nthreads,
const ga_size num, const ga_size channels, const ga_size pooled_height,
const ga_size pooled_width, const ga_size height, const ga_size width,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size kernel_h, const ga_size kernel_w,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size x_off, const ga_size kernel_h, const ga_size kernel_w,
const ga_size stride_h, const ga_size stride_w, const ga_size pad_h, const ga_size pad_w,
const ga_bool inc_pad, const ga_bool sum_mode,
GLOBAL_MEM DTYPE_OUTPUT_0 *z)
GLOBAL_MEM DTYPE_OUTPUT_0 *z, const ga_size z_off)
{
x = (GLOBAL_MEM DTYPE_INPUT_0 *)(((char *)x) + x_off);
z = (GLOBAL_MEM DTYPE_OUTPUT_0 *)(((char *)z) + z_off);
// grid stride looping
for (ga_size index = GID_0 * LDIM_0 + LID_0;
index < nthreads;
......@@ -143,20 +149,22 @@ KERNEL void ave_pool2d_kernel(const ga_size nthreads,
}
}
#kernel ave_pool3d_kernel : size, size, size, size, size, size, size, size, size, *, size, size, size, size, size, size, size, size, size, size, size, * :
#kernel ave_pool3d_kernel : size, size, size, size, size, size, size, size, size, *, size, size, size, size, size, size, size, size, size, size, size, size, *, size :
// (adopted from Caffe: https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cu)
KERNEL void ave_pool3d_kernel(const ga_size nthreads,
const ga_size num, const ga_size channels, const ga_size pooled_depth,
const ga_size pooled_height, const ga_size pooled_width,
const ga_size depth, const ga_size height, const ga_size width,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size kernel_d, const ga_size kernel_h,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size x_off, const ga_size kernel_d, const ga_size kernel_h,
const ga_size kernel_w, const ga_size stride_d, const ga_size stride_h,
const ga_size stride_w, const ga_size pad_d, const ga_size pad_h, const ga_size pad_w,
const ga_bool inc_pad, const ga_bool sum_mode,
GLOBAL_MEM DTYPE_OUTPUT_0 *z)
GLOBAL_MEM DTYPE_OUTPUT_0 *z, const ga_size z_off)
{
// grid stride looping
x = (GLOBAL_MEM DTYPE_INPUT_0 *)(((char *)x) + x_off);
z = (GLOBAL_MEM DTYPE_OUTPUT_0 *)(((char *)z) + z_off);
for (ga_size index = GID_0 * LDIM_0 + LID_0;
index < nthreads;
index += LDIM_0 * GDIM_0) {
......@@ -273,8 +281,8 @@ int APPLY_SPECIFIC(pool)(PyGpuArrayObject *x,
err = max_pool2d_kernel_scall(1, &num_kernels, 0, num_kernels,
z_dims[0], z_dims[1], z_dims[2], z_dims[3],
x_dims[2], x_dims[3],
x->ga.data, w[0], w[1], s[0], s[1], p[0], p[1],
(*z)->ga.data);
x->ga.data, x->ga.offset, w[0], w[1], s[0], s[1], p[0], p[1],
(*z)->ga.data, (*z)->ga.offset);
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
"GpuPool: max_pool2d_kernel %s.",
......@@ -285,8 +293,10 @@ int APPLY_SPECIFIC(pool)(PyGpuArrayObject *x,
err = ave_pool2d_kernel_scall(1, &num_kernels, 0, num_kernels,
z_dims[0], z_dims[1], z_dims[2], z_dims[3],
x_dims[2], x_dims[3],
x->ga.data, w[0], w[1], s[0], s[1], p[0], p[1],
INC_PAD, SUM_MODE, (*z)->ga.data);
x->ga.data, x->ga.offset,
w[0], w[1], s[0], s[1], p[0], p[1],
INC_PAD, SUM_MODE,
(*z)->ga.data, (*z)->ga.offset);
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
"GpuPool: ave_pool2d_kernel %s.",
......@@ -301,8 +311,8 @@ int APPLY_SPECIFIC(pool)(PyGpuArrayObject *x,
err = max_pool3d_kernel_scall(1, &num_kernels, 0, num_kernels,
z_dims[0], z_dims[1], z_dims[2], z_dims[3], z_dims[4],
x_dims[2], x_dims[3], x_dims[4],
x->ga.data, w[0], w[1], w[2], s[0], s[1], s[2],
p[0], p[1], p[2], (*z)->ga.data);
x->ga.data, x->ga.offset, w[0], w[1], w[2], s[0], s[1], s[2],
p[0], p[1], p[2], (*z)->ga.data, (*z)->ga.offset);
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
"GpuPool: max_pool3d_kernel %s.",
......@@ -313,9 +323,11 @@ int APPLY_SPECIFIC(pool)(PyGpuArrayObject *x,
err = ave_pool3d_kernel_scall(1, &num_kernels, 0, num_kernels,
z_dims[0], z_dims[1], z_dims[2], z_dims[3], z_dims[4],
x_dims[2], x_dims[3], x_dims[4],
x->ga.data, w[0], w[1], w[2], s[0], s[1], s[2],
x->ga.data, x->ga.offset,
w[0], w[1], w[2], s[0], s[1], s[2],
p[0], p[1], p[2],
INC_PAD, SUM_MODE, (*z)->ga.data);
INC_PAD, SUM_MODE,
(*z)->ga.data, (*z)->ga.offset);
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
"GpuPool: ave_pool3d_kernel %s.",
......
theano/gpuarray/pool_ave_grad.c
浏览文件 @ e5ba1b08
#section kernels
#kernel ave_pool2d_grad_kernel : size, size, size, size, size, size, size, *, *, size, size, size, size, size, size, size, size, * :
#kernel ave_pool2d_grad_kernel : size, size, size, size, size, size, size, *, size, *, size, size, size, size, size, size, size, size, size, *, size :
// (adopted from Caffe: https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cu)
KERNEL void ave_pool2d_grad_kernel(const ga_size nthreads,
const ga_size num, const ga_size channels, const ga_size height,
const ga_size width, const ga_size pooled_height, const ga_size pooled_width,
GLOBAL_MEM const DTYPE_INPUT_0 *x, GLOBAL_MEM const DTYPE_INPUT_1 *gz,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size x_off, GLOBAL_MEM const DTYPE_INPUT_1 *gz, const ga_size gz_off,
const ga_size kernel_h, const ga_size kernel_w, const ga_size stride_h, const ga_size stride_w,
const ga_size pad_h, const ga_size pad_w, const ga_bool inc_pad, const ga_bool sum_mode,
GLOBAL_MEM DTYPE_OUTPUT_0 *gx)
GLOBAL_MEM DTYPE_OUTPUT_0 *gx, const ga_size gx_off)
{
x = (GLOBAL_MEM const DTYPE_INPUT_0 *)(((char *)x) + x_off);
gz = (GLOBAL_MEM const DTYPE_INPUT_1 *)(((char *)gz) + gz_off);
gx = (GLOBAL_MEM DTYPE_OUTPUT_0 *)(((char *)gx) + gx_off);
// grid stride looping
for (ga_size index = GID_0 * LDIM_0 + LID_0;
index < nthreads; index += LDIM_0 * GDIM_0) {
......@@ -46,19 +49,22 @@ KERNEL void ave_pool2d_grad_kernel(const ga_size nthreads,
}
}
#kernel ave_pool3d_grad_kernel : size, size, size, size, size, size, size, size, size, *, *, size, size, size, size, size, size, size, size, size, size, size, * :
#kernel ave_pool3d_grad_kernel : size, size, size, size, size, size, size, size, size, *, size, *, size, size, size, size, size, size, size, size, size, size, size, size, *, size :
// (adopted from Caffe: https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cu)
KERNEL void ave_pool3d_grad_kernel(const ga_size nthreads,
const ga_size num, const ga_size channels, const ga_size depth,
const ga_size height, const ga_size width, const ga_size pooled_depth,
const ga_size pooled_height, const ga_size pooled_width,
GLOBAL_MEM const DTYPE_INPUT_0 *x, GLOBAL_MEM const DTYPE_INPUT_1 *gz,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size x_off, GLOBAL_MEM const DTYPE_INPUT_1 *gz, const ga_size gz_off,
const ga_size kernel_d, const ga_size kernel_h, const ga_size kernel_w,
const ga_size stride_d, const ga_size stride_h, const ga_size stride_w,
const ga_size pad_d, const ga_size pad_h, const ga_size pad_w,
const ga_bool inc_pad, const ga_bool sum_mode, GLOBAL_MEM DTYPE_OUTPUT_0 *gx)
const ga_bool inc_pad, const ga_bool sum_mode, GLOBAL_MEM DTYPE_OUTPUT_0 *gx, const ga_size gx_off)
{
x = (GLOBAL_MEM const DTYPE_INPUT_0 *)(((char *)x) + x_off);
gz = (GLOBAL_MEM const DTYPE_INPUT_1 *)(((char *)gz) + gz_off);
gx = (GLOBAL_MEM DTYPE_OUTPUT_0 *)(((char *)gx) + gx_off);
// grid stride looping
for (ga_size index = GID_0 * LDIM_0 + LID_0;
index < nthreads; index += LDIM_0 * GDIM_0) {
......@@ -152,9 +158,11 @@ int APPLY_SPECIFIC(ave_pool_grad)(PyGpuArrayObject *x,
err = ave_pool2d_grad_kernel_scall(1, &num_kernels, 0, num_kernels,
x_dims[0], x_dims[1], x_dims[2], x_dims[3],
z_dims[2], z_dims[3],
x->ga.data, gz->ga.data,
x->ga.data, x->ga.offset,
gz->ga.data, gz->ga.offset,
w[0], w[1], s[0], s[1], p[0], p[1],
INC_PAD, SUM_MODE, (*gx)->ga.data);
INC_PAD, SUM_MODE,
(*gx)->ga.data, (*gx)->ga.offset);
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
"GpuAveragePoolGrad: ave_pool2d_grad_kernel %s.",
......@@ -166,10 +174,11 @@ int APPLY_SPECIFIC(ave_pool_grad)(PyGpuArrayObject *x,
err = ave_pool3d_grad_kernel_scall(1, &num_kernels, 0, num_kernels,
x_dims[0], x_dims[1], x_dims[2], x_dims[3], x_dims[4],
z_dims[2], z_dims[3], z_dims[4],
x->ga.data, gz->ga.data,
x->ga.data, x->ga.offset,
gz->ga.data, gz->ga.offset,
w[0], w[1], w[2], s[0], s[1], s[2],
p[0], p[1], p[2], INC_PAD, SUM_MODE,
(*gx)->ga.data);
(*gx)->ga.data, (*gx)->ga.offset);
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
"GpuAveragePoolGrad: ave_pool3d_grad_kernel %s.",
......
theano/gpuarray/pool_grad_grad.c
浏览文件 @ e5ba1b08
#section kernels
#kernel max_pool2d_grad_grad_kernel : size, size, size, size, size, size, size, *, *, *, size, size, size, size, size, size, * :
#kernel max_pool2d_grad_grad_kernel : size, size, size, size, size, size, size, *, size, *, size, *, size, size, size, size, size, size, size, *, size :
KERNEL void max_pool2d_grad_grad_kernel(const ga_size nthreads,
const ga_size num, const ga_size channels, const ga_size pooled_height,
const ga_size pooled_width, const ga_size height, const ga_size width,
GLOBAL_MEM const DTYPE_INPUT_0 *x, GLOBAL_MEM const DTYPE_INPUT_1 *z, GLOBAL_MEM const DTYPE_INPUT_2 *gx,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size x_off, GLOBAL_MEM const DTYPE_INPUT_1 *z, const ga_size z_off, GLOBAL_MEM const DTYPE_INPUT_2 *gx, const ga_size gx_off,
const ga_size kernel_h, const ga_size kernel_w, const ga_size stride_h, const ga_size stride_w,
const ga_size pad_h, const ga_size pad_w,
GLOBAL_MEM DTYPE_OUTPUT_0 *gz)
GLOBAL_MEM DTYPE_OUTPUT_0 *gz, const ga_size gz_off)
{
x = (GLOBAL_MEM DTYPE_INPUT_0 *)(((char *)x) + x_off);
z = (GLOBAL_MEM DTYPE_INPUT_1 *)(((char *)z) + z_off);
gx = (GLOBAL_MEM DTYPE_INPUT_2 *)(((char *)gx) + gx_off);
gz = (GLOBAL_MEM DTYPE_OUTPUT_0 *)(((char *)gz) + gz_off);
// grid stride looping
for (ga_size index = GID_0 * LDIM_0 + LID_0;
index < nthreads; index += LDIM_0 * GDIM_0) {
......@@ -42,18 +46,22 @@ KERNEL void max_pool2d_grad_grad_kernel(const ga_size nthreads,
}
}
#kernel max_pool3d_grad_grad_kernel : size, size, size, size, size, size, size, size, size, *, *, *, size, size, size, size, size, size, size, size, size, * :
#kernel max_pool3d_grad_grad_kernel : size, size, size, size, size, size, size, size, size, *, size, *, size, *, size, size, size, size, size, size, size, size, size, size, *, size :
KERNEL void max_pool3d_grad_grad_kernel(const ga_size nthreads,
const ga_size num, const ga_size channels, const ga_size pooled_depth,
const ga_size pooled_height, const ga_size pooled_width,
const ga_size depth, const ga_size height, const ga_size width,
GLOBAL_MEM const DTYPE_INPUT_0 *x, GLOBAL_MEM const DTYPE_INPUT_1 *z, GLOBAL_MEM const DTYPE_INPUT_2 *gx,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size x_off, GLOBAL_MEM const DTYPE_INPUT_1 *z, const ga_size z_off, GLOBAL_MEM const DTYPE_INPUT_2 *gx, const ga_size gx_off,
const ga_size kernel_d, const ga_size kernel_h, const ga_size kernel_w,
const ga_size stride_d, const ga_size stride_h, const ga_size stride_w,
const ga_size pad_d, const ga_size pad_h, const ga_size pad_w,
GLOBAL_MEM DTYPE_OUTPUT_0 *gz)
GLOBAL_MEM DTYPE_OUTPUT_0 *gz, const ga_size gz_off)
{
x = (GLOBAL_MEM DTYPE_INPUT_0 *)(((char *)x) + x_off);
z = (GLOBAL_MEM DTYPE_INPUT_1 *)(((char *)z) + z_off);
gx = (GLOBAL_MEM DTYPE_INPUT_2 *)(((char *)gx) + gx_off);
gz = (GLOBAL_MEM DTYPE_OUTPUT_0 *)(((char *)gz) + gz_off);
// grid stride looping
for (ga_size index = GID_0 * LDIM_0 + LID_0;
index < nthreads; index += LDIM_0 * GDIM_0) {
......@@ -146,9 +154,11 @@ int APPLY_SPECIFIC(pool_grad_grad)(PyGpuArrayObject *x,
err = max_pool2d_grad_grad_kernel_scall(1, &num_kernels, 0, num_kernels,
z_dims[0], z_dims[1], z_dims[2], z_dims[3],
x_dims[2], x_dims[3],
x->ga.data, z->ga.data, gx->ga.data,
x->ga.data, x->ga.offset,
z->ga.data, z->ga.offset,
gx->ga.data, gx->ga.offset,
w[0], w[1], s[0], s[1], p[0], p[1],
(*gz)->ga.data);
(*gz)->ga.data, (*gz)->ga.offset);
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
"GpuPoolingGradGrad: max_pool2d_grad_grad_kernel %s.",
......@@ -161,9 +171,11 @@ int APPLY_SPECIFIC(pool_grad_grad)(PyGpuArrayObject *x,
err = max_pool3d_grad_grad_kernel_scall(1, &num_kernels, 0, num_kernels,
z_dims[0], z_dims[1], z_dims[2], z_dims[3], z_dims[4],
x_dims[2], x_dims[3], x_dims[4],
x->ga.data, z->ga.data, gx->ga.data,
x->ga.data, x->ga.offset,
z->ga.data, z->ga.offset,
gx->ga.data, gx->ga.offset,
w[0], w[1], w[2], s[0], s[1], s[2], p[0], p[1], p[2],
(*gz)->ga.data);
(*gz)->ga.data, (*gz)->ga.offset);
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
"GpuPoolingGradGrad: max_pool3d_grad_grad_kernel %s.",
......
theano/gpuarray/pool_max_grad.c
浏览文件 @ e5ba1b08
#section kernels
#kernel max_pool2d_grad_kernel : size, size, size, size, size, size, size, *, *, *, size, size, size, size, size, size, * :
#kernel max_pool2d_grad_kernel : size, size, size, size, size, size, size, *, size, *, size, *, size, size, size, size, size, size, size, *, size :
// (borrowed from Caffe: https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cu)
KERNEL void max_pool2d_grad_kernel(const ga_size nthreads,
const ga_size num, const ga_size channels, const ga_size height,
const ga_size width, const ga_size pooled_height, const ga_size pooled_width,
GLOBAL_MEM const DTYPE_INPUT_0 *x, GLOBAL_MEM const DTYPE_INPUT_1 *z, GLOBAL_MEM const DTYPE_INPUT_2 *gz,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size x_off, GLOBAL_MEM const DTYPE_INPUT_1 *z, const ga_size z_off, GLOBAL_MEM const DTYPE_INPUT_2 *gz, const ga_size gz_off,
const ga_size kernel_h, const ga_size kernel_w, const ga_size stride_h, const ga_size stride_w,
const ga_size pad_h, const ga_size pad_w, GLOBAL_MEM DTYPE_OUTPUT_0 *gx)
const ga_size pad_h, const ga_size pad_w, GLOBAL_MEM DTYPE_OUTPUT_0 *gx, const ga_size gx_off)
{
x = (GLOBAL_MEM const DTYPE_INPUT_0 *)(((char *)x) + x_off);
z = (GLOBAL_MEM const DTYPE_INPUT_1 *)(((char *)z) + z_off);
gz = (GLOBAL_MEM const DTYPE_INPUT_2 *)(((char *)gz) + gz_off);
gx = (GLOBAL_MEM DTYPE_OUTPUT_0 *)(((char *)gx) + gx_off);
// grid stride looping
for (ga_size index = GID_0 * LDIM_0 + LID_0;
index < nthreads; index += LDIM_0 * GDIM_0) {
......@@ -38,19 +42,23 @@ KERNEL void max_pool2d_grad_kernel(const ga_size nthreads,
}
}
#kernel max_pool3d_grad_kernel : size, size, size, size, size, size, size, size, size, *, *, *, size, size, size, size, size, size, size, size, size, * :
#kernel max_pool3d_grad_kernel : size, size, size, size, size, size, size, size, size, *, size, *, size, *, size, size, size, size, size, size, size, size, size, size, *, size :
// (adopted from Caffe: https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cu)
KERNEL void max_pool3d_grad_kernel(const ga_size nthreads,
const ga_size num, const ga_size channels, const ga_size depth,
const ga_size height, const ga_size width, const ga_size pooled_depth,
const ga_size pooled_height, const ga_size pooled_width,
GLOBAL_MEM const DTYPE_INPUT_0 *x, GLOBAL_MEM const DTYPE_INPUT_1 *z, GLOBAL_MEM const DTYPE_INPUT_2 *gz,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size x_off, GLOBAL_MEM const DTYPE_INPUT_1 *z, const ga_size z_off, GLOBAL_MEM const DTYPE_INPUT_2 *gz, const ga_size gz_off,
const ga_size kernel_d, const ga_size kernel_h, const ga_size kernel_w,
const ga_size stride_d, const ga_size stride_h, const ga_size stride_w,
const ga_size pad_d, const ga_size pad_h, const ga_size pad_w,
GLOBAL_MEM DTYPE_OUTPUT_0 *gx)
GLOBAL_MEM DTYPE_OUTPUT_0 *gx, const ga_size gx_off)
{
x = (GLOBAL_MEM const DTYPE_INPUT_0 *)(((char *)x) + x_off);
z = (GLOBAL_MEM const DTYPE_INPUT_1 *)(((char *)z) + z_off);
gz = (GLOBAL_MEM const DTYPE_INPUT_2 *)(((char *)gz) + gz_off);
gx = (GLOBAL_MEM DTYPE_OUTPUT_0 *)(((char *)gx) + gx_off);
// grid stride looping
for (ga_size index = GID_0 * LDIM_0 + LID_0;
index < nthreads; index += LDIM_0 * GDIM_0) {
......@@ -138,9 +146,11 @@ int APPLY_SPECIFIC(max_pool_grad)(PyGpuArrayObject *x,
err = max_pool2d_grad_kernel_scall(1, &num_kernels, 0, num_kernels,
x_dims[0], x_dims[1], x_dims[2], x_dims[3],
z_dims[2], z_dims[3],
x->ga.data, z->ga.data, gz->ga.data,
x->ga.data, x->ga.offset,
z->ga.data, z->ga.offset,
gz->ga.data, gz->ga.offset,
w[0], w[1], s[0], s[1], p[0], p[1],
(*gx)->ga.data);
(*gx)->ga.data, (*gx)->ga.offset);
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
"GpuMaxPoolGrad: max_pool2d_grad_kernel %s.",
......@@ -152,9 +162,11 @@ int APPLY_SPECIFIC(max_pool_grad)(PyGpuArrayObject *x,
err = max_pool3d_grad_kernel_scall(1, &num_kernels, 0, num_kernels,
x_dims[0], x_dims[1], x_dims[2], x_dims[3], x_dims[4],
z_dims[2], z_dims[3], z_dims[4],
x->ga.data, z->ga.data, gz->ga.data,
x->ga.data, x->ga.offset,
z->ga.data, z->ga.offset,
gz->ga.data, gz->ga.offset,
w[0], w[1], w[2], s[0], s[1], s[2],
p[0], p[1], p[2], (*gx)->ga.data);
p[0], p[1], p[2], (*gx)->ga.data, (*gx)->ga.offset);
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
"GpuMaxPoolGrad: max_pool3d_grad_kernel %s.",
......
theano/gpuarray/pool_max_rop.c
浏览文件 @ e5ba1b08
#section kernels
#kernel max_pool2d_rop_kernel : size, size, size, size, size, size, size, *, *, size, size, size, size, size, size, * :
#kernel max_pool2d_rop_kernel : size, size, size, size, size, size, size, *, size, *, size, size, size, size, size, size, size, *, size :
// (borrowed from Caffe: https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cu)
KERNEL void max_pool2d_rop_kernel(const ga_size nthreads,
const ga_size num, const ga_size channels, const ga_size pooled_height,
const ga_size pooled_width, const ga_size height, const ga_size width,
GLOBAL_MEM const DTYPE_INPUT_0 *x, GLOBAL_MEM const DTYPE_INPUT_1 *ex,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size x_off, GLOBAL_MEM const DTYPE_INPUT_1 *ex, const ga_size ex_off,
const ga_size kernel_h, const ga_size kernel_w,
const ga_size stride_h, const ga_size stride_w,
const ga_size pad_h, const ga_size pad_w,
GLOBAL_MEM DTYPE_OUTPUT_0 *z)
GLOBAL_MEM DTYPE_OUTPUT_0 *z, const ga_size z_off)
{
x = (GLOBAL_MEM DTYPE_INPUT_0 *x)(((char *)x) + x_off);
ex = (GLOBAL_MEM DTYPE_INPUT_1 *x)(((char *)ex) + ex_off);
z = (GLOBAL_MEM DTYPE_OUTPUT_0 *x)(((char *)z) + z_off);
// grid stride looping
for (ga_size index = GID_0 * LDIM_0 + LID_0;
index < nthreads;
......@@ -46,19 +49,22 @@ KERNEL void max_pool2d_rop_kernel(const ga_size nthreads,
}
}
#kernel max_pool3d_rop_kernel : size, size, size, size, size, size, size, size, size, *, *, size, size, size, size, size, size, size, size, size, * :
#kernel max_pool3d_rop_kernel : size, size, size, size, size, size, size, size, size, *, size, *, size, size, size, size, size, size, size, size, size, size, *, size :
// (adopted from Caffe: https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cu)
KERNEL void max_pool3d_rop_kernel(const ga_size nthreads,
const ga_size num, const ga_size channels, const ga_size pooled_depth,
const ga_size pooled_height, const ga_size pooled_width,
const ga_size depth, const ga_size height, const ga_size width,
GLOBAL_MEM const DTYPE_INPUT_0 *x, GLOBAL_MEM const DTYPE_INPUT_1 *ex,
GLOBAL_MEM const DTYPE_INPUT_0 *x, const ga_size x_off, GLOBAL_MEM const DTYPE_INPUT_1 *ex, const ga_size ex_off,
const ga_size kernel_d, const ga_size kernel_h, const ga_size kernel_w,
const ga_size stride_d, const ga_size stride_h, const ga_size stride_w,
const ga_size pad_d, const ga_size pad_h, const ga_size pad_w,
GLOBAL_MEM DTYPE_OUTPUT_0 *z)
GLOBAL_MEM DTYPE_OUTPUT_0 *z, const ga_size x_off)
{
x = (GLOBAL_MEM DTYPE_INPUT_0 *x)(((char *)x) + x_off);
ex = (GLOBAL_MEM DTYPE_INPUT_1 *x)(((char *)ex) + ex_off);
z = (GLOBAL_MEM DTYPE_OUTPUT_0 *x)(((char *)z) + z_off);
// grid stride looping
for (ga_size index = GID_0 * LDIM_0 + LID_0;
index < nthreads;
......@@ -167,9 +173,10 @@ int APPLY_SPECIFIC(max_pool_rop)(PyGpuArrayObject *x,
err = max_pool2d_rop_kernel_scall(1, &num_kernels, 0, num_kernels,
z_dims[0], z_dims[1], z_dims[2], z_dims[3],
x_dims[2], x_dims[3],
x->ga.data, ex->ga.data,
x->ga.data, x->ga.offset,
ex->ga.data, ex->ga.offset,
w[0], w[1], s[0], s[1], p[0], p[1],
(*z)->ga.data);
(*z)->ga.data, (*z)->ga.offset);
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
"GpuMaxPoolRop: max_pool2d_rop_kernel %s.",
......@@ -182,9 +189,11 @@ int APPLY_SPECIFIC(max_pool_rop)(PyGpuArrayObject *x,
err = max_pool3d_rop_kernel_scall(1, &num_kernels, 0, num_kernels,
z_dims[0], z_dims[1], z_dims[2], z_dims[3], z_dims[4],
x_dims[2], x_dims[3], x_dims[4],
x->ga.data, ex->ga.data,
x->ga.data, x->ga.offset,
ex->ga.data, ex->ga.offset,
w[0], w[1], w[2], s[0], s[1], s[2],
p[0], p[1], p[2], (*z)->ga.data);
p[0], p[1], p[2],
(*z)->ga.data, (*z)->ga.offset);
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
"GpuMaxPoolRop: max_pool3d_rop_kernel %s.",
......
theano/gpuarray/rng_mrg.py
浏览文件 @ e5ba1b08
......@@ -75,10 +75,14 @@ class GPUA_mrg_uniform(GpuKernelBase, mrg_uniform_base):
code = """
KERNEL void mrg_uniform(
GLOBAL_MEM %(otype)s *sample_data,
ga_size sample_offset,
GLOBAL_MEM ga_int *state_data,
ga_size state_offset,
const ga_uint Nsamples,
const ga_uint Nstreams_used)
{
sample_data = (GLOBAL_MEM %(otype)s *)(((char *)sample_data) + sample_offset);
state_data = (GLOBAL_MEM ga_int *)(((char *)state_data) + state_offset);
/*
* The cluda backend makes sure that ga_int corresponds to
* a 32 bit signed type on the target device. It is not a
......@@ -157,7 +161,8 @@ class GPUA_mrg_uniform(GpuKernelBase, mrg_uniform_base):
from pygpu import gpuarray
return [Kernel(code=code, name="mrg_uniform",
params=[gpuarray.GpuArray, gpuarray.GpuArray,
params=[gpuarray.GpuArray, gpuarray.SIZE,
gpuarray.GpuArray, gpuarray.SIZE,
'uint32', 'uint32'],
flags=Kernel.get_flags(self.output_type.dtype, 'int32'))
]
......@@ -273,7 +278,7 @@ class GPUA_mrg_uniform(GpuKernelBase, mrg_uniform_base):
}
// Make sure we run as many blocks as we need to cover the whole n_streams
gs = (n_streams + ls - 1)/ls;
err = mrg_uniform_call(1, &ls, &gs, 0, %(o_sample)s->ga.data, %(o_rstate)s->ga.data, n_elements, n_streams);
err = mrg_uniform_call(1, &ls, &gs, 0, %(o_sample)s->ga.data, %(o_sample)s->ga.offset, %(o_rstate)s->ga.data, %(o_rstate)s->ga.offset, n_elements, n_streams);
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError, "mrg_uniform_call: %%s\\n",
GpuKernel_error(&%(kname)s, err));
......@@ -283,7 +288,7 @@ class GPUA_mrg_uniform(GpuKernelBase, mrg_uniform_base):
""" % locals()
def c_code_cache_version(self):
return (12,)
return (13,)
@register_opt2([mrg_uniform], 'fast_compile')
......
theano/gpuarray/tests/tstgpueye.c
浏览文件 @ e5ba1b08
#section kernels
#kernel eye : *, size, size :
#kernel eye : *, size, size, size :
/* The eye name will be used to generate supporting objects. The only
you probably need to care about is the kernel object which will be
named 'k_' + <the name above> (k_eye in this case). This name also
has to match the kernel function name below.
*/
KERNEL void eye(GLOBAL_MEM DTYPE_OUTPUT_0 *a, ga_size n, ga_size m) {
KERNEL void eye(GLOBAL_MEM DTYPE_OUTPUT_0 *a, ga_size a_off, ga_size n, ga_size m) {
a = (GLOBAL_MEM DTYPE_OUTPUT_0 *)(((char *)a) + a_off);
ga_size nb = n < m ? n : m;
for (ga_size i = LID_0; i < nb; i += LDIM_0) {
a[i*m + i] = 1;
......@@ -37,7 +38,7 @@ int APPLY_SPECIFIC(tstgpueye)(PyArrayObject *n, PyArrayObject *m,
ls = 1;
gs = 256;
/* The eye_call name comes from the kernel declaration above. */
err = eye_call(1, &gs, &ls, 0, (*z)->ga.data, dims[0], dims[1]);
err = eye_call(1, &gs, &ls, 0, (*z)->ga.data, (*z)->ga.offset, dims[0], dims[1]);
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
"gpuarray error: kEye: %s. n%lu, m=%lu.",
......
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