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pytensor
提交 116f1fa6 authored 作者: Pierre Luc Carrier's avatar Pierre Luc Carrier
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Changed float to npy_%(dtype)s in ops GpuSoftmax and GpuSoftmaxWithBias.

上级 cc2cdc3f
隐藏空白字符变更
内嵌 并排
正在显示 包含 27 行增加 和 24 行删除
theano/sandbox/gpuarray/nnet.py
浏览文件 @ 116f1fa6
......@@ -471,6 +471,7 @@ class GpuSoftmax (Op):
return NVCC_compiler
def c_code(self, node, nodename, inp, out, sub):
dtype = self.dtype
x, = inp
z, = out
fail = sub['fail']
......@@ -503,7 +504,7 @@ class GpuSoftmax (Op):
//TODO, detect the maximum number of thread per block.
int n_threads = std::min(CudaNdarray_HOST_DIMS(%(x)s)[1], 512);
int n_shared_bytes = CudaNdarray_HOST_DIMS(%(x)s)[1] *
2 * sizeof(float);
2 * sizeof(npy_%(dtype)s);
if (CudaNdarray_HOST_DIMS(%(x)s)[0] > 0)
{
......@@ -533,7 +534,7 @@ class GpuSoftmax (Op):
<<<
n_blocks,
n_threads,
n_threads * sizeof(float)
n_threads * sizeof(npy_%(dtype)s)
>>>(
CudaNdarray_HOST_DIMS(%(x)s)[0],
CudaNdarray_HOST_DIMS(%(x)s)[1],
......@@ -567,11 +568,11 @@ class GpuSoftmax (Op):
def c_support_code_apply(self, node, nodename):
ret1 = nvcc_kernel("kSoftmax_%s" % nodename,
params=['int M', 'int N',
'const float * x', 'const int sx0', 'const int sx1',
'float * sm', 'const int sm_s0', 'const int sm_s1'],
'const npy_%(dtype)s * x', 'const int sx0', 'const int sx1',
'npy_%(dtype)s * sm', 'const int sm_s0', 'const int sm_s1'],
body=[
"extern __shared__ float buf[]",
"float * buf2 = buf + N",
"extern __shared__ npy_%(dtype)s buf[]",
"npy_%(dtype)s * buf2 = buf + N",
"for (int blockIDX = blockIdx.x; blockIDX < M;"
" blockIDX += gridDim.x){",
"for (int tx = threadIdx.x; tx< N; tx += blockDim.x){",
......@@ -590,14 +591,14 @@ class GpuSoftmax (Op):
])
ret2 = nvcc_kernel("kSoftmax_fixed_shared%s" % nodename,
params=['int M', 'int N',
'const float * x', 'const int sx0', 'const int sx1',
'float * sm', 'const int sm_s0', 'const int sm_s1'],
'const npy_%(dtype)s * x', 'const int sx0', 'const int sx1',
'npy_%(dtype)s * sm', 'const int sm_s0', 'const int sm_s1'],
body=[
"extern __shared__ float buf[]",
"extern __shared__ npy_%(dtype)s buf[]",
"for (int blockIDX = blockIdx.x; blockIDX < M;"
" blockIDX += gridDim.x){",
"const float *x_ptr = &x[blockIDX * sx0]",
"float *sm_ptr = &sm[blockIDX * sm_s0]",
"const npy_%(dtype)s *x_ptr = &x[blockIDX * sx0]",
"npy_%(dtype)s *sm_ptr = &sm[blockIDX * sm_s0]",
inline_softmax_fixed_shared('N', 'buf', 'x_ptr', 'sx1',
'sm_ptr', 'sm_s1',
'threadIdx.x', 'blockDim.x'),
......@@ -642,6 +643,7 @@ class GpuSoftmaxWithBias (GpuOp):
return NVCC_compiler
def c_code(self, node, nodename, inp, out, sub):
dtype = self.dtype
x, b = inp
z, = out
fail = sub['fail']
......@@ -688,7 +690,7 @@ class GpuSoftmaxWithBias (GpuOp):
//TODO, detect the maximum number of thread per block.
int n_threads = std::min(CudaNdarray_HOST_DIMS(%(x)s)[1], 512);
int n_shared_bytes = CudaNdarray_HOST_DIMS(%(x)s)[1] *
2 * sizeof(float);
2 * sizeof(npy_%(dtype)s);
if (CudaNdarray_HOST_DIMS(%(x)s)[0] > 0)
{
if(n_shared_bytes < (32 * 1024 - 500)){
......@@ -717,7 +719,7 @@ class GpuSoftmaxWithBias (GpuOp):
<<<
n_blocks,
n_threads,
n_threads * sizeof(float)
n_threads * sizeof(npy_%(dtype)s)
>>>(
CudaNdarray_HOST_DIMS(%(x)s)[0],
CudaNdarray_HOST_DIMS(%(x)s)[1],
......@@ -750,14 +752,15 @@ class GpuSoftmaxWithBias (GpuOp):
""" % locals()
def c_support_code_apply(self, node, nodename):
dtype = self.dtype
ret1 = nvcc_kernel("kSoftmaxWithBias_%s" % nodename,
params=['int M', 'int N',
'const float * x', 'const int sx0', 'const int sx1',
'const float * b', 'const int sb0',
'float * sm', 'const int sm_s0', 'const int sm_s1'],
'const npy_%(dtype)s * x', 'const int sx0', 'const int sx1',
'const npy_%(dtype)s * b', 'const int sb0',
'npy_%(dtype)s * sm', 'const int sm_s0', 'const int sm_s1'],
body=[
"extern __shared__ float buf[]",
"float * buf2 = buf + N",
"extern __shared__ npy_%(dtype)s buf[]",
"npy_%(dtype)s * buf2 = buf + N",
"for (int blockIDX = blockIdx.x; blockIDX < M;"
" blockIDX += gridDim.x){",
"for (int tx = threadIdx.x; tx< N; tx += blockDim.x){",
......@@ -776,17 +779,17 @@ class GpuSoftmaxWithBias (GpuOp):
])
ret2 = nvcc_kernel("kSoftmaxWithBias_fixed_shared%s" % nodename,
params=['int M', 'int N',
'const float * x',
'const npy_%(dtype)s * x',
'const int sx0', 'const int sx1',
'const float * b', 'const int sb0',
'float * sm',
'const npy_%(dtype)s * b', 'const int sb0',
'npy_%(dtype)s * sm',
'const int sm_s0', 'const int sm_s1'],
body=[
"extern __shared__ float buf[]",
"extern __shared__ npy_%(dtype)s buf[]",
"for (int blockIDX = blockIdx.x; blockIDX < M;"
" blockIDX += gridDim.x){",
"const float *x_ptr = &x[blockIDX * sx0]",
"float *sm_ptr = &sm[blockIDX * sm_s0]",
"const npy_%(dtype)s *x_ptr = &x[blockIDX * sx0]",
"npy_%(dtype)s *sm_ptr = &sm[blockIDX * sm_s0]",
inline_softmax_fixed_shared('N', 'buf',
'x_ptr', 'sx1',
'sm_ptr', 'sm_s1',
......
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