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Commit ea932b63 authored by Shucai Xiao's avatar Shucai Xiao
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add std namespace for size_t

parent 070d4904
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Showing with 37 additions and 37 deletions
src/targets/cpu/lowering.cpp
View file @ ea932b63
......@@ -533,7 +533,7 @@ struct cpu_softmax
{
argument result{output_shape};
auto batch_lens = output_shape.lens();
size_t n_dims = batch_lens[op.axis];
std::size_t n_dims = batch_lens[op.axis];
batch_lens[op.axis] = 1;
shape batch_shape{shape::int32_type, batch_lens};
......@@ -544,26 +544,26 @@ struct cpu_softmax
std::vector<value_type> batch_sum(batch_shape.elements(), value_type(0));
par_for(batch_shape.elements(), [&](auto i) {
auto idx = batch_shape.multi(i);
for(size_t j = 0; j < n_dims; ++j)
for(std::size_t j = 0; j < n_dims; ++j)
{
idx[op.axis] = j;
batch_max[i] = std::max(batch_max[i], input(idx.begin(), idx.end()));
}
for(size_t j = 0; j < n_dims; ++j)
for(std::size_t j = 0; j < n_dims; ++j)
{
idx[op.axis] = j;
size_t index = output_shape.index(idx);
std::size_t index = output_shape.index(idx);
output[index] = std::exp(input[index] - batch_max[i]);
}
for(size_t j = 0; j < n_dims; ++j)
for(std::size_t j = 0; j < n_dims; ++j)
{
idx[op.axis] = j;
batch_sum[i] += output(idx.begin(), idx.end());
}
for(size_t j = 0; j < n_dims; ++j)
for(std::size_t j = 0; j < n_dims; ++j)
{
idx[op.axis] = j;
output(idx.begin(), idx.end()) /= batch_sum[i];
......@@ -591,7 +591,7 @@ struct cpu_logsoftmax
{
argument result{output_shape};
auto batch_lens = output_shape.lens();
size_t n_dims = batch_lens[op.axis];
std::size_t n_dims = batch_lens[op.axis];
batch_lens[op.axis] = 1;
shape batch_shape{shape::int32_type, batch_lens};
......@@ -605,20 +605,20 @@ struct cpu_logsoftmax
par_for(batch_shape.elements(), [&](auto i) {
auto idx = batch_shape.multi(i);
for(size_t j = 0; j < n_dims; ++j)
for(std::size_t j = 0; j < n_dims; ++j)
{
idx[op.axis] = j;
batch_max[i] = std::max(batch_max[i], input(idx.begin(), idx.end()));
}
for(size_t j = 0; j < n_dims; ++j)
for(std::size_t j = 0; j < n_dims; ++j)
{
idx[op.axis] = j;
size_t index = output_shape.index(idx);
std::size_t index = output_shape.index(idx);
output[index] = input[index] - batch_max[i];
}
for(size_t j = 0; j < n_dims; ++j)
for(std::size_t j = 0; j < n_dims; ++j)
{
idx[op.axis] = j;
batch_sum[i] += std::exp(output(idx.begin(), idx.end()));
......@@ -626,7 +626,7 @@ struct cpu_logsoftmax
batch_sum[i] = std::log(batch_sum[i]);
for(size_t j = 0; j < n_dims; ++j)
for(std::size_t j = 0; j < n_dims; ++j)
{
idx[op.axis] = j;
output(idx.begin(), idx.end()) -= batch_sum[i];
......
src/targets/gpu/device/logsoftmax.cpp
View file @ ea932b63
......@@ -23,26 +23,26 @@ void logsoftmax(hipStream_t stream, const argument& result, const argument& arg,
hip_visit_all(result, arg, batch_shape)([&](auto output, auto input, auto batch) {
// use one block for items in one batch.
const size_t max_block_size = 1024;
size_t block_size = 1;
const std::size_t max_block_size = 1024;
std::size_t block_size = 1;
while(block_size < max_block_size and block_size < batch_item_num)
{
block_size *= 2;
}
launch(stream, batch_shape.elements() * block_size, block_size)([=](auto idx) __device__ {
size_t thr_idx = idx.local;
size_t blk_idx = idx.group;
std::size_t thr_idx = idx.local;
std::size_t blk_idx = idx.group;
using type = device_type<std::remove_cv_t<typename decltype(output)::value_type>>;
MIGRAPHX_DEVICE_SHARED type lds_data[max_block_size + 1];
auto batch_idx = batch.multi(blk_idx);
auto data_idx = batch_idx;
// load data to lds and compute the batch max
size_t remaining_item_num = batch_item_num;
size_t round_item_num = (batch_item_num + block_size - 1) / block_size * block_size;
std::size_t remaining_item_num = batch_item_num;
std::size_t round_item_num = (batch_item_num + block_size - 1) / block_size * block_size;
lds_data[max_block_size] = input[0];
for(size_t i = thr_idx; i < round_item_num; i += block_size)
for(std::size_t i = thr_idx; i < round_item_num; i += block_size)
{
if(i < batch_item_num)
{
......@@ -62,7 +62,7 @@ void logsoftmax(hipStream_t stream, const argument& result, const argument& arg,
lds_data[max_block_size] = 0;
remaining_item_num = batch_item_num;
for(size_t i = thr_idx; i < round_item_num; i += block_size)
for(std::size_t i = thr_idx; i < round_item_num; i += block_size)
{
if(i < batch_item_num)
{
......@@ -81,7 +81,7 @@ void logsoftmax(hipStream_t stream, const argument& result, const argument& arg,
auto log_batch_sum = ::log(to_hip_type(lds_data[max_block_size])) + batch_max;
for(size_t i = thr_idx; i < batch_item_num; i += block_size)
for(std::size_t i = thr_idx; i < batch_item_num; i += block_size)
{
data_idx[axis] = i;
output[data_idx] = input[data_idx] - log_batch_sum;
......
src/targets/gpu/device/softmax.cpp
View file @ ea932b63
......@@ -17,32 +17,32 @@ void softmax(hipStream_t stream, const argument& result, const argument& arg, in
{
auto lens = result.get_shape().lens();
auto batch_lens = lens;
size_t batch_item_num = lens[axis];
std::size_t batch_item_num = lens[axis];
batch_lens[axis] = 1;
migraphx::shape batch_shape{result.get_shape().type(), batch_lens};
hip_visit_all(result, arg, batch_shape)([&](auto output, auto input, auto batch) {
// use one block for items in one batch.
const size_t max_block_size = 1024;
size_t block_size = 1;
const std::size_t max_block_size = 1024;
std::size_t block_size = 1;
while(block_size < max_block_size and block_size < batch_item_num)
{
block_size *= 2;
}
launch(stream, batch_shape.elements() * block_size, block_size)([=](auto idx) __device__ {
size_t thr_idx = idx.local;
size_t blk_idx = idx.group;
std::size_t thr_idx = idx.local;
std::size_t blk_idx = idx.group;
using type = device_type<std::remove_cv_t<typename decltype(output)::value_type>>;
MIGRAPHX_DEVICE_SHARED type lds_data[max_block_size + 1];
auto batch_idx = batch.multi(blk_idx);
auto data_idx = batch_idx;
// load data to lds and compute the batch max
size_t remaining_item_num = batch_item_num;
size_t round_item_num = (batch_item_num + block_size - 1) / block_size * block_size;
std::size_t remaining_item_num = batch_item_num;
std::size_t round_item_num = (batch_item_num + block_size - 1) / block_size * block_size;
lds_data[max_block_size] = input[0];
for(size_t i = thr_idx; i < round_item_num; i += block_size)
for(std::size_t i = thr_idx; i < round_item_num; i += block_size)
{
if(i < batch_item_num)
{
......@@ -63,7 +63,7 @@ void softmax(hipStream_t stream, const argument& result, const argument& arg, in
lds_data[max_block_size] = 0;
remaining_item_num = batch_item_num;
for(size_t i = thr_idx; i < round_item_num; i += block_size)
for(std::size_t i = thr_idx; i < round_item_num; i += block_size)
{
if(i < batch_item_num)
{
......@@ -81,7 +81,7 @@ void softmax(hipStream_t stream, const argument& result, const argument& arg, in
}
auto batch_sum = lds_data[max_block_size];
for(size_t i = thr_idx; i < batch_item_num; i += block_size)
for(std::size_t i = thr_idx; i < batch_item_num; i += block_size)
{
data_idx[axis] = i;
auto val = input[data_idx] - batch_max;
......
src/targets/gpu/include/migraphx/gpu/device/reduce_opers.hpp
View file @ ea932b63
......@@ -12,13 +12,13 @@ namespace device {
template <class T>
inline __device__ void
reduce_max(T* data_ptr, size_t block_size, size_t thr_idx, size_t item_num, size_t max_index)
reduce_max(T* data_ptr, std::size_t block_size, std::size_t thr_idx, std::size_t item_num, std::size_t max_index)
{
while(true)
{
auto stride = (item_num + 1) / 2;
auto size = item_num / 2;
for(size_t i = thr_idx; i < size; i += block_size)
for(std::size_t i = thr_idx; i < size; i += block_size)
{
data_ptr[i] = ::max(to_hip_type(data_ptr[i]), to_hip_type(data_ptr[i + stride]));
}
......@@ -40,13 +40,13 @@ reduce_max(T* data_ptr, size_t block_size, size_t thr_idx, size_t item_num, size
template <class T>
inline __device__ void
reduce_min(T* data_ptr, size_t block_size, size_t thr_idx, size_t item_num, size_t min_index)
reduce_min(T* data_ptr, std::size_t block_size, std::size_t thr_idx, std::size_t item_num, std::size_t min_index)
{
while(true)
{
auto stride = (item_num + 1) / 2;
auto size = item_num / 2;
for(size_t i = thr_idx; i < size; i += block_size)
for(std::size_t i = thr_idx; i < size; i += block_size)
{
data_ptr[i] = ::min(to_hip_type(data_ptr[i]), to_hip_type(data_ptr[i + stride]));
}
......@@ -68,13 +68,13 @@ reduce_min(T* data_ptr, size_t block_size, size_t thr_idx, size_t item_num, size
template <class T>
inline __device__ void
reduce_sum(T* data_ptr, size_t block_size, size_t thr_idx, size_t item_num, size_t sum_index)
reduce_sum(T* data_ptr, std::size_t block_size, std::size_t thr_idx, std::size_t item_num, std::size_t sum_index)
{
while(true)
{
auto stride = (item_num + 1) / 2;
auto size = item_num / 2;
for(size_t i = thr_idx; i < size; i += block_size)
for(std::size_t i = thr_idx; i < size; i += block_size)
{
data_ptr[i] += data_ptr[i + stride];
}
......
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