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Commit 4f5024b7 authored by Khalique's avatar Khalique
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testing changes

parent 894af2c6
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Showing with 91 additions and 91 deletions
src/eliminate_pad.cpp
View file @ 4f5024b7
......@@ -44,8 +44,8 @@ void eliminate_pad::update_op(T,
std::array<size_t, 2> new_pads{static_cast<size_t>(pads[2]), static_cast<size_t>(pads[3])};
T op = any_cast<T>(ins->get_operator());
if(op.padding_mode != op::padding_mode_t::default_)
return;
// if(op.padding_mode != op::padding_mode_t::default_)
// return;
op.padding = new_pads;
std::vector<instruction_ref> new_inputs{ins->inputs()};
......
src/include/migraphx/op/convolution.hpp
View file @ 4f5024b7
......@@ -44,8 +44,8 @@ struct convolution
const shape& input = inputs.at(0);
const shape& weights = inputs.at(1);
auto t = input.type();
if(padding_mode == default_)
{
// if(padding_mode == default_)
// {
return {t,
{
input.lens()[0],
......@@ -63,32 +63,32 @@ struct convolution
stride[1] +
1)),
}};
}
else if(padding_mode == same)
{
return {t,
{input.lens()[0],
weights.lens()[0],
static_cast<std::size_t>(
std::ceil(static_cast<double>(input.lens()[2]) / stride[0])),
static_cast<std::size_t>(
std::ceil(static_cast<double>(input.lens()[3]) / stride[1]))}};
}
else if(padding_mode == valid)
{
return {
t,
{input.lens()[0],
weights.lens()[0],
static_cast<std::size_t>(std::ceil(
static_cast<double>(input.lens()[2] - weights.lens()[2] + 1) / stride[0])),
static_cast<std::size_t>(std::ceil(
static_cast<double>(input.lens()[3] - weights.lens()[3] + 1) / stride[1]))}};
}
else
{
MIGRAPHX_THROW("Invalid padding mode");
}
// }
// else if(padding_mode == same)
// {
// return {t,
// {input.lens()[0],
// weights.lens()[0],
// static_cast<std::size_t>(
// std::ceil(static_cast<double>(input.lens()[2]) / stride[0])),
// static_cast<std::size_t>(
// std::ceil(static_cast<double>(input.lens()[3]) / stride[1]))}};
// }
// else if(padding_mode == valid)
// {
// return {
// t,
// {input.lens()[0],
// weights.lens()[0],
// static_cast<std::size_t>(std::ceil(
// static_cast<double>(input.lens()[2] - weights.lens()[2] + 1) / stride[0])),
// static_cast<std::size_t>(std::ceil(
// static_cast<double>(input.lens()[3] - weights.lens()[3] + 1) / stride[1]))}};
// }
// else
// {
// MIGRAPHX_THROW("Invalid padding mode");
// }
}
};
......
src/include/migraphx/op/pooling.hpp
View file @ 4f5024b7
......@@ -48,8 +48,8 @@ struct pooling
assert(lengths[0] <= (input.lens()[2] + 2 * padding[0]));
assert(lengths[1] <= (input.lens()[3] + 2 * padding[1]));
if(padding_mode == default_)
{
// if(padding_mode == default_)
// {
return {t,
{
input.lens()[0],
......@@ -65,34 +65,34 @@ struct pooling
input.lens()[3] + 2 * padding[1] - lengths[1], stride[1]) +
1)),
}};
}
else if(padding_mode == same)
{
return {t,
{input.lens()[0],
input.lens()[1],
ceil_divide<std::size_t>(input.lens()[2], stride[0]),
ceil_divide<std::size_t>(input.lens()[3], stride[1])}};
}
else if(padding_mode == valid)
{
return {
t,
{
input.lens()[0],
input.lens()[1],
std::size_t(std::max<std::ptrdiff_t>(
1,
floor_divide<std::ptrdiff_t>(input.lens()[2] - lengths[0], stride[0]) + 1)),
std::size_t(std::max<std::ptrdiff_t>(
1,
floor_divide<std::ptrdiff_t>(input.lens()[3] - lengths[1], stride[1]) + 1)),
}};
}
else
{
MIGRAPHX_THROW("Invalid padding mode");
}
// }
// else if(padding_mode == same)
// {
// return {t,
// {input.lens()[0],
// input.lens()[1],
// ceil_divide<std::size_t>(input.lens()[2], stride[0]),
// ceil_divide<std::size_t>(input.lens()[3], stride[1])}};
// }
// else if(padding_mode == valid)
// {
// return {
// t,
// {
// input.lens()[0],
// input.lens()[1],
// std::size_t(std::max<std::ptrdiff_t>(
// 1,
// floor_divide<std::ptrdiff_t>(input.lens()[2] - lengths[0], stride[0]) + 1)),
// std::size_t(std::max<std::ptrdiff_t>(
// 1,
// floor_divide<std::ptrdiff_t>(input.lens()[3] - lengths[1], stride[1]) + 1)),
// }};
// }
// else
// {
// MIGRAPHX_THROW("Invalid padding mode");
// }
}
};
......
src/targets/gpu/device/include/migraphx/gpu/device/types.hpp
View file @ 4f5024b7
......@@ -64,9 +64,9 @@ host_type<T>* host_cast(T* x)
}
template <class T>
device_type<T> device_cast(T x)
device_type<T> device_cast(const T& x)
{
return reinterpret_cast<device_type<T>>(x);
return reinterpret_cast<const device_type<T>&>(x);
}
template <class T>
......
src/targets/gpu/device/pad.cpp
View file @ 4f5024b7
......@@ -4,6 +4,7 @@
#include <migraphx/gpu/device/pad.hpp>
#include <migraphx/gpu/device/tensor.hpp>
#include <migraphx/gpu/device/launch.hpp>
#include <migraphx/float_equal.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -14,28 +15,30 @@ argument
pad(hipStream_t stream, argument result, argument arg1, float value, std::vector<std::int64_t> pads)
{
std::size_t nelements = arg1.get_shape().elements();
// if(value == std::numeric_limits<float>::lowest())
// {
// visit_all(result)([&](auto output) {
// auto* outptr = output.data();
// gs_launch(stream, nelements)([=](auto i) {
// outptr[i] = std::numeric_limits<typename
// decltype(output)::value_type>::lowest();
// });
// });
// }
if(float_equal(value,std::numeric_limits<float>::lowest()))
{
visit_all(result)([&](auto output) {
auto* outptr = device_cast(output.data());
auto val = device_cast(std::numeric_limits<typename
decltype(output)::value_type>::lowest());
gs_launch(stream, nelements)([=](auto i) {
outptr[i] = val;
});
});
}
// else
// {
// visit_all(result)([&](auto output) {
// auto* outptr = output.data();
// gs_launch(stream, nelements)([=](auto i) {
// outptr[i] = static_cast<typename decltype(output)::value_type>(value);
// });
// });
// }
else
{
visit_all(result)([&](auto output) {
auto* outptr = device_cast(output.data());
gs_launch(stream, nelements)([=](auto i) {
outptr[i] = value;
});
});
}
nary(stream, result)([=] { return value; });
// nary(stream, result)([=] { return value; });
visit_all(result, arg1)([&](auto output, auto input) {
visit_tensor_size(result.get_shape().lens().size(), [&](auto ndim) {
std::size_t offsets[ndim];
......
src/targets/gpu/include/migraphx/gpu/softmax.hpp
View file @ 4f5024b7
......@@ -34,7 +34,7 @@ struct miopen_softmax
return migraphx::reflect(self.op, f);
}
std::string name() const { return "gpu::softmax"; }
std::string name() const { return "gpu::miopen_softmax"; }
shape compute_shape(const std::vector<shape>& inputs) const;
argument
compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
......
src/targets/gpu/lowering.cpp
View file @ 4f5024b7
......@@ -100,6 +100,7 @@ struct miopen_apply
add_extend_op<miopen_contiguous, op::contiguous>("contiguous");
add_extend_op<hip_concat, op::concat>("concat");
add_extend_op<hip_softmax, op::softmax>("softmax");
// add_extend_op<miopen_softmax, op::softmax>("softmax");
add_extend_op<hip_logsoftmax, op::logsoftmax>("logsoftmax");
add_extend_op<hip_gather, op::gather>("gather");
add_extend_op<hip_pad, op::pad>("pad");
......
src/tf/CMakeLists.txt
View file @ 4f5024b7
......@@ -31,7 +31,7 @@ rocm_install_targets(
add_executable(read_tf read_tf.cpp)
rocm_clang_tidy_check(read_tf)
target_link_libraries(read_tf migraphx_tf)
target_link_libraries(read_tf migraphx_tf migraphx_cpu)
if(MIGRAPHX_ENABLE_GPU)
add_executable(verify_tf verify_tf.cpp)
......
src/tf/tf.cpp
View file @ 4f5024b7
......@@ -323,7 +323,7 @@ struct tf_parser
const std::string& pad_mode = attributes.at("padding").s();
if(pad_mode.find("SAME") != std::string::npos)
{
// op.padding_mode = op::padding_mode_t::same;
op.padding_mode = op::padding_mode_t::same;
std::vector<size_t> weight_dims = weights->get_shape().lens();
size_t weight_h = weight_dims[2];
size_t weight_w = weight_dims[3];
......@@ -424,7 +424,7 @@ struct tf_parser
if(pad_mode.find("SAME") != std::string::npos)
{
// op.padding_mode = op::padding_mode_t::same;
op.padding_mode = op::padding_mode_t::same;
std::vector<size_t> weight_dims = weights->get_shape().lens();
size_t weight_h = weight_dims[2];
size_t weight_w = weight_dims[3];
......@@ -609,17 +609,13 @@ struct tf_parser
const std::string& pad_mode = attributes.at("padding").s();
if(pad_mode.find("SAME") != std::string::npos)
{
// op.padding_mode = op::padding_mode_t::same;
op.padding_mode = op::padding_mode_t::same;
auto input_dims = l0->get_shape().lens();
size_t input_h = input_dims[2];
size_t input_w = input_dims[3];
std::vector<int64_t> pads(input_dims.size());
calculate_padding(0, pads, input_h, op.stride[0], 1, op.lengths[0]);
calculate_padding(1, pads, input_w, op.stride[1], 1, op.lengths[1]);
// for(auto pad : pads)
// {
// std::cout << pad << std::endl;
// }
if(pads[0] != pads[2] || pads[1] != pads[3])
{
......
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