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Commit 099e9ce8 authored by Shucai Xiao's avatar Shucai Xiao
Browse files

Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/AMDMIGraphX into argmax_min

parents 274c772b 15eb1987
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Showing with 263 additions and 311 deletions
src/eliminate_pad.cpp
View file @ 099e9ce8
...@@ -44,8 +44,6 @@ void eliminate_pad::update_op(T, ...@@ -44,8 +44,6 @@ 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])}; 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()); T op = any_cast<T>(ins->get_operator());
if(op.padding_mode != op::padding_mode_t::default_)
return;
op.padding = new_pads; op.padding = new_pads;
std::vector<instruction_ref> new_inputs{ins->inputs()}; std::vector<instruction_ref> new_inputs{ins->inputs()};
......
src/include/migraphx/op/binary.hpp
View file @ 099e9ce8
...@@ -28,8 +28,10 @@ struct binary : op_name<Derived> ...@@ -28,8 +28,10 @@ struct binary : op_name<Derived>
argument compute(const shape& output_shape, std::vector<argument> args) const argument compute(const shape& output_shape, std::vector<argument> args) const
{ {
argument result{output_shape}; argument result{output_shape};
auto s1 = args[0].get_shape();
auto s2 = args[1].get_shape();
visit_all(result, args[0], args[1])([&](auto output, auto input1, auto input2) { visit_all(result, args[0], args[1])([&](auto output, auto input1, auto input2) {
if(input1.get_shape().packed() and input2.get_shape().packed()) if(s1 == s2 and input1.get_shape().packed() and input2.get_shape().packed())
{ {
std::transform(input1.begin(), std::transform(input1.begin(),
input1.end(), input1.end(),
......
src/include/migraphx/op/convolution.hpp
View file @ 099e9ce8
...@@ -44,8 +44,7 @@ struct convolution ...@@ -44,8 +44,7 @@ struct convolution
const shape& input = inputs.at(0); const shape& input = inputs.at(0);
const shape& weights = inputs.at(1); const shape& weights = inputs.at(1);
auto t = input.type(); auto t = input.type();
if(padding_mode == default_)
{
return {t, return {t,
{ {
input.lens()[0], input.lens()[0],
...@@ -64,32 +63,6 @@ struct convolution ...@@ -64,32 +63,6 @@ struct convolution
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");
}
}
}; };
} // namespace op } // namespace op
......
src/include/migraphx/op/pooling.hpp
View file @ 099e9ce8
...@@ -48,52 +48,22 @@ struct pooling ...@@ -48,52 +48,22 @@ struct pooling
assert(lengths[0] <= (input.lens()[2] + 2 * padding[0])); assert(lengths[0] <= (input.lens()[2] + 2 * padding[0]));
assert(lengths[1] <= (input.lens()[3] + 2 * padding[1])); assert(lengths[1] <= (input.lens()[3] + 2 * padding[1]));
if(padding_mode == default_)
{
return {t, return {t,
{ {
input.lens()[0], input.lens()[0],
input.lens()[1], input.lens()[1],
std::size_t(std::max<std::ptrdiff_t>( std::size_t(std::max<std::ptrdiff_t>(
1, 1,
floor_divide<std::ptrdiff_t>( floor_divide<std::ptrdiff_t>(input.lens()[2] + 2 * padding[0] - lengths[0],
input.lens()[2] + 2 * padding[0] - lengths[0], stride[0]) + stride[0]) +
1)), 1)),
std::size_t(std::max<std::ptrdiff_t>( std::size_t(std::max<std::ptrdiff_t>(
1, 1,
floor_divide<std::ptrdiff_t>( floor_divide<std::ptrdiff_t>(input.lens()[3] + 2 * padding[1] - lengths[1],
input.lens()[3] + 2 * padding[1] - lengths[1], stride[1]) + stride[1]) +
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");
}
}
}; };
} // namespace op } // namespace op
......
src/include/migraphx/pad_calc.hpp
View file @ 099e9ce8
...@@ -2,13 +2,24 @@ ...@@ -2,13 +2,24 @@
#define MIGRAPHX_GUARD_OPERATORS_PAD_CALC_HPP #define MIGRAPHX_GUARD_OPERATORS_PAD_CALC_HPP
#include <utility> #include <utility>
#include <cstdint>
#include <vector>
namespace migraphx { namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS { inline namespace MIGRAPHX_INLINE_NS {
inline std::size_t calculate_padding(std::size_t weight_dim, std::size_t dilation) inline void calculate_padding(int64_t idx,
std::vector<int64_t>& pads,
int64_t input_dim,
int64_t stride,
int64_t dilation,
int64_t weight_dim)
{ {
return (dilation * (weight_dim - 1)) / 2; int64_t output_dim = input_dim / stride;
int64_t pad = std::max(static_cast<int64_t>(0),
(output_dim - 1) * stride + dilation * weight_dim - input_dim);
pads[idx] = pad / 2;
pads[idx + 2] = pad - pad / 2;
} }
} // namespace MIGRAPHX_INLINE_NS } // namespace MIGRAPHX_INLINE_NS
......
src/include/migraphx/stringutils.hpp
View file @ 099e9ce8
...@@ -52,6 +52,8 @@ inline std::string transform_string(std::string s, F f) ...@@ -52,6 +52,8 @@ inline std::string transform_string(std::string s, F f)
inline std::string to_upper(std::string s) { return transform_string(std::move(s), ::toupper); } inline std::string to_upper(std::string s) { return transform_string(std::move(s), ::toupper); }
inline std::string to_lower(std::string s) { return transform_string(std::move(s), ::tolower); }
inline bool starts_with(const std::string& value, const std::string& prefix) inline bool starts_with(const std::string& value, const std::string& prefix)
{ {
if(prefix.size() > value.size()) if(prefix.size() > value.size())
......
src/onnx/CMakeLists.txt
View file @ 099e9ce8
...@@ -19,7 +19,7 @@ rocm_install_targets( ...@@ -19,7 +19,7 @@ rocm_install_targets(
add_executable(read_onnx read_onnx.cpp) add_executable(read_onnx read_onnx.cpp)
rocm_clang_tidy_check(read_onnx) rocm_clang_tidy_check(read_onnx)
target_link_libraries(read_onnx migraphx_onnx) target_link_libraries(read_onnx migraphx_cpu migraphx_onnx)
if(MIGRAPHX_ENABLE_GPU) if(MIGRAPHX_ENABLE_GPU)
......
src/onnx/onnx.cpp
View file @ 099e9ce8
...@@ -100,6 +100,7 @@ struct onnx_parser ...@@ -100,6 +100,7 @@ struct onnx_parser
void init_actv_func() void init_actv_func()
{ {
// Support name format of all lower case or the first letter capital
map_actv_funcs.insert(std::make_pair("tanh", op::tanh{})); map_actv_funcs.insert(std::make_pair("tanh", op::tanh{}));
map_actv_funcs.insert(std::make_pair("relu", op::relu{})); map_actv_funcs.insert(std::make_pair("relu", op::relu{}));
map_actv_funcs.insert(std::make_pair("sigmoid", op::sigmoid{})); map_actv_funcs.insert(std::make_pair("sigmoid", op::sigmoid{}));
...@@ -352,7 +353,8 @@ struct onnx_parser ...@@ -352,7 +353,8 @@ struct onnx_parser
{ {
// insert zeros for pad op (args[0] has 4 dims) // insert zeros for pad op (args[0] has 4 dims)
padding = {0, 0, padding[0], padding[1], 0, 0, padding[2], padding[3]}; padding = {0, 0, padding[0], padding[1], 0, 0, padding[2], padding[3]};
l0 = prog.add_instruction(op::pad{padding}, l0); l0 = prog.add_instruction(op::pad{padding, std::numeric_limits<float>::lowest()},
l0);
} }
else else
{ {
...@@ -870,7 +872,9 @@ struct onnx_parser ...@@ -870,7 +872,9 @@ struct onnx_parser
auto names = attributes.at("activations").strings(); auto names = attributes.at("activations").strings();
vec_names.clear(); vec_names.clear();
vec_names.resize(names.size()); vec_names.resize(names.size());
std::copy(names.begin(), names.end(), vec_names.begin()); std::transform(names.begin(), names.end(), vec_names.begin(), [](auto name) {
return to_lower(name);
});
} }
auto name_it = std::find_if(vec_names.begin(), vec_names.end(), [&](auto& name) { auto name_it = std::find_if(vec_names.begin(), vec_names.end(), [&](auto& name) {
...@@ -961,7 +965,9 @@ struct onnx_parser ...@@ -961,7 +965,9 @@ struct onnx_parser
auto names = attributes.at("activations").strings(); auto names = attributes.at("activations").strings();
vec_names.clear(); vec_names.clear();
vec_names.resize(names.size()); vec_names.resize(names.size());
std::copy(names.begin(), names.end(), vec_names.begin()); std::transform(names.begin(), names.end(), vec_names.begin(), [](auto name) {
return to_lower(name);
});
} }
// need 4 activation functions // need 4 activation functions
...@@ -1088,7 +1094,9 @@ struct onnx_parser ...@@ -1088,7 +1094,9 @@ struct onnx_parser
auto names = attributes.at("activations").strings(); auto names = attributes.at("activations").strings();
vec_names.clear(); vec_names.clear();
vec_names.resize(names.size()); vec_names.resize(names.size());
std::copy(names.begin(), names.end(), vec_names.begin()); std::transform(names.begin(), names.end(), vec_names.begin(), [](auto name) {
return to_lower(name);
});
} }
// need 6 activation functions for bidirectional directions // need 6 activation functions for bidirectional directions
......
src/py/migraphx_py.cpp
View file @ 099e9ce8
...@@ -8,6 +8,7 @@ ...@@ -8,6 +8,7 @@
#include <migraphx/stringutils.hpp> #include <migraphx/stringutils.hpp>
#include <migraphx/tf.hpp> #include <migraphx/tf.hpp>
#include <migraphx/onnx.hpp> #include <migraphx/onnx.hpp>
#include <migraphx/type_name.hpp>
#ifdef HAVE_GPU #ifdef HAVE_GPU
#include <migraphx/gpu/target.hpp> #include <migraphx/gpu/target.hpp>
...@@ -101,8 +102,13 @@ migraphx::shape to_shape(const py::buffer_info& info) ...@@ -101,8 +102,13 @@ migraphx::shape to_shape(const py::buffer_info& info)
t = as.type_enum(); t = as.type_enum();
n = sizeof(as()); n = sizeof(as());
} }
}); });
if(n == 0)
{
MIGRAPHX_THROW("MIGRAPHX PYTHON: Unsupported data type" + info.format);
}
auto strides = info.strides; auto strides = info.strides;
std::transform(strides.begin(), strides.end(), strides.begin(), [&](auto i) -> std::size_t { std::transform(strides.begin(), strides.end(), strides.begin(), [&](auto i) -> std::size_t {
return n > 0 ? i / n : 0; return n > 0 ? i / n : 0;
......
src/rewrite_rnn.cpp
View file @ 099e9ce8
...@@ -205,16 +205,18 @@ std::vector<instruction_ref> rewrite_rnn::vanilla_rnn_cell(bool is_forward, ...@@ -205,16 +205,18 @@ std::vector<instruction_ref> rewrite_rnn::vanilla_rnn_cell(bool is_forward,
// initial hidden state // initial hidden state
auto sih = prog.insert_instruction(ins, op::squeeze{{0}}, ih); auto sih = prog.insert_instruction(ins, op::squeeze{{0}}, ih);
auto sih_lens = sih->get_shape().lens();
// bias // bias
instruction_ref bb{};
if(bias != prog.end()) if(bias != prog.end())
{ {
long hs = r->get_shape().lens()[2]; long hs = static_cast<long>(r->get_shape().lens()[2]);
auto sbias = prog.insert_instruction(ins, op::squeeze{{0}}, bias); auto sbias = prog.insert_instruction(ins, op::squeeze{{0}}, bias);
auto wb = prog.insert_instruction(ins, op::slice{{0}, {0}, {hs}}, sbias); auto wb = prog.insert_instruction(ins, op::slice{{0}, {0}, {hs}}, sbias);
auto rb = prog.insert_instruction(ins, op::slice{{0}, {hs}, {2 * hs}}, sbias); auto rb = prog.insert_instruction(ins, op::slice{{0}, {hs}, {2 * hs}}, sbias);
auto b = prog.insert_instruction(ins, op::add{}, wb, rb); auto wrb = prog.insert_instruction(ins, op::add{}, wb, rb);
bias = prog.insert_instruction(ins, op::broadcast{1, sih->get_shape().lens()}, b); bb = prog.insert_instruction(ins, op::broadcast{1, sih_lens}, wrb);
} }
instruction_ref hidden_out = prog.end(); instruction_ref hidden_out = prog.end();
...@@ -228,19 +230,14 @@ std::vector<instruction_ref> rewrite_rnn::vanilla_rnn_cell(bool is_forward, ...@@ -228,19 +230,14 @@ std::vector<instruction_ref> rewrite_rnn::vanilla_rnn_cell(bool is_forward,
xt = prog.insert_instruction(ins, op::squeeze{{0}}, xt); xt = prog.insert_instruction(ins, op::squeeze{{0}}, xt);
auto xt_wi = prog.insert_instruction(ins, op::dot{}, xt, tran_sw); auto xt_wi = prog.insert_instruction(ins, op::dot{}, xt, tran_sw);
auto ht_ri = prog.insert_instruction(ins, op::dot{}, sih, tran_sr); auto ht_ri = prog.insert_instruction(ins, op::dot{}, sih, tran_sr);
auto xt_ht = prog.insert_instruction(ins, op::add{}, xt_wi, ht_ri);
instruction_ref ht;
if(bias != prog.end()) if(bias != prog.end())
{ {
ht = prog.insert_instruction(ins, op::add{}, xt_ht, bias); xt_wi = prog.insert_instruction(ins, op::add{}, xt_wi, bb);
}
else
{
ht = xt_ht;
} }
auto xt_ht = prog.insert_instruction(ins, op::add{}, xt_wi, ht_ri);
// apply activation function // apply activation function
ht = prog.insert_instruction(ins, actv_func, ht); auto ht = prog.insert_instruction(ins, actv_func, xt_ht);
sih = ht; sih = ht;
// add the dimensions of sequence length (axis 0 for sequence length, // add the dimensions of sequence length (axis 0 for sequence length,
...@@ -485,62 +482,41 @@ std::vector<instruction_ref> rewrite_rnn::gru_cell(bool is_forward, ...@@ -485,62 +482,41 @@ std::vector<instruction_ref> rewrite_rnn::gru_cell(bool is_forward,
long hs = static_cast<long>(r_shape.lens()[2]); long hs = static_cast<long>(r_shape.lens()[2]);
migraphx::shape s(seq_shape.type(), {seq_shape.lens()[1], r_shape.lens()[2]}); migraphx::shape s(seq_shape.type(), {seq_shape.lens()[1], r_shape.lens()[2]});
std::vector<int> data(s.elements(), 1); std::vector<float> data(s.elements(), 1.0f);
auto l1 = prog.add_literal(migraphx::literal{s, data}); auto l1 = prog.add_literal(migraphx::literal{s, data});
// weight matrix // w matrix squeeze to 2-dim and do a transpose
std::vector<int64_t> perm{1, 0}; std::vector<int64_t> perm{1, 0};
auto sw = prog.insert_instruction(ins, op::squeeze{{0}}, w); auto sw = prog.insert_instruction(ins, op::squeeze{{0}}, w);
auto wz = prog.insert_instruction(ins, op::slice{{0}, {0}, {hs}}, sw); auto tw = prog.insert_instruction(ins, op::transpose{perm}, sw);
auto tran_wz = prog.insert_instruction(ins, op::transpose{perm}, wz);
auto wr = prog.insert_instruction(ins, op::slice{{0}, {hs}, {2 * hs}}, sw);
auto tran_wr = prog.insert_instruction(ins, op::transpose{perm}, wr);
auto wh = prog.insert_instruction(ins, op::slice{{0}, {2 * hs}, {3 * hs}}, sw);
auto tran_wh = prog.insert_instruction(ins, op::transpose{perm}, wh);
// r slide to two part, zr and h
auto sr = prog.insert_instruction(ins, op::squeeze{{0}}, r); auto sr = prog.insert_instruction(ins, op::squeeze{{0}}, r);
auto rz = prog.insert_instruction(ins, op::slice{{0}, {0}, {hs}}, sr); auto rzr = prog.insert_instruction(ins, op::slice{{0}, {0}, {2 * hs}}, sr);
auto tran_rz = prog.insert_instruction(ins, op::transpose{perm}, rz); auto trzr = prog.insert_instruction(ins, op::transpose{perm}, rzr);
auto rr = prog.insert_instruction(ins, op::slice{{0}, {hs}, {2 * hs}}, sr);
auto tran_rr = prog.insert_instruction(ins, op::transpose{perm}, rr);
auto rh = prog.insert_instruction(ins, op::slice{{0}, {2 * hs}, {3 * hs}}, sr); auto rh = prog.insert_instruction(ins, op::slice{{0}, {2 * hs}, {3 * hs}}, sr);
auto tran_rh = prog.insert_instruction(ins, op::transpose{perm}, rh); auto trh = prog.insert_instruction(ins, op::transpose{perm}, rh);
// initial states // initial states
auto sih = prog.insert_instruction(ins, op::squeeze{{0}}, ih); auto sih = prog.insert_instruction(ins, op::squeeze{{0}}, ih);
size_t bs = ih->get_shape().lens()[1];
// bias // bias
instruction_ref brcst_bz{}; instruction_ref bwb{};
instruction_ref brcst_br{}; instruction_ref brb_zr{};
instruction_ref brcst_wbh{}; instruction_ref brb_h{};
instruction_ref brcst_rbh{};
instruction_ref brcst_bh{};
if(bias != prog.end()) if(bias != prog.end())
{ {
auto broadcast_lens = sih->get_shape().lens();
auto sbias = prog.insert_instruction(ins, op::squeeze{{0}}, bias); auto sbias = prog.insert_instruction(ins, op::squeeze{{0}}, bias);
auto wbz = prog.insert_instruction(ins, op::slice{{0}, {0}, {hs}}, sbias); auto wb = prog.insert_instruction(ins, op::slice{{0}, {0}, {3 * hs}}, sbias);
auto wbr = prog.insert_instruction(ins, op::slice{{0}, {hs}, {2 * hs}}, sbias); bwb = prog.insert_instruction(ins, op::broadcast{1, {bs, static_cast<size_t>(3 * hs)}}, wb);
auto wbh = prog.insert_instruction(ins, op::slice{{0}, {2 * hs}, {3 * hs}}, sbias);
brcst_wbh = prog.insert_instruction(ins, op::broadcast{1, broadcast_lens}, wbh);
auto rbz = prog.insert_instruction(ins, op::slice{{0}, {3 * hs}, {4 * hs}}, sbias);
auto rbr = prog.insert_instruction(ins, op::slice{{0}, {4 * hs}, {5 * hs}}, sbias);
auto rbh = prog.insert_instruction(ins, op::slice{{0}, {5 * hs}, {6 * hs}}, sbias);
brcst_rbh = prog.insert_instruction(ins, op::broadcast{1, broadcast_lens}, rbh);
auto bz = prog.insert_instruction(ins, op::add{}, wbz, rbz);
brcst_bz = prog.insert_instruction(ins, op::broadcast{1, broadcast_lens}, bz);
auto br = prog.insert_instruction(ins, op::add{}, wbr, rbr); auto rb_zr = prog.insert_instruction(ins, op::slice{{0}, {3 * hs}, {5 * hs}}, sbias);
brcst_br = prog.insert_instruction(ins, op::broadcast{1, broadcast_lens}, br); auto rb_h = prog.insert_instruction(ins, op::slice{{0}, {5 * hs}, {6 * hs}}, sbias);
brb_zr = prog.insert_instruction(
auto bh = prog.insert_instruction(ins, op::add{}, wbh, rbh); ins, op::broadcast{1, {bs, static_cast<size_t>(2 * hs)}}, rb_zr);
brcst_bh = prog.insert_instruction(ins, op::broadcast{1, broadcast_lens}, bh); brb_h = prog.insert_instruction(ins, op::broadcast{1, {bs, static_cast<size_t>(hs)}}, rb_h);
} }
for(long i = 0; i < seq_len; i++) for(long i = 0; i < seq_len; i++)
...@@ -549,56 +525,58 @@ std::vector<instruction_ref> rewrite_rnn::gru_cell(bool is_forward, ...@@ -549,56 +525,58 @@ std::vector<instruction_ref> rewrite_rnn::gru_cell(bool is_forward,
auto xt = prog.insert_instruction(ins, op::slice{{0}, {seq_index}, {seq_index + 1}}, seq); auto xt = prog.insert_instruction(ins, op::slice{{0}, {seq_index}, {seq_index + 1}}, seq);
xt = prog.insert_instruction(ins, op::squeeze{{0}}, xt); xt = prog.insert_instruction(ins, op::squeeze{{0}}, xt);
// equation f(xt*(Wz^T) + Ht-1 * (Rz^T) + Wbz + Rbz) auto xt_w = prog.insert_instruction(ins, op::dot{}, xt, tw);
auto xt_wz = prog.insert_instruction(ins, op::dot{}, xt, tran_wz); auto ih1_rzr = prog.insert_instruction(ins, op::dot{}, sih, trzr);
auto ht_rz = prog.insert_instruction(ins, op::dot{}, sih, tran_rz);
auto xht_z = prog.insert_instruction(ins, op::add{}, xt_wz, ht_rz);
if(bias != prog.end()) if(bias != prog.end())
{ {
xht_z = prog.insert_instruction(ins, op::add{}, xht_z, brcst_bz); xt_w = prog.insert_instruction(ins, op::add{}, xt_w, bwb);
ih1_rzr = prog.insert_instruction(ins, op::add{}, ih1_rzr, brb_zr);
} }
auto zt = prog.insert_instruction(ins, actv_func1, xht_z);
// equation f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) auto xw_z = prog.insert_instruction(ins, op::slice{{1}, {0}, {hs}}, xt_w);
auto xt_wr = prog.insert_instruction(ins, op::dot{}, xt, tran_wr); auto xw_r = prog.insert_instruction(ins, op::slice{{1}, {hs}, {2 * hs}}, xt_w);
auto ht_rr = prog.insert_instruction(ins, op::dot{}, sih, tran_rr); auto xw_h = prog.insert_instruction(ins, op::slice{{1}, {2 * hs}, {3 * hs}}, xt_w);
auto xht_r = prog.insert_instruction(ins, op::add{}, xt_wr, ht_rr);
if(bias != prog.end()) auto hr_z = prog.insert_instruction(ins, op::slice{{1}, {0}, {hs}}, ih1_rzr);
{ auto hr_r = prog.insert_instruction(ins, op::slice{{1}, {hs}, {2 * hs}}, ih1_rzr);
xht_r = prog.insert_instruction(ins, op::add{}, xht_r, brcst_br);
} auto xw_hr_z = prog.insert_instruction(ins, op::add{}, xw_z, hr_z);
auto rt = prog.insert_instruction(ins, actv_func1, xht_r); auto zt = prog.insert_instruction(ins, actv_func1, xw_hr_z);
auto xw_hr_r = prog.insert_instruction(ins, op::add{}, xw_r, hr_r);
auto rt = prog.insert_instruction(ins, actv_func1, xw_hr_r);
instruction_ref xht_h; instruction_ref hr_h{};
if(linear_before_reset == 0) if(linear_before_reset == 0)
{ {
// equation g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) // equation g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh)
auto xt_wh = prog.insert_instruction(ins, op::dot{}, xt, tran_wh);
auto rt_ht1 = prog.insert_instruction(ins, op::mul{}, rt, sih); auto rt_ht1 = prog.insert_instruction(ins, op::mul{}, rt, sih);
auto rt_rh = prog.insert_instruction(ins, op::dot{}, rt_ht1, tran_rh);
xht_h = prog.insert_instruction(ins, op::add{}, xt_wh, rt_rh);
if(bias != prog.end()) if(bias != prog.end())
{ {
xht_h = prog.insert_instruction(ins, op::add{}, xht_h, brcst_bh); hr_h = prog.insert_instruction(ins, op::dot{}, rt_ht1, trh, brb_h);
}
else
{
hr_h = prog.insert_instruction(ins, op::dot{}, rt_ht1, trh);
} }
} }
else else
{ {
// equation ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) // equation ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh)
auto xt_wh = prog.insert_instruction(ins, op::dot{}, xt, tran_wh); instruction_ref ht1_rh{};
auto ht1_rh = prog.insert_instruction(ins, op::dot{}, sih, tran_rh);
if(bias != prog.end()) if(bias != prog.end())
{ {
ht1_rh = prog.insert_instruction(ins, op::add{}, ht1_rh, brcst_rbh); ht1_rh = prog.insert_instruction(ins, op::dot{}, sih, trh, brb_h);
} }
auto rt_rh = prog.insert_instruction(ins, op::mul{}, rt, ht1_rh); else
xht_h = prog.insert_instruction(ins, op::add{}, xt_wh, rt_rh);
if(bias != prog.end())
{ {
xht_h = prog.insert_instruction(ins, op::add{}, xht_h, brcst_wbh); ht1_rh = prog.insert_instruction(ins, op::dot{}, sih, trh);
} }
hr_h = prog.insert_instruction(ins, op::mul{}, rt, ht1_rh);
} }
auto ht = prog.insert_instruction(ins, actv_func2, xht_h);
auto xw_hr_h = prog.insert_instruction(ins, op::add{}, xw_h, hr_h);
auto ht = prog.insert_instruction(ins, actv_func2, xw_hr_h);
// equation Ht = (1 - zt) (.) ht + zt (.) Ht-1 // equation Ht = (1 - zt) (.) ht + zt (.) Ht-1
auto one_minus_zt = prog.insert_instruction(ins, op::sub{}, l1, zt); auto one_minus_zt = prog.insert_instruction(ins, op::sub{}, l1, zt);
...@@ -913,35 +891,16 @@ std::vector<instruction_ref> rewrite_rnn::lstm_cell(bool is_forward, ...@@ -913,35 +891,16 @@ std::vector<instruction_ref> rewrite_rnn::lstm_cell(bool is_forward,
migraphx::shape r_shape = r->get_shape(); migraphx::shape r_shape = r->get_shape();
long seq_len = static_cast<long>(seq_shape.lens()[0]); long seq_len = static_cast<long>(seq_shape.lens()[0]);
long hs = static_cast<long>(r_shape.lens()[2]); long hs = static_cast<long>(r_shape.lens()[2]);
auto bs = ih->get_shape().lens()[1];
std::vector<int64_t> perm{1, 0}; std::vector<int64_t> perm{1, 0};
// w matrix // w matrix, squeeze and transpose
auto sw = prog.insert_instruction(ins, op::squeeze{{0}}, w); auto sw = prog.insert_instruction(ins, op::squeeze{{0}}, w);
auto wi = prog.insert_instruction(ins, op::slice{{0}, {0}, {hs}}, sw); auto tsw = prog.insert_instruction(ins, op::transpose{perm}, sw);
auto tran_wi = prog.insert_instruction(ins, op::transpose{perm}, wi);
auto wo = prog.insert_instruction(ins, op::slice{{0}, {hs}, {2 * hs}}, sw);
auto tran_wo = prog.insert_instruction(ins, op::transpose{perm}, wo);
auto wf = prog.insert_instruction(ins, op::slice{{0}, {2 * hs}, {3 * hs}}, sw);
auto tran_wf = prog.insert_instruction(ins, op::transpose{perm}, wf);
auto wc = prog.insert_instruction(ins, op::slice{{0}, {3 * hs}, {4 * hs}}, sw);
auto tran_wc = prog.insert_instruction(ins, op::transpose{perm}, wc);
// r matrix // r matrix, squeeze and transpose
auto sr = prog.insert_instruction(ins, op::squeeze{{0}}, r); auto sr = prog.insert_instruction(ins, op::squeeze{{0}}, r);
auto ri = prog.insert_instruction(ins, op::slice{{0}, {0}, {hs}}, sr); auto tsr = prog.insert_instruction(ins, op::transpose{perm}, sr);
auto tran_ri = prog.insert_instruction(ins, op::transpose{perm}, ri);
auto ro = prog.insert_instruction(ins, op::slice{{0}, {hs}, {2 * hs}}, sr);
auto tran_ro = prog.insert_instruction(ins, op::transpose{perm}, ro);
auto rf = prog.insert_instruction(ins, op::slice{{0}, {2 * hs}, {3 * hs}}, sr);
auto tran_rf = prog.insert_instruction(ins, op::transpose{perm}, rf);
auto rc = prog.insert_instruction(ins, op::slice{{0}, {3 * hs}, {4 * hs}}, sr);
auto tran_rc = prog.insert_instruction(ins, op::transpose{perm}, rc);
// initial hidden state // initial hidden state
auto sih = prog.insert_instruction(ins, op::squeeze{{0}}, ih); auto sih = prog.insert_instruction(ins, op::squeeze{{0}}, ih);
...@@ -951,40 +910,23 @@ std::vector<instruction_ref> rewrite_rnn::lstm_cell(bool is_forward, ...@@ -951,40 +910,23 @@ std::vector<instruction_ref> rewrite_rnn::lstm_cell(bool is_forward,
auto ic_lens = sic->get_shape().lens(); auto ic_lens = sic->get_shape().lens();
// bias // bias
instruction_ref bi_brcst{}; instruction_ref wrb{};
instruction_ref bo_brcst{};
instruction_ref bf_brcst{};
instruction_ref bc_brcst{};
if(bias != prog.end()) if(bias != prog.end())
{ {
auto sbias = prog.insert_instruction(ins, op::squeeze{{0}}, bias); auto sbias = prog.insert_instruction(ins, op::squeeze{{0}}, bias);
auto bxi = prog.insert_instruction(ins, op::slice{{0}, {0}, {hs}}, sbias); auto ub_wb = prog.insert_instruction(ins, op::slice{{0}, {0}, {4 * hs}}, sbias);
auto bhi = prog.insert_instruction(ins, op::slice{{0}, {4 * hs}, {5 * hs}}, sbias); auto ub_rb = prog.insert_instruction(ins, op::slice{{0}, {4 * hs}, {8 * hs}}, sbias);
auto bi = prog.insert_instruction(ins, op::add{}, bxi, bhi); auto ub_wrb = prog.insert_instruction(ins, op::add{}, ub_wb, ub_rb);
bi_brcst = prog.insert_instruction(ins, op::broadcast{1, ic_lens}, bi);
auto bxo = prog.insert_instruction(ins, op::slice{{0}, {hs}, {2 * hs}}, sbias);
auto bho = prog.insert_instruction(ins, op::slice{{0}, {5 * hs}, {6 * hs}}, sbias);
auto bo = prog.insert_instruction(ins, op::add{}, bxo, bho);
bo_brcst = prog.insert_instruction(ins, op::broadcast{1, ic_lens}, bo);
auto bxf = prog.insert_instruction(ins, op::slice{{0}, {2 * hs}, {3 * hs}}, sbias); wrb = prog.insert_instruction(
auto bhf = prog.insert_instruction(ins, op::slice{{0}, {6 * hs}, {7 * hs}}, sbias); ins, op::broadcast{1, {bs, 4 * static_cast<size_t>(hs)}}, ub_wrb);
auto bf = prog.insert_instruction(ins, op::add{}, bxf, bhf);
bf_brcst = prog.insert_instruction(ins, op::broadcast{1, ic_lens}, bf);
auto bxc = prog.insert_instruction(ins, op::slice{{0}, {3 * hs}, {4 * hs}}, sbias);
auto bhc = prog.insert_instruction(ins, op::slice{{0}, {7 * hs}, {8 * hs}}, sbias);
auto bc = prog.insert_instruction(ins, op::add{}, bxc, bhc);
bc_brcst = prog.insert_instruction(ins, op::broadcast{1, ic_lens}, bc);
} }
// peep hole // peep hole
instruction_ref pphi_brcst{}; instruction_ref pphi_brcst{};
instruction_ref ppho_brcst{}; instruction_ref ppho_brcst{};
instruction_ref pphf_brcst{}; instruction_ref pphf_brcst{};
if(pph != prog.end()) if(pph != prog.end())
{ {
auto spph = prog.insert_instruction(ins, op::squeeze{{0}}, pph); auto spph = prog.insert_instruction(ins, op::squeeze{{0}}, pph);
...@@ -1004,44 +946,31 @@ std::vector<instruction_ref> rewrite_rnn::lstm_cell(bool is_forward, ...@@ -1004,44 +946,31 @@ std::vector<instruction_ref> rewrite_rnn::lstm_cell(bool is_forward,
auto xt = prog.insert_instruction(ins, op::slice{{0}, {seq_index}, {seq_index + 1}}, seq); auto xt = prog.insert_instruction(ins, op::slice{{0}, {seq_index}, {seq_index + 1}}, seq);
xt = prog.insert_instruction(ins, op::squeeze{{0}}, xt); xt = prog.insert_instruction(ins, op::squeeze{{0}}, xt);
// equation it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) auto xt_tsw = prog.insert_instruction(ins, op::dot{}, xt, tsw);
auto xt_wi = prog.insert_instruction(ins, op::dot{}, xt, tran_wi); auto sih_tsr = prog.insert_instruction(ins, op::dot{}, sih, tsr);
auto ht_ri = prog.insert_instruction(ins, op::dot{}, sih, tran_ri); auto xt_sih = prog.insert_instruction(ins, op::add{}, xt_tsw, sih_tsr);
auto it_before_actv = prog.insert_instruction(ins, op::add{}, xt_wi, ht_ri);
if(pph != prog.end())
{
auto pphi_ct = prog.insert_instruction(ins, op::mul{}, pphi_brcst, sic);
it_before_actv = prog.insert_instruction(ins, op::add{}, it_before_actv, pphi_ct);
}
if(bias != prog.end()) if(bias != prog.end())
{ {
it_before_actv = prog.insert_instruction(ins, op::add{}, it_before_actv, bi_brcst); xt_sih = prog.insert_instruction(ins, op::add{}, xt_sih, wrb);
} }
auto it = prog.insert_instruction(ins, actv_func1, it_before_actv);
// equation ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) auto it_before_actv = prog.insert_instruction(ins, op::slice{{1}, {0}, {hs}}, xt_sih);
auto xt_wf = prog.insert_instruction(ins, op::dot{}, xt, tran_wf); auto ot_before_actv = prog.insert_instruction(ins, op::slice{{1}, {hs}, {2 * hs}}, xt_sih);
auto ht_rf = prog.insert_instruction(ins, op::dot{}, sih, tran_rf); auto ft_before_actv =
auto ft_before_actv = prog.insert_instruction(ins, op::add{}, xt_wf, ht_rf); prog.insert_instruction(ins, op::slice{{1}, {2 * hs}, {3 * hs}}, xt_sih);
auto ct_before_actv =
prog.insert_instruction(ins, op::slice{{1}, {3 * hs}, {4 * hs}}, xt_sih);
if(pph != prog.end()) if(pph != prog.end())
{ {
auto pphi_ct = prog.insert_instruction(ins, op::mul{}, pphi_brcst, sic);
it_before_actv = prog.insert_instruction(ins, op::add{}, it_before_actv, pphi_ct);
auto pphf_ct = prog.insert_instruction(ins, op::mul{}, pphf_brcst, sic); auto pphf_ct = prog.insert_instruction(ins, op::mul{}, pphf_brcst, sic);
ft_before_actv = prog.insert_instruction(ins, op::add{}, ft_before_actv, pphf_ct); ft_before_actv = prog.insert_instruction(ins, op::add{}, ft_before_actv, pphf_ct);
} }
if(bias != prog.end()) auto it = prog.insert_instruction(ins, actv_func1, it_before_actv);
{
ft_before_actv = prog.insert_instruction(ins, op::add{}, ft_before_actv, bf_brcst);
}
auto ft = prog.insert_instruction(ins, actv_func1, ft_before_actv); auto ft = prog.insert_instruction(ins, actv_func1, ft_before_actv);
// equation ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
auto xt_wc = prog.insert_instruction(ins, op::dot{}, xt, tran_wc);
auto ht_rc = prog.insert_instruction(ins, op::dot{}, sih, tran_rc);
auto ct_before_actv = prog.insert_instruction(ins, op::add{}, xt_wc, ht_rc);
if(bias != prog.end())
{
ct_before_actv = prog.insert_instruction(ins, op::add{}, ct_before_actv, bc_brcst);
}
auto ct = prog.insert_instruction(ins, actv_func2, ct_before_actv); auto ct = prog.insert_instruction(ins, actv_func2, ct_before_actv);
// equation Ct = ft (.) Ct-1 + it (.) ct // equation Ct = ft (.) Ct-1 + it (.) ct
...@@ -1050,19 +979,11 @@ std::vector<instruction_ref> rewrite_rnn::lstm_cell(bool is_forward, ...@@ -1050,19 +979,11 @@ std::vector<instruction_ref> rewrite_rnn::lstm_cell(bool is_forward,
auto cellt = prog.insert_instruction(ins, op::add{}, ft_cell, it_ct); auto cellt = prog.insert_instruction(ins, op::add{}, ft_cell, it_ct);
last_cell_output = cellt; last_cell_output = cellt;
// ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)
auto xt_wo = prog.insert_instruction(ins, op::dot{}, xt, tran_wo);
auto ht_ro = prog.insert_instruction(ins, op::dot{}, sih, tran_ro);
auto ot_before_actv = prog.insert_instruction(ins, op::add{}, xt_wo, ht_ro);
if(pph != prog.end()) if(pph != prog.end())
{ {
auto ppho_cellt = prog.insert_instruction(ins, op::mul{}, ppho_brcst, cellt); auto ppho_cellt = prog.insert_instruction(ins, op::mul{}, ppho_brcst, cellt);
ot_before_actv = prog.insert_instruction(ins, op::add{}, ot_before_actv, ppho_cellt); ot_before_actv = prog.insert_instruction(ins, op::add{}, ot_before_actv, ppho_cellt);
} }
if(bias != prog.end())
{
ot_before_actv = prog.insert_instruction(ins, op::add{}, ot_before_actv, bo_brcst);
}
auto ot = prog.insert_instruction(ins, actv_func1, ot_before_actv); auto ot = prog.insert_instruction(ins, actv_func1, ot_before_actv);
// Ht = ot (.) h(Ct) // Ht = ot (.) h(Ct)
......
src/targets/gpu/device/include/migraphx/gpu/device/types.hpp
View file @ 099e9ce8
...@@ -64,9 +64,9 @@ host_type<T>* host_cast(T* x) ...@@ -64,9 +64,9 @@ host_type<T>* host_cast(T* x)
} }
template <class T> 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> template <class T>
......
src/targets/gpu/device/pad.cpp
View file @ 099e9ce8
...@@ -4,6 +4,7 @@ ...@@ -4,6 +4,7 @@
#include <migraphx/gpu/device/pad.hpp> #include <migraphx/gpu/device/pad.hpp>
#include <migraphx/gpu/device/tensor.hpp> #include <migraphx/gpu/device/tensor.hpp>
#include <migraphx/gpu/device/launch.hpp> #include <migraphx/gpu/device/launch.hpp>
#include <migraphx/float_equal.hpp>
namespace migraphx { namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS { inline namespace MIGRAPHX_INLINE_NS {
...@@ -14,8 +15,17 @@ argument ...@@ -14,8 +15,17 @@ argument
pad(hipStream_t stream, argument result, argument arg1, float value, std::vector<std::int64_t> pads) pad(hipStream_t stream, argument result, argument arg1, float value, std::vector<std::int64_t> pads)
{ {
std::size_t nelements = arg1.get_shape().elements(); std::size_t nelements = arg1.get_shape().elements();
visit_all(result)([&](auto output) {
auto* outptr = device_cast(output.data());
using type = typename decltype(output)::value_type;
device_type<type> device_val = value;
if(float_equal(value, std::numeric_limits<float>::lowest()))
{
device_val = device_cast(std::numeric_limits<type>::lowest());
}
gs_launch(stream, result.get_shape().elements())([=](auto i) { outptr[i] = device_val; });
});
nary(stream, result)([=] { return value; });
visit_all(result, arg1)([&](auto output, auto input) { visit_all(result, arg1)([&](auto output, auto input) {
visit_tensor_size(result.get_shape().lens().size(), [&](auto ndim) { visit_tensor_size(result.get_shape().lens().size(), [&](auto ndim) {
std::size_t offsets[ndim]; std::size_t offsets[ndim];
......
src/targets/gpu/include/migraphx/gpu/softmax.hpp
View file @ 099e9ce8
...@@ -34,7 +34,7 @@ struct miopen_softmax ...@@ -34,7 +34,7 @@ struct miopen_softmax
return migraphx::reflect(self.op, f); return migraphx::reflect(self.op, f);
} }
std::string name() const { return "gpu::softmax"; } std::string name() const { return "miopen::softmax"; }
shape compute_shape(const std::vector<shape>& inputs) const; shape compute_shape(const std::vector<shape>& inputs) const;
argument argument
compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const; compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
......
src/tf/CMakeLists.txt
View file @ 099e9ce8
...@@ -31,7 +31,7 @@ rocm_install_targets( ...@@ -31,7 +31,7 @@ rocm_install_targets(
add_executable(read_tf read_tf.cpp) add_executable(read_tf read_tf.cpp)
rocm_clang_tidy_check(read_tf) 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) if(MIGRAPHX_ENABLE_GPU)
add_executable(verify_tf verify_tf.cpp) add_executable(verify_tf verify_tf.cpp)
......
src/tf/tf.cpp
View file @ 099e9ce8
...@@ -317,6 +317,7 @@ struct tf_parser ...@@ -317,6 +317,7 @@ struct tf_parser
} }
} }
auto l0 = args[0];
if(contains(attributes, "padding")) if(contains(attributes, "padding"))
{ {
const std::string& pad_mode = attributes.at("padding").s(); const std::string& pad_mode = attributes.at("padding").s();
...@@ -326,8 +327,24 @@ struct tf_parser ...@@ -326,8 +327,24 @@ struct tf_parser
std::vector<size_t> weight_dims = weights->get_shape().lens(); std::vector<size_t> weight_dims = weights->get_shape().lens();
size_t weight_h = weight_dims[2]; size_t weight_h = weight_dims[2];
size_t weight_w = weight_dims[3]; size_t weight_w = weight_dims[3];
op.padding[0] = calculate_padding(weight_h, op.dilation[0]);
op.padding[1] = calculate_padding(weight_w, op.dilation[1]); 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], op.dilation[0], weight_h);
calculate_padding(1, pads, input_w, op.stride[1], op.dilation[1], weight_w);
if(pads[0] != pads[2] || pads[1] != pads[3])
{
std::vector<int64_t> padding = {0, 0, pads[0], pads[1], 0, 0, pads[2], pads[3]};
l0 = prog.add_instruction(migraphx::op::pad{padding}, l0);
}
else
{
op.padding[0] = pads[0];
op.padding[1] = pads[1];
}
} }
else if(pad_mode.find("VALID") != std::string::npos) else if(pad_mode.find("VALID") != std::string::npos)
{ {
...@@ -350,7 +367,7 @@ struct tf_parser ...@@ -350,7 +367,7 @@ struct tf_parser
} }
} }
return prog.add_instruction(op, {args[0], weights}); return prog.add_instruction(op, {l0, weights});
} }
instruction_ref parse_depthwiseconv(const std::string&, instruction_ref parse_depthwiseconv(const std::string&,
...@@ -400,17 +417,35 @@ struct tf_parser ...@@ -400,17 +417,35 @@ struct tf_parser
} }
} }
auto l0 = args[0];
if(contains(attributes, "padding")) if(contains(attributes, "padding"))
{ {
const std::string& pad_mode = attributes.at("padding").s(); 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;
std::vector<size_t> weight_dims = weights->get_shape().lens(); std::vector<size_t> weight_dims = weights->get_shape().lens();
size_t weight_h = weight_dims[2]; size_t weight_h = weight_dims[2];
size_t weight_w = weight_dims[3]; size_t weight_w = weight_dims[3];
if(pad_mode.find("SAME") != std::string::npos)
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], op.dilation[0], weight_h);
calculate_padding(1, pads, input_w, op.stride[1], op.dilation[1], weight_w);
if(pads[0] != pads[2] || pads[1] != pads[3])
{ {
op.padding_mode = op::padding_mode_t::same; std::vector<int64_t> padding = {0, 0, pads[0], pads[1], 0, 0, pads[2], pads[3]};
op.padding[0] = calculate_padding(weight_h, op.dilation[0]); l0 = prog.add_instruction(migraphx::op::pad{padding}, l0);
op.padding[1] = calculate_padding(weight_w, op.dilation[1]); }
else
{
op.padding[0] = pads[0];
op.padding[1] = pads[1];
}
} }
else if(pad_mode.find("VALID") != std::string::npos) else if(pad_mode.find("VALID") != std::string::npos)
{ {
...@@ -432,7 +467,7 @@ struct tf_parser ...@@ -432,7 +467,7 @@ struct tf_parser
auto cweights = prog.add_instruction(op::contiguous{}, weights); auto cweights = prog.add_instruction(op::contiguous{}, weights);
auto new_weights = prog.add_instruction(op::reshape{new_weights_shape}, cweights); auto new_weights = prog.add_instruction(op::reshape{new_weights_shape}, cweights);
return prog.add_instruction(op, {args[0], new_weights}); return prog.add_instruction(op, {l0, new_weights});
} }
instruction_ref instruction_ref
...@@ -567,21 +602,39 @@ struct tf_parser ...@@ -567,21 +602,39 @@ struct tf_parser
op.lengths[0] = ksize[2]; op.lengths[0] = ksize[2];
op.lengths[1] = ksize[3]; op.lengths[1] = ksize[3];
} }
auto l0 = args[0];
if(contains(attributes, "padding")) if(contains(attributes, "padding"))
{ {
const std::string& pad_mode = attributes.at("padding").s(); const std::string& pad_mode = attributes.at("padding").s();
if(pad_mode.find("SAME") != std::string::npos) if(pad_mode.find("SAME") != std::string::npos)
{ {
op.padding_mode = op::padding_mode_t::same; op.padding_mode = op::padding_mode_t::same;
op.padding[0] = calculate_padding(op.lengths[0], 1); auto input_dims = l0->get_shape().lens();
op.padding[1] = calculate_padding(op.lengths[1], 1); 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]);
if(pads[0] != pads[2] || pads[1] != pads[3])
{
std::vector<int64_t> padding = {0, 0, pads[0], pads[1], 0, 0, pads[2], pads[3]};
l0 = prog.add_instruction(
migraphx::op::pad{padding, std::numeric_limits<float>::lowest()}, l0);
}
else
{
op.padding[0] = pads[0];
op.padding[1] = pads[1];
}
} }
else if(pad_mode.find("VALID") != std::string::npos) else if(pad_mode.find("VALID") != std::string::npos)
{ {
op.padding_mode = op::padding_mode_t::valid; op.padding_mode = op::padding_mode_t::valid;
} }
} }
return prog.add_instruction(op, args[0]); return prog.add_instruction(op, l0);
} }
instruction_ref instruction_ref
......
test/CMakeLists.txt
View file @ 099e9ce8
...@@ -119,7 +119,7 @@ foreach(ONNX_TEST ${ONNX_TESTS}) ...@@ -119,7 +119,7 @@ foreach(ONNX_TEST ${ONNX_TESTS})
set(TEST_NAME test_${BASE_NAME}) set(TEST_NAME test_${BASE_NAME})
add_executable(${TEST_NAME} ${TES_ONNX_DIR}/${ONNX_TEST}) add_executable(${TEST_NAME} ${TES_ONNX_DIR}/${ONNX_TEST})
rocm_clang_tidy_check(${TEST_NAME}) rocm_clang_tidy_check(${TEST_NAME})
target_link_libraries(${TEST_NAME} migraphx_onnx) target_link_libraries(${TEST_NAME} migraphx_onnx migraphx_cpu)
target_include_directories(${TEST_NAME} PUBLIC include) target_include_directories(${TEST_NAME} PUBLIC include)
add_test(NAME ${TEST_NAME} COMMAND $<TARGET_FILE:${TEST_NAME}> WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/onnx) add_test(NAME ${TEST_NAME} COMMAND $<TARGET_FILE:${TEST_NAME}> WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/onnx)
add_dependencies(tests ${TEST_NAME}) add_dependencies(tests ${TEST_NAME})
...@@ -129,7 +129,7 @@ endforeach() ...@@ -129,7 +129,7 @@ endforeach()
# tf test # tf test
add_executable(test_tf tf/tf_test.cpp) add_executable(test_tf tf/tf_test.cpp)
rocm_clang_tidy_check(test_tf) rocm_clang_tidy_check(test_tf)
target_link_libraries(test_tf migraphx_tf) target_link_libraries(test_tf migraphx_tf migraphx_cpu)
target_include_directories(test_tf PUBLIC include) target_include_directories(test_tf PUBLIC include)
add_test(NAME test_tf COMMAND $<TARGET_FILE:test_tf> WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/tf) add_test(NAME test_tf COMMAND $<TARGET_FILE:test_tf> WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/tf)
add_dependencies(tests test_tf) add_dependencies(tests test_tf)
......
test/eliminate_pad_test.cpp
View file @ 099e9ce8
...@@ -83,23 +83,4 @@ TEST_CASE(rewrite_test_asymmetric) ...@@ -83,23 +83,4 @@ TEST_CASE(rewrite_test_asymmetric)
p.begin(), p.end(), [](const migraphx::instruction& ins) { return ins.name() == "pad"; })); p.begin(), p.end(), [](const migraphx::instruction& ins) { return ins.name() == "pad"; }));
} }
TEST_CASE(rewrite_test_same_padding)
{
migraphx::program p;
size_t img_dim[2] = {2, 2};
size_t channels = 1;
std::vector<int32_t> input(channels * img_dim[0] * img_dim[1]);
std::iota(input.begin(), input.end(), 0);
migraphx::shape s_img{migraphx::shape::int32_type, {1, channels, img_dim[0], img_dim[1]}};
auto l_img = p.add_literal(migraphx::literal{s_img, input});
auto padded_img = p.add_instruction(migraphx::op::pad{{0, 0, 1, 1, 0, 0, 1, 1}}, l_img);
create_conv(padded_img, channels, p, migraphx::op::padding_mode_t::same);
p.compile(eliminate_pad_target{});
EXPECT(std::any_of(
p.begin(), p.end(), [](const migraphx::instruction& ins) { return ins.name() == "pad"; }));
}
int main(int argc, const char* argv[]) { test::run(argc, argv); } int main(int argc, const char* argv[]) { test::run(argc, argv); }
test/gpu/miopen.cpp
View file @ 099e9ce8
...@@ -1460,6 +1460,22 @@ struct test_pad : verify_program<test_pad> ...@@ -1460,6 +1460,22 @@ struct test_pad : verify_program<test_pad>
} }
}; };
struct test_pad_int8 : verify_program<test_pad_int8>
{
migraphx::program create_program() const
{
migraphx::program p;
std::vector<int8_t> data0 = {0, 1, 2, 3};
migraphx::shape s0{migraphx::shape::float_type, {2, 2}};
auto l0 = p.add_literal(migraphx::literal{s0, data0});
migraphx::op::pad op{};
op.value = std::numeric_limits<int8_t>::lowest();
op.pads = {0, 0, 1, 1};
p.add_instruction(op, l0);
return p;
}
};
struct test_pooling_autopad : verify_program<test_pooling_autopad> struct test_pooling_autopad : verify_program<test_pooling_autopad>
{ {
migraphx::program create_program() const migraphx::program create_program() const
...@@ -2650,7 +2666,8 @@ struct test_lstm_forward_last : verify_program<test_lstm_forward_last> ...@@ -2650,7 +2666,8 @@ struct test_lstm_forward_last : verify_program<test_lstm_forward_last>
auto und = p.add_instruction(migraphx::op::undefined{}); auto und = p.add_instruction(migraphx::op::undefined{});
auto output = p.add_instruction( auto output = p.add_instruction(
migraphx::op::gru{hidden_size, migraphx::op::lstm{
hidden_size,
{migraphx::op::sigmoid{}, migraphx::op::tanh{}, migraphx::op::tanh{}}, {migraphx::op::sigmoid{}, migraphx::op::tanh{}, migraphx::op::tanh{}},
migraphx::op::rnn_direction::forward, migraphx::op::rnn_direction::forward,
clip}, clip},
......
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