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Commit f17d6246 authored by Paul's avatar Paul
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Merge branch 'develop' into ck-gemm-fused

parents 87673e1b fdc3f00a
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examples/nlp/python_bert_squad/requirements_bertsquad.txt
View file @ f17d6246
......@@ -21,6 +21,6 @@
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#####################################################################################
tensorflow==2.7.2
tensorflow==2.9.3
onnxruntime
tokenizers
\ No newline at end of file
src/include/migraphx/int_divide.hpp deleted 100644 → 0
View file @ 87673e1b
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#ifndef MIGRAPHX_GUARD_RTGLIB_INT_DIVIDE_HPP
#define MIGRAPHX_GUARD_RTGLIB_INT_DIVIDE_HPP
#include <migraphx/config.hpp>
#include <cmath>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
template <class R, class T, class U>
R floor_divide(T x, U y)
{
return R(std::floor(double(x) / double(y)));
}
template <class R, class T, class U>
R ceil_divide(T x, U y)
{
return R(std::ceil(double(x) / double(y)));
}
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/literal.hpp
View file @ f17d6246
......@@ -80,6 +80,7 @@ struct literal : raw_data<literal>
fill(start, end);
}
// Directly copies buffer of x
template <class T, MIGRAPHX_REQUIRES(sizeof(T) == 1)>
literal(const shape& s, T* x) : buffer(make_shared_array<char>(s.bytes())), m_shape(s)
{
......@@ -107,25 +108,15 @@ struct literal : raw_data<literal>
std::shared_ptr<char> buffer;
shape m_shape;
// Keeps the same data ordering as the given container
template <class Iterator>
void fill(Iterator start, Iterator end)
{
assert(std::distance(start, end) == m_shape.elements());
if(m_shape.standard())
{
m_shape.visit_type([&](auto as) { std::copy(start, end, as.from(buffer.get())); });
}
else
{
auto it = start;
m_shape.visit_type([&](auto as) {
auto output = make_view(m_shape, as.from(buffer.get()));
shape_for_each(output.get_shape(), [&](const auto& idx) {
output(idx.begin(), idx.end()) = *it; // NOLINT(bugprone-signed-char-misuse)
it++;
});
});
}
m_shape.visit_type([&](auto as) {
auto output = make_view(m_shape, as.from(buffer.get()));
std::copy(start, end, output.begin());
});
}
};
......
src/include/migraphx/op/pooling.hpp
View file @ f17d6246
......@@ -31,7 +31,7 @@
#include <migraphx/argument.hpp>
#include <migraphx/par_for.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/int_divide.hpp>
#include <migraphx/dyn_output.hpp>
#include <cmath>
#include <utility>
......@@ -49,6 +49,9 @@ struct pooling
bool ceil_mode = false;
int lp_order = 2;
// Global pooling with dynamic shape input
bool dyn_global = false;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
......@@ -57,7 +60,8 @@ struct pooling
f(self.stride, "stride"),
f(self.lengths, "lengths"),
f(self.ceil_mode, "ceil_mode"),
f(self.lp_order, "lp_order"));
f(self.lp_order, "lp_order"),
f(self.dyn_global, "dyn_global"));
}
std::string name() const { return "pooling"; }
......@@ -65,51 +69,111 @@ struct pooling
void check_attribute_size() const
{
if((padding.size() != stride.size() and (padding.size() / 2) != stride.size()) or
stride.size() != lengths.size())
(not dyn_global and stride.size() != lengths.size()))
{
MIGRAPHX_THROW("POOLING: inconsistent attribute sizes");
}
}
size_t kdims() const
{
check_attribute_size();
return stride.size();
}
value attributes() const { return {{"normalize_padding", "padding"}}; }
std::vector<std::size_t> calc_spatial_dim_out(const std::vector<std::size_t>& input_lens,
std::size_t kdims) const
{
std::vector<std::size_t> output_lens{};
for(size_t i = 0; i < kdims; ++i)
{
if(input_lens[i + 2] == 0)
{
// handle opt = 0
output_lens.push_back(0);
}
else
{
std::size_t padding_factor = 2 * padding[i];
if(padding.size() == 2 * kdims)
padding_factor = padding[i] + padding[i + kdims];
assert(input_lens[i + 2] + padding_factor >= lengths[i]);
std::size_t dim_size = input_lens[i + 2] + padding_factor - lengths[i];
std::size_t len =
(ceil_mode)
? dim_size / stride[i] + static_cast<std::size_t>((dim_size % stride[i] !=
0)) // ceil uint divide
: dim_size / stride[i]; // floor divide
output_lens.push_back(len + 1);
}
}
return output_lens;
}
shape normalize_compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
check_shapes{inputs, *this, true}.has(1);
check_attribute_size();
const shape& input = inputs.at(0);
auto input_lens = input.lens();
size_t kdims = input_lens.size() - 2;
auto input_size = inputs[0].lens().size();
auto padding_size = padding.size();
if(input_size != padding_size / 2 + 2 and input_size != padding_size + 2)
auto padding_size = padding.size();
size_t kdims = input.ndim() - 2;
if(input.ndim() != padding_size / 2 + 2 and input.ndim() != padding_size + 2)
{
MIGRAPHX_THROW("POOLING: input and attribute size mismatch!");
}
std::vector<std::size_t> output_lens(input_lens.begin(), input_lens.begin() + 2);
for(size_t i = 0; i < kdims; i++)
if(input.dynamic())
{
std::ptrdiff_t dim_size;
auto padding_factor = 2 * padding[i];
if(padding_size == 2 * kdims)
padding_factor = padding[i] + padding[i + kdims];
dim_size = input_lens[i + 2] + padding_factor - lengths[i];
assert(dim_size >= 0);
std::size_t len = (ceil_mode) ? ceil_divide<std::ptrdiff_t>(dim_size, stride[i])
: floor_divide<std::ptrdiff_t>(dim_size, stride[i]);
output_lens.push_back(std::size_t(std::max<std::ptrdiff_t>(1, len + 1)));
auto input_dyn_dims = input.dyn_dims();
std::vector<shape::dynamic_dimension> output_dyn_dims(input_dyn_dims.begin(),
input_dyn_dims.begin() + 2);
if(dyn_global)
{
for(size_t i = 0; i < kdims; ++i)
{
output_dyn_dims.push_back(shape::dynamic_dimension{1, 1, 1});
}
return {input.type(), output_dyn_dims};
}
else
{
auto min_spatial_dims = calc_spatial_dim_out(input.min_lens(), kdims);
auto max_spatial_dims = calc_spatial_dim_out(input.max_lens(), kdims);
auto opt_spatial_dims = calc_spatial_dim_out(input.opt_lens(), kdims);
for(size_t i = 0; i < kdims; ++i)
{
output_dyn_dims.push_back(shape::dynamic_dimension{
min_spatial_dims[i], max_spatial_dims[i], opt_spatial_dims[i]});
}
return {input.type(), output_dyn_dims};
}
}
return inputs[0].with_lens(output_lens);
}
else
{
auto input_lens = input.lens();
size_t kdims() const
{
check_attribute_size();
return stride.size();
std::vector<std::size_t> output_lens(input_lens.begin(), input_lens.begin() + 2);
// Used for when normalize_compute_shape() is called again at model eval time
// for an originally dynamic shape. Since kernel shape is not used with dyn_global.
if(dyn_global)
{
for(size_t i = 0; i < kdims; ++i)
{
output_lens.push_back(1);
}
return {input.type(), output_lens};
}
else
{
auto output_spatial_lens = calc_spatial_dim_out(input_lens, kdims);
output_lens.insert(
output_lens.end(), output_spatial_lens.begin(), output_spatial_lens.end());
return inputs[0].with_lens(output_lens);
}
}
}
struct lpnorm_pool
......@@ -158,7 +222,11 @@ struct pooling
};
template <class Type, class Out, class In, class Op>
void calc_pooling(const shape& output_shape, Out& output, const In& input, Op op) const
void calc_pooling(const shape& output_shape,
Out& output,
const In& input,
const std::vector<std::size_t>& kernel_dims,
Op op) const
{
auto in_s = input.get_shape();
auto in_lens = in_s.lens();
......@@ -172,7 +240,7 @@ struct pooling
auto d_2 = dim - 2;
int start =
static_cast<int>(idx_o[dim] * stride[d_2]) - static_cast<int>(padding[d_2]);
int end = std::min(start + lengths[d_2], in_lens[dim]);
int end = std::min(start + kernel_dims[d_2], in_lens[dim]);
start = std::max(start, 0);
win_start.push_back(start);
win_size.push_back(end - start);
......@@ -198,21 +266,32 @@ struct pooling
});
}
argument compute(const shape& output_shape, std::vector<argument> args) const
argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
{
argument result{output_shape};
argument result{dyn_out.computed_shape};
auto input_lens = args[0].get_shape().lens();
std::vector<std::size_t> kernel_dims;
if(dyn_global)
{
kernel_dims.insert(kernel_dims.end(), input_lens.begin() + 2, input_lens.end());
}
else
{
kernel_dims = this->lengths;
}
visit_all(result, args[0])([&](auto output, auto input) {
using type = typename decltype(output)::value_type;
switch(mode)
{
case migraphx::op::pooling_mode::average:
calc_pooling<type>(output_shape, output, input, avg_pool{});
calc_pooling<type>(dyn_out.computed_shape, output, input, kernel_dims, avg_pool{});
break;
case migraphx::op::pooling_mode::max:
calc_pooling<type>(output_shape, output, input, max_pool{});
calc_pooling<type>(dyn_out.computed_shape, output, input, kernel_dims, max_pool{});
break;
case migraphx::op::pooling_mode::lpnorm:
calc_pooling<type>(output_shape, output, input, lpnorm_pool{lp_order});
calc_pooling<type>(
dyn_out.computed_shape, output, input, kernel_dims, lpnorm_pool{lp_order});
break;
}
});
......
src/include/migraphx/op/transpose.hpp
View file @ f17d6246
......@@ -29,6 +29,7 @@
#include <migraphx/config.hpp>
#include <migraphx/value.hpp>
#include <migraphx/op/normalize_attribute.hpp>
#include <migraphx/dyn_output.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -45,17 +46,15 @@ struct transpose
}
std::string name() const { return "transpose"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto input = inputs.at(0);
auto input_lens = input.lens();
auto input_strides = input.strides();
auto t = input.type();
check_shapes{inputs, *this, true}.has(1);
auto input = inputs.at(0);
if(dims.size() != input_lens.size())
if(dims.size() != input.ndim())
{
MIGRAPHX_THROW("Permutation has wrong number of axes");
MIGRAPHX_THROW("TRANSPOSE: Permutation has wrong number of axes");
}
std::vector<int64_t> axes(dims.size());
std::iota(axes.begin(), axes.end(), 0);
......@@ -63,19 +62,36 @@ struct transpose
{
MIGRAPHX_THROW("TRANSPOSE: Invalid permutation");
}
std::vector<size_t> output_lens(input_lens.size());
std::vector<size_t> output_strides(input_lens.size());
for(std::size_t i = 0; i < output_lens.size(); i++)
if(input.dynamic())
{
output_lens[i] = input_lens[dims[i]];
output_strides[i] = input_strides[dims[i]];
std::vector<shape::dynamic_dimension> output_dyn_dims(input.ndim());
std::transform(dims.cbegin(), dims.cend(), output_dyn_dims.begin(), [&](auto dim) {
return input.dyn_dims()[dim];
});
return {input.type(), output_dyn_dims};
}
else
{
auto input_lens = input.lens();
auto input_strides = input.strides();
std::vector<size_t> output_lens(input.ndim());
std::vector<size_t> output_strides(input.ndim());
for(std::size_t i = 0; i < input.ndim(); i++)
{
output_lens[i] = input_lens[dims[i]];
output_strides[i] = input_strides[dims[i]];
}
return {input.type(), output_lens, output_strides};
}
return {t, output_lens, output_strides};
}
argument compute(shape output_shape, std::vector<argument> args) const
argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
{
return args[0].reshape(output_shape);
return args[0].reshape(dyn_out.computed_shape);
}
std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
};
......
src/include/migraphx/shape_for_each.hpp
View file @ f17d6246
......@@ -31,6 +31,9 @@
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
/**
* Iterates the given function over the indices from the shape in order.
*/
template <class F>
void shape_for_each(const migraphx::shape& s, F f)
{
......@@ -51,7 +54,6 @@ void shape_for_each(const migraphx::shape& s, F f)
call(indices);
}
}
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
......
src/insert_pad.cpp
View file @ f17d6246
......@@ -77,14 +77,14 @@ static void update_pooling(const instruction_ref& input, const instruction_ref&
{
return;
}
auto kdims = input->get_shape().lens().size() - 2;
auto kdims = input->get_shape().ndim() - 2;
if(std::equal(op.padding.begin(),
op.padding.begin() + kdims,
op.padding.begin() + kdims,
op.padding.end()))
return;
std::vector<int64_t> padding(input->get_shape().lens().size() * 2, 0);
std::vector<int64_t> padding(input->get_shape().ndim() * 2, 0);
std::vector<size_t> pads_l(op.padding.begin(), op.padding.begin() + kdims);
std::vector<size_t> pads_r(op.padding.begin() + kdims, op.padding.end());
op.padding = std::vector<size_t>(kdims * 2, 0);
......
src/onnx/parse_pooling.cpp
View file @ f17d6246
......@@ -47,52 +47,42 @@ struct parse_pooling : op_parser<parse_pooling>
{"GlobalLpPool", "lpnorm"}};
}
instruction_ref parse(const op_desc& opd,
const onnx_parser& /*parser*/,
onnx_parser::node_info info,
std::vector<instruction_ref> args) const
value handle_values(const op_desc& opd,
onnx_parser::node_info info,
const shape& in_shape,
value values) const
{
const std::unordered_map<std::string, op::pooling_mode> mode_map = {
{"max", op::pooling_mode::max},
{"average", op::pooling_mode::average},
{"lpnorm", op::pooling_mode::lpnorm}};
std::string mode = opd.op_name;
if(not contains(mode_map, mode))
{
MIGRAPHX_THROW("onnx pooling mode must be [\"max\", \"average\", \"lpnorm\"]");
}
operation op = make_op("pooling", {{"mode", mode_map.at(mode)}});
value values = op.to_value();
auto l0 = args[0];
auto in_lens = l0->get_shape().lens();
assert(in_lens.size() > 2);
auto kdims = in_lens.size() - 2;
auto kdims = in_shape.ndim() - 2;
if(starts_with(opd.onnx_name, "Global"))
{
values["lengths"] = std::vector<size_t>(in_lens.begin() + 2, in_lens.end());
// if spatial dimensions are dynamic use dyn_global flag
if(in_shape.dynamic() and std::any_of(in_shape.dyn_dims().cbegin() + 2,
in_shape.dyn_dims().cend(),
[](auto dd) { return not dd.is_fixed(); }))
{
values["dyn_global"] = true;
values["lengths"] = std::vector<size_t>();
}
else
{
// works with static and fixed dynamic shape
auto m_lens = in_shape.max_lens();
values["lengths"] = std::vector<size_t>(m_lens.begin() + 2, m_lens.end());
}
}
// does not support ceil_mode
if(contains(info.attributes, "ceil_mode"))
{
values["ceil_mode"] = static_cast<bool>(info.attributes.at("ceil_mode").i());
}
// count include padding, if count include pad is 1, we always use
// explicit pad
int count_include_pad = 0;
if(contains(info.attributes, "count_include_pad"))
{
count_include_pad = info.attributes.at("count_include_pad").i();
}
if(contains(info.attributes, "strides"))
{
values["stride"].clear();
copy(info.attributes["strides"].ints(), std::back_inserter(values["stride"]));
check_attr_sizes(kdims, values["stride"].size(), "PARSE_POOLING: inconsistent strides");
}
if(contains(info.attributes, "kernel_shape"))
{
values["lengths"].clear();
......@@ -110,6 +100,46 @@ struct parse_pooling : op_parser<parse_pooling>
// ensure pads availabe only when auto_pad is "NOT_SET"
check_padding_mode(info, "POOLING");
return values;
}
instruction_ref parse(const op_desc& opd,
const onnx_parser& /*parser*/,
onnx_parser::node_info info,
std::vector<instruction_ref> args) const
{
std::string mode = opd.op_name;
const std::unordered_map<std::string, op::pooling_mode> mode_map = {
{"max", op::pooling_mode::max},
{"average", op::pooling_mode::average},
{"lpnorm", op::pooling_mode::lpnorm}};
if(not contains(mode_map, mode))
{
MIGRAPHX_THROW(
"PARSE_POOLING: onnx pooling mode must be [\"max\", \"average\", \"lpnorm\"]");
}
operation op = make_op("pooling", {{"mode", mode_map.at(mode)}});
value values = op.to_value();
auto l0 = args[0];
auto in_shape = l0->get_shape();
assert(in_shape.ndim() > 2);
auto kdims = in_shape.ndim() - 2;
values = handle_values(opd, info, in_shape, values);
// count include padding, if count include pad is 1, we always use
// explicit pad
int count_include_pad = 0;
if(contains(info.attributes, "count_include_pad"))
{
if(in_shape.dynamic())
{
MIGRAPHX_THROW("PARSE_POOLING: count_include_pad attribute is not supported for "
"dynamic input shape");
}
count_include_pad = info.attributes.at("count_include_pad").i();
}
std::vector<int64_t> paddings;
float pad_val = ((mode == "max") ? std::numeric_limits<float>::lowest() : 0.0f);
......@@ -123,14 +153,22 @@ struct parse_pooling : op_parser<parse_pooling>
if(contains(info.attributes, "auto_pad"))
{
values["padding"].clear();
// return paddings could be empty, then setting to 0 for no padding
cal_auto_padding_size(info,
values,
values["lengths"].to_vector<std::size_t>(),
{1, 1},
in_lens,
paddings);
if(in_shape.dynamic())
{
MIGRAPHX_THROW(
"PARSE_POOLING: Auto padding pooling with dynamic input shape not supported");
}
else
{
values["padding"].clear();
// return paddings could be empty, then setting to 0 for no padding
cal_auto_padding_size(info,
values,
values["lengths"].to_vector<std::size_t>(),
{1, 1},
in_shape.lens(),
paddings);
}
}
if(paddings.size() != 2 * kdims)
......@@ -150,6 +188,7 @@ struct parse_pooling : op_parser<parse_pooling>
values["stride"].resize(kdims);
std::fill_n(values["stride"].begin(), kdims, 1);
}
// used to calculate the supposed output shape
std::vector<int64_t> orig_padding = paddings;
......@@ -159,6 +198,11 @@ struct parse_pooling : op_parser<parse_pooling>
if(not slice_start.empty())
{
if(in_shape.dynamic())
{
MIGRAPHX_THROW(
"PARSE_POOLING: asymmetric padding not supported for dynamic input shape");
}
// calculate expected output shape
orig_padding.insert(orig_padding.begin() + kdims, 2, 0);
orig_padding.insert(orig_padding.begin(), 2, 0);
......
src/onnx/parse_transpose.cpp
View file @ f17d6246
......@@ -47,7 +47,7 @@ struct parse_transpose : op_parser<parse_transpose>
}
// if perm is empty, use the default value
auto n_dim = args.front()->get_shape().lens().size();
auto n_dim = args.front()->get_shape().ndim();
if(perm.empty())
{
perm.resize(n_dim);
......
src/targets/gpu/target.cpp
View file @ f17d6246
......@@ -149,8 +149,6 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
dead_code_elimination{},
pack_int8_args{},
dead_code_elimination{},
adjust_allocation{gpu_allocation_model{}},
dead_code_elimination{},
fuse_ops{&ctx, options.fast_math},
dead_code_elimination{},
replace_allocate{gpu_allocation_model{}, options.offload_copy},
......
test/literal_test.cpp
View file @ f17d6246
......@@ -49,6 +49,25 @@ TEST_CASE(literal_test)
EXPECT(l4.empty());
}
TEST_CASE(literal_nstd_shape_vector)
{
migraphx::shape nstd_shape{migraphx::shape::float_type, {1, 3, 2, 2}, {12, 1, 6, 3}};
std::vector<float> data(12);
std::iota(data.begin(), data.end(), 0);
auto l0 = migraphx::literal{nstd_shape, data};
// check data buffer is read in correctly
std::vector<float> expected_buffer = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
const auto* start = reinterpret_cast<const float*>(l0.data());
std::vector<float> l0_data{start, start + 12};
EXPECT(l0_data == expected_buffer);
// check that using visit() (that uses a tensor view) gives data in correct order
std::vector<float> results_vector(12);
l0.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
EXPECT(results_vector == data);
}
TEST_CASE(literal_os1)
{
migraphx::literal l{1};
......
test/onnx/gen_onnx.py
View file @ f17d6246
......@@ -237,6 +237,64 @@ def averagepool_3d_test():
return ([node], [x], [out])
@onnx_test
def averagepool_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[None, 3, 5, 5, 5])
out = helper.make_tensor_value_info('1', TensorProto.FLOAT,
[None, 3, 3, 3, 3])
node = onnx.helper.make_node('AveragePool',
inputs=['0'],
outputs=['1'],
kernel_shape=[3, 3, 3])
return ([node], [x], [out])
@onnx_test
def averagepool_dyn_autopad_error_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None, 1, 5, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, 1, 5, 5])
node = onnx.helper.make_node('AveragePool',
inputs=['x'],
outputs=['y'],
kernel_shape=[2, 2],
auto_pad='SAME_LOWER')
return ([node], [x], [y])
@onnx_test
def averagepool_dyn_asym_padding_error_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None, 1, 5, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, 1, 3, 3])
node = onnx.helper.make_node('AveragePool',
inputs=['x'],
outputs=['y'],
kernel_shape=[2, 2],
strides=[2, 2],
pads=[0, 0, 1, 1])
return ([node], [x], [y])
@onnx_test
def averagepool_dyn_cip_error_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None, 1, 5, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, 1, 1, 1])
node = onnx.helper.make_node('AveragePool',
inputs=['x'],
outputs=['y'],
kernel_shape=[2, 2],
count_include_pad=1)
return ([node], [x], [y])
@onnx_test
def averagepool_notset_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
......@@ -2069,6 +2127,21 @@ def globalavgpool_test():
return ([node], [x], [y])
@onnx_test
def globalavgpool_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[None, 3, 16, 16])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, 3, 1, 1])
node = onnx.helper.make_node(
'GlobalAveragePool',
inputs=['0'],
outputs=['1'],
)
return ([node], [x], [y])
@onnx_test
def globallppool_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 16, 16])
......@@ -2083,6 +2156,21 @@ def globallppool_test():
return ([node], [x], [y])
@onnx_test
def globallppool_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[1, 3, None, None])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1])
node = onnx.helper.make_node(
'GlobalLpPool',
inputs=['0'],
outputs=['1'],
)
return ([node], [x], [y])
@onnx_test
def globalmaxpool_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 16, 16])
......@@ -2097,6 +2185,21 @@ def globalmaxpool_test():
return ([node], [x], [y])
@onnx_test
def globalmaxpool_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[None, 3, 32, 32])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, 3, 1, 1])
node = onnx.helper.make_node(
'GlobalMaxPool',
inputs=['0'],
outputs=['1'],
)
return ([node], [x], [y])
@onnx_test
def greater_test():
ax1 = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
......@@ -6277,6 +6380,21 @@ def transpose_test():
return ([node], [x], [y])
@onnx_test
def transpose_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, 2, 2, 3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, 3, 2, 2])
node = onnx.helper.make_node(
'Transpose',
perm=[0, 3, 1, 2],
inputs=['0'],
outputs=['1'],
)
return ([node], [x], [y])
@onnx_test
def transpose_gather_test():
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 5, 4, 6])
......
test/onnx/onnx_test.cpp
View file @ f17d6246
......@@ -273,6 +273,51 @@ TEST_CASE(averagepool_3d_test)
EXPECT(p == prog);
}
TEST_CASE(averagepool_dyn_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto l0 = mm->add_parameter(
"0",
{migraphx::shape::float_type, {{1, 4, 0}, {3, 3, 0}, {5, 5, 0}, {5, 5, 0}, {5, 5, 0}}});
auto ret = mm->add_instruction(migraphx::make_op("pooling",
{{"mode", migraphx::op::pooling_mode::average},
{"padding", {0, 0, 0, 0, 0, 0}},
{"stride", {1, 1, 1}},
{"lengths", {3, 3, 3}}}),
l0);
mm->add_return({ret});
migraphx::onnx_options options;
options.default_dyn_dim_value = {1, 4, 0};
auto prog = migraphx::parse_onnx("averagepool_dyn_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(averagepool_dyn_autopad_error_test)
{
migraphx::onnx_options options;
options.default_dyn_dim_value = {1, 4, 0};
EXPECT(test::throws(
[&] { migraphx::parse_onnx("averagepool_dyn_autopad_error_test.onnx", options); }));
}
TEST_CASE(averagepool_dyn_asym_padding_error_test)
{
migraphx::onnx_options options;
options.default_dyn_dim_value = {1, 4, 0};
EXPECT(test::throws(
[&] { migraphx::parse_onnx("averagepool_dyn_asym_padding_error_test.onnx", options); }));
}
TEST_CASE(averagepool_dyn_cip_error_test)
{
migraphx::onnx_options options;
options.default_dyn_dim_value = {1, 4, 0};
EXPECT(test::throws(
[&] { migraphx::parse_onnx("averagepool_dyn_cip_error_test.onnx", options); }));
}
TEST_CASE(averagepool_notset_test)
{
migraphx::program p;
......@@ -2144,6 +2189,28 @@ TEST_CASE(globalavgpool_test)
EXPECT(p == prog);
}
TEST_CASE(globalavgpool_dyn_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto input =
mm->add_parameter("0",
migraphx::shape{migraphx::shape::float_type,
{{1, 4, 0}, {3, 3, 0}, {16, 16, 0}, {16, 16, 0}}});
auto ret = mm->add_instruction(migraphx::make_op("pooling",
{{"mode", migraphx::op::pooling_mode::average},
{"lengths", {16, 16}},
{"padding", {0, 0, 0, 0}}}),
input);
mm->add_return({ret});
migraphx::onnx_options options;
options.default_dyn_dim_value = {1, 4, 0};
auto prog = parse_onnx("globalavgpool_dyn_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(globallppool_test)
{
migraphx::program p;
......@@ -2161,6 +2228,29 @@ TEST_CASE(globallppool_test)
EXPECT(p == prog);
}
TEST_CASE(globallppool_dyn_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto input =
mm->add_parameter("0",
migraphx::shape{migraphx::shape::float_type,
{{1, 1, 0}, {3, 3, 0}, {16, 32, 0}, {16, 32, 0}}});
auto ret = mm->add_instruction(migraphx::make_op("pooling",
{{"mode", migraphx::op::pooling_mode::lpnorm},
{"dyn_global", true},
{"padding", {0, 0, 0, 0}},
{"lengths", {}}}),
input);
mm->add_return({ret});
migraphx::onnx_options options;
options.default_dyn_dim_value = {16, 32, 0};
auto prog = migraphx::parse_onnx("globallppool_dyn_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(globalmaxpool_test)
{
migraphx::program p;
......@@ -2178,6 +2268,28 @@ TEST_CASE(globalmaxpool_test)
EXPECT(p == prog);
}
TEST_CASE(globalmaxpool_dyn_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto input =
mm->add_parameter("0",
migraphx::shape{migraphx::shape::float_type,
{{1, 4, 0}, {3, 3, 0}, {32, 32, 0}, {32, 32, 0}}});
auto ret = mm->add_instruction(migraphx::make_op("pooling",
{{"mode", migraphx::op::pooling_mode::max},
{"lengths", {32, 32}},
{"padding", {0, 0, 0, 0}}}),
input);
mm->add_return({ret});
migraphx::onnx_options options;
options.default_dyn_dim_value = {1, 4, 0};
auto prog = parse_onnx("globalmaxpool_dyn_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(greater_test)
{
migraphx::program p;
......@@ -5973,6 +6085,24 @@ TEST_CASE(transpose_test)
EXPECT(p == prog);
}
TEST_CASE(transpose_dyn_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto input = mm->add_parameter(
"0",
migraphx::shape{migraphx::shape::float_type, {{1, 4, 0}, {2, 2, 0}, {2, 2, 0}, {3, 3, 0}}});
std::vector<int64_t> perm{0, 3, 1, 2};
auto t0 = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), input);
mm->add_return({t0});
migraphx::onnx_options options;
options.default_dyn_dim_value = {1, 4, 0};
auto prog = migraphx::parse_onnx("transpose_dyn_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(topk_attrk_test)
{
migraphx::program p;
......
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