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Commit bc062ca3 authored by charlie's avatar charlie
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Merge branch 'develop' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dot_broadcast

parents 47a07c3a a41cd5c0
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Showing with 971 additions and 219 deletions
src/onnx/include/migraphx/onnx/pooling.hpp 0 → 100644
View file @ bc062ca3
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2023 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_AMDMIGRAPHX_ONNX_POOLING_HPP
#define MIGRAPHX_GUARD_AMDMIGRAPHX_ONNX_POOLING_HPP
#include <migraphx/config.hpp>
#include <migraphx/onnx/onnx_parser.hpp>
#include <migraphx/onnx/op_parser.hpp>
#include <migraphx/instruction.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace onnx {
value handle_pooling_values(const op_desc& opd,
onnx_parser::node_info info,
const shape& in_shape,
value values);
instruction_ref add_pooling_op(const op_desc& opd, onnx_parser::node_info info, instruction_ref l0);
} // namespace onnx
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/onnx/parse_pooling.cpp
View file @ bc062ca3
...@@ -22,14 +22,8 @@ ...@@ -22,14 +22,8 @@
* THE SOFTWARE. * THE SOFTWARE.
*/ */
#include <migraphx/onnx/op_parser.hpp> #include <migraphx/onnx/op_parser.hpp>
#include <migraphx/onnx/checks.hpp> #include <migraphx/onnx/pooling.hpp>
#include <migraphx/onnx/padding.hpp>
#include <migraphx/op/pad.hpp>
#include <migraphx/op/pooling.hpp>
#include <migraphx/instruction.hpp> #include <migraphx/instruction.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/make_op.hpp>
namespace migraphx { namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS { inline namespace MIGRAPHX_INLINE_NS {
...@@ -39,76 +33,14 @@ struct parse_pooling : op_parser<parse_pooling> ...@@ -39,76 +33,14 @@ struct parse_pooling : op_parser<parse_pooling>
{ {
std::vector<op_desc> operators() const std::vector<op_desc> operators() const
{ {
return {{"AveragePool", "average"}, return {
{"GlobalAveragePool", "average"}, {"AveragePool", "average"},
{"GlobalMaxPool", "max"}, {"GlobalAveragePool", "average"},
{"MaxPool", "max"}, {"GlobalMaxPool", "max"},
{"LpPool", "lpnorm"}, {"MaxPool", "max"},
{"GlobalLpPool", "lpnorm"}}; {"LpPool", "lpnorm"},
} {"GlobalLpPool", "lpnorm"},
};
value handle_values(const op_desc& opd,
onnx_parser::node_info info,
const shape& in_shape,
value values) const
{
auto kdims = in_shape.ndim() - 2;
if(starts_with(opd.onnx_name, "Global"))
{
// 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());
}
}
if(contains(info.attributes, "ceil_mode"))
{
values["ceil_mode"] = static_cast<bool>(info.attributes.at("ceil_mode").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();
copy(info.attributes["kernel_shape"].ints(), std::back_inserter(values["lengths"]));
check_attr_sizes(
kdims, values["lengths"].size(), "PARSE_POOLING: inconsistent lengths");
}
if(contains(info.attributes, "dilations"))
{
values["dilations"].clear();
copy(info.attributes["dilations"].ints(), std::back_inserter(values["dilations"]));
check_attr_sizes(
kdims, values["dilations"].size(), "PARSE_POOLING: inconsistent dilations");
}
// lp_order attribute
if(contains(info.attributes, "p"))
{
values["lp_order"] = info.attributes.at("p").i();
}
// ensure pads available only when auto_pad is "NOT_SET"
check_padding_mode(info, "POOLING");
return values;
} }
instruction_ref parse(const op_desc& opd, instruction_ref parse(const op_desc& opd,
...@@ -116,148 +48,8 @@ struct parse_pooling : op_parser<parse_pooling> ...@@ -116,148 +48,8 @@ struct parse_pooling : op_parser<parse_pooling>
onnx_parser::node_info info, onnx_parser::node_info info,
std::vector<instruction_ref> args) const std::vector<instruction_ref> args) const
{ {
std::string mode = opd.op_name; return add_pooling_op(opd, std::move(info), args[0]);
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);
if(contains(info.attributes, "pads"))
{
values["padding"].clear();
copy(info.attributes["pads"].ints(), std::back_inserter(paddings));
check_attr_sizes(
kdims, paddings.size() / 2, "PARSE_POOLING: inconsistent explicit paddings");
}
if(paddings.size() != 2 * kdims)
{
paddings.resize(kdims * 2);
std::fill_n(paddings.begin(), 2 * kdims, 0);
}
if(values["padding"].size() != kdims)
{
values["padding"].resize(kdims);
std::fill_n(values["padding"].begin(), kdims, 0);
}
if(values["stride"].size() != kdims)
{
values["stride"].resize(kdims);
std::fill_n(values["stride"].begin(), kdims, 1);
}
if(values["dilations"].size() != kdims)
{
values["dilations"].resize(kdims);
std::fill_n(values["dilations"].begin(), kdims, 1);
}
// used to calculate the supposed output shape
std::vector<int64_t> orig_padding = paddings;
if(contains(info.attributes, "auto_pad") and
to_upper(info.attributes["auto_pad"].s()) != "NOTSET")
{
auto auto_pad = to_upper(info.attributes["auto_pad"].s());
// don't use the given padding sizes, if any
// values["padding"].clear();
if(in_shape.dynamic())
{
// set padding_mode to trigger auto padding at runtime
bool is_same_upper = (auto_pad.find("SAME_UPPER") != std::string::npos);
values["padding_mode"] = is_same_upper ? to_value(op::padding_mode_t::same_upper)
: to_value(op::padding_mode_t::same_lower);
}
else
{
// Calculate auto padding
cal_auto_padding_size(info,
values,
values["lengths"].to_vector<std::size_t>(),
values["dilations"].to_vector<std::size_t>(),
in_shape.lens(),
paddings);
values["padding"] = paddings;
// default padding_mode indicates that padding sizes are not calculated dynamically
values["padding_mode"] = migraphx::op::padding_mode_t::default_;
}
}
std::vector<int64_t> slice_start;
std::vector<int64_t> slice_end;
tune_padding_size(values, paddings, count_include_pad, slice_start);
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);
op::pad pad{orig_padding, 0.0f};
shape padded_shape = pad.compute_shape({l0->get_shape()});
// make an op just to get its output shape
auto out_lens = make_op("pooling", values).compute_shape({padded_shape}).lens();
// compute slice_end information
slice_end.resize(slice_start.size());
std::transform(out_lens.begin() + 2,
out_lens.end(),
slice_start.begin(),
slice_end.begin(),
[](auto i, auto j) { return i + j; });
}
values["padding"] = std::vector<size_t>(paddings.begin(), paddings.end());
check_asym_padding(info, l0, paddings, values, count_include_pad, pad_val);
op.from_value(values);
auto l1 = info.add_instruction(op, l0);
if(not slice_start.empty())
{
std::vector<int64_t> axes(kdims);
std::iota(axes.begin(), axes.end(), 2);
l1 = info.add_instruction(
make_op("slice", {{"axes", axes}, {"starts", slice_start}, {"ends", slice_end}}),
l1);
}
return l1;
}
}; };
} // namespace onnx } // namespace onnx
......
src/onnx/parse_qlinearglavgpool.cpp → src/onnx/parse_qlinearpooling.cpp
View file @ bc062ca3
...@@ -23,6 +23,7 @@ ...@@ -23,6 +23,7 @@
*/ */
#include <migraphx/onnx/op_parser.hpp> #include <migraphx/onnx/op_parser.hpp>
#include <migraphx/onnx/pooling.hpp>
#include <migraphx/ranges.hpp> #include <migraphx/ranges.hpp>
#include <migraphx/op/pooling.hpp> #include <migraphx/op/pooling.hpp>
#include <migraphx/make_op.hpp> #include <migraphx/make_op.hpp>
...@@ -36,90 +37,56 @@ namespace onnx { ...@@ -36,90 +37,56 @@ namespace onnx {
/* /*
********************************************************************************* *********************************************************************************
* Reference: see QLinearGlobalAveragePool in * * Reference: see QLinearAveragePool and QLinearGlobalAveragePool in *
* github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md * * github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md *
********************************************************************************* *********************************************************************************
*/
QLinearGlobalAveragePool consumes an input tensor X and applies struct parse_qlinearpooling : op_parser<parse_qlinearpooling>
Average pooling across the values in the same channel. This is
equivalent to AveragePool with kernel size equal to the spatial
dimension of input tensor. Input is of type uint8_t or int8_t.
Version
This version of the operator has been available since version 1 of the 'com.microsoft' operator set.
Attributes
channels_last : int
Inputs
X : T
Input data tensor from the previous operator; According to channels_last, dimensions for image case
are (N x C x H x W), or (N x H x W x C) where N is the batch size, C is the number of channels, and
H and W are the height and the width of the data. For non image case, the dimensions are in the form
of (N x C x D1 x D2 ... Dn), or (N x D1 X D2 ... Dn x C) where N is the batch size.
x_scale : tensor(float)
Scale of quantized input 'X'. It must be a scalar.
x_zero_point : T
Zero point tensor for input 'X'. It must be a scalar.
y_scale : tensor(float)
Scale of quantized output 'Y'. It must be a scalar.
y_zero_point : T
Zero point tensor for output 'Y'. It must be a scalar.
Outputs
Y : T
Output data tensor from pooling across the input tensor. The output tensor has the same rank as the
input. with the N and C value keep it value, while the other dimensions are all 1. Type Constraints
T : tensor(uint8), tensor(int8)
Constrain input and output types to signed/unsigned int8 tensors.
*/
struct parse_qlinearglobalaveragepool : op_parser<parse_qlinearglobalaveragepool>
{ {
std::vector<op_desc> operators() const { return {{"QLinearGlobalAveragePool"}}; } std::vector<op_desc> operators() const
// basic type checking for QLinearGlobalAveragePool Operator
void check_inputs(const std::vector<instruction_ref>& args) const
{ {
if(args.size() < 5) return {{"QLinearGlobalAveragePool", "average"}, {"QLinearAveragePool", "average"}};
MIGRAPHX_THROW("QLINEARGLOBALAVERAGEPOOL: missing inputs"); }
const auto& in_x = args[0]; void check_inputs(const op_desc& opd, const std::vector<instruction_ref>& args) const
const auto& zero_pt_x = args[2]; {
const auto& zero_pt_y = args[4]; const auto& in_x = args[0];
const auto onnx_name = opd.onnx_name;
if(in_x->get_shape().ndim() <= 2) if(in_x->get_shape().ndim() <= 2)
MIGRAPHX_THROW("QLINEARGLOBALAVERAGEPOOL: input dimensions too small"); MIGRAPHX_THROW(onnx_name + ": input dimensions too small");
auto type_x = in_x->get_shape().type(); auto type_x = in_x->get_shape().type();
if(type_x != migraphx::shape::int8_type and type_x != migraphx::shape::uint8_type) if(type_x != migraphx::shape::int8_type and type_x != migraphx::shape::uint8_type)
MIGRAPHX_THROW("QLINEARGLOBALAVERAGEPOOL: unsupported input type"); MIGRAPHX_THROW(onnx_name + ": unsupported input type");
const auto& zero_pt_x = args[2];
if(type_x != zero_pt_x->get_shape().type()) if(type_x != zero_pt_x->get_shape().type())
MIGRAPHX_THROW("QLINEARGLOBALAVERAGEPOOL: mismatched type: input zero point"); MIGRAPHX_THROW(onnx_name + ": mismatched type: input zero point");
if(type_x != zero_pt_y->get_shape().type()) if(args.size() == 5)
MIGRAPHX_THROW("QLINEARGLOBALAVERAGEPOOL: mismatched type: output zero point"); {
const auto& zero_pt_y = args[4];
if(type_x != zero_pt_y->get_shape().type())
MIGRAPHX_THROW(onnx_name + ": mismatched type: output zero point");
}
} }
instruction_ref parse(const op_desc& /* opd */, instruction_ref parse(const op_desc& opd,
const onnx_parser& parser, const onnx_parser& parser,
const onnx_parser::node_info& info, const onnx_parser::node_info& info,
const std::vector<instruction_ref>& args) const const std::vector<instruction_ref>& args) const
{ {
int channels_last = if(contains(info.attributes, "channel_last"))
parser.parse_value(info.attributes.at("channels_last")).template at<int>(); {
if(channels_last != 0) int channels_last =
MIGRAPHX_THROW( parser.parse_value(info.attributes.at("channels_last")).template at<int>();
"QLINEARGLOBALAVERAGEPOOL: channels_last (N x D1..Dn x C) is not supported"); if(channels_last != 0)
MIGRAPHX_THROW(opd.onnx_name + ": channels_last (N x D1..Dn x C) is not supported");
}
check_inputs(args); check_inputs(opd, args);
// Input: X // Input: X
...@@ -128,21 +95,18 @@ struct parse_qlinearglobalaveragepool : op_parser<parse_qlinearglobalaveragepool ...@@ -128,21 +95,18 @@ struct parse_qlinearglobalaveragepool : op_parser<parse_qlinearglobalaveragepool
const auto& zero_pt_x = args[2]; const auto& zero_pt_x = args[2];
auto dquant_x = bcast_qdq_instr("dequantizelinear", in_x, scale_x, zero_pt_x, info); auto dquant_x = bcast_qdq_instr("dequantizelinear", in_x, scale_x, zero_pt_x, info);
// Output Y = globalaveragepool(X) // Output Y = pooling_op(X)
auto op = migraphx::op::pooling{migraphx::op::pooling_mode::average};
auto lens = in_x->get_shape().lens();
std::vector<size_t> lengths(lens.begin() + 2, lens.end());
op.lengths = lengths;
op.padding = std::vector<size_t>(lens.size());
auto out_y = info.add_instruction(op, dquant_x);
const auto& scale_y = args[3]; auto out_y = add_pooling_op(opd, info, dquant_x);
const auto& zero_pt_y = args[4];
auto out_quant_y = bcast_qdq_instr("quantizelinear", out_y, scale_y, zero_pt_y, info); const auto& in_scale_y = args[3];
// zero_pt for Y is supplied as the last optional argument..
if(args.size() == 5)
return (bcast_qdq_instr("quantizelinear", out_y, in_scale_y, args[4], info));
return out_quant_y; // if no zero_pt: just broadcast the scale..
auto bcast_scale_y = bcast_scalar_instr(out_y->get_shape(), in_scale_y, info);
return (info.add_instruction(migraphx::make_op("quantizelinear"), out_y, bcast_scale_y));
} }
}; };
......
src/onnx/pooling.cpp 0 → 100644
View file @ bc062ca3
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2023 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.
*/
#include <migraphx/onnx/pooling.hpp>
#include <migraphx/onnx/checks.hpp>
#include <migraphx/onnx/padding.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/op/pooling.hpp>
#include <migraphx/op/pad.hpp>
#include <migraphx/ranges.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace onnx {
value handle_pooling_values(const op_desc& opd,
onnx_parser::node_info info,
const shape& in_shape,
value values)
{
auto kdims = in_shape.ndim() - 2;
if(starts_with(opd.onnx_name, "Global") or starts_with(opd.onnx_name, "QLinearGlobal"))
{
// 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());
}
}
if(contains(info.attributes, "ceil_mode"))
{
values["ceil_mode"] = static_cast<bool>(info.attributes.at("ceil_mode").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();
copy(info.attributes["kernel_shape"].ints(), std::back_inserter(values["lengths"]));
check_attr_sizes(kdims, values["lengths"].size(), "PARSE_POOLING: inconsistent lengths");
}
if(contains(info.attributes, "dilations"))
{
values["dilations"].clear();
copy(info.attributes["dilations"].ints(), std::back_inserter(values["dilations"]));
check_attr_sizes(
kdims, values["dilations"].size(), "PARSE_POOLING: inconsistent dilations");
}
// lp_order attribute
if(contains(info.attributes, "p"))
{
values["lp_order"] = info.attributes.at("p").i();
}
// ensure pads available only when auto_pad is "NOT_SET"
check_padding_mode(info, "POOLING");
return values;
}
instruction_ref add_pooling_op(const op_desc& opd, onnx_parser::node_info info, instruction_ref l0)
{
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 in_shape = l0->get_shape();
assert(in_shape.ndim() > 2);
auto kdims = in_shape.ndim() - 2;
values = handle_pooling_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);
if(contains(info.attributes, "pads"))
{
values["padding"].clear();
copy(info.attributes["pads"].ints(), std::back_inserter(paddings));
check_attr_sizes(
kdims, paddings.size() / 2, "PARSE_POOLING: inconsistent explicit paddings");
}
if(paddings.size() != 2 * kdims)
{
paddings.resize(kdims * 2);
std::fill_n(paddings.begin(), 2 * kdims, 0);
}
if(values["padding"].size() != kdims)
{
values["padding"].resize(kdims);
std::fill_n(values["padding"].begin(), kdims, 0);
}
if(values["stride"].size() != kdims)
{
values["stride"].resize(kdims);
std::fill_n(values["stride"].begin(), kdims, 1);
}
if(values["dilations"].size() != kdims)
{
values["dilations"].resize(kdims);
std::fill_n(values["dilations"].begin(), kdims, 1);
}
// used to calculate the supposed output shape
std::vector<int64_t> orig_padding = paddings;
// TODO: add parsing for dilations
if(contains(info.attributes, "auto_pad") and
to_upper(info.attributes["auto_pad"].s()) != "NOTSET")
{
auto auto_pad = to_upper(info.attributes["auto_pad"].s());
// don't use the given padding sizes, if any
// values["padding"].clear();
if(in_shape.dynamic())
{
// set padding_mode to trigger auto padding at runtime
bool is_same_upper = (auto_pad.find("SAME_UPPER") != std::string::npos);
values["padding_mode"] = is_same_upper ? to_value(op::padding_mode_t::same_upper)
: to_value(op::padding_mode_t::same_lower);
}
else
{
// Calculate auto padding
// dilations (argument 4) not supported; default to all 1's
cal_auto_padding_size(info,
values,
values["lengths"].to_vector<std::size_t>(),
values["dilations"].to_vector<std::size_t>(),
in_shape.lens(),
paddings);
values["padding"] = paddings;
// default padding_mode indicates that padding sizes are not calculated dynamically
values["padding_mode"] = migraphx::op::padding_mode_t::default_;
}
}
std::vector<int64_t> slice_start;
std::vector<int64_t> slice_end;
tune_padding_size(values, paddings, count_include_pad, slice_start);
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);
op::pad pad{orig_padding, 0.0f};
shape padded_shape = pad.compute_shape({l0->get_shape()});
// make an op just to get its output shape
auto out_lens = make_op("pooling", values).compute_shape({padded_shape}).lens();
// compute slice_end information
slice_end.resize(slice_start.size());
std::transform(out_lens.begin() + 2,
out_lens.end(),
slice_start.begin(),
slice_end.begin(),
[](auto i, auto j) { return i + j; });
}
values["padding"] = std::vector<size_t>(paddings.begin(), paddings.end());
check_asym_padding(info, l0, paddings, values, count_include_pad, pad_val);
op.from_value(values);
auto l1 = info.add_instruction(op, l0);
if(not slice_start.empty())
{
std::vector<int64_t> axes(kdims);
std::iota(axes.begin(), axes.end(), 2);
l1 = info.add_instruction(
make_op("slice", {{"axes", axes}, {"starts", slice_start}, {"ends", slice_end}}), l1);
}
return l1;
}
} // namespace onnx
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
test/onnx/gen_onnx.py
View file @ bc062ca3
...@@ -5994,6 +5994,263 @@ def qlinearadd_bcast_test(): ...@@ -5994,6 +5994,263 @@ def qlinearadd_bcast_test():
[sc_a, zero_pt_a, sc_b, zero_pt_b, sc_c, zero_pt_c]) [sc_a, zero_pt_a, sc_b, zero_pt_b, sc_c, zero_pt_c])
@onnx_test()
def qlinearaveragepool_1d_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 32])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.05])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 3, 31])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.05])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[16])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[2],
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_2d_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 4, 4])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.05])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 3, 3, 3])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.015])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[16])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[2, 2],
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_2d_ceil_test():
x = helper.make_tensor_value_info('x', TensorProto.UINT8, [1, 1, 4, 4])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.UINT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.UINT8, [1, 1, 2, 2])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.05])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.UINT8, [],
[0])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[3, 3],
strides=[2, 2],
ceil_mode=True,
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_2d_dilations_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 1, 4, 4])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 1, 2, 2])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.25])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[84])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[2, 2],
strides=[1, 1],
dilations=[2, 2],
ceil_mode=True,
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_2d_pads_count_include_pad_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 4, 4])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.05])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 3, 6, 6])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.01])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[32])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[3, 3],
pads=[2, 2, 2, 2],
count_include_pad=1,
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_2d_same_lower_test():
x = helper.make_tensor_value_info('x', TensorProto.UINT8, [1, 3, 4, 4])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.UINT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.UINT8, [1, 3, 4, 4])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.5])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.UINT8, [],
[0])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[2, 2],
auto_pad="SAME_LOWER",
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_2d_same_upper_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 4, 4])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[32])
y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 3, 4, 4])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.25])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[0])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[2, 2],
auto_pad="SAME_UPPER",
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_2d_strides_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 8, 8])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.05])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 3, 2, 2])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.05])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[8])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[5, 5],
strides=[2, 2],
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_3d_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 3, 3, 3])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.05])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 3, 2, 2, 2])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.02])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[0])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[2, 2, 2],
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_notset_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 1, 5, 5])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 1, 1, 1])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.5])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[10])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[6, 6],
strides=[2, 2],
pads=[0, 0, 1, 1],
channels_last=0,
auto_pad='NOTSET')
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_nt_cip_test():
x = helper.make_tensor_value_info('x', TensorProto.UINT8, [1, 1, 5, 5])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.UINT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.UINT8, [1, 1, 1, 1])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.5])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.UINT8, [],
[10])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[6, 6],
strides=[2, 2],
pads=[0, 0, 1, 1],
channels_last=0,
auto_pad='NOTSET',
count_include_pad=1)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test() @onnx_test()
def qlinearconv_test(): def qlinearconv_test():
# https://xadupre.github.io/draft/onnx/onnx_doc_folder/onnx__QLinearConv.html # https://xadupre.github.io/draft/onnx/onnx_doc_folder/onnx__QLinearConv.html
......
test/onnx/onnx_test.cpp
View file @ bc062ca3
...@@ -5595,6 +5595,54 @@ TEST_CASE(qlinearadd_test) ...@@ -5595,6 +5595,54 @@ TEST_CASE(qlinearadd_test)
EXPECT(p.sort() == prog.sort()); EXPECT(p.sort() == prog.sort());
} }
TEST_CASE(qlinearaveragepool_notset_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto sc_x = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.5}});
auto z_pt_x = mm->add_literal(migraphx::literal{migraphx::shape::int8_type, {0}});
auto sc_y = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.5}});
auto z_pt_y = mm->add_literal(migraphx::literal{migraphx::shape::int8_type, {10}});
auto x = mm->add_parameter("x", migraphx::shape{migraphx::shape::int8_type, {1, 1, 5, 5}});
auto scale_x_bcast = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 5, 5}}}), sc_x);
auto z_pt_x_bcast = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 5, 5}}}), z_pt_x);
auto fp_x =
mm->add_instruction(migraphx::make_op("dequantizelinear"), x, scale_x_bcast, z_pt_x_bcast);
auto fp_y =
mm->add_instruction(migraphx::make_op("pooling",
{{"mode", migraphx::op::pooling_mode::average},
{"padding", {2, 2, 2, 2}},
{"stride", {2, 2}},
{"lengths", {6, 6}}}),
fp_x);
fp_y = mm->add_instruction(
migraphx::make_op("slice", {{"axes", {2, 3}}, {"starts", {1, 1}}, {"ends", {2, 2}}}), fp_y);
auto scale_y_bcast = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 1, 1}}}), sc_y);
auto z_pt_y_bcast = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 1, 1}}}), z_pt_y);
auto y =
mm->add_instruction(migraphx::make_op("quantizelinear"), fp_y, scale_y_bcast, z_pt_y_bcast);
mm->add_return({y});
auto prog = migraphx::parse_onnx("qlinearaveragepool_notset_test.onnx");
EXPECT(p == prog);
}
TEST_CASE(qlinearconv_test) TEST_CASE(qlinearconv_test)
{ {
migraphx::program p; migraphx::program p;
......
test/onnx/verify_onnx.cpp
View file @ bc062ca3
...@@ -1686,6 +1686,252 @@ TEST_CASE(qlinearadd_bcast_test) ...@@ -1686,6 +1686,252 @@ TEST_CASE(qlinearadd_bcast_test)
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold)); EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
} }
TEST_CASE(qlinearaveragepool_1d_test)
{
auto p = migraphx::parse_onnx("qlinearaveragepool_1d_test.onnx");
p.compile(migraphx::make_target("ref"));
std::vector<int8_t> data_x = {
-31, 51, 125, 30, -17, -125, 121, -19, -13, 52, 18, -70, 97, 15, 56, 42,
-65, -26, 40, -109, -70, 83, 110, -94, 34, 70, 5, -23, -60, -68, 19, 48,
-113, 3, -44, 20, -99, -103, -49, -38, 122, 75, 38, -7, -65, -56, 96, 99,
50, -27, -114, 49, -65, 105, -3, 54, 8, 38, -81, -46, -86, -46, -104, 36,
22, -51, 48, 59, -116, 6, 93, 16, -111, 98, 51, -87, -111, -74, -39, 7,
107, 115, 59, 60, -66, -14, -106, -23, 119, -122, -51, -100, 26, 125, 45, 90};
migraphx::shape s_x{migraphx::shape::int8_type, {1, 3, 32}};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s_x, data_x.data());
auto result = p.eval(pp).back();
std::vector<int8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<int8_t> gold = {
26, 104, 94, 22, -55, 14, 67, 0, 36, 51, -10, 29, 72, 52, 65, 5,
-30, 23, -19, -74, 23, 112, 24, -14, 68, 54, 7, -26, -48, -8, 50, -39,
-4, 4, -24, -85, -60, -28, 58, 114, 72, 31, -20, -44, 36, 114, 90, 28,
-54, -16, 8, 36, 67, 42, 47, 39, -6, -48, -50, -50, -59, -18, 2, 15,
70, -13, -39, 66, 71, -32, 9, 90, -2, -83, -76, -40, 0, 73, 127, 103,
75, 13, -24, -44, -48, 64, 15, -70, -60, -21, 92, 101, 84};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearaveragepool_2d_test)
{
auto p = migraphx::parse_onnx("qlinearaveragepool_2d_test.onnx");
p.compile(migraphx::make_target("ref"));
std::vector<int8_t> data_x = {84, -73, 117, -2, -97, 72, 67, 27, 1, -44, 110, 51,
9, 7, 58, 113, -34, 34, 124, -20, 6, 66, 68, 98,
31, -84, 25, 101, -69, -100, -68, 116, 33, -121, 78, 49,
102, -86, 65, 69, -87, -89, 16, -125, 51, -54, -86, 79};
migraphx::shape s_x{migraphx::shape::int8_type, {1, 3, 4, 4}};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s_x, data_x.data());
auto result = p.eval(pp).back();
std::vector<int8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<int8_t> gold = {4, 127, 127, -41, 127, 127, -6, 125, 127,
76, 127, 127, 32, 78, 127, -128, -128, 127,
-44, -37, 127, -117, -62, 37, -128, -128, -81};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearaveragepool_2d_ceil_test)
{
auto p = migraphx::parse_onnx("qlinearaveragepool_2d_ceil_test.onnx");
p.compile(migraphx::make_target("ref"));
std::vector<uint8_t> data_x = {2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32};
migraphx::shape s_x{migraphx::shape::uint8_type, {1, 1, 4, 4}};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s_x, data_x.data());
auto result = p.eval(pp).back();
std::vector<uint8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<uint8_t> gold = {120, 150, 240, 255};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearaveragepool_2d_dilations_test)
{
auto p = migraphx::parse_onnx("qlinearaveragepool_2d_dilations_test.onnx");
p.compile(migraphx::make_target("ref"));
std::vector<int8_t> data_x = {2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32};
migraphx::shape s_x{migraphx::shape::int8_type, {1, 1, 4, 4}};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s_x, data_x.data());
auto result = p.eval(pp).back();
std::vector<int8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<int8_t> gold = {108, 112, 124, 127};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearaveragepool_2d_pads_count_include_pad_test)
{
auto p = migraphx::parse_onnx("qlinearaveragepool_2d_pads_count_include_pad_test.onnx");
p.compile(migraphx::make_target("ref"));
std::vector<int8_t> data_x = {-30, 50, 91, -87, -21, -113, -16, 6, -128, 104, 82, -126,
54, 41, -71, 62, -11, -111, 13, 104, -43, -48, 30, 85,
-62, -33, -27, -114, 32, -17, 30, -26, -18, 15, 17, 100,
-122, 115, 84, -34, -86, 82, 102, -117, -91, -105, 112, 91};
migraphx::shape s_x{migraphx::shape::int8_type, {1, 3, 4, 4}};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s_x, data_x.data());
auto result = p.eval(pp).back();
std::vector<int8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<int8_t> gold = {
15, 43, 94, 62, 34, -16, 4, -31, 10, -6, 29, -13, -67, -45, 43, 27, 4, -83,
-21, -3, -6, 15, -3, 0, -9, 71, 78, 83, 3, -4, 62, 85, 45, 50, 27, 66,
26, -36, -29, 35, 97, 90, 2, -86, -62, 73, 127, 127, -32, -128, -128, -24, 83, 74,
-9, -63, -45, -35, 20, 1, 15, -12, -11, -72, -44, -46, 50, 40, 57, 25, 34, 18,
22, 30, 40, 105, 97, 88, -46, 26, 83, 127, 125, 69, -94, 24, 127, 127, 116, 4,
-128, -83, 83, 127, 127, -1, -66, -79, 40, 124, 127, 18, -19, -77, -15, 86, 127, 83};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearaveragepool_2d_same_lower_test)
{
auto p = migraphx::parse_onnx("qlinearaveragepool_2d_same_lower_test.onnx");
p.compile(migraphx::make_target("ref"));
std::vector<uint8_t> data_x = {195, 102, 250, 61, 222, 6, 243, 218, 230, 105, 36, 116,
194, 31, 113, 85, 126, 204, 80, 38, 115, 167, 221, 67,
69, 140, 11, 209, 136, 120, 39, 96, 29, 5, 167, 40,
58, 51, 157, 179, 244, 149, 76, 243, 126, 144, 192, 199};
migraphx::shape s_x{migraphx::shape::uint8_type, {1, 3, 4, 4}};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s_x, data_x.data());
auto result = p.eval(pp).back();
std::vector<uint8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<uint8_t> gold = {195, 148, 176, 156, 208, 131, 150, 193, 226, 141, 98, 153,
212, 140, 71, 88, 126, 165, 142, 59, 120, 153, 168, 102,
92, 123, 135, 127, 102, 116, 78, 89, 29, 17, 86, 104,
44, 36, 95, 136, 151, 126, 108, 164, 185, 166, 140, 178};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearaveragepool_2d_same_upper_test)
{
auto p = migraphx::parse_onnx("qlinearaveragepool_2d_same_upper_test.onnx");
p.compile(migraphx::make_target("ref"));
std::vector<int8_t> data_x = {-61, 102, -6, 61, -34, 6, -13, -38, -26, 105, 36, 116,
-62, 31, 113, 85, 126, -52, 80, 38, 115, -89, -35, 67,
69, -116, 11, -47, -120, 120, 39, 96, 29, 5, -89, 40,
58, 51, -99, -77, -12, -107, 76, -13, 126, -112, -64, -57};
migraphx::shape s_x{migraphx::shape::int8_type, {1, 3, 4, 4}};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s_x, data_x.data());
auto result = p.eval(pp).back();
std::vector<int8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<int8_t> gold = {
-58, -20, -62, -41, -38, 3, -14, 14, -40, 78, 111, 127, -95, 80, 127, 106,
-14, -112, 11, 41, -74, -128, -66, -44, -88, -37, -14, -15, -64, 95, 71, 127,
8, -128, -128, -101, -69, -104, -120, -128, -116, -128, -93, -128, -50, -128, -128, -128};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearaveragepool_2d_strides_test)
{
auto p = migraphx::parse_onnx("qlinearaveragepool_2d_strides_test.onnx");
p.compile(migraphx::make_target("ref"));
std::vector<int8_t> data_x = {
84, -73, 117, -2, -97, 72, 67, 27, 1, -44, 110, 51, 9, 7, 58, 113,
-34, 34, 124, -20, 6, 66, 68, 98, 31, -84, 25, 101, -69, -100, -68, 116,
33, -121, 78, 49, 102, -86, 65, 69, -87, -89, 16, -125, 51, -54, -86, 79,
-112, -37, -6, 74, 118, -75, -41, 52, 101, -22, -28, -92, -59, -128, 32, 78,
-20, 121, 11, -107, -92, -31, 81, 117, -55, -3, 80, 119, 126, -98, -11, 52,
-4, -66, 37, -57, -16, -33, -12, 100, 55, 2, 27, 62, -15, 64, -74, -21,
-123, 22, -45, 12, 30, 24, 20, 120, -36, -102, -75, -39, -76, 55, 74, -120,
103, 67, -80, -89, -112, 36, 69, 98, 110, -82, 60, 119, 98, 88, 5, 42,
-88, -86, -58, -33, 93, 80, -57, -56, 87, 7, -4, 114, -73, -91, -12, -123,
96, -99, -31, -99, 85, 34, -126, 106, 88, 126, -60, 14, 75, -117, -15, 6,
55, -14, 117, -87, -75, -50, -85, 54, 70, 125, 74, -100, 25, -112, 74, -66,
-116, -102, 1, -75, -107, 83, -120, -66, 57, 29, 62, -45, -103, -56, 90, -53};
migraphx::shape s_x{migraphx::shape::int8_type, {1, 3, 8, 8}};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s_x, data_x.data());
auto result = p.eval(pp).back();
std::vector<int8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<int8_t> gold = {24, 37, 10, 17, 12, 12, -13, -1, 14, -10, 7, -19};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearaveragepool_3d_test)
{
auto p = migraphx::parse_onnx("qlinearaveragepool_3d_test.onnx");
p.compile(migraphx::make_target("ref"));
std::vector<int8_t> data_x = {
-61, 102, -6, 61, -34, 6, -13, -38, -26, 105, 36, 116, -62, 31, 113, 85, 126,
-52, 80, 38, 115, -89, -35, 67, 69, -116, 11, -47, -120, 120, 39, 96, 29, 5,
-89, 40, 58, 51, -99, -77, -12, -107, 76, -13, 126, -112, -64, -57, 99, -54, 27,
99, 126, -46, -7, 109, 17, 77, 94, -92, 84, -92, 48, 71, 45, -102, 95, 118,
24, 13, -70, 33, 35, -60, 102, 81, 34, 108, -79, 14, -42};
migraphx::shape s_x{migraphx::shape::int8_type, {1, 3, 3, 3, 3}};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s_x, data_x.data());
auto result = p.eval(pp).back();
std::vector<int8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<int8_t> gold = {56, 114, 49, 39, 32, 127, 3, 45, -4, -13, 8, 22,
-35, -98, 76, 15, 127, 67, 100, 20, 127, 84, 64, 68};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearaveragepool_notset_test)
{
auto p = migraphx::parse_onnx("qlinearaveragepool_notset_test.onnx");
p.compile(migraphx::make_target("ref"));
std::vector<int8_t> data_x = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24};
migraphx::shape s_x{migraphx::shape::int8_type, {1, 1, 5, 5}};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s_x, data_x.data());
auto result = p.eval(pp).back();
std::vector<int8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<int8_t> gold = {22};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearaveragepool_nt_cip_test)
{
// github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.QLinearAveragePool
auto p = migraphx::parse_onnx("qlinearaveragepool_nt_cip_test.onnx");
p.compile(migraphx::make_target("ref"));
std::vector<uint8_t> data_x = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24};
migraphx::shape s_x{migraphx::shape::uint8_type, {1, 1, 5, 5}};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s_x, data_x.data());
auto result = p.eval(pp).back();
std::vector<uint8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<uint8_t> gold = {18};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearconv_test) TEST_CASE(qlinearconv_test)
{ {
// https://xadupre.github.io/draft/onnx/onnx_doc_folder/onnx__QLinearConv.html // https://xadupre.github.io/draft/onnx/onnx_doc_folder/onnx__QLinearConv.html
......
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