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Commit c6ec6638 authored by Khalique Ahmed's avatar Khalique Ahmed
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Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/AMDMIGraphX into auto_contig_fix

parents b42c7b41 a6d1540f
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src/onnx/include/migraphx/onnx/pooling.hpp 0 → 100644
View file @ c6ec6638
/*
* 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_lstm.cpp
View file @ c6ec6638
...@@ -116,6 +116,37 @@ void lstm_actv_functions(op::rnn_direction dirct, std::vector<std::string>& actv ...@@ -116,6 +116,37 @@ void lstm_actv_functions(op::rnn_direction dirct, std::vector<std::string>& actv
} }
} }
void lstm_transpose_inputs(onnx_parser::node_info& info, std::vector<instruction_ref>& args)
{
std::vector<int64_t> perm{1, 0, 2};
args[0] = info.add_instruction(make_op("transpose", {{"permutation", perm}}), args[0]);
if(args.size() >= 6 and not args[5]->is_undefined())
{
args[5] = info.add_instruction(make_op("transpose", {{"permutation", perm}}), args[5]);
}
if(args.size() >= 7 and not args[6]->is_undefined())
{
args[6] = info.add_instruction(make_op("transpose", {{"permutation", perm}}), args[6]);
}
}
void lstm_transpose_outputs(onnx_parser::node_info& info,
instruction_ref& hidden_states,
instruction_ref& last_output,
instruction_ref& last_cell_output)
{
std::vector<int64_t> perm_hs{2, 0, 1, 3};
hidden_states =
info.add_instruction(make_op("transpose", {{"permutation", perm_hs}}), hidden_states);
std::vector<int64_t> perm_last{1, 0, 2};
last_output =
info.add_instruction(make_op("transpose", {{"permutation", perm_last}}), last_output);
last_cell_output =
info.add_instruction(make_op("transpose", {{"permutation", perm_last}}), last_cell_output);
}
struct parse_lstm : op_parser<parse_lstm> struct parse_lstm : op_parser<parse_lstm>
{ {
std::vector<op_desc> operators() const { return {{"LSTM"}}; } std::vector<op_desc> operators() const { return {{"LSTM"}}; }
...@@ -202,6 +233,12 @@ struct parse_lstm : op_parser<parse_lstm> ...@@ -202,6 +233,12 @@ struct parse_lstm : op_parser<parse_lstm>
input_forget = parser.parse_value(info.attributes.at("input_forget")).at<int>(); input_forget = parser.parse_value(info.attributes.at("input_forget")).at<int>();
} }
int layout = 0;
if(contains(info.attributes, "layout"))
{
layout = parser.parse_value(info.attributes.at("layout")).at<int>();
}
// append undefined opeator to make 6 arguments // append undefined opeator to make 6 arguments
if(args.size() < 8) if(args.size() < 8)
{ {
...@@ -209,6 +246,11 @@ struct parse_lstm : op_parser<parse_lstm> ...@@ -209,6 +246,11 @@ struct parse_lstm : op_parser<parse_lstm>
args.insert(args.end(), 8 - args.size(), ins); args.insert(args.end(), 8 - args.size(), ins);
} }
if(layout != 0)
{
lstm_transpose_inputs(info, args);
}
// first output for concatenation of hidden states // first output for concatenation of hidden states
auto hidden_states = info.add_instruction(make_op("lstm", auto hidden_states = info.add_instruction(make_op("lstm",
{{"hidden_size", hidden_size}, {{"hidden_size", hidden_size},
...@@ -224,6 +266,11 @@ struct parse_lstm : op_parser<parse_lstm> ...@@ -224,6 +266,11 @@ struct parse_lstm : op_parser<parse_lstm>
auto last_cell_output = auto last_cell_output =
info.add_instruction(make_op("rnn_last_cell_output"), hidden_states); info.add_instruction(make_op("rnn_last_cell_output"), hidden_states);
if(layout != 0)
{
lstm_transpose_outputs(info, hidden_states, last_output, last_cell_output);
}
return {hidden_states, last_output, last_cell_output}; return {hidden_states, last_output, last_cell_output};
} }
}; };
......
src/onnx/parse_pooling.cpp
View file @ c6ec6638
...@@ -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 {
...@@ -39,68 +33,14 @@ struct parse_pooling : op_parser<parse_pooling> ...@@ -39,68 +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 {
{"AveragePool", "average"},
{"GlobalAveragePool", "average"}, {"GlobalAveragePool", "average"},
{"GlobalMaxPool", "max"}, {"GlobalMaxPool", "max"},
{"MaxPool", "max"}, {"MaxPool", "max"},
{"LpPool", "lpnorm"}, {"LpPool", "lpnorm"},
{"GlobalLpPool", "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");
}
// 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,
...@@ -108,144 +48,8 @@ struct parse_pooling : op_parser<parse_pooling> ...@@ -108,144 +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);
}
// 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>(),
std::vector<size_t>(in_shape.ndim() - 2, 1),
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_qlinearadd.cpp → src/onnx/parse_qlinearbinary.cpp
View file @ c6ec6638
...@@ -36,7 +36,7 @@ namespace onnx { ...@@ -36,7 +36,7 @@ namespace onnx {
/* /*
********************************************************************************* *********************************************************************************
* Reference: see QLinearAdd in * * Reference: see QLinearAdd, QLinearMul in *
* https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md * * https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md *
********************************************************************************* *********************************************************************************
...@@ -49,6 +49,17 @@ namespace onnx { ...@@ -49,6 +49,17 @@ namespace onnx {
This version of the operator has been available since version 1 of the 'com.microsoft' operator This version of the operator has been available since version 1 of the 'com.microsoft' operator
set. set.
com.microsoft.QLinearMul
Performs element-wise binary multiplication on 8 bit data types (with Numpy-style broadcasting
support).
C = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_point
Version
This version of the operator has been available since version 1 of the 'com.microsoft' operator
set.
General definition of binary QLinear* ops:
Inputs (7 - 8) Inputs (7 - 8)
A : T A : T
First operand. First operand.
...@@ -88,15 +99,18 @@ namespace onnx { ...@@ -88,15 +99,18 @@ namespace onnx {
*/ */
struct parse_qlinearadd : op_parser<parse_qlinearadd> struct parse_qlinearbinary : op_parser<parse_qlinearbinary>
{ {
std::vector<op_desc> operators() const { return {{"QLinearAdd"}}; } std::vector<op_desc> operators() const
{
return {{"QLinearAdd", "add"}, {"QLinearMul", "mul"}};
}
// basic type checking for QLinearAdd Operator // basic type checking for binary QLinear Operator
void check_inputs(const std::vector<instruction_ref>& args) const void check_inputs(const std::vector<instruction_ref>& args, const std::string& op_name) const
{ {
if(args.size() < 7) if(args.size() < 7)
MIGRAPHX_THROW("QLINEARADD: missing inputs"); MIGRAPHX_THROW(op_name + ": missing inputs");
const auto& in_a = args[0]; const auto& in_a = args[0];
const auto& in_b = args[3]; const auto& in_b = args[3];
...@@ -107,19 +121,19 @@ struct parse_qlinearadd : op_parser<parse_qlinearadd> ...@@ -107,19 +121,19 @@ struct parse_qlinearadd : op_parser<parse_qlinearadd>
auto type_a = sh_a.type(); auto type_a = sh_a.type();
auto type_b = sh_b.type(); auto type_b = sh_b.type();
if(type_a != migraphx::shape::int8_type and type_a != migraphx::shape::uint8_type) if(type_a != migraphx::shape::int8_type and type_a != migraphx::shape::uint8_type)
MIGRAPHX_THROW("QLINEARADD: unsupported input type"); MIGRAPHX_THROW(op_name + ": unsupported input type");
if(type_b != migraphx::shape::int8_type and type_b != migraphx::shape::uint8_type) if(type_b != migraphx::shape::int8_type and type_b != migraphx::shape::uint8_type)
MIGRAPHX_THROW("QLINEARADD: unsupported input type"); MIGRAPHX_THROW(op_name + ": unsupported input type");
if(type_a != type_b) if(type_a != type_b)
MIGRAPHX_THROW("QLINEARADD: mismatched input types"); MIGRAPHX_THROW(op_name + ": mismatched input types");
} }
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
{ {
check_inputs(args); check_inputs(args, opd.op_name);
// A // A
const auto& in_a = args[0]; const auto& in_a = args[0];
...@@ -134,8 +148,8 @@ struct parse_qlinearadd : op_parser<parse_qlinearadd> ...@@ -134,8 +148,8 @@ struct parse_qlinearadd : op_parser<parse_qlinearadd>
const auto& in_zero_pt_b = args[5]; const auto& in_zero_pt_b = args[5];
auto dquant_b = bcast_qdq_instr("dequantizelinear", in_b, in_scale_b, in_zero_pt_b, info); auto dquant_b = bcast_qdq_instr("dequantizelinear", in_b, in_scale_b, in_zero_pt_b, info);
// C = A + B // C = op(A, B)
auto out_c = info.add_common_op("add", dquant_a, dquant_b); auto out_c = info.add_common_op(opd.op_name, dquant_a, dquant_b);
const auto& in_scale_c = args[6]; const auto& in_scale_c = args[6];
......
src/onnx/parse_qlinearglavgpool.cpp → src/onnx/parse_qlinearpooling.cpp
View file @ c6ec6638
...@@ -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"); }
void check_inputs(const op_desc& opd, const std::vector<instruction_ref>& args) const
{
const auto& in_x = args[0]; const auto& in_x = args[0];
const auto& zero_pt_x = args[2]; const auto onnx_name = opd.onnx_name;
const auto& zero_pt_y = args[4];
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(args.size() == 5)
{
const auto& zero_pt_y = args[4];
if(type_x != zero_pt_y->get_shape().type()) if(type_x != zero_pt_y->get_shape().type())
MIGRAPHX_THROW("QLINEARGLOBALAVERAGEPOOL: mismatched type: output zero point"); 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
{
if(contains(info.attributes, "channel_last"))
{ {
int channels_last = int channels_last =
parser.parse_value(info.attributes.at("channels_last")).template at<int>(); parser.parse_value(info.attributes.at("channels_last")).template at<int>();
if(channels_last != 0) if(channels_last != 0)
MIGRAPHX_THROW( MIGRAPHX_THROW(opd.onnx_name + ": channels_last (N x D1..Dn x C) is not supported");
"QLINEARGLOBALAVERAGEPOOL: 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/parse_qlinearunary.cpp 0 → 100644
View file @ c6ec6638
/*
* 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/op_parser.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/common.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/onnx/checks.hpp>
#include <migraphx/onnx/broadcast_qdq.hpp>
#include <migraphx/op/pooling.hpp>
#include <migraphx/instruction.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace onnx {
/*
*********************************************************************************
* Reference: see QLinearSigmoid, QLinearLeakyRelu in *
* https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md *
*********************************************************************************
com.microsoft.QLinearSigmoid
QLinearSigmoid takes quantized input data (Tensor), and quantize parameter for output, and produces
one output data (Tensor) where the function f(x) = quantize(Sigmoid(dequantize(x))), is applied to
the data tensor elementwise. Where the function Sigmoid(x) = 1 / (1 + exp(-x))
Version
This version of the operator has been available since version 1 of the 'com.microsoft' operator
set.
*****************************************************************************************************
com.microsoft.QLinearLeakyRelu
QLinearLeakyRelu takes quantized input data (Tensor), an argument alpha, and quantize parameter for
output, and produces one output data (Tensor) where the function f(x) = quantize(alpha *
dequantize(x)) for dequantize(x) < 0, f(x) = quantize(dequantize(x)) for dequantize(x) >= 0, is
applied to the data tensor elementwise.
Version
This version of the operator has been available since version 1 of the 'com.microsoft' operator set.
Attributes
alpha : float
Coefficient of leakage.
******************************************************************************************************
Generic input layout of QLinear unary operators:
Inputs (4 - 5)
X : T
Input tensor
X_scale : tensor(float)
Input X's scale. It's a scalar, which means a per-tensor/layer quantization.
X_zero_point (optional) : T
Input X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a
per-tensor/layer quantization.
Y_scale : tensor(float) Output Y's scale. It's a scalar, which means
a per-tensor/layer quantization.
Y_zero_point (optional) : T Output Y's zero point. Default value is
0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.
Outputs
Y : T
Output tensor
Type Constraints
T : tensor(uint8), tensor(int8)
Constrain input and output types to 8 bit tensors.
*/
struct parse_qlinearunary : op_parser<parse_qlinearunary>
{
std::vector<op_desc> operators() const
{
return {{"QLinearSigmoid", "sigmoid"}, {"QLinearLeakyRelu", "leaky_relu"}};
}
void check_inputs(const op_desc& opd, const std::vector<instruction_ref>& args) const
{
if(args.size() < 4)
MIGRAPHX_THROW(opd.op_name + ": missing inputs");
const auto& in_x = args[0];
auto sh_x = in_x->get_shape();
auto type_x = sh_x.type();
if(type_x != migraphx::shape::int8_type and type_x != migraphx::shape::uint8_type)
MIGRAPHX_THROW(opd.op_name + ": unsupported input type");
}
instruction_ref parse(const op_desc& opd,
const onnx_parser& parser,
const onnx_parser::node_info& info,
const std::vector<instruction_ref>& args) const
{
check_inputs(opd, args);
// X
const auto& in_x = args[0];
const auto& in_scale_x = args[1];
const auto& in_zero_pt_x = args[2];
auto dquant_x = bcast_qdq_instr("dequantizelinear", in_x, in_scale_x, in_zero_pt_x, info);
// Y = (op(dequantizelinear(x))
auto op = parser.load(opd.op_name, info);
auto y = info.add_instruction(op, dquant_x);
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", y, in_scale_y, args[4], info));
// if no zero_pt: just broadcast the scale..
auto bcast_scale_sigm = bcast_scalar_instr(y->get_shape(), in_scale_y, info);
return (info.add_instruction(migraphx::make_op("quantizelinear"), y, bcast_scale_sigm));
}
};
} // namespace onnx
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/onnx/parse_scatternd.cpp
View file @ c6ec6638
...@@ -39,15 +39,17 @@ struct parse_scatternd : op_parser<parse_scatternd> ...@@ -39,15 +39,17 @@ struct parse_scatternd : op_parser<parse_scatternd>
const onnx_parser::node_info& info, const onnx_parser::node_info& info,
std::vector<instruction_ref>& args) const std::vector<instruction_ref>& args) const
{ {
std::string reduction = "none";
if(contains(info.attributes, "reduction")) if(contains(info.attributes, "reduction"))
{ {
if(info.attributes.at("reduction").s() == "add") reduction = info.attributes.at("reduction").s();
return info.add_instruction(migraphx::make_op("scatternd_add"), args); if(not contains({"none", "add", "mul", "min", "max"}, reduction))
if(info.attributes.at("reduction").s() == "mul") {
return info.add_instruction(migraphx::make_op("scatternd_mul"), args); MIGRAPHX_THROW("PARSE_SCATTERND: unsupported reduction mode " + reduction);
}
} }
return info.add_instruction(migraphx::make_op("scatternd_none"), args); return info.add_instruction(migraphx::make_op("scatternd_" + reduction), args);
} }
}; };
......
src/onnx/pooling.cpp 0 → 100644
View file @ c6ec6638
/*
* 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
src/py/migraphx_py.cpp
View file @ c6ec6638
...@@ -40,7 +40,7 @@ ...@@ -40,7 +40,7 @@
#include <migraphx/json.hpp> #include <migraphx/json.hpp>
#include <migraphx/make_op.hpp> #include <migraphx/make_op.hpp>
#include <migraphx/op/common.hpp> #include <migraphx/op/common.hpp>
#include <migraphx/float8.hpp>
#ifdef HAVE_GPU #ifdef HAVE_GPU
#include <migraphx/gpu/hip.hpp> #include <migraphx/gpu/hip.hpp>
#endif #endif
...@@ -144,6 +144,18 @@ struct npy_format_descriptor<half> ...@@ -144,6 +144,18 @@ struct npy_format_descriptor<half>
static constexpr auto name() { return _("half"); } static constexpr auto name() { return _("half"); }
}; };
template <>
struct npy_format_descriptor<migraphx::fp8::fp8e4m3fnuz>
{
static std::string format()
{
// following: https://docs.python.org/3/library/struct.html#format-characters
// TODO: need to figure out correct encoding
return "z";
}
static constexpr auto name() { return _("fp8e4m3fnuz"); }
};
} // namespace detail } // namespace detail
} // namespace pybind11 } // namespace pybind11
......
src/register_target.cpp
View file @ c6ec6638
...@@ -56,7 +56,11 @@ target make_target(const std::string& name) ...@@ -56,7 +56,11 @@ target make_target(const std::string& name)
{ {
if(not contains(target_map(), name)) if(not contains(target_map(), name))
{ {
#ifdef _WIN32
std::string target_name = "migraphx_" + name + ".dll";
#else
std::string target_name = "libmigraphx_" + name + ".so"; std::string target_name = "libmigraphx_" + name + ".so";
#endif
store_target_lib(dynamic_loader(target_name)); store_target_lib(dynamic_loader(target_name));
} }
const auto it = target_map().find(name); const auto it = target_map().find(name);
......
src/rewrite_pooling.cpp
View file @ c6ec6638
...@@ -35,25 +35,14 @@ ...@@ -35,25 +35,14 @@
namespace migraphx { namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS { inline namespace MIGRAPHX_INLINE_NS {
void rewrite_pooling::apply(module& m) const static void replace_with_reduce(module& m, instruction_ref ins)
{ {
for(auto ins : iterator_for(m))
{
if(ins->name() != "pooling")
continue;
if(ins->inputs().empty())
continue;
auto&& s = ins->inputs().front()->get_shape(); auto&& s = ins->inputs().front()->get_shape();
auto&& op = any_cast<op::pooling>(ins->get_operator()); auto&& op = any_cast<op::pooling>(ins->get_operator());
if(not std::all_of(op.padding.begin(), op.padding.end(), [](auto i) { return i == 0; }))
continue;
if(not std::all_of(op.stride.begin(), op.stride.end(), [](auto i) { return i == 1; }))
continue;
auto lens = s.lens(); auto lens = s.lens();
if(not std::equal(lens.begin() + 2, lens.end(), op.lengths.begin(), op.lengths.end()))
continue;
std::vector<std::int64_t> axes(lens.size() - 2); std::vector<std::int64_t> axes(lens.size() - 2);
std::iota(axes.begin(), axes.end(), 2); std::iota(axes.begin(), axes.end(), 2);
// average pooling // average pooling
if(op.mode == op::pooling_mode::average) if(op.mode == op::pooling_mode::average)
{ {
...@@ -64,6 +53,131 @@ void rewrite_pooling::apply(module& m) const ...@@ -64,6 +53,131 @@ void rewrite_pooling::apply(module& m) const
{ {
m.replace_instruction(ins, make_op("reduce_max", {{"axes", axes}}), ins->inputs()); m.replace_instruction(ins, make_op("reduce_max", {{"axes", axes}}), ins->inputs());
} }
}
static void replace_dilations_with_gather_pooling(module& m, instruction_ref ins)
{
// TODO remove this when MIOpen supports dilated pooling
auto&& s = ins->inputs().front()->get_shape();
auto&& op = any_cast<op::pooling>(ins->get_operator());
// Ignore N, C axes
std::vector<size_t> dims = {s.lens().cbegin() + 2, s.lens().cend()};
bool default_padding =
std::all_of(op.padding.cbegin(), op.padding.cend(), [](auto i) { return i == 0; });
if(not default_padding)
{
for(size_t idx{0}; idx < op.padding.size(); ++idx)
{
// We need to pad both ends
dims[idx] += op.padding.at(idx) * 2;
}
}
std::vector<size_t> kernels = op.lengths;
std::vector<size_t> strides = op.stride;
std::vector<size_t> dilations = op.dilations;
std::vector<std::vector<int>> axis_indices;
axis_indices.resize(dims.size());
for(auto idx{0}; idx < dims.size(); ++idx)
{
// Only consider if iw fits into the window
for(size_t stride{0}; stride < dims.at(idx) - dilations.at(idx) * (kernels.at(idx) - 1);
stride += strides.at(idx))
{
for(size_t step{0}; step < kernels.at(idx); ++step)
{
axis_indices.at(idx).push_back(stride + dilations.at(idx) * step);
}
}
}
auto elements = ins->inputs().front();
if(not default_padding)
{
// Pad supports asym, we need to provide both ends
std::vector<size_t> padding(2 * s.lens().size(), 0);
// Format will be e.g {N, C, P1, P2, N, C, P1, P2}
for(size_t idx{0}; idx < op.padding.size(); ++idx)
{
// Ignore N, C axes
padding.at(2 + idx) = op.padding.at(idx);
padding.at(2 + idx + s.lens().size()) = op.padding.at(idx);
}
// Default value needed for Max pooling
elements = m.insert_instruction(
ins,
make_op("pad", {{"pads", padding}, {"value", std::numeric_limits<float>::lowest()}}),
elements);
}
for(auto idx{0}; idx < axis_indices.size(); ++idx)
{
migraphx::shape s_indices{migraphx::shape::int32_type, {axis_indices.at(idx).size()}};
auto indices = m.add_literal(migraphx::literal{s_indices, axis_indices.at(idx)});
elements = m.insert_instruction(
ins, make_op("gather", {{"axis", idx + 2 /*ignore N,C*/}}), elements, indices);
}
// Ignore padding
std::vector<size_t> new_padding(kernels.size(), 0);
// The kernel window elements are places next to each other. E.g. {x1, y1, x2, y2, ...}
// We need to skip them to not overlap
std::vector<size_t> new_strides(kernels);
// Ignore dilations
std::vector<size_t> new_dilations(kernels.size(), 1);
m.replace_instruction(ins,
make_op("pooling",
{{"mode", op.mode},
{"padding", new_padding},
{"stride", new_strides},
{"lengths", kernels},
{"dilations", new_dilations}}),
elements);
}
void rewrite_pooling::apply(module& m) const
{
for(auto ins : iterator_for(m))
{
if(ins->name() != "pooling")
continue;
if(ins->inputs().empty())
continue;
auto&& s = ins->inputs().front()->get_shape();
auto&& op = any_cast<op::pooling>(ins->get_operator());
bool same_kernel_as_shape = std::equal(
s.lens().cbegin() + 2, s.lens().cend(), op.lengths.cbegin(), op.lengths.cend());
bool default_strides =
std::all_of(op.stride.cbegin(), op.stride.cend(), [](auto i) { return i == 1; });
bool default_padding =
std::all_of(op.padding.cbegin(), op.padding.cend(), [](auto i) { return i == 0; });
bool default_dilations =
std::all_of(op.dilations.cbegin(), op.dilations.cend(), [](auto i) { return i == 1; });
if(same_kernel_as_shape and default_strides and default_padding and default_dilations)
{
replace_with_reduce(m, ins);
}
else if(not default_dilations)
{
// Dilated AvgPool with padding is not supported
if(not default_padding and op.mode == op::pooling_mode::average)
{
continue;
}
auto size =
std::accumulate(s.lens().cbegin(), s.lens().cend(), 1, std::multiplies<size_t>());
// Can't handle too much size because of literal size
if(size > 100000)
{
continue;
}
replace_dilations_with_gather_pooling(m, ins);
}
} }
} }
......
src/schedule.cpp
View file @ c6ec6638
...@@ -27,7 +27,7 @@ ...@@ -27,7 +27,7 @@
#include <migraphx/iterator_for.hpp> #include <migraphx/iterator_for.hpp>
#include <migraphx/iterator.hpp> #include <migraphx/iterator.hpp>
#include <migraphx/dfor.hpp> #include <migraphx/dfor.hpp>
#include <migraphx/par_for.hpp> #include <migraphx/simple_par_for.hpp>
#include <migraphx/functional.hpp> #include <migraphx/functional.hpp>
#include <migraphx/ranges.hpp> #include <migraphx/ranges.hpp>
#include <migraphx/dom_info.hpp> #include <migraphx/dom_info.hpp>
...@@ -461,7 +461,7 @@ struct stream_info ...@@ -461,7 +461,7 @@ struct stream_info
std::back_inserter(index_to_ins), std::back_inserter(index_to_ins),
[](auto&& it) { return it.first; }); [](auto&& it) { return it.first; });
par_for(concur_ins.size(), [&](auto ins_index, auto tid) { simple_par_for(concur_ins.size(), [&](auto ins_index, auto tid) {
auto merge_first = index_to_ins[ins_index]; auto merge_first = index_to_ins[ins_index];
assert(concur_ins.count(merge_first) > 0); assert(concur_ins.count(merge_first) > 0);
auto& merge_second = concur_ins.at(merge_first); auto& merge_second = concur_ins.at(merge_first);
......
src/simplify_algebra.cpp
View file @ c6ec6638
...@@ -952,15 +952,6 @@ struct find_splits ...@@ -952,15 +952,6 @@ struct find_splits
{ {
auto split = i->inputs()[split_idx]; auto split = i->inputs()[split_idx];
assert(split->name() == "slice"); assert(split->name() == "slice");
// Insert contiguous for reshapes
auto outputs = i->outputs();
for(auto output : outputs)
{
if(output->name() != "reshape")
continue;
auto x = m.insert_instruction(output, make_op("contiguous"), i);
m.replace_instruction(output, output->get_operator(), x);
}
m.replace_instruction(i, split->get_operator(), c); m.replace_instruction(i, split->get_operator(), c);
} }
...@@ -1192,13 +1183,6 @@ struct find_conv_dot_horiz_fusion ...@@ -1192,13 +1183,6 @@ struct find_conv_dot_horiz_fusion
for(auto arg : range(start, last)) for(auto arg : range(start, last))
{ {
auto outputs = arg->outputs(); auto outputs = arg->outputs();
for(auto output : outputs)
{
if(output->name() != "reshape")
continue;
auto x = m.insert_instruction(output, make_op("contiguous"), arg);
m.replace_instruction(output, output->get_operator(), x);
}
int64_t len = arg->get_shape().lens()[axis]; int64_t len = arg->get_shape().lens()[axis];
m.replace_instruction( m.replace_instruction(
...@@ -1498,11 +1482,6 @@ struct find_split_reshape ...@@ -1498,11 +1482,6 @@ struct find_split_reshape
slc_axis_len; slc_axis_len;
}); });
// insert the reshape instruction and add contiguous if needed
if(not input->get_shape().standard())
{
input = m.insert_instruction(std::next(input), make_op("contiguous"), input);
}
auto rsp_ins = m.insert_instruction( auto rsp_ins = m.insert_instruction(
std::next(input), make_op("reshape", {{"dims", rsp_out_lens}}), input); std::next(input), make_op("reshape", {{"dims", rsp_out_lens}}), input);
......
src/simplify_dyn_ops.cpp
View file @ c6ec6638
...@@ -22,6 +22,7 @@ ...@@ -22,6 +22,7 @@
* THE SOFTWARE. * THE SOFTWARE.
*/ */
#include <migraphx/simplify_dyn_ops.hpp> #include <migraphx/simplify_dyn_ops.hpp>
#include <migraphx/op/slice.hpp>
#include <migraphx/matcher.hpp> #include <migraphx/matcher.hpp>
#include <migraphx/make_op.hpp> #include <migraphx/make_op.hpp>
#include <migraphx/literal.hpp> #include <migraphx/literal.hpp>
...@@ -33,6 +34,10 @@ inline namespace MIGRAPHX_INLINE_NS { ...@@ -33,6 +34,10 @@ inline namespace MIGRAPHX_INLINE_NS {
* Convert 2 input static shape broadcast/multibroadcast into 1 input version. * Convert 2 input static shape broadcast/multibroadcast into 1 input version.
* Some compiler passes (ex. simplify_algebra) only support the 1 input versions * Some compiler passes (ex. simplify_algebra) only support the 1 input versions
* of the broadcasting operators. * of the broadcasting operators.
* From:
* broadcast_op(argument_with_static_shape, argument_with_static_shape)
* To:
* broadcast_op(argument_with_static_shape); broadcast_op.out_lens = constant_output_dims
*/ */
struct find_static_2in_broadcasts struct find_static_2in_broadcasts
{ {
...@@ -61,8 +66,65 @@ struct find_static_2in_broadcasts ...@@ -61,8 +66,65 @@ struct find_static_2in_broadcasts
}; };
/** /**
* Simplify slice with variable `starts` and `ends` to the constant version if * Simplify slice with 2 inputs to the 1 input version if inputs[1] is constant.
* the `input_starts` and `input_ends` inputs are constant. * From:
* slice(data, constant_input); two attributes set
* To:
* slice(data); slice.starts, slice.ends. slice.axes set
*/
struct find_const_2in_slice
{
auto matcher() const
{
return match::name("slice")(match::nargs(2), match::arg(1)(match::is_constant()));
}
void apply(module& m, const match::matcher_result& mr) const
{
auto ins = mr.result;
auto inputs = ins->inputs();
auto slice_op = any_cast<op::slice>(ins->get_operator());
auto set_attrs = slice_op.get_set_attributes();
std::vector<int64_t> starts_vec;
std::vector<int64_t> ends_vec;
std::vector<int64_t> axes_vec;
if(set_attrs == op::slice::ends_axes)
{
// slice(data, starts)
inputs.at(1)->eval().visit(
[&](auto output) { starts_vec.assign(output.begin(), output.end()); });
ends_vec = slice_op.ends;
axes_vec = slice_op.axes;
}
else if(set_attrs == op::slice::starts_axes)
{
// slice(data, ends)
inputs.at(1)->eval().visit(
[&](auto output) { ends_vec.assign(output.begin(), output.end()); });
starts_vec = slice_op.starts;
axes_vec = slice_op.axes;
}
else
{
// slice(data, axes)
inputs.at(1)->eval().visit(
[&](auto output) { axes_vec.assign(output.begin(), output.end()); });
starts_vec = slice_op.starts;
ends_vec = slice_op.ends;
}
m.replace_instruction(
ins,
make_op("slice", {{"starts", starts_vec}, {"ends", ends_vec}, {"axes", axes_vec}}),
inputs.at(0));
}
};
/**
* Simplify slice with 3 inputs to the 1 input version if inputs[1:2] are constant.
* From:
* slice(data, constant_input1, constant_input2); one attribute set
* To:
* slice(data); slice.starts, slice.ends. slice.axes set
*/ */
struct find_const_3in_slice struct find_const_3in_slice
{ {
...@@ -77,27 +139,51 @@ struct find_const_3in_slice ...@@ -77,27 +139,51 @@ struct find_const_3in_slice
{ {
auto ins = mr.result; auto ins = mr.result;
auto inputs = ins->inputs(); auto inputs = ins->inputs();
argument starts_arg = inputs.at(1)->eval(); auto slice_op = any_cast<op::slice>(ins->get_operator());
argument ends_arg = inputs.at(2)->eval(); auto set_attrs = slice_op.get_set_attributes();
if(not starts_arg.empty() and not ends_arg.empty())
{
std::vector<int64_t> starts_vec; std::vector<int64_t> starts_vec;
std::vector<int64_t> ends_vec; std::vector<int64_t> ends_vec;
starts_arg.visit([&](auto output) { starts_vec.assign(output.begin(), output.end()); }); std::vector<int64_t> axes_vec;
ends_arg.visit([&](auto output) { ends_vec.assign(output.begin(), output.end()); }); if(set_attrs == op::slice::axes_only)
auto slice_val = ins->get_operator().to_value(); {
auto axes_vec = slice_val.at("axes").to_vector<int64_t>(); // slice(data, starts, ends)
inputs.at(1)->eval().visit(
[&](auto output) { starts_vec.assign(output.begin(), output.end()); });
inputs.at(2)->eval().visit(
[&](auto output) { ends_vec.assign(output.begin(), output.end()); });
axes_vec = slice_op.axes;
}
else if(set_attrs == op::slice::ends_only)
{
// slice(data, starts, axes)
inputs.at(1)->eval().visit(
[&](auto output) { starts_vec.assign(output.begin(), output.end()); });
inputs.at(2)->eval().visit(
[&](auto output) { axes_vec.assign(output.begin(), output.end()); });
ends_vec = slice_op.ends;
}
else
{
// slice(data, ends, axes)
inputs.at(1)->eval().visit(
[&](auto output) { ends_vec.assign(output.begin(), output.end()); });
inputs.at(2)->eval().visit(
[&](auto output) { axes_vec.assign(output.begin(), output.end()); });
starts_vec = slice_op.starts;
}
m.replace_instruction( m.replace_instruction(
ins, ins,
make_op("slice", {{"starts", starts_vec}, {"ends", ends_vec}, {"axes", axes_vec}}), make_op("slice", {{"starts", starts_vec}, {"ends", ends_vec}, {"axes", axes_vec}}),
inputs.at(0)); inputs.at(0));
} }
}
}; };
/** /**
* Simplify slice with variable `starts`, `ends`, and `input_axes` to the constant version if * Simplify slice with 4 inputs to the 1 input version if inputs[1:3] are constant.
* the `input_starts`, `input_ends`, and `input_axes` inputs are constant. * From:
* slice(data, constant_starts, constant_ends, constant_axes)
* To:
* slice(data); slice.starts, slice.ends. slice.axes set
*/ */
struct find_const_4in_slice struct find_const_4in_slice
{ {
...@@ -113,9 +199,9 @@ struct find_const_4in_slice ...@@ -113,9 +199,9 @@ struct find_const_4in_slice
{ {
auto ins = mr.result; auto ins = mr.result;
auto inputs = ins->inputs(); auto inputs = ins->inputs();
argument starts_arg = inputs.at(1)->eval(); argument starts_arg = inputs.at(1)->eval(false);
argument ends_arg = inputs.at(2)->eval(); argument ends_arg = inputs.at(2)->eval(false);
argument axes_arg = inputs.at(3)->eval(); argument axes_arg = inputs.at(3)->eval(false);
if(not starts_arg.empty() and not ends_arg.empty() and not axes_arg.empty()) if(not starts_arg.empty() and not ends_arg.empty() and not axes_arg.empty())
{ {
std::vector<int64_t> starts_vec; std::vector<int64_t> starts_vec;
...@@ -172,13 +258,76 @@ struct find_static_dimensions_of ...@@ -172,13 +258,76 @@ struct find_static_dimensions_of
} }
}; };
/**
* Simplify allocate into 2 argument reshape that has constant output dimensions into a static 1
* argument reshape. Intended to simplify what ONNX parse_reshape creates for dynamic reshapes.
* This matcher can be generalized to matching reshape(data, static_shape_output_tensor).
* From:
* x = allocate(constant_output_dims) -> reshape(data, x)
* To:
* reshape(data); reshape.dims = constant_output_dims
*/
struct find_const_alloc_reshapes
{
auto matcher() const
{
return match::name("reshape")(match::nargs(2),
match::arg(1)(match::name("allocate")(match::is_constant())));
}
void apply(module& m, const match::matcher_result& mr) const
{
auto reshape_ins = mr.result;
auto reshape_inputs = reshape_ins->inputs();
auto alloc_ins = reshape_inputs.at(1);
argument output_dims_arg = alloc_ins->inputs().at(0)->eval(false);
std::vector<int64_t> output_dims_vec;
output_dims_arg.visit(
[&](auto output) { output_dims_vec.assign(output.begin(), output.end()); });
m.replace_instruction(
reshape_ins, make_op("reshape", {{"dims", output_dims_vec}}), reshape_inputs.at(0));
// have dead_code_elimination remove the previous allocate
}
};
/**
* Simplify allocate into fill operator that has constant output dimensions and constant value.
* The allocate into fill instructions is what is produced when parsing the ONNX
* ConstantOfShape operator. This replacement could be handled with propagate_constant, but
* would rather have the simplification happen earlier during compiling.
* This matcher can be generalized to matching fill(constant_value, static_shape_output_tensor).
* From:
* x = allocate(constant_ouptut_dims) -> fill(constant_value, x)
* To:
* literal
*/
struct find_const_alloc_fill
{
auto matcher() const
{
return match::name("fill")(match::arg(0)(match::is_constant()),
match::arg(1)(match::name("allocate")(match::is_constant())));
}
void apply(module& m, const match::matcher_result& mr) const
{
auto fill_ins = mr.result;
auto fill_arg = fill_ins->eval(false);
auto l = m.add_literal(fill_arg.get_shape(), fill_arg.data());
m.replace_instruction(fill_ins, l);
}
};
void simplify_dyn_ops::apply(module& m) const void simplify_dyn_ops::apply(module& m) const
{ {
match::find_matches(m, match::find_matches(m,
find_static_2in_broadcasts{},
find_static_dimensions_of{}, find_static_dimensions_of{},
find_const_alloc_reshapes{},
find_static_2in_broadcasts{},
find_const_2in_slice{},
find_const_3in_slice{}, find_const_3in_slice{},
find_const_4in_slice{}); find_const_4in_slice{},
find_const_alloc_fill{});
} }
} // namespace MIGRAPHX_INLINE_NS } // namespace MIGRAPHX_INLINE_NS
......
src/simplify_qdq.cpp
View file @ c6ec6638
...@@ -45,77 +45,145 @@ std::unordered_set<std::string> get_quantizable_op_names() ...@@ -45,77 +45,145 @@ std::unordered_set<std::string> get_quantizable_op_names()
return s; return s;
} }
MIGRAPHX_PRED_MATCHER(has_same_value, instruction_ref ins) struct match_find_quantizable_ops
{ {
if(ins->name() != "@literal") static bool
is_valid_scale(instruction_ref scale, std::vector<std::size_t> lens, std::size_t axis)
{
return scale->get_shape().scalar() or scale->get_shape().elements() == lens.at(axis);
}
static bool is_valid_zero_point(instruction_ref zp)
{
if(not zp->can_eval())
return false; return false;
bool all_same = false;
ins->get_literal().visit([&](auto s) { bool all_zeros = false;
all_same = std::all_of(s.begin() + 1, s.end(), [&](const auto& scale) { zp->eval().visit([&](auto z) {
return float_equal(scale, s.front()); all_zeros =
}); std::all_of(z.begin(), z.end(), [&](auto val) { return float_equal(val, 0); });
}); });
return all_same; return all_zeros;
} }
struct match_find_quantizable_ops static auto
{ scale_broadcast_op(instruction_ref scale, std::vector<std::size_t> lens, std::size_t axis)
{
if(scale->get_shape().scalar())
{
return migraphx::make_op("multibroadcast", {{"out_lens", lens}});
}
else
{
return migraphx::make_op("broadcast", {{"out_lens", lens}, {"axis", axis}});
}
}
// Helper function to insert quantized versions of any broadcasts and transpose ops that
// occur between dequantizelinear and the quantized op
static auto
propagate_quantized_ins(module& m, const instruction_ref dqins, const instruction_ref qop)
{
auto qinp = dqins->inputs().front();
auto next_ins = dqins;
static auto dequantizelinear_op(const std::string& name, const std::string& scale) while(next_ins != qop)
{
if(next_ins->name() != "dequantizelinear")
{
qinp = m.insert_instruction(qop, next_ins->get_operator(), qinp);
}
next_ins = next_ins->outputs().front();
}
return qinp;
}
static auto dequantizelinear_op(const std::string& scale, const std::string& zp)
{ {
return match::name("dequantizelinear")( return match::name("dequantizelinear")(
match::arg(0)(match::skip(match::name("quantizelinear"))(match::any().bind(name))), match::arg(0)(match::skip(match::name("quantizelinear"))(match::any())),
match::arg(1)(match::skip_broadcasts(has_same_value().bind(scale))), match::arg(1)(match::skip_broadcasts(match::is_constant().bind(scale))),
match::arg(2)(match::skip_broadcasts(match::all_of(match::has_value(0))))); match::arg(2)(match::skip_broadcasts(match::is_constant().bind(zp))));
} }
auto matcher() const auto matcher() const
{ {
return match::name(get_quantizable_op_names())( return match::name(get_quantizable_op_names())(
match::arg(0)(dequantizelinear_op("x1", "scale1")), match::arg(0)(match::skip_broadcasts_transposes_contiguous(
match::arg(1)(dequantizelinear_op("x2", "scale2"))); dequantizelinear_op("scale1", "zp1").bind("dq1"))),
match::arg(1)(match::skip_broadcasts_transposes_contiguous(
dequantizelinear_op("scale2", "zp2").bind("dq2"))));
} }
void apply(module& m, const match::matcher_result& r) const void apply(module& m, const match::matcher_result& r) const
{ {
auto qop = r.result; auto qop = r.result;
auto q1 = r.instructions["x1"]; auto dq1 = r.instructions["dq1"];
auto q2 = r.instructions["x2"]; auto dq2 = r.instructions["dq2"];
auto scale1 = r.instructions["scale1"]; auto scale1 = r.instructions["scale1"];
auto scale2 = r.instructions["scale2"]; auto scale2 = r.instructions["scale2"];
auto zp1 = r.instructions["zp1"];
auto zp2 = r.instructions["zp2"];
// Only INT8 type currently supported // Only INT8 type currently supported
if(q1->get_shape().type() != migraphx::shape::int8_type or if(dq1->inputs().front()->get_shape().type() != migraphx::shape::int8_type or
q2->get_shape().type() != migraphx::shape::int8_type) dq2->inputs().front()->get_shape().type() != migraphx::shape::int8_type)
return;
// Only symmetric quantization supported (ie. non-zero zero_points not allowed)
if(not(is_valid_zero_point(zp1) and is_valid_zero_point(zp2)))
return; return;
double scale; // Only support scalar and 1D scales
visit_all(scale1->get_literal(), scale2->get_literal())( if(scale1->get_shape().lens().size() != 1 or scale2->get_shape().lens().size() != 1)
[&](const auto s1, const auto s2) { scale = s1.front() * s2.front(); }); return;
// Propagate q1 and q2 through any broadcasts and transposes before qop
auto qop_args = qop->inputs(); auto qop_args = qop->inputs();
qop_args.at(0) = q1; qop_args.at(0) = propagate_quantized_ins(m, dq1, qop);
qop_args.at(1) = q2; qop_args.at(1) = propagate_quantized_ins(m, dq2, qop);
instruction_ref dq; instruction_ref dq;
instruction_ref dq_scale; instruction_ref out_scale;
instruction_ref zero_point; instruction_ref zero_point;
if(qop->name() == "convolution") if(qop->name() == "convolution")
{ {
auto conv_val = qop->get_operator().to_value(); auto conv_val = qop->get_operator().to_value();
dq = m.insert_instruction( dq = m.insert_instruction(
qop, migraphx::make_op("quant_convolution", conv_val), qop_args); qop, migraphx::make_op("quant_convolution", conv_val), qop_args);
auto out_lens = dq->get_shape().lens();
// Input scale should always be scalar and weight scale can be scalar or 1D of the
// same lens as the output channel dim (dim 1 in the output)
if(not(is_valid_scale(scale1, out_lens, 1) and is_valid_scale(scale2, out_lens, 1)))
return;
auto s1_bcast =
m.insert_instruction(qop, scale_broadcast_op(scale1, out_lens, 1), scale1);
auto s2_bcast =
m.insert_instruction(qop, scale_broadcast_op(scale2, out_lens, 1), scale2);
out_scale = m.insert_instruction(qop, migraphx::make_op("mul"), s1_bcast, s2_bcast);
} }
else if(qop->name() == "dot") else if(qop->name() == "dot")
{ {
dq = m.insert_instruction(qop, migraphx::make_op("quant_dot"), qop_args); dq = m.insert_instruction(qop, migraphx::make_op("quant_dot"), qop_args);
auto out_lens = dq->get_shape().lens();
// For (..., M, N) x (..., N, K) dot, only support cases where quantization axis is M
// for input1 and K for input 2
if(not(is_valid_scale(scale1, out_lens, out_lens.size() - 2) and
is_valid_scale(scale2, out_lens, out_lens.size() - 1)))
return;
auto s1_bcast = m.insert_instruction(
qop, scale_broadcast_op(scale1, out_lens, out_lens.size() - 2), scale1);
auto s2_bcast = m.insert_instruction(
qop, scale_broadcast_op(scale2, out_lens, out_lens.size() - 1), scale2);
out_scale = m.insert_instruction(qop, migraphx::make_op("mul"), s1_bcast, s2_bcast);
} }
auto ins_type = qop->get_shape().type();
dq_scale = m.add_literal(literal({ins_type}, {scale}));
auto lens = dq->get_shape().lens(); dq = m.insert_instruction(qop, make_op("dequantizelinear"), dq, out_scale);
auto scale_mb =
m.insert_instruction(qop, make_op("multibroadcast", {{"out_lens", lens}}), dq_scale);
dq = m.insert_instruction(qop, make_op("dequantizelinear"), dq, scale_mb);
m.replace_instruction(qop, dq); m.replace_instruction(qop, dq);
} }
}; };
......
src/simplify_reshapes.cpp
View file @ c6ec6638
...@@ -103,8 +103,6 @@ struct find_reshaper ...@@ -103,8 +103,6 @@ struct find_reshaper
auto input = mr.instructions["x"]; auto input = mr.instructions["x"];
auto dims = ins->get_shape().lens(); auto dims = ins->get_shape().lens();
if(not input->get_shape().standard())
input = m.insert_instruction(ins, make_op("contiguous"), input);
m.replace_instruction(ins, make_op("reshape", {{"dims", dims}}), input); m.replace_instruction(ins, make_op("reshape", {{"dims", dims}}), input);
} }
}; };
...@@ -475,9 +473,8 @@ struct find_resize ...@@ -475,9 +473,8 @@ struct find_resize
ins_rsp, migraphx::make_op("reshape", {{"dims", in_dims}}), in_rsp); ins_rsp, migraphx::make_op("reshape", {{"dims", in_dims}}), in_rsp);
auto mb_rsp = m.insert_instruction( auto mb_rsp = m.insert_instruction(
ins_rsp, migraphx::make_op("multibroadcast", {{"out_lens", out_dims}}), rsp_data); ins_rsp, migraphx::make_op("multibroadcast", {{"out_lens", out_dims}}), rsp_data);
auto std_mb = m.insert_instruction(ins, migraphx::make_op("contiguous"), mb_rsp);
std::vector<int64_t> rsp_dims(out_lens.begin(), out_lens.end()); std::vector<int64_t> rsp_dims(out_lens.begin(), out_lens.end());
m.replace_instruction(ins, migraphx::make_op("reshape", {{"dims", rsp_dims}}), std_mb); m.replace_instruction(ins, migraphx::make_op("reshape", {{"dims", rsp_dims}}), mb_rsp);
} }
}; };
...@@ -626,9 +623,8 @@ struct find_transpose_contiguous_reshaper_unary ...@@ -626,9 +623,8 @@ struct find_transpose_contiguous_reshaper_unary
auto cont_ins = r.instructions["cont_ins"]; auto cont_ins = r.instructions["cont_ins"];
auto unary_op_name = ins->get_operator().name(); auto unary_op_name = ins->get_operator().name();
auto unary_ins = m.insert_instruction(cont_ins, make_op(unary_op_name), trans_ins); auto unary_ins = m.insert_instruction(cont_ins, make_op(unary_op_name), trans_ins);
auto new_cont_ins = m.insert_instruction(cont_ins, make_op("contiguous"), unary_ins);
// older cont and reshape are removed by deadcode elimination // older cont and reshape are removed by deadcode elimination
m.replace_instruction(ins, reshaper_ins->get_operator(), new_cont_ins); m.replace_instruction(ins, reshaper_ins->get_operator(), unary_ins);
} }
}; };
......
src/targets/cpu/CMakeLists.txt
View file @ c6ec6638
...@@ -74,21 +74,27 @@ if(MIGRAPHX_ENABLE_ZENDNN) ...@@ -74,21 +74,27 @@ if(MIGRAPHX_ENABLE_ZENDNN)
target_link_libraries(migraphx_cpu PRIVATE ${BLIS_LIB}) target_link_libraries(migraphx_cpu PRIVATE ${BLIS_LIB})
target_link_libraries(migraphx_cpu PRIVATE ${ZENDNN_LIB}) target_link_libraries(migraphx_cpu PRIVATE ${ZENDNN_LIB})
else() else()
target_link_libraries(migraphx_cpu PRIVATE DNNL::dnnl) target_link_libraries(migraphx_cpu PUBLIC DNNL::dnnl)
endif() endif()
target_link_libraries(migraphx_cpu PRIVATE migraphx) target_link_libraries(migraphx_cpu PRIVATE migraphx)
migraphx_generate_export_header(migraphx_cpu) migraphx_generate_export_header(migraphx_cpu)
find_package(OpenMP) find_package(OpenMP)
target_link_libraries(migraphx_cpu PUBLIC OpenMP::OpenMP_CXX) if(WIN32)
# Add library path to rpath to workaround issues with our broken packages target_link_libraries(migraphx_cpu PUBLIC libomp)
foreach(LIBRARY ${OpenMP_CXX_LIBRARIES}) target_include_directories(migraphx_cpu PUBLIC ${OpenMP_CXX_INCLUDE_DIRS})
target_compile_options(migraphx_cpu PUBLIC ${OpenMP_CXX_FLAGS})
else()
target_link_libraries(migraphx_cpu PUBLIC OpenMP::OpenMP_CXX)
# Add library path to rpath to workaround issues with our broken packages
foreach(LIBRARY ${OpenMP_CXX_LIBRARIES})
if(LIBRARY MATCHES "libomp") if(LIBRARY MATCHES "libomp")
get_filename_component(LIBRARY_PATH "${LIBRARY}" PATH) get_filename_component(LIBRARY_PATH "${LIBRARY}" PATH)
target_link_libraries(migraphx_cpu PUBLIC -Wl,-rpath=${LIBRARY_PATH} -Wl,-rpath-link=${LIBRARY_PATH}) target_link_libraries(migraphx_cpu PUBLIC -Wl,-rpath=${LIBRARY_PATH} -Wl,-rpath-link=${LIBRARY_PATH})
endif() endif()
endforeach() endforeach()
endif()
rocm_install_targets( rocm_install_targets(
TARGETS migraphx_cpu TARGETS migraphx_cpu
......
src/targets/cpu/pooling.cpp
View file @ c6ec6638
...@@ -34,23 +34,32 @@ namespace migraphx { ...@@ -34,23 +34,32 @@ namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS { inline namespace MIGRAPHX_INLINE_NS {
namespace cpu { namespace cpu {
struct dnnl_pooling : dnnl_extend_op<dnnl_pooling, dnnl::pooling_forward, op::pooling> struct dnnl_pooling : dnnl_extend_op<dnnl_pooling, dnnl::pooling_v2_forward, op::pooling>
{ {
std::vector<int> arg_map(int) const { return {MIGRAPHX_DNNL_PREFIX(ARG_SRC)}; } std::vector<int> arg_map(int) const { return {MIGRAPHX_DNNL_PREFIX(ARG_SRC)}; }
dnnl::pooling_forward::desc get_desc(const std::unordered_map<int, dnnl::memory::desc>& m) const dnnl::pooling_v2_forward::desc
get_desc(const std::unordered_map<int, dnnl::memory::desc>& m) const
{ {
auto algo = op.mode == op::pooling_mode::max ? dnnl::algorithm::pooling_max auto algo = op.mode == op::pooling_mode::max ? dnnl::algorithm::pooling_max
: dnnl::algorithm::pooling_avg; : dnnl::algorithm::pooling_avg;
auto kdims = op.kdims(); auto kdims = op.kdims();
std::vector<size_t> padding_l(op.padding.begin(), op.padding.begin() + kdims); std::vector<size_t> padding_l(op.padding.begin(), op.padding.begin() + kdims);
std::vector<size_t> padding_r(op.padding.begin() + kdims, op.padding.end()); std::vector<size_t> padding_r(op.padding.begin() + kdims, op.padding.end());
// Note: It is not documented, but the default dilation seems to be 0 instead of 1.
// We need to offset dilations with -1.
std::vector<size_t> dilations;
std::transform(op.dilations.cbegin(),
op.dilations.cend(),
std::back_inserter(dilations),
[](size_t d) { return d - 1; });
return {dnnl::prop_kind::forward_inference, return {dnnl::prop_kind::forward_inference,
algo, algo,
m.at(MIGRAPHX_DNNL_PREFIX(ARG_SRC)), m.at(MIGRAPHX_DNNL_PREFIX(ARG_SRC)),
m.at(MIGRAPHX_DNNL_PREFIX(ARG_DST)), m.at(MIGRAPHX_DNNL_PREFIX(ARG_DST)),
to_dnnl_dims(op.stride), to_dnnl_dims(op.stride),
to_dnnl_dims(op.lengths), to_dnnl_dims(op.lengths),
to_dnnl_dims(dilations),
to_dnnl_dims(padding_l), to_dnnl_dims(padding_l),
to_dnnl_dims(padding_r)}; to_dnnl_dims(padding_r)};
} }
......
src/targets/gpu/CMakeLists.txt
View file @ c6ec6638
...@@ -22,20 +22,20 @@ ...@@ -22,20 +22,20 @@
# THE SOFTWARE. # THE SOFTWARE.
# #################################################################################### # ####################################################################################
list(APPEND CMAKE_PREFIX_PATH /opt/rocm) find_package(hip REQUIRED)
find_package(hip)
if(NOT GPU_TARGETS) if(NOT GPU_TARGETS)
message(FATAL_ERROR "HIP package is broken and has no GPU_TARGETS, please pass -DGPU_TARGETS=$(/opt/rocm/bin/rocminfo | grep -o -m1 'gfx.*') to cmake to build for your gpu.") set(fatal_msg "HIP package is broken and has no GPU_TARGETS. Please pass GPU_TARGETS to cmake.")
if(NOT WIN32)
set(fatal_msg "${fatal_msg}\nUse -DGPU_TARGETS=$(/opt/rocm/bin/rocminfo | grep -o -m1 'gfx.*') to build for your GPU.")
endif()
message(FATAL_ERROR ${fatal_msg})
endif() endif()
find_package(miopen) find_package(miopen REQUIRED)
message(STATUS "MIGraphX is using MIOpen")
# rocblas # rocblas
find_package(rocblas REQUIRED PATHS /opt/rocm) find_package(rocblas REQUIRED)
message(STATUS "Build with rocblas") message(STATUS "MIGraphX build with rocBLAS")
if(NOT TARGET MIOpen)
message(SEND_ERROR "Cant find miopen")
endif()
if(MIGRAPHX_USE_COMPOSABLEKERNEL) if(MIGRAPHX_USE_COMPOSABLEKERNEL)
find_package(composable_kernel 1.0.0 REQUIRED COMPONENTS jit_library) find_package(composable_kernel 1.0.0 REQUIRED COMPONENTS jit_library)
...@@ -49,7 +49,6 @@ endif() ...@@ -49,7 +49,6 @@ endif()
file(GLOB KERNEL_FILES CONFIGURE_DEPENDS file(GLOB KERNEL_FILES CONFIGURE_DEPENDS
${CMAKE_CURRENT_SOURCE_DIR}/kernels/include/migraphx/kernels/*.hpp) ${CMAKE_CURRENT_SOURCE_DIR}/kernels/include/migraphx/kernels/*.hpp)
message(STATUS "KERNEL_FILES: ${KERNEL_FILES}")
if(NOT MIGRAPHX_USE_COMPOSABLEKERNEL) if(NOT MIGRAPHX_USE_COMPOSABLEKERNEL)
list(REMOVE_ITEM KERNEL_FILES list(REMOVE_ITEM KERNEL_FILES
...@@ -66,8 +65,10 @@ file(GLOB DEVICE_GPU_SRCS CONFIGURE_DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/device/* ...@@ -66,8 +65,10 @@ file(GLOB DEVICE_GPU_SRCS CONFIGURE_DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/device/*
add_library(migraphx_device ${DEVICE_GPU_SRCS}) add_library(migraphx_device ${DEVICE_GPU_SRCS})
add_library(compile_for_gpu INTERFACE) add_library(compile_for_gpu INTERFACE)
target_compile_options(compile_for_gpu INTERFACE -std=c++17 -fno-gpu-rdc -Wno-cuda-compat -Wno-unused-command-line-argument -Xclang -fallow-half-arguments-and-returns) target_compile_features(compile_for_gpu INTERFACE cxx_std_17)
target_link_libraries(compile_for_gpu INTERFACE hip::device -fno-gpu-rdc -Wno-invalid-command-line-argument -Wno-unused-command-line-argument -Wno-option-ignored) target_compile_options(compile_for_gpu INTERFACE -fno-gpu-rdc -Wno-cuda-compat -Wno-unused-command-line-argument -Xclang -fallow-half-arguments-and-returns)
target_link_options(compile_for_gpu INTERFACE -fno-gpu-rdc -Wno-invalid-command-line-argument -Wno-unused-command-line-argument -Wno-option-ignored)
target_link_libraries(compile_for_gpu INTERFACE hip::device)
check_cxx_compiler_flag("--cuda-host-only -fhip-lambda-host-device -x hip" HAS_HIP_LAMBDA_HOST_DEVICE) check_cxx_compiler_flag("--cuda-host-only -fhip-lambda-host-device -x hip" HAS_HIP_LAMBDA_HOST_DEVICE)
if(HAS_HIP_LAMBDA_HOST_DEVICE) if(HAS_HIP_LAMBDA_HOST_DEVICE)
...@@ -211,8 +212,10 @@ if(MIGRAPHX_ENABLE_MLIR) ...@@ -211,8 +212,10 @@ if(MIGRAPHX_ENABLE_MLIR)
endif() endif()
if(MIGRAPHX_USE_HIPRTC) if(MIGRAPHX_USE_HIPRTC)
find_package(hiprtc REQUIRED)
message(STATUS "MIGraphX is using hipRTC") message(STATUS "MIGraphX is using hipRTC")
target_compile_definitions(migraphx_gpu PRIVATE -DMIGRAPHX_USE_HIPRTC=1) target_compile_definitions(migraphx_gpu PRIVATE -DMIGRAPHX_USE_HIPRTC=1)
target_link_libraries(migraphx_gpu PUBLIC hiprtc::hiprtc)
else() else()
message(STATUS "MIGraphX is using HIP Clang") message(STATUS "MIGraphX is using HIP Clang")
...@@ -221,12 +224,15 @@ else() ...@@ -221,12 +224,15 @@ else()
target_flags(HIP_COMPILER_FLAGS hip::device) target_flags(HIP_COMPILER_FLAGS hip::device)
# Remove cuda arch flags # Remove cuda arch flags
string(REGEX REPLACE --cuda-gpu-arch=[a-z0-9]+ "" HIP_COMPILER_FLAGS "${HIP_COMPILER_FLAGS}") string(REGEX REPLACE "--cuda-gpu-arch=[a-z0-9]+ ?" "" HIP_COMPILER_FLAGS "${HIP_COMPILER_FLAGS}")
string(REGEX REPLACE --offload-arch=[a-z0-9:+-]+ "" HIP_COMPILER_FLAGS "${HIP_COMPILER_FLAGS}") string(REGEX REPLACE "--offload-arch=[a-z0-9:+-]+ ?" "" HIP_COMPILER_FLAGS "${HIP_COMPILER_FLAGS}")
# Skip library paths since hip will incorrectly treat it as a source file # Skip library paths since hip will incorrectly treat it as a source file
string(APPEND HIP_COMPILER_FLAGS " ") string(APPEND HIP_COMPILER_FLAGS " ")
if(WIN32)
string(REPLACE "\\" "/" HIP_COMPILER_FLAGS "${HIP_COMPILER_FLAGS}")
endif()
foreach(_unused RANGE 2) foreach(_unused RANGE 2)
string(REGEX REPLACE " /[^ ]+\\.(a|so) " " " HIP_COMPILER_FLAGS "${HIP_COMPILER_FLAGS}") string(REGEX REPLACE " /[^ ]+\\.(a|so) " " " HIP_COMPILER_FLAGS "${HIP_COMPILER_FLAGS}")
endforeach() endforeach()
...@@ -238,7 +244,11 @@ else() ...@@ -238,7 +244,11 @@ else()
) )
if(DEFINED CMAKE_CXX_COMPILER_LAUNCHER) if(DEFINED CMAKE_CXX_COMPILER_LAUNCHER)
if(WIN32)
execute_process(COMMAND where ${CMAKE_CXX_COMPILER_LAUNCHER} OUTPUT_VARIABLE MIGRAPHX_HIP_COMPILER_LAUNCHER)
else()
execute_process(COMMAND which ${CMAKE_CXX_COMPILER_LAUNCHER} OUTPUT_VARIABLE MIGRAPHX_HIP_COMPILER_LAUNCHER) execute_process(COMMAND which ${CMAKE_CXX_COMPILER_LAUNCHER} OUTPUT_VARIABLE MIGRAPHX_HIP_COMPILER_LAUNCHER)
endif()
string(STRIP "${MIGRAPHX_HIP_COMPILER_LAUNCHER}" MIGRAPHX_HIP_COMPILER_LAUNCHER) string(STRIP "${MIGRAPHX_HIP_COMPILER_LAUNCHER}" MIGRAPHX_HIP_COMPILER_LAUNCHER)
target_compile_definitions(migraphx_gpu PRIVATE -DMIGRAPHX_HIP_COMPILER_LAUNCHER="${MIGRAPHX_HIP_COMPILER_LAUNCHER}") target_compile_definitions(migraphx_gpu PRIVATE -DMIGRAPHX_HIP_COMPILER_LAUNCHER="${MIGRAPHX_HIP_COMPILER_LAUNCHER}")
endif() endif()
......
src/targets/gpu/compile_hip.cpp
View file @ c6ec6638
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