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Unverified Commit 0662a9a3 authored by Brian Pickrell's avatar Brian Pickrell Committed by GitHub
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Merge branch 'develop' into dyn_resize_gather

parents b74d3a8f 35e5298e
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Showing with 618 additions and 221 deletions
src/driver/main.cpp
View file @ 0662a9a3
......@@ -59,6 +59,13 @@ namespace migraphx {
namespace driver {
inline namespace MIGRAPHX_INLINE_NS {
inline std::string get_version()
{
return "MIGraphX Version: " + std::to_string(MIGRAPHX_VERSION_MAJOR) + "." +
std::to_string(MIGRAPHX_VERSION_MINOR) + "." + std::to_string(MIGRAPHX_VERSION_PATCH) +
"." MIGRAPHX_VERSION_TWEAK;
}
struct loader
{
std::string model;
......@@ -597,17 +604,6 @@ struct verify : command<verify>
}
};
struct version : command<version>
{
void parse(const argument_parser&) {}
void run() const
{
std::cout << "MIGraphX Version: " << MIGRAPHX_VERSION_MAJOR << "." << MIGRAPHX_VERSION_MINOR
<< "." << MIGRAPHX_VERSION_PATCH << "."
<< MIGRAPHX_STRINGIZE(MIGRAPHX_VERSION_TWEAK) << std::endl;
}
};
struct compile : command<compile>
{
compiler c;
......@@ -760,16 +756,14 @@ struct main_command
}
void parse(argument_parser& ap)
{
std::string version_str = "MIGraphX Version: " + std::to_string(MIGRAPHX_VERSION_MAJOR) +
"." + std::to_string(MIGRAPHX_VERSION_MINOR) + "." +
std::to_string(MIGRAPHX_VERSION_PATCH) + "." +
MIGRAPHX_STRINGIZE(MIGRAPHX_VERSION_TWEAK);
std::string version_str = get_version();
ap(wrong_commands, {}, ap.metavar("<command>"), ap.append());
ap(nullptr, {"-h", "--help"}, ap.help("Show help"), ap.show_help(get_command_help()));
ap(nullptr,
{"-v", "--version"},
ap.help("Show MIGraphX version"),
ap.show_help(version_str));
ap(nullptr, {"--ort-sha"}, ap.help("Show MIGraphX onnx runtime SHA"));
// Trim command off of exe name
ap.set_exe_name(ap.get_exe_name().substr(0, ap.get_exe_name().size() - 5));
......@@ -812,7 +806,6 @@ using namespace migraphx::driver; // NOLINT
int main(int argc, const char* argv[])
{
std::vector<std::string> args(argv + 1, argv + argc);
// no argument, print the help infomration by default
if(args.empty())
{
......@@ -822,15 +815,27 @@ int main(int argc, const char* argv[])
auto&& m = get_commands();
auto cmd = args.front();
if(cmd == "ort-sha")
if(cmd == "--ort-sha")
{
std::cout << MIGRAPHX_ORT_SHA1 << std::endl;
return 0;
}
if(cmd == "-v" or cmd == "--version")
{
std::cout << get_version() << std::endl;
return 0;
}
if(m.count(cmd) > 0)
{
m.at(cmd)(argv[0], {args.begin() + 1, args.end()});
std::string driver_invocation =
std::string(argv[0]) + " " + migraphx::to_string_range(args, " ");
std::cout << "Running [ " << get_version() << " ]: " << driver_invocation << std::endl;
m.at(cmd)(argv[0],
{args.begin() + 1, args.end()}); // run driver command found in commands map
std::cout << "[ " << get_version() << " ] Complete: " << driver_invocation << std::endl;
}
else
{
......
src/include/migraphx/onnx.hpp
View file @ 0662a9a3
......@@ -48,8 +48,12 @@ struct onnx_options
bool skip_unknown_operators = false;
/// Print program if an error occurs
bool print_program_on_error = false;
/// Max iter num for the loop operator
/// Max iter num for the loop operator if trip count is not set
int64_t max_loop_iterations = 10;
/// Max iter limit for the loop operator.
/// Since loop will become a tensor of max iter size a huge number can cause overflow during
/// shape computations.
int64_t limit_max_iterations = std::numeric_limits<uint16_t>::max();
/// Use dynamic output for operators when available
bool use_dyn_output = false;
};
......
src/include/migraphx/op/allocate.hpp
View file @ 0662a9a3
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* 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
......@@ -37,20 +37,22 @@ namespace op {
* Static allocate:
* No inputs: `allocate()`
* `this.s` attribute set to the static output shape of the buffer.
* `this.s` attribute can be set to a dynamic output shape; however this will allocate the maximum
* buffer size for that case
*
* Dynamic allocate:
* One input: `allocate(output_dims)`
* `output_dims` are the output buffer dimensions and has a static shape.
* Either `this.s` or `this.buf_type` must be set to calculate the dynamic output shape at compute
* time. If `this.buf_type` is set, the compute_shape() of allocate at compile time will have
* dynamic_dimensions from {0, max_int} with rank = output_dims.ndim(). If `this.s` is set then the
* compute_shape() will output `this.s`; `this.s` should be a dynamic shape.
* Either `this.s` or `this.buf_type` (but not both) must be set to calculate the dynamic output
* shape at compute time. If `this.buf_type` is set, the compute_shape() of allocate at compile time
* will have dynamic_dimensions from {0, max_int} with rank = output_dims.ndim(). If `this.s` is set
* then the compute_shape() will output `this.s`; `this.s` should be a dynamic shape.
*/
struct allocate
{
shape s{};
optional<shape> s;
// for dynamic allocate to set the buffer type
shape::type_t buf_type = shape::half_type;
optional<shape::type_t> buf_type;
template <class Self, class F>
static auto reflect(Self& self, F f)
......@@ -62,8 +64,12 @@ struct allocate
shape compute_shape(const std::vector<shape>& inputs) const
{
if(s != shape())
if(s.has_value())
{
if(buf_type.has_value())
{
MIGRAPHX_THROW("ALLOCATE: shape and buf_type attributes both set");
}
if(inputs.size() == 1)
{
migraphx::check_shapes{inputs, *this, false}.only_dims(1);
......@@ -72,16 +78,20 @@ struct allocate
{
migraphx::check_shapes{inputs, *this, false}.has(0);
}
return s;
return s.value();
}
else
{
if(not buf_type.has_value())
{
MIGRAPHX_THROW("ALLOCATE: shape and buf_type attributes both not set");
}
migraphx::check_shapes{inputs, *this, false}.has(1).only_dims(1);
const auto& out_dims = inputs.at(0);
std::size_t max_val = std::numeric_limits<std::size_t>::max();
std::vector<shape::dynamic_dimension> dyn_dims(out_dims.lens().at(0),
shape::dynamic_dimension{0, max_val});
return {buf_type, dyn_dims};
return {buf_type.value(), dyn_dims};
}
}
argument compute(const shape& output_shape, const std::vector<argument>& args) const
......@@ -94,7 +104,11 @@ struct allocate
{
std::vector<std::size_t> output_dims(output_shape.ndim());
args.at(0).visit([&](auto a) { output_dims.assign(a.begin(), a.end()); });
return argument{shape{buf_type, output_dims}};
if(s)
{
return argument{shape{s->type(), output_dims}};
}
return argument{shape{buf_type.value(), output_dims}};
}
}
};
......
src/targets/gpu/include/migraphx/gpu/pack_int8_args.hpp → src/include/migraphx/op/isinf.hpp
View file @ 0662a9a3
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* 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
......@@ -21,25 +21,32 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#ifndef MIGRAPHX_GUARD_RTGLIB_PACK_INT8_ARGS_HPP
#define MIGRAPHX_GUARD_RTGLIB_PACK_INT8_ARGS_HPP
#ifndef MIGRAPHX_GUARD_OPERATORS_ISINF_HPP
#define MIGRAPHX_GUARD_OPERATORS_ISINF_HPP
#include <migraphx/program.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/op/unary.hpp>
#include <migraphx/config.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
namespace gpu {
struct MIGRAPHX_GPU_EXPORT pack_int8_args
struct isinf : unary<isinf>
{
std::string name() const { return "gpu::pack_int8_args"; }
void apply(module& m) const;
shape pack_int8_shape(const shape& s) const;
auto apply() const
{
return [&](auto x) { return std::isinf(x); };
}
std::string name() const { return "isinf"; }
shape compute_shape(std::vector<shape> inputs) const
{
return unary<isinf>::compute_shape(std::move(inputs)).with_type(shape::bool_type);
}
};
} // namespace gpu
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
......
src/include/migraphx/op/multinomial.hpp
View file @ 0662a9a3
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* 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
......@@ -21,11 +21,52 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
/**
* * Multinomial or categorical distribution. Performs a sampling of random input
* and returns a count of
* each category, or bucket. This does not require the standard multinomial
* distribution but instead takes a probability distribution, i.e. cumulative
* distribution function (CDF) as its first input.
*
* Inputs: args[0] - a tensor of probabilities for each category. Values are
* cumulative density function
* totals as provided by operation prefix_scan_sum. Values are
* cumulative probabilities (i.e. start with any set of numbers > 0
* and then apply prefix_scan_sum). Values do not need to be
* normalized to sum to 1; this is done in runtime computation.
*
* This input has Rank 2. Dimension 0 is batch #, so that there can be
* a different CDF for each iteration in the batch. The size of dimension
* 1 is the number of categories.
*
* args[1] - a tensor of random numbers. The last dimension is the sample
* size, i.e. the number of
* random samples in each iteration of the batch. Nominally
* has two dimensions where the first dimension is batch size, but
* any reshaping such that the total
* number of elements is (batch_size * sample_size) is legal.
*
* Values as created by a std::mt19937 like this:
*
* size_t sample_size = 100000;
* float seed = 0.0f;
* std::mt19937 gen(seed);
* std::uniform_real_distribution<> dis(0.0, 1.0);
* std::vector<float> rand_samples(sample_size);
* std::generate(rand_samples.begin(), rand_samples.end(), [&]() { return
* dis(gen); });
*
* Output: A 2D vector of category each input. Dimensions are (Input 1[first], Input
2[last]).
*
*/
#ifndef MIGRAPHX_GUARD_OPERATORS_MULTINOMIAL_HPP
#define MIGRAPHX_GUARD_OPERATORS_MULTINOMIAL_HPP
#include <migraphx/check_shapes.hpp>
#include <migraphx/argument.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/dyn_output.hpp>
#include <migraphx/par_for.hpp>
#include <migraphx/reflect.hpp>
#include <random>
......@@ -47,22 +88,35 @@ struct multinomial
std::string name() const { return "multinomial"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(2).only_dims(2);
size_t sample_size = inputs.back().lens().back();
check_shapes{inputs, *this, true}.has(2).only_dims(2);
if(not contains({shape::int32_type, shape::int64_type}, dtype))
MIGRAPHX_THROW(
"Multinomial: Invalid output type. Valid types are int32_type and int64_type.");
if(inputs.back().ndim() < 1)
MIGRAPHX_THROW("Multinomial: Second input shape (sample) has no dimensions");
if(dtype == shape::bool_type)
MIGRAPHX_THROW("Multinomial: boolean output type invalid.");
return {dtype, {inputs.front().lens().front(), sample_size}};
// Output takes one dimension from each of the two input shapes. If they are both fixed,
// return a static shape
if((not inputs.front().dynamic()) or (inputs.front().dyn_dims().front().is_fixed()))
{
if((not inputs.back().dynamic()) or (inputs.back().dyn_dims().back().is_fixed()))
{
size_t batch = {inputs.front().max_lens().front()};
size_t sample_size{inputs.back().max_lens().back()};
return {dtype, {batch, sample_size}};
}
}
return {dtype,
{inputs.front().to_dynamic().dyn_dims().front(),
inputs.back().to_dynamic().dyn_dims().back()}};
}
argument compute(const shape& output_shape, std::vector<argument> args) const
argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
{
argument result{output_shape};
size_t batch_size = output_shape.lens().front();
argument result{dyn_out.computed_shape};
size_t batch_size = dyn_out.computed_shape.lens().front();
size_t class_size = args[0].get_shape().lens().back();
size_t sample_size = output_shape.lens().back();
size_t sample_size = dyn_out.computed_shape.lens().back();
visit_all(args[0], args[1])([&](auto cdf, auto dist) {
result.visit([&](auto output) {
......@@ -70,13 +124,16 @@ struct multinomial
auto idx = args[1].get_shape().multi(i);
auto cdf_begin = cdf.begin() + (idx[0] * class_size);
auto cdf_end = cdf_begin + class_size;
// std::upper_bound returns an iterator to the bucket the value belongs in,
// when normalized by the probability distribution dist
auto sample_iter =
std::upper_bound(cdf_begin, cdf_end, dist[i] * *(std::prev(cdf_end)));
// convert iterator to an integer index
output[i] = std::distance(cdf_begin, sample_iter);
});
});
});
return result;
}
};
......
src/include/migraphx/op/round.hpp → src/include/migraphx/op/nearbyint.hpp
View file @ 0662a9a3
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* 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
......@@ -21,24 +21,28 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#ifndef MIGRAPHX_GUARD_OPERATORS_ROUND_HPP
#define MIGRAPHX_GUARD_OPERATORS_ROUND_HPP
#ifndef MIGRAPHX_GUARD_OPERATORS_NEARBYINT_HPP
#define MIGRAPHX_GUARD_OPERATORS_NEARBYINT_HPP
#include <migraphx/op/unary.hpp>
#include <migraphx/config.hpp>
#include <fenv.h>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct round : unary<round>
struct nearbyint : unary<nearbyint>
{
auto apply() const
{
return [](auto x) { return std::round(x); };
return [](auto x) {
auto rounding_mode = fegetround();
fesetround(FE_TONEAREST);
return std::nearbyint(x);
fesetround(rounding_mode);
};
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
......
src/include/migraphx/op/normalize_attribute.hpp
View file @ 0662a9a3
......@@ -40,6 +40,8 @@ namespace op {
* 2. use_rank (default) vs use_len:
* `use_rank` sets the max value/index of the attribute as the rank of lens.
* `use_lens` sets the max value/index as the corresponding value in lens at the axes index.
* Uses the dynamic_dimension.max value for dynamic shapes. Returns the original vector
* (no normalization) if any of dynamic_dimension[axes] are not fixed.
* 3. `clip_min` vs. `not_clip_min` (default):
* Clip values less than the minimum to the minimum or not.
* 4. `include_min` vs. `exclude_min` (default):
......
src/include/migraphx/op/prefix_scan_op.hpp
View file @ 0662a9a3
......@@ -22,6 +22,12 @@
* THE SOFTWARE.
*/
/**
* Parent struct for prefix scan ops. A prefix scan is a mathematical entity useful
* in parallelizing various computations. Given a list of numbers, a prefix scan
* op returns an equal size list of running totals of the values. Other operations
* besides addition can be supported by child ops.
*/
#ifndef MIGRAPHX_GUARD_OPERATORS_SCAN_OP_HPP
#define MIGRAPHX_GUARD_OPERATORS_SCAN_OP_HPP
......
src/include/migraphx/op/quantizelinear.hpp
View file @ 0662a9a3
......@@ -30,11 +30,11 @@
#include <migraphx/par_for.hpp>
#include <migraphx/value.hpp>
#include <cmath>
#include <fenv.h>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct quantizelinear
{
std::string name() const { return "quantizelinear"; }
......@@ -71,26 +71,26 @@ struct quantizelinear
{
y_zero_point = args.at(2);
}
argument result{output_shape};
auto rounding_mode = fegetround();
fesetround(FE_TONEAREST);
visit_all(result, y_zero_point)([&](auto output, auto zero_pts) {
visit_all(x, y_scale)([&](auto input, auto scales) {
using quant_type = typename decltype(output)::value_type;
auto min_value = std::numeric_limits<quant_type>::min();
auto max_value = std::numeric_limits<quant_type>::max();
par_for(output_shape.elements(), [&](auto i) {
int64_t quantized = static_cast<int64_t>(std::round(input[i] / scales[i])) +
int64_t quantized = static_cast<int64_t>(std::nearbyint(input[i] / scales[i])) +
static_cast<int64_t>(zero_pts[i]);
output[i] = std::max(static_cast<int64_t>(min_value),
std::min(static_cast<int64_t>(max_value), quantized));
});
});
});
fesetround(rounding_mode);
return result;
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
......
src/include/migraphx/op/random_uniform.hpp
View file @ 0662a9a3
......@@ -65,11 +65,10 @@ struct random_uniform
return inputs.at(1);
}
argument compute(const shape&, std::vector<argument> args) const
argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
{
// Output goes into the passed buffer, not the shape output.
auto result = args[1];
argument result{dyn_out.computed_shape};
uint64_t local_seed = args[0].at<uint64_t>(0);
std::mt19937 gen(local_seed);
......
src/include/migraphx/op/slice.hpp
View file @ 0662a9a3
......@@ -38,6 +38,18 @@ namespace op {
/**
* Slice operator that accepts variable axes, starts and ends.
* All of `starts`, `ends`, and `axes` must be supplied by either
* their attribute or an input (but not both).
*
* Valid calls:
* slice(input); axes, starts, ends set
* slice(input, starts); axes, ends set
* slice(input, ends); starts, axes set
* slice(input, axes); starts, ends set
* slice(input, starts, ends); axes set
* slice(input, starts, axes); ends set
* slice(input, ends, axes); starts set
* slice(input, start, ends, axes); none set
*
* Attributes:
* axes: constant axes to slice over (optional)
......@@ -46,8 +58,8 @@ namespace op {
*
* Parameters:
* data: the input tensor to slice (dynamic or static shape)
* input_starts: starting indicies of slice (optional, static shape)
* input_ends: ending indicies of slice (optional, static shape)
* input_starts: starting indices of slice (optional, static shape)
* input_ends: ending indices of slice (optional, static shape)
* input_axes: axes to slice over (optional, static shape)
*/
struct slice
......@@ -56,6 +68,18 @@ struct slice
std::vector<int64_t> starts{};
std::vector<int64_t> ends{};
/**
* Named arrays for the set attribute possibilities.
*/
static constexpr std::array<bool, 3> all_set = {true, true, true};
static constexpr std::array<bool, 3> ends_axes = {false, true, true};
static constexpr std::array<bool, 3> starts_axes = {true, false, true};
static constexpr std::array<bool, 3> starts_ends = {true, true, false};
static constexpr std::array<bool, 3> axes_only = {false, false, true};
static constexpr std::array<bool, 3> ends_only = {false, true, false};
static constexpr std::array<bool, 3> starts_only = {true, false, false};
static constexpr std::array<bool, 3> none_set = {false, false, false};
template <class Self, class F>
static auto reflect(Self& self, F f)
{
......@@ -63,24 +87,26 @@ struct slice
}
/**
* Ensure that attribute vectors axes, starts, and ends are all the same size and values are
* within limits.
* Ensure that attribute axes is within limits.
* Will attempt to normalize starts and ends; but will use the dynamic_dimension.max
* values for dynamic shapes. This makes it so you have to renormalize for
* non-fixed dynamic_dimensions.
*/
value attributes() const
{
value normalize = value::object{};
normalize["axes"] = value::array{normalize_attribute::include_min};
normalize["starts"] = value::array{normalize_attribute::clip_max,
normalize_attribute::clip_min,
normalize_attribute::include_max,
normalize_attribute::use_len,
normalize_attribute::include_min};
normalize["ends"] = value::array{normalize_attribute::clip_max,
normalize_attribute::clip_min,
normalize_attribute::include_max,
normalize_attribute::use_len,
normalize_attribute::include_min};
return {{"normalize_axes", normalize}};
value normalize_axes = value::object{};
normalize_axes["axes"] = value::array{normalize_attribute::include_min};
normalize_axes["starts"] = value::array{normalize_attribute::clip_max,
normalize_attribute::clip_min,
normalize_attribute::include_max,
normalize_attribute::use_len,
normalize_attribute::include_min};
normalize_axes["ends"] = value::array{normalize_attribute::clip_max,
normalize_attribute::clip_min,
normalize_attribute::include_max,
normalize_attribute::use_len,
normalize_attribute::include_min};
return {{"normalize_axes", normalize_axes}};
}
std::string name() const { return "slice"; }
......@@ -88,7 +114,7 @@ struct slice
/**
* Computes the slice output shape dimensions for given starts, ends,and axes.
* Templated to also handle tensor views.
* Possibily different type between [in_starts, in_ends] and [in_axes] if in_axes is this
* Possibly different type between [in_starts, in_ends] and [in_axes] if in_axes is this
* object's axes attribute. Assumes in_starts and in_ends are normalized; in_axes are valid.
*/
template <class A, class B>
......@@ -104,62 +130,160 @@ struct slice
return new_lens;
}
shape normalize_compute_shape(std::vector<shape> inputs) const
/// Get the attributes that are non-empty
std::array<bool, 3> get_set_attributes() const
{
check_shapes{inputs, *this, true}.has(1, 3, 4);
auto input_shape = inputs[0];
if(inputs.size() == 1)
std::array<std::vector<int64_t>, 3> attrs = {this->starts, this->ends, this->axes};
std::array<bool, 3> bool_vec;
std::transform(
attrs.cbegin(), attrs.cend(), bool_vec.begin(), [](auto a) { return not a.empty(); });
return bool_vec;
}
/// Helper function for normalize_compute_shape()
shape compute_two_or_more(std::vector<shape> inputs) const
{
auto input_shape = inputs[0];
auto set_attributes = get_set_attributes();
// check that inputs [1, end) are all 1D, have the same
// dimension, and are static
check_shapes{inputs.begin() + 1,
inputs.end(),
std::string("SLICE: inputs (starts, ends, and input_axes)"),
false}
.only_dims(1)
.same_dims();
auto dds = input_shape.to_dynamic().dyn_dims();
if(inputs.size() == 2)
{
auto t = input_shape.type();
if(input_shape.dynamic() and std::any_of(axes.begin(), axes.end(), [&](auto axis) {
return not input_shape.dyn_dims()[axis].is_fixed();
}))
if(set_attributes == ends_axes)
{
MIGRAPHX_THROW("SLICE: slicing is not allowed on non-fixed dynamic input axis ");
// attr ends and axes set; inputs are (data, input_starts)
if(inputs[1].lens().at(0) != axes.size())
{
MIGRAPHX_THROW("SLICE: 2 input and attributes mismatch");
}
std::for_each(axes.cbegin(), axes.cend(), [&](const auto& axis) {
dds.at(axis) = {0, dds.at(axis).max};
});
}
if(input_shape.dynamic())
else if(set_attributes == starts_axes)
{
return shape{t,
lens_calc(input_shape.min_lens(), starts, ends, axes),
lens_calc(input_shape.max_lens(), starts, ends, axes),
{}};
// attr starts and axes set; inputs are (data, input_ends)
if(inputs[1].lens().at(0) != axes.size())
{
MIGRAPHX_THROW("SLICE: 2 input and attributes mismatch");
}
std::for_each(axes.cbegin(), axes.cend(), [&](const auto& axis) {
dds.at(axis) = {0, dds.at(axis).max};
});
}
else if(set_attributes == starts_ends)
{
// attr starts and ends set; inputs are (data, input_axes)
if(inputs[1].lens().at(0) != starts.size())
{
MIGRAPHX_THROW("SLICE: 2 input and attributes mismatch");
}
std::transform(dds.begin(), dds.end(), dds.begin(), [](auto dd) {
return shape::dynamic_dimension{0, dd.max};
});
}
else
{
return shape{
t, lens_calc(input_shape.lens(), starts, ends, axes), input_shape.strides()};
MIGRAPHX_THROW("SLICE: Invalid 2 input and attributes configuration");
}
}
else
else if(inputs.size() == 3)
{
// check that starts, ends, and optionally input_axes are all 1D, have the same
// dimension, and are static
check_shapes{inputs.begin() + 1,
inputs.end(),
std::string("SLICE: inputs (starts, ends, and input_axes)"),
false}
.only_dims(1)
.same_dims();
auto dds = input_shape.to_dynamic().dyn_dims();
if(inputs.size() == 3)
if(set_attributes == axes_only)
{
// attr axes set; inputs are (data, input_starts, input_ends)
if(inputs[1].lens().at(0) != axes.size())
{
MIGRAPHX_THROW("SLICE: inputs starts and ends do not have the same dimension "
"as the axes attribute");
MIGRAPHX_THROW("SLICE: 3 input and attributes mismatch");
}
std::for_each(axes.cbegin(), axes.cend(), [&](const auto& axis) {
dds.at(axis) = {0, dds.at(axis).max};
});
}
else
else if(set_attributes == ends_only)
{
// attr ends set; inputs are (data, input_starts, input_axes)
if(inputs[1].lens().at(0) != ends.size())
{
MIGRAPHX_THROW("SLICE: 3 input and attributes mismatch");
}
std::transform(dds.begin(), dds.end(), dds.begin(), [](auto dd) {
return shape::dynamic_dimension{0, dd.max};
});
}
else if(set_attributes == starts_only)
{
// if axes is an input, then all the output dimensions could be 0 to the max value
// attr starts set; inputs are (data, input_ends, input_axes)
if(inputs[1].lens().at(0) != starts.size())
{
MIGRAPHX_THROW("SLICE: 3 input and attributes mismatch");
}
std::transform(dds.begin(), dds.end(), dds.begin(), [](auto dd) {
return shape::dynamic_dimension{0, dd.max};
});
}
return shape{input_shape.type(), dds};
else
{
MIGRAPHX_THROW("Invalid 3 input and attributes configuration");
}
}
else
{
// all 4 inputs (data, inputs_starts, input_ends, input_axes)
std::transform(dds.begin(), dds.end(), dds.begin(), [](auto dd) {
return shape::dynamic_dimension{0, dd.max};
});
}
return shape{input_shape.type(), dds};
}
// uses the normalize_axes flag to normalize axes, starts, and ends
shape normalize_compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this, true}.has(1, 2, 3, 4);
if(inputs.size() == 1)
{
auto input_shape = inputs[0];
auto set_attributes = get_set_attributes();
if(set_attributes != all_set)
{
MIGRAPHX_THROW("SLICE 1_arg: Invalid 1 input and attributes configuration");
}
// NOTE: make sure to update how normalization works here if this type of slicing is
// changed to be allowed
if(input_shape.dynamic() and std::any_of(axes.begin(), axes.end(), [&](auto axis) {
return not input_shape.dyn_dims()[axis].is_fixed();
}))
{
MIGRAPHX_THROW(
"SLICE 1_arg: slicing is not allowed on non-fixed dynamic input axis ");
}
if(input_shape.dynamic())
{
return shape{
input_shape.type(),
lens_calc(input_shape.min_lens(), this->starts, this->ends, this->axes),
lens_calc(input_shape.max_lens(), this->starts, this->ends, this->axes),
{}};
}
else
{
return shape{input_shape.type(),
lens_calc(input_shape.lens(), this->starts, this->ends, this->axes),
input_shape.strides()};
}
}
else
{
return compute_two_or_more(inputs);
}
}
......@@ -194,14 +318,14 @@ struct slice
/**
* Calculates the starting offset for the sliced tensor (for aliasing).
* Used when the starts and/or the axes are inputs.
* Used for 2-4 inputs to `slice.
*
* \param s static input shape
* \param input_starts starting indices of slice
* \param ax_vec axes to slice on
*/
template <class IndView, class Axes>
auto compute_offset(const shape& s, const IndView& input_starts, const Axes& ax_vec) const
template <class T>
auto compute_offset(const shape& s, const T& input_starts, const T& ax_vec) const
{
auto ret = 0;
for(std::size_t i = 0; i < ax_vec.size(); ++i)
......@@ -212,106 +336,168 @@ struct slice
return ret * s.type_size();
}
std::unordered_map<std::string, std::vector<int64_t>>
normalize_inputs(const shape& input_shape,
const std::vector<int64_t>& input_starts,
const std::vector<int64_t>& input_ends) const
{
auto attrs = this->attributes().at("normalize_axes");
return {{"input_starts",
normalize_indices(input_starts,
this->axes,
input_shape,
attrs.at("starts"),
"Slice variable input_starts")},
{"input_ends",
normalize_indices(input_ends,
this->axes,
input_shape,
attrs.at("ends"),
"Slice variable input_ends")}};
}
/**
* Three input version of the normalize_inputs.
* This one also checks that the input_axes are valid.
* If given, normalize the inputs. Otherwise get from operator attributes.
* Return the values in a map.
*
* Parameters
* input_shape: static shape of the input
* input_starts: optional
* input_ends: optional
* input_ends: optional
*/
std::unordered_map<std::string, std::vector<int64_t>>
normalize_inputs(shape input_shape,
const std::vector<int64_t>& input_starts,
const std::vector<int64_t>& input_ends,
const std::vector<int64_t>& input_axes) const
normalize_starts_ends_axes(shape input_shape,
const optional<std::vector<int64_t>>& input_starts,
const optional<std::vector<int64_t>>& input_ends,
const optional<std::vector<int64_t>>& input_axes) const
{
auto attrs = this->attributes().at("normalize_axes");
auto norm_axes =
normalize_axes(input_axes, input_shape, attrs.at("axes"), "Slice variable input_axes");
return {{"input_starts",
normalize_indices(input_starts,
norm_axes,
input_shape,
attrs.at("starts"),
"Slice variable input_starts")},
{"input_ends",
normalize_indices(input_ends,
norm_axes,
input_shape,
attrs.at("ends"),
"Slice variable input ends")},
{"input_axes", norm_axes}};
auto axes_attrs = this->attributes().at("normalize_axes");
std::vector<int64_t> norm_starts;
std::vector<int64_t> norm_ends;
std::vector<int64_t> norm_axes;
if(input_axes)
{
norm_axes = normalize_axes(input_axes.value(),
input_shape,
axes_attrs.at("axes"),
"Slice variable input_axes");
}
else
{
norm_axes = this->axes;
}
if(input_starts)
{
norm_starts = normalize_indices(input_starts.value(),
norm_axes,
input_shape,
axes_attrs.at("starts"),
"Slice variable input_starts");
}
else
{
norm_starts = this->starts;
}
if(input_ends)
{
norm_ends = normalize_indices(input_ends.value(),
norm_axes,
input_shape,
axes_attrs.at("ends"),
"Slice variable input ends");
}
else
{
norm_ends = this->ends;
}
return {{"norm_starts", norm_starts}, {"norm_ends", norm_ends}, {"norm_axes", norm_axes}};
}
argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
{
auto input = args[0];
auto input_shape = input.get_shape();
switch(args.size())
if(args.size() == 1)
{
case 1: {
std::size_t offset = compute_offset(input_shape);
return {dyn_out.computed_shape, [=] { return input.data() + offset; }};
}
case 3: {
shape calc_shape;
std::size_t offset = 0;
visit_all(args[1], args[2])([&](auto input_starts, auto input_ends) {
auto norm_inputs = normalize_inputs(input_shape,
input_starts.template to_vector<int64_t>(),
input_ends.template to_vector<int64_t>());
offset = compute_offset(input_shape, norm_inputs.at("input_starts"), this->axes);
calc_shape = {input_shape.type(),
lens_calc(input_shape.lens(),
norm_inputs.at("input_starts"),
norm_inputs.at("input_ends"),
this->axes),
input_shape.strides()};
});
return {calc_shape, [=] { return input.data() + offset; }};
}
case 4: {
shape calc_shape;
std::size_t offset = 0;
visit_all(args[1], args[2], args[3])(
[&](auto input_starts, auto input_ends, auto input_axes) {
auto norm_inputs = normalize_inputs(input_shape,
input_starts.template to_vector<int64_t>(),
input_ends.template to_vector<int64_t>(),
input_axes.template to_vector<int64_t>());
offset = compute_offset(
input_shape, norm_inputs.at("input_starts"), norm_inputs.at("input_axes"));
calc_shape = shape{input_shape.type(),
lens_calc(input_shape.lens(),
norm_inputs.at("input_starts"),
norm_inputs.at("input_ends"),
norm_inputs.at("input_axes")),
input_shape.strides()};
else
{
// Note that we re-normalize both the attributes and inputs because of the non-fixed
// dynamic input shape case. It's possible to only re-normalize if slicing over
// non-fixed dynamic_dimensions.
auto set_attributes = get_set_attributes();
std::unordered_map<std::string, std::vector<int64_t>> norm_inputs;
if(set_attributes == ends_axes)
{
// attr ends and axes set; inputs are (data, input_starts)
args[1].visit([&](auto input_starts) {
norm_inputs =
normalize_starts_ends_axes(input_shape,
input_starts.template to_vector<int64_t>(),
this->ends,
this->axes);
});
}
else if(set_attributes == starts_axes)
{
// attr starts and axes set; inputs are (data, input_ends)
args[1].visit([&](auto input_ends) {
norm_inputs =
normalize_starts_ends_axes(input_shape,
this->starts,
input_ends.template to_vector<int64_t>(),
this->axes);
});
}
else if(set_attributes == starts_ends)
{
// attr starts and ends set; inputs are (data, input_axes)
args[1].visit([&](auto input_axes) {
norm_inputs =
normalize_starts_ends_axes(input_shape,
this->starts,
this->ends,
input_axes.template to_vector<int64_t>());
});
}
else if(set_attributes == axes_only)
{
// attr axes set; inputs are (data, input_starts, input_ends)
visit_all(args[1], args[2])([&](auto input_starts, auto input_ends) {
norm_inputs =
normalize_starts_ends_axes(input_shape,
input_starts.template to_vector<int64_t>(),
input_ends.template to_vector<int64_t>(),
this->axes);
});
}
else if(set_attributes == ends_only)
{
// attr ends set; inputs are (data, input_starts, input_axes)
visit_all(args[1], args[2])([&](auto input_starts, auto input_axes) {
norm_inputs =
normalize_starts_ends_axes(input_shape,
input_starts.template to_vector<int64_t>(),
this->ends,
input_axes.template to_vector<int64_t>());
});
}
else if(set_attributes == starts_only)
{
// attr starts set; inputs are (data, input_ends, input_axes)
visit_all(args[1], args[2])([&](auto input_ends, auto input_axes) {
norm_inputs =
normalize_starts_ends_axes(input_shape,
this->starts,
input_ends.template to_vector<int64_t>(),
input_axes.template to_vector<int64_t>());
});
}
else
{
// no attr set, all inputs
visit_all(args[1], args[2], args[3])(
[&](auto input_starts, auto input_ends, auto input_axes) {
norm_inputs =
normalize_starts_ends_axes(input_shape,
input_starts.template to_vector<int64_t>(),
input_ends.template to_vector<int64_t>(),
input_axes.template to_vector<int64_t>());
});
}
auto offset = compute_offset(
input_shape, norm_inputs.at("norm_starts"), norm_inputs.at("norm_axes"));
shape calc_shape = shape{input_shape.type(),
lens_calc(input_shape.lens(),
norm_inputs.at("norm_starts"),
norm_inputs.at("norm_ends"),
norm_inputs.at("norm_axes")),
input_shape.strides()};
return {calc_shape, [=] { return input.data() + offset; }};
}
default: {
// Should never get here; covering in case some code change occurs
MIGRAPHX_THROW("SLICE: invalid number of inputs");
}
}
}
std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
......
src/include/migraphx/operators.hpp
View file @ 0662a9a3
......@@ -84,6 +84,7 @@
#include <migraphx/op/mod.hpp>
#include <migraphx/op/mul.hpp>
#include <migraphx/op/multibroadcast.hpp>
#include <migraphx/op/nearbyint.hpp>
#include <migraphx/op/neg.hpp>
#include <migraphx/op/nonmaxsuppression.hpp>
#include <migraphx/op/nonzero.hpp>
......@@ -110,7 +111,6 @@
#include <migraphx/op/rnn_variable_seq_lens.hpp>
#include <migraphx/op/rnn_var_sl_last_output.hpp>
#include <migraphx/op/roialign.hpp>
#include <migraphx/op/round.hpp>
#include <migraphx/op/rsqrt.hpp>
#include <migraphx/op/scalar.hpp>
#include <migraphx/op/scatter_add.hpp>
......
src/include/migraphx/streamutils.hpp
View file @ 0662a9a3
......@@ -30,6 +30,7 @@
#include <migraphx/rank.hpp>
#include <migraphx/requires.hpp>
#include <migraphx/config.hpp>
#include <migraphx/optional.hpp>
#include <vector>
namespace migraphx {
......@@ -68,6 +69,19 @@ auto stream_write_value_impl(rank<1>, std::ostream& os, const T& x) -> decltype(
os << x;
}
template <class T>
auto stream_write_value_impl(rank<1>, std::ostream& os, const optional<T>& x)
{
if(x.has_value())
{
os << *x;
}
else
{
os << "nullopt";
}
}
template <class T>
void stream_write_value_impl(rank<1>, std::ostream& os, const std::vector<T>& r)
{
......
src/normalize_attributes.cpp
View file @ 0662a9a3
......@@ -66,15 +66,15 @@ auto tune_attribute(const std::vector<int64_t>& vec,
{
if(input_shape.dynamic())
{
// return the unchanged `vec` if the dynamic_dimensions at `axes` are not fixed
if(std::any_of(axes.begin(), axes.end(), [&](auto ax) {
return not input_shape.dyn_dims().at(ax).is_fixed();
}))
{
return vec;
}
std::transform(axes.begin(), axes.end(), max_vals.begin(), [&](auto i) {
const auto& dd = input_shape.dyn_dims().at(i);
if(not dd.is_fixed())
{
MIGRAPHX_THROW(
"NORMALIZE_ATTR: 'use_lens' on a non-fixed dynamic dimension, axis=" +
std::to_string(i));
}
return dd.max;
return input_shape.dyn_dims().at(i).max;
});
}
else
......
src/onnx/include/migraphx/onnx/onnx_parser.hpp
View file @ 0662a9a3
......@@ -97,10 +97,11 @@ struct onnx_parser
shape::dynamic_dimension default_dyn_dim_value = {1, 1};
std::unordered_map<std::string, std::vector<std::size_t>> map_input_dims;
std::unordered_map<std::string, std::vector<shape::dynamic_dimension>> map_dyn_input_dims;
bool use_dyn_output = false;
bool skip_unknown_operators = false;
int64_t max_loop_iterations = 10;
int64_t opset_version = 13;
bool use_dyn_output = false;
bool skip_unknown_operators = false;
int64_t max_loop_iterations = 10;
int64_t limit_max_iterations = std::numeric_limits<uint16_t>::max();
int64_t opset_version = 13;
std::unordered_map<std::string, op_func> ops;
......
src/onnx/onnx.cpp
View file @ 0662a9a3
......@@ -67,6 +67,7 @@ program parse_onnx_from(const onnx_options& options, Ts&&... xs)
}
parser.skip_unknown_operators = options.skip_unknown_operators;
parser.max_loop_iterations = options.max_loop_iterations;
parser.limit_max_iterations = options.limit_max_iterations;
parser.use_dyn_output = options.use_dyn_output;
if(options.print_program_on_error)
......
src/onnx/parse_clip.cpp
View file @ 0662a9a3
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* 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
......
src/onnx/parse_generic_op.cpp
View file @ 0662a9a3
......@@ -60,7 +60,7 @@ struct parse_generic_op : op_parser<parse_generic_op>
{"Neg", "neg"},
{"Reciprocal", "recip"},
{"Relu", "relu"},
{"Round", "round"},
{"Round", "nearbyint"},
{"Sigmoid", "sigmoid"},
{"Sign", "sign"},
{"Sin", "sin"},
......
src/targets/gpu/include/migraphx/gpu/int8_gemm_pack.hpp → src/onnx/parse_isinf.cpp
View file @ 0662a9a3
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* 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
......@@ -21,43 +21,67 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#ifndef MIGRAPHX_GUARD_RTGLIB_INT8_GEMM_PACK_HPP
#define MIGRAPHX_GUARD_RTGLIB_INT8_GEMM_PACK_HPP
#include <migraphx/argument.hpp>
#include <migraphx/config.hpp>
#include <utility>
#include <migraphx/onnx/op_parser.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/instruction.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
struct context;
namespace onnx {
struct hip_int8_gemm_pack_a
struct parse_isinf : op_parser<parse_isinf>
{
std::string name() const { return "gpu::int8_gemm_pack_a"; }
shape compute_shape(const std::vector<shape>& inputs) const;
argument compute(context& ctx, const shape&, const std::vector<argument>& args) const;
std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
{
return shapes.size() - 1;
}
};
std::vector<op_desc> operators() const { return {{"IsInf", "isinf"}}; }
struct hip_int8_gemm_pack_b
{
std::string name() const { return "gpu::int8_gemm_pack_b"; }
shape compute_shape(const std::vector<shape>& inputs) const;
argument compute(context& ctx, const shape&, const std::vector<argument>& args) const;
std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
instruction_ref parse(const op_desc& /*opd*/,
const onnx_parser& parser,
onnx_parser::node_info info,
const std::vector<instruction_ref>& args) const
{
return shapes.size() - 1;
bool detect_negative = true;
bool detect_positive = true;
if(contains(info.attributes, "detect_negative"))
{
detect_negative = static_cast<bool>(
parser.parse_value(info.attributes.at("detect_negative")).at<int>());
}
if(contains(info.attributes, "detect_positive"))
{
detect_positive = static_cast<bool>(
parser.parse_value(info.attributes.at("detect_positive")).at<int>());
}
auto x_shape = args[0]->get_shape();
if(not detect_negative and not detect_positive)
{
return info.add_instruction(
make_op("multibroadcast", {{"out_lens", x_shape.lens()}}),
info.add_literal(migraphx::literal{migraphx::shape{shape::bool_type}, {false}}));
}
auto is_inf = info.add_instruction(make_op("isinf"), args[0]);
if(detect_negative and detect_positive)
{
return is_inf;
}
auto zero_l = info.add_literal(migraphx::literal{migraphx::shape{x_shape.type()}, {0}});
auto mb_zero =
info.add_instruction(make_op("multibroadcast", {{"out_lens", x_shape.lens()}}), zero_l);
auto cond = info.add_broadcastable_binary_op(
detect_negative ? "less" : "greater", args[0], mb_zero);
if(cond->get_shape().type() != shape::bool_type)
{
cond =
info.add_instruction(make_op("convert", {{"target_type", shape::bool_type}}), cond);
}
return info.add_instruction(make_op("logical_and"), is_inf, cond);
}
};
} // namespace gpu
} // namespace onnx
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/onnx/parse_loop.cpp
View file @ 0662a9a3
......@@ -58,6 +58,16 @@ struct parse_loop : op_parser<parse_loop>
}
}
// cap max_iter because loop uses static shapes with max_iter size and huge numbers
// here can cause overflow
if(max_iterations > parser.limit_max_iterations)
{
std::cerr << "WARNING: PARSE_LOOP max_iterations exceeds the maximum loop "
"iterations limit, it will be changed from "
<< max_iterations << " to " << parser.limit_max_iterations << ".\n";
max_iterations = parser.limit_max_iterations;
}
// condition input is empty
if(args.at(1)->name() == "undefined")
{
......
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