normalize_attributes.cpp

/*
 * 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/operation.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/normalize_attributes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/op/normalize_attribute.hpp>

namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {

/**
 * Parameters:
 * vec: the vector attribute to normalize
 * axes: the operator's axes attribute if it exists, empty otherwise
 * val: the normalize_axes key and options. Ex: normalize["axes"] =
 * value::array{normalize_attribute::include_min};
 * input_shape: input shape passed when calling
 * normalize_attributes(op&, input_shape)
 *
 * See normalize_attribute.hpp for explaining the options.
 */
template <class Message>
auto tune_attribute(const std::vector<int64_t>& vec,
                    const std::vector<int64_t>& axes,
                    const value& val,
                    const shape& input_shape,
                    Message m)
{
    std::vector<int64_t> result(vec);
    if(result.empty())
    {
        return result;
    };
    int64_t n_rank                                 = input_shape.ndim();
    std::vector<op::normalize_attribute> vec_attrs = val.to_vector<op::normalize_attribute>();
    if(contains(vec_attrs, op::normalize_attribute::use_output))
    {
        n_rank = n_rank + vec.size();
    }

    std::vector<int64_t> max_vals(vec.size(), n_rank);

    if(contains(vec_attrs, op::normalize_attribute::use_len))
    {
        if(input_shape.dynamic())
        {
            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;
            });
        }
        else
        {
            std::transform(axes.begin(), axes.end(), max_vals.begin(), [&](auto i) {
                return input_shape.lens().at(i);
            });
        }
    }

    if(contains(vec_attrs, op::normalize_attribute::clip_max))
    {
        if(contains(vec_attrs, op::normalize_attribute::include_max))
        {
            std::transform(result.begin(),
                           result.end(),
                           max_vals.begin(),
                           result.begin(),
                           [](auto v, auto mv) { return v > mv ? mv : v; });
        }
        else
        {
            std::transform(result.begin(),
                           result.end(),
                           max_vals.begin(),
                           result.begin(),
                           [](auto v, auto mv) { return v >= mv ? mv - 1 : v; });
        }
    }
    else
    {
        if(contains(vec_attrs, op::normalize_attribute::include_max))
        {
            if(not std::equal(result.begin(), result.end(), max_vals.begin(), std::less_equal<>{}))
            {
                MIGRAPHX_THROW(m() + "value out of range!");
            }
        }
        else
        {
            if(not std::equal(result.begin(), result.end(), max_vals.begin(), std::less<>{}))
            {
                MIGRAPHX_THROW(m() + "value out of range!");
            }
        }
    }

    std::vector<int64_t> min_vals = max_vals;
    std::transform(min_vals.begin(), min_vals.end(), min_vals.begin(), [](auto v) { return -v; });
    if(contains(vec_attrs, op::normalize_attribute::clip_min))
    {
        if(contains(vec_attrs, op::normalize_attribute::include_min))
        {
            std::transform(result.begin(),
                           result.end(),
                           min_vals.begin(),
                           result.begin(),
                           [](auto v, auto mv) { return v < mv ? mv : v; });
        }
        else
        {
            std::transform(result.begin(),
                           result.end(),
                           min_vals.begin(),
                           result.begin(),
                           [](auto v, auto mv) { return v < mv + 1 ? mv + 1 : v; });
        }
    }
    else
    {
        if(contains(vec_attrs, op::normalize_attribute::include_min))
        {
            if(not std::equal(
                   min_vals.begin(), min_vals.end(), result.begin(), std::less_equal<>{}))
            {
                MIGRAPHX_THROW(m() + "attribute out of range!");
            }
        }
        else
        {
            if(not std::equal(result.begin(), result.end(), min_vals.begin(), std::less<>{}))
            {
                MIGRAPHX_THROW(m() + "attribute out of range!");
            }
        }
    }

    std::transform(
        result.begin(), result.end(), max_vals.begin(), result.begin(), [](auto v, auto mv) {
            return v < 0 ? v + mv : v;
        });

    return result;
}

auto tune_pad_attribute(const value& val)
{

    std::vector<size_t> vec_attrs = val.to_vector<size_t>();
    std::vector<size_t> result(vec_attrs.begin(), vec_attrs.end());
    std::copy(vec_attrs.begin(), vec_attrs.end(), std::back_inserter(result));

    return result;
}

/**
 * Assumptions:
 *  Dimensions to pad start from the third dimension (index 2).
 *  Called by compute_shape_op() with the shape of the first input.
 */
bool normalize_attributes(operation& op, const shape& input_shape)
{
    bool tuned = false;
    auto attrs = op.attributes();
    auto val   = op.to_value();
    if(attrs.contains("normalize_padding"))
    {
        auto padding       = val.at(attrs.at("normalize_padding").to<std::string>());
        auto padding_size  = padding.size();
        auto padding_start = 2;

        if(padding_size == 2 * (input_shape.ndim() - padding_start))
            tuned = true;
        else if(padding_size != (input_shape.ndim() - padding_start))
            MIGRAPHX_THROW("inconsistent padding size");
        else
        {
            auto result    = tune_pad_attribute(padding);
            val["padding"] = result;
            op.from_value(val);
            tuned = true;
        }
    }
    if(not attrs.contains("normalize_axes"))
    {
        return tuned;
    }

    auto attr_v = attrs.at("normalize_axes").without_key();
    for(const auto& rv : attr_v)
    {
        const auto& key = rv.get_key();
        if(val.contains(key))
        {
            auto message = [&] { return op.name() + ": " + key + ": "; };
            auto vv      = val.at(key).without_key();
            if(vv.is_array())
            {
                std::vector<int64_t> axes;
                if(val.contains("axes"))
                {
                    axes = val.at("axes").without_key().to_vector<int64_t>();
                }
                auto vec    = vv.to_vector<int64_t>();
                auto result = tune_attribute(vec, axes, rv.without_key(), input_shape, message);
                val[key]    = result;
                op.from_value(val);
                val   = op.to_value();
                tuned = true;
            }
            else
            {
                auto num    = vv.to<int64_t>();
                auto result = tune_attribute({num}, {num}, rv.without_key(), input_shape, message);
                val[key]    = result.front();
                op.from_value(val);
                val   = op.to_value();
                tuned = true;
            }
        }
        else
        {
            MIGRAPHX_THROW("NORMALIZE_ATTR : op " + op.name() + " attribute \"" + key +
                           "\" not exist!");
        }
    }

    return tuned;
}

std::vector<int64_t> normalize_axes(const std::vector<int64_t>& axes,
                                    const shape& input_shape,
                                    const value& attr_val,
                                    const std::string& prefix)
{
    return tune_attribute(axes, {}, attr_val, input_shape, [&] { return prefix; });
}

std::vector<int64_t> normalize_indices(const std::vector<int64_t>& indices,
                                       const std::vector<int64_t>& axes,
                                       const shape& input_shape,
                                       const value& attr_val,
                                       const std::string& prefix)
{
    return tune_attribute(indices, axes, attr_val, input_shape, [&] { return prefix; });
}

} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx