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Commit 8d32c6b8 authored by Paul's avatar Paul
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Merge branch 'develop' into blas_tuning

parents 23cb7917 f25606f9
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Showing with 956 additions and 167 deletions
src/include/migraphx/op/nonzero.hpp
View file @ 8d32c6b8
/*
* 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
......@@ -56,10 +56,10 @@ struct nonzero
std::vector<std::vector<std::size_t>> vec_idx;
auto s = args.front().get_shape();
args.front().visit([&](auto v) {
shape_for_each(s, [&](auto idx) {
if(not float_equal(v[s.index(idx)], 0))
shape_for_each(s, [&](const auto& idx_v, size_t idx) {
if(not float_equal(v[idx], 0))
{
vec_idx.push_back(idx);
vec_idx.push_back(idx_v);
}
});
});
......
src/include/migraphx/op/pooling.hpp
View file @ 8d32c6b8
......@@ -29,6 +29,7 @@
#include <migraphx/config.hpp>
#include <migraphx/value.hpp>
#include <migraphx/argument.hpp>
#include <migraphx/pad_calc.hpp>
#include <migraphx/par_for.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/dyn_output.hpp>
......@@ -40,10 +41,20 @@ namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
// The Pooling operator mostly follows the specifications for the Onnx pooling op.
// It assumes an NCHW layout, extended to support any number of spatial dimensions
// from 1 on up; dimensions are <batch index, channels, spatial dimensions...>
//
struct pooling
{
// Class members mode, ceil_mode, padding_mode have similar names but refer to separate
// concepts.
pooling_mode mode = {pooling_mode::average};
// If the input has rank other than 4 then padding, stride, lengths must all be specified
// since the defaults have 2-dimensions. Exception: padding not required if
// padding_mode != default_
// Padding along each spatial input dimension
// Can be ndim or 2*ndim values where ndim is size of lengths
// ndim values means pad the same before and after each dimension
......@@ -63,13 +74,14 @@ struct pooling
// ceiling mode is a flag affecting output size
// or equivalently, placements of the pooling kernel.
// When true, round the size upwards, possibly
// including partial placements where the kernel extends beyond the edge
// of input and even padding. When false, round down so that all
// When true, round the size upwards. When false, round down so that all
// kernel placements fit but some input values may be dropped.
bool ceil_mode = false;
int lp_order = 2;
// Mode for auto padding. default_ indicates no auto padding.
padding_mode_t padding_mode = padding_mode_t::default_;
// Global pooling with dynamic shape input
bool dyn_global = false;
......@@ -84,6 +96,7 @@ struct pooling
{
return pack(f(self.mode, "mode"),
f(self.padding, "padding"),
f(self.padding_mode, "padding_mode"),
f(self.stride, "stride"),
f(self.lengths, "lengths"),
f(self.ceil_mode, "ceil_mode"),
......@@ -97,7 +110,8 @@ struct pooling
{
if(dyn_global)
return;
if((padding.size() != stride.size() and (padding.size()) != stride.size() * 2) or
if((padding_mode != default_ and padding.size() != stride.size() and
(padding.size()) != stride.size() * 2) or
stride.size() != lengths.size())
{
MIGRAPHX_THROW("POOLING: inconsistent attribute sizes");
......@@ -137,8 +151,19 @@ struct pooling
std::size_t padding_factor = 2 * padding[i];
if(padding.size() == 2 * kdims)
padding_factor = padding[i] + padding[i + kdims];
assert(input_lens[i + 2] + padding_factor >= lengths[i]);
std::size_t dim_size = input_lens[i + 2] + padding_factor - lengths[i];
std::size_t dim_size;
if(input_lens[i + 2] + padding_factor < lengths[i])
{
if(padding_mode == default_)
MIGRAPHX_THROW("POOLING: not enough padding for the given kernel size");
// lengths can be legitimately larger only if we're doing auto padding
// with a dynamic shape, in which case given padding is ignored. Set a dummy value.
dim_size = 2;
}
else
{
dim_size = input_lens[i + 2] + padding_factor - lengths[i];
}
std::size_t len =
(ceil_mode)
? dim_size / stride[i] +
......@@ -151,17 +176,13 @@ struct pooling
shape normalize_compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this, true}.has(1);
check_shapes{inputs, *this, true}.has(1).min_ndims(3);
check_attribute_size();
const shape& input = inputs.at(0);
auto padding_size = padding.size();
auto stride_size = stride.size();
size_t kdims = input.ndim() - 2;
if(input.ndim() < 3)
{
MIGRAPHX_THROW("POOLING: input must have 3 or more dimensions and be nonempty");
}
if(input.ndim() * 2 != padding_size + 4 and input.ndim() != padding_size + 2)
if(input.ndim() != stride_size + 2)
{
MIGRAPHX_THROW("POOLING: input and attribute size mismatch!");
}
......@@ -179,6 +200,28 @@ struct pooling
}
return {input.type(), output_dyn_dims};
}
else if(padding_mode != default_)
{
const size_t num_spatial_dims = inputs[0].ndim() - 2;
const shape& x_shape = inputs[0];
// same as convolution::dynamic_compute_shape()
for(std::size_t i = 0; i < num_spatial_dims; ++i)
{
auto ceil_div = [](std::size_t x, std::size_t y) { return (x + y - 1) / y; };
auto s = stride[i];
auto x = x_shape.dyn_dims()[i + 2];
std::set<std::size_t> optimals{};
std::transform(x.optimals.begin(),
x.optimals.end(),
std::inserter(optimals, optimals.begin()),
[&](auto o) { return ceil_div(o, s); });
output_dyn_dims.push_back(
shape::dynamic_dimension{ceil_div(x.min, s), ceil_div(x.max, s), optimals});
}
return {input.type(), output_dyn_dims};
}
else
{
// does not compute optimals
......@@ -267,6 +310,7 @@ struct pooling
Out& output,
const In& input,
const std::vector<std::size_t>& kernel_dims,
const std::vector<std::size_t>& padding_vals,
Op op) const
{
auto in_s = input.get_shape();
......@@ -283,9 +327,9 @@ struct pooling
// For each spatial dimension, find starting and ending index of pooling kernel
for(std::size_t dim = 2; dim < n_dim; ++dim)
{
auto d_2 = dim - 2;
int start =
static_cast<int>(idx_o[dim] * stride[d_2]) - static_cast<int>(padding[d_2]);
auto d_2 = dim - 2;
int start = static_cast<int>(idx_o[dim] * stride[d_2]) -
static_cast<int>(padding_vals[d_2]);
int end;
// NOLINT
if(count_include_pad and ceil_mode and (mode != pooling_mode::max))
......@@ -297,7 +341,7 @@ struct pooling
// Check if this kernel extends beyond the padding at end of dimension
end = std::min(start + kernel_dims[d_2],
in_lens[dim] + static_cast<int>(padding[d_2]));
in_lens[dim] + static_cast<int>(padding_vals[d_2]));
}
else
{
......@@ -316,11 +360,12 @@ struct pooling
}
shape win_shape{output_shape.type(), win_size};
auto pool_size = win_shape.elements();
double output_val = op.template init<Type>();
// for each element in the window...
shape_for_each(win_shape, [&](auto idx_w) {
shape_for_each(win_shape, [&](const auto& idx_w) {
// the coordinates of this element
auto idx = idx_o;
......@@ -354,30 +399,65 @@ struct pooling
argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
{
argument result{dyn_out.computed_shape};
argument result;
auto input_lens = args[0].get_shape().lens();
std::vector<std::size_t> kernel_dims;
shape output_shape;
// If we have to auto-calculate padding, it will be passed to calc_pooling() as an argument
// instead of the member variable padding.
std::vector<std::size_t> temp_padding(padding);
if(dyn_global)
{
// for dynamic GlobalPooling, there's no padding
kernel_dims.insert(kernel_dims.end(), input_lens.begin() + 2, input_lens.end());
output_shape = dyn_out.computed_shape;
result = dyn_out.computed_shape;
}
else
else if((padding_mode != op::padding_mode_t::default_))
{
// if padding_mode is set, input was a dynamic size. Calculate padded size now.
// kernel_lens is the same as kernel_dims, but prepended with the 2 non-
// spatial dimensions. For size computations, it's used like the weights
// tensor for convolutions.
std::vector<std::size_t> kernel_lens;
kernel_lens.insert(kernel_lens.end(), input_lens.begin(), input_lens.begin() + 2);
kernel_lens.insert(kernel_lens.end(), lengths.begin(), lengths.end());
kernel_dims = this->lengths;
auto type = args[0].get_shape().type();
// dilation not currently supported for pooling, so default to all 1's
temp_padding = calc_dyn_auto_pad(
input_lens, kernel_lens, stride, {1, 1}, bool(padding_mode == op::same_upper));
output_shape = compute_padded_pool_shape(
args[0].get_shape(), shape(type, kernel_dims), temp_padding, stride, {1, 1});
result = argument(output_shape);
}
else // fixed/static input
{
kernel_dims = this->lengths;
output_shape = dyn_out.computed_shape;
result = dyn_out.computed_shape;
}
// Perform the computation and populate result
visit_all(result, args[0])([&](auto output, auto input) {
using type = typename decltype(output)::value_type;
switch(mode)
{
case migraphx::op::pooling_mode::average:
calc_pooling<type>(dyn_out.computed_shape, output, input, kernel_dims, avg_pool{});
calc_pooling<type>(
output_shape, output, input, kernel_dims, temp_padding, avg_pool{});
break;
case migraphx::op::pooling_mode::max:
calc_pooling<type>(dyn_out.computed_shape, output, input, kernel_dims, max_pool{});
calc_pooling<type>(
output_shape, output, input, kernel_dims, temp_padding, max_pool{});
break;
case migraphx::op::pooling_mode::lpnorm:
calc_pooling<type>(
dyn_out.computed_shape, output, input, kernel_dims, lpnorm_pool{lp_order});
output_shape, output, input, kernel_dims, temp_padding, lpnorm_pool{lp_order});
break;
}
});
......
src/include/migraphx/op/random_seed.hpp 0 → 100644
View file @ 8d32c6b8
/*
* 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_OPERATORS_RANDOM_SEED_HPP
#define MIGRAPHX_GUARD_OPERATORS_RANDOM_SEED_HPP
#include <migraphx/check_shapes.hpp>
#include <migraphx/argument.hpp>
#include <random>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
/**
* Generates a random seed for the use of random number generators. Generating the seed
* at runtime guarantees there will be a different random sequence on every execution.
* This operation has no inputs or attributes, and outputs an unsigned integer tensor with
* a single value.
*/
struct random_seed
{
shape::type_t dtype = shape::type_t::uint64_type;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.dtype, "dtype"));
}
std::string name() const { return "random_seed"; }
shape compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(0);
return shape{dtype};
}
argument compute(const shape& output_shape, const std::vector<argument>&) const
{
argument result(output_shape);
result.visit([&](auto output) { output.front() = std::random_device{}(); });
return result;
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/random_uniform.hpp 0 → 100644
View file @ 8d32c6b8
/*
* 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.
*/
/**
* Random Uniform distribution operator. Given a shape, populate it with random
* values. Calls to random_uniform using the same randomization seed as a
* literal input will
* always generate the same pseudo-random sequence.
*
* Inputs: (1) randomization seed (any type is allowed)
* (2) output buffer argument to be populated.
*
* Attributes: none
*
* Output: Returns the buffer from input #2.
*
*/
#ifndef MIGRAPHX_GUARD_OPERATORS_RANDOM_UNIFORM_HPP
#define MIGRAPHX_GUARD_OPERATORS_RANDOM_UNIFORM_HPP
#include <migraphx/check_shapes.hpp>
#include <migraphx/argument.hpp>
#include <random>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
/**
* random_uniform populates the passed shape with random numbers, in a uniform
* distribution. Range for floating-point data types is (0, 1);
* for integer types it is [0, <max value for the type>]
*/
struct random_uniform
{
// The random_uniform operation needs the random number generator seed
// to be passed as a runtime input.
std::string name() const { return "random_uniform"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this, true}.has(2);
return inputs.at(1);
}
argument compute(const shape&, std::vector<argument> args) const
{
// Output goes into the passed buffer, not the shape output.
auto result = args[1];
uint64_t local_seed = args[0].at<uint64_t>(0);
std::mt19937 gen(local_seed);
result.visit([&](auto output) {
using type = typename decltype(output)::value_type;
if constexpr(std::is_integral<type>{})
{
#ifdef _MSC_VER
// According to the C++ specification, the effect is undefined if the result type
// for the generator is not one of short, int, long, long long, unsigned short,
// unsigned int, unsigned long, or unsigned long long. See
// https://en.cppreference.com/w/cpp/numeric/random/uniform_int_distribution.
if constexpr(sizeof(type) == 1)
{
std::uniform_int_distribution<int> dis{std::numeric_limits<type>::min(),
std::numeric_limits<type>::max()};
std::generate(output.begin(), output.end(), [&] { return dis(gen); });
}
else
#endif
{
// default range for all integer types is
// (0, std::uniform_int_distribution<type>::max()).
// Todo: enable different ranges
std::uniform_int_distribution<type> dis;
std::generate(output.begin(), output.end(), [&] { return dis(gen); });
}
}
else
{
// default real distribution type is double with range (0, 1);
std::uniform_real_distribution<> dis;
std::generate(output.begin(), output.end(), [&] { return dis(gen); });
}
});
return result;
}
std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 1; }
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/reduce_op.hpp
View file @ 8d32c6b8
/*
* 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
......@@ -163,7 +163,7 @@ struct reduce_op : op_name<Derived>
auto& self = static_cast<const Derived&>(*this);
auto data_idx = out_idx;
accumulator val = self.init();
shape_for_each(batch_shape, [&](auto b_idx) {
shape_for_each(batch_shape, [&](const auto& b_idx) {
this->tune_dims(tuned_axes, b_idx, data_idx);
accumulator x = input(data_idx.begin(), data_idx.end());
val = self.op()(accumulator{self.input()(x)}, val);
......
src/include/migraphx/op/reshape.hpp
View file @ 8d32c6b8
/*
* 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
......@@ -29,12 +29,29 @@
#include <migraphx/config.hpp>
#include <migraphx/value.hpp>
#include <migraphx/dyn_output.hpp>
#include <migraphx/optional.hpp>
#include <algorithm>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
/**
* 1 input version:
* reshape(input_data)
* this.dims = output_dims
* Makes a copy of input_data to the output shape.
*
* 2 input version:
* reshape(input_data, output_buffer)
* this.dims = unset
* Copies input_data to output_buffer; output_buffer already has the output shape.
* This version will not fail gracefully if the input shape and output_buffer shape are
* incompatible. There's a throw that will catch when the number of elements do not match at
* runtime. This version should only be used for dynamic reshapes (output dimensions only known at
* runtime). If output_buffer has a static shape during compile/parse, you can use the 1 input
* version.
*/
struct reshape
{
std::vector<int64_t> dims;
......@@ -45,8 +62,6 @@ struct reshape
return pack(f(self.dims, "dims"));
}
value attributes() const { return {{"require_std_shape", true}}; }
std::string name() const { return "reshape"; }
shape dyn_compute_shape(shape s0) const
......@@ -110,27 +125,9 @@ struct reshape
return it;
}
template <class DimIterator, class StrideIterator>
static auto can_strides_merge(DimIterator dim_start,
DimIterator dim_last,
StrideIterator stride_start,
StrideIterator stride_last)
{
assert(std::distance(dim_start, dim_last) == std::distance(stride_start, stride_last));
auto cstride = *std::prev(stride_last);
return std::equal(std::make_reverse_iterator(dim_last),
std::make_reverse_iterator(dim_start + 1),
std::make_reverse_iterator(stride_last - 1),
std::make_reverse_iterator(stride_start),
[&](auto dim, auto stride) {
cstride *= dim;
return stride == cstride;
});
}
// This will reshape the dimesions of the input shape to use the lens of
// `rdims`. If this can't be done without changing memory layout then it
// will return nullopt
// This will attempt to alias the dimensions of the input shape to the lens of
// `rdims`. Unlike reshape_lazy though we can modify memory layout with copies and this
// can remove previous nullopts that were sent back for the alias case
static optional<shape> reshape_dims(const shape& input, const std::vector<std::size_t>& rdims)
{
if(input.standard())
......@@ -155,13 +152,8 @@ struct reshape
{
auto start = idims.begin() + i;
auto it = compute_end_dim(start, idims.end(), rdim);
if(it == start)
return nullopt;
auto n = it - start;
assert((i + n) <= istrides.size());
if(not can_strides_merge(
start, it + 1, istrides.begin() + i, istrides.begin() + i + n + 1))
return nullopt;
i += n;
rstrides.push_back(istrides[i]);
}
......@@ -170,8 +162,7 @@ struct reshape
{
auto start = rdims.begin() + i;
auto it = compute_end_dim(start, rdims.end(), idim);
if(it == start)
return nullopt;
auto n = it - start;
assert((r + n) <= rdims.size());
auto stride = istrides[i] * idim;
......@@ -191,15 +182,11 @@ struct reshape
auto stride = rstrides.back();
for(auto d : range(rdims.begin() + rstrides.size(), rdims.end()))
{
if(d != 1)
return nullopt;
(void)d;
rstrides.push_back(stride);
}
}
if(rdims.size() != rstrides.size())
return nullopt;
return shape{input.type(), rdims, rstrides};
}
......@@ -233,41 +220,68 @@ struct reshape
}
auto s = reshape_dims(inputs.front(), rdims);
if(not s.has_value())
MIGRAPHX_THROW("Reshape on axis that is not packed.");
if(s->elements() != inputs.front().elements())
MIGRAPHX_THROW("Reshape: Wrong number of elements for reshape: reshape has " +
MIGRAPHX_THROW("reshape: Wrong number of elements for reshape: reshape has " +
std::to_string(s->elements()) + " elements whereas the input has " +
std::to_string(inputs.front().elements()));
assert(s->bytes() == inputs.front().bytes());
return *s;
}
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this, true}.has(1);
check_shapes{inputs, *this, true}.has(1, 2);
auto n_neg_dims = std::count(dims.begin(), dims.end(), -1);
if(n_neg_dims > 1)
MIGRAPHX_THROW("Reshape: Dimensions for reshape can only have one -1 dim");
auto s0 = inputs[0];
if(s0.dynamic())
MIGRAPHX_THROW("reshape: Dimensions for reshape can only have one -1 dim");
auto s0 = inputs.front();
if(inputs.size() == 1)
{
return dyn_compute_shape(s0);
if(s0.dynamic())
{
return dyn_compute_shape(s0);
}
else
{
return static_compute_shape(inputs, n_neg_dims);
}
}
else
{
return static_compute_shape(inputs, n_neg_dims);
return inputs.back();
}
}
argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
{
return args[0].reshape(dyn_out.computed_shape);
}
assert(dyn_out.computed_shape.standard());
if(args.size() == 1)
{
argument result{dyn_out.computed_shape};
std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
visit_all(result, args[0])([&](auto output, auto input) {
std::copy(input.begin(), input.end(), output.begin());
});
return result;
}
else
{
// 2 arg
if(args[0].get_shape().elements() != args[1].get_shape().elements())
{
MIGRAPHX_THROW("Reshape: Number of elements must match at runtime. Input: " +
std::to_string(args[0].get_shape().elements()) +
" Output buffer: " + std::to_string(args[1].get_shape().elements()));
}
visit_all(args[1], args[0])([&](auto output, auto input) {
std::copy(input.begin(), input.end(), output.begin());
});
return args[1];
}
}
};
} // namespace op
......
src/include/migraphx/op/reshape_lazy.hpp 0 → 100644
View file @ 8d32c6b8
/*
* 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_OPERATORS_RESHAPE_LAZY_HPP
#define MIGRAPHX_GUARD_OPERATORS_RESHAPE_LAZY_HPP
#include <migraphx/check_shapes.hpp>
#include <migraphx/argument.hpp>
#include <migraphx/config.hpp>
#include <migraphx/value.hpp>
#include <migraphx/dyn_output.hpp>
#include <migraphx/optional.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct reshape_lazy
{
std::vector<int64_t> dims;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.dims, "dims"));
}
value attributes() const { return {{"require_std_shape", true}}; }
std::string name() const { return "reshape_lazy"; }
shape dyn_compute_shape(shape s0) const
{
auto dyn_dims = s0.dyn_dims();
auto num_not_fixed = std::count_if(
dyn_dims.cbegin(), dyn_dims.cend(), [](auto dd) { return not dd.is_fixed(); });
if(num_not_fixed != 1)
{
MIGRAPHX_THROW("reshape_lazy: Only supports one non-fixed dynamic_dimension");
}
// track number of fixed elements in input and output
std::size_t num_dims_ele = 1;
std::size_t num_dd_ele = 1;
for(std::size_t i = 0; i < dyn_dims.size(); ++i)
{
if(dyn_dims[i].is_fixed())
{
num_dims_ele *= dims[i];
num_dd_ele *= dyn_dims[i].min;
}
else
{
if(dims[i] != 0 and dims[i] != -1)
{
MIGRAPHX_THROW(
"reshape_lazy: Non-fixed dynamic_dimension doesn't match with 0 or -1 "
"output dimension");
}
}
}
if(num_dims_ele != num_dd_ele)
{
MIGRAPHX_THROW("reshape_lazy: Number of fixed elements must match. Input: " +
std::to_string(num_dd_ele) + " Output: " + std::to_string(num_dims_ele));
}
// construct output dynamic shape from dims attribute
std::vector<shape::dynamic_dimension> output_dyn_dims(dims.size());
std::transform(dims.cbegin(),
dims.cend(),
dyn_dims.cbegin(),
output_dyn_dims.begin(),
[](std::size_t dim, auto dyn_dim) {
if(not dyn_dim.is_fixed())
return dyn_dim;
return shape::dynamic_dimension{dim, dim};
});
return {s0.type(), output_dyn_dims};
}
template <class Iterator>
static auto compute_end_dim(Iterator start, Iterator last, std::size_t dim)
{
std::size_t x = 1;
auto it = std::find_if(start, last, [&](auto i) {
x *= i;
return x >= dim;
});
if(x != dim)
return start;
return it;
}
template <class DimIterator, class StrideIterator>
static auto can_strides_merge(DimIterator dim_start,
DimIterator dim_last,
StrideIterator stride_start,
StrideIterator stride_last)
{
assert(std::distance(dim_start, dim_last) == std::distance(stride_start, stride_last));
auto cstride = *std::prev(stride_last);
return std::equal(std::make_reverse_iterator(dim_last),
std::make_reverse_iterator(dim_start + 1),
std::make_reverse_iterator(stride_last - 1),
std::make_reverse_iterator(stride_start),
[&](auto dim, auto stride) {
cstride *= dim;
return stride == cstride;
});
}
// This will attempt to alias the dimensions of the input shape to the lens of
// `rdims`. If this can't be done without changing memory layout then it
// will return nullopt
static optional<shape> reshape_lazy_dims(const shape& input,
const std::vector<std::size_t>& rdims)
{
if(input.standard())
return shape{input.type(), rdims};
const auto& idims = input.lens();
const auto& istrides = input.strides();
std::vector<std::size_t> rstrides;
std::size_t i = 0;
std::size_t r = 0;
while(i < idims.size() and r < rdims.size())
{
auto idim = idims[i];
auto rdim = rdims[r];
if(rdim == idim)
{
rstrides.push_back(istrides[i]);
}
// squeeze
else if(rdim > idim)
{
auto start = idims.begin() + i;
auto it = compute_end_dim(start, idims.end(), rdim);
if(it == start)
return nullopt;
auto n = it - start;
assert((i + n) <= istrides.size());
if(not can_strides_merge(
start, it + 1, istrides.begin() + i, istrides.begin() + i + n + 1))
return nullopt;
i += n;
rstrides.push_back(istrides[i]);
}
// unsqueeze
else // if(rdim < idim)
{
auto start = rdims.begin() + i;
auto it = compute_end_dim(start, rdims.end(), idim);
if(it == start)
return nullopt;
auto n = it - start;
assert((r + n) <= rdims.size());
auto stride = istrides[i] * idim;
std::for_each(start, it + 1, [&](auto dim) {
stride /= dim;
rstrides.push_back(stride);
});
r += n;
}
i++;
r++;
}
// Handle trailing 1s
if(rstrides.size() < rdims.size() and not rstrides.empty())
{
auto stride = rstrides.back();
for(auto d : range(rdims.begin() + rstrides.size(), rdims.end()))
{
if(d != 1)
return nullopt;
rstrides.push_back(stride);
}
}
if(rdims.size() != rstrides.size())
return nullopt;
return shape{input.type(), rdims, rstrides};
}
shape static_compute_shape(std::vector<shape> inputs, std::size_t n_neg_dims) const
{
check_shapes{inputs, *this}.has(1);
auto&& idims = inputs.front().lens();
std::vector<std::size_t> rdims(dims.begin(), dims.end());
for(std::size_t i = 0; i < dims.size(); i++)
{
if(dims[i] == 0)
rdims[i] = idims[i];
// since rdims using size_t type, -1 is the max value
// is size_t that cause later compuation incorrect
if(dims[i] == -1)
rdims[i] = 1;
}
if(n_neg_dims > 0)
{
size_t missing_dim =
inputs.front().elements() /
std::accumulate(rdims.begin(), rdims.end(), 1, std::multiplies<int64_t>());
for(std::size_t i = 0; i < rdims.size(); i++)
{
if(dims[i] == -1)
rdims[i] = missing_dim;
}
}
auto s = reshape_lazy_dims(inputs.front(), rdims);
if(not s.has_value())
MIGRAPHX_THROW("reshape_lazy on axis that is not packed.");
if(s->elements() != inputs.front().elements())
MIGRAPHX_THROW(
"reshape_lazy: Wrong number of elements for reshape_lazy: reshape_lazy has " +
std::to_string(s->elements()) + " elements whereas the input has " +
std::to_string(inputs.front().elements()));
assert(s->bytes() == inputs.front().bytes());
return *s;
}
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this, true}.has(1);
auto n_neg_dims = std::count(dims.begin(), dims.end(), -1);
if(n_neg_dims > 1)
MIGRAPHX_THROW("reshape_lazy: Dimensions for reshape_lazy can only have one -1 dim");
auto s0 = inputs[0];
if(s0.dynamic())
{
return dyn_compute_shape(s0);
}
else
{
return static_compute_shape(inputs, n_neg_dims);
}
}
argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
{
return args[0].reshape(dyn_out.computed_shape);
}
std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/reverse.hpp
View file @ 8d32c6b8
/*
* 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
......@@ -70,13 +70,13 @@ struct reverse
argument result{s};
auto lens = s.lens();
visit_all(result, args.front())([&](auto output, auto input) {
shape_for_each(s, [&](const auto& out_idx) {
auto in_idx = out_idx;
shape_for_each(s, [&](const auto& out_idx_v, size_t out_idx) {
auto in_idx = out_idx_v;
for(const auto& axis : axes)
{
in_idx[axis] = lens[axis] - 1 - out_idx[axis];
in_idx[axis] = lens[axis] - 1 - out_idx_v[axis];
}
output[s.index(out_idx)] = input[s.index(in_idx)];
output[out_idx] = input[s.index(in_idx)];
});
});
......
src/include/migraphx/op/roialign.hpp
View file @ 8d32c6b8
/*
* 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
......@@ -33,6 +33,7 @@
#include <migraphx/dfor.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/shape_for_each.hpp>
#include <array>
#include <cmath>
#include <numeric>
#include <utility>
......@@ -113,10 +114,9 @@ struct roialign
{
std::vector<pos_weight> results(bin_grid_size[0] * bin_grid_size[1] * output_height *
output_width);
shape_for_each(comp_s, [&](auto idx) {
std::array<std::size_t, 2> p = {idx[0], idx[1]};
std::array<std::size_t, 2> i = {idx[2], idx[3]};
auto index = comp_s.index(idx);
shape_for_each(comp_s, [&](const auto& idx_v, size_t index) {
std::array<std::size_t, 2> p = {idx_v[0], idx_v[1]};
std::array<std::size_t, 2> i = {idx_v[2], idx_v[3]};
std::array<float, 2> xy{};
std::array<int64_t, 2> low{};
......@@ -125,7 +125,7 @@ struct roialign
{
xy[ii] = roi_start[ii] + p[ii] * bin_size[ii] +
(i[ii] + .5f) * bin_size[ii] / bin_grid_size[ii];
xy[ii] = (coord_trans_mode == "output_half_pixel") ? (xy[ii] - 0.5f) : xy[ii];
xy[ii] = (coord_trans_mode == "half_pixel") ? (xy[ii] - 0.5f) : xy[ii];
if(xy[ii] < -1.0 or xy[ii] > dims[ii])
{
results[index] = pos_weight{};
......@@ -255,7 +255,7 @@ struct roialign
std::vector<std::size_t> comp_lens1 = {channels, out_dims[0], out_dims[1]};
shape comp_s1{migraphx::shape::float_type, comp_lens1};
std::vector<int64_t> vec_index(channels, 0);
shape_for_each(comp_s1, [&](auto idx) {
shape_for_each(comp_s1, [&](const auto& idx) {
auto c = idx[0];
auto ph = idx[1];
auto pw = idx[2];
......
src/include/migraphx/op/scatter.hpp
View file @ 8d32c6b8
/*
* 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
......@@ -66,7 +66,7 @@ struct scatter : op_name<Derived>
shape normalize_compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(3).standard();
check_shapes{inputs, *this}.has(3);
// If non-packed, this converts to a packed output while preserving permutation of tensor
return inputs.front().with_lens(inputs.front().lens());
}
......
src/include/migraphx/op/slice.hpp
View file @ 8d32c6b8
......@@ -27,19 +27,34 @@
#include <migraphx/check_shapes.hpp>
#include <migraphx/argument.hpp>
#include <migraphx/config.hpp>
#include <migraphx/dyn_output.hpp>
#include <migraphx/value.hpp>
#include <migraphx/dyn_output.hpp>
#include <migraphx/op/normalize_attribute.hpp>
#include <migraphx/normalize_attributes.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
/**
* Slice operator that accepts variable axes, starts and ends.
*
* Attributes:
* axes: constant axes to slice over (optional)
* starts: constant slice starting indices (optional)
* ends: constant slice ending indices (optional)
*
* 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_axes: axes to slice over (optional, static shape)
*/
struct slice
{
std::vector<int64_t> axes;
std::vector<int64_t> starts;
std::vector<int64_t> ends;
std::vector<int64_t> axes{};
std::vector<int64_t> starts{};
std::vector<int64_t> ends{};
template <class Self, class F>
static auto reflect(Self& self, F f)
......@@ -48,8 +63,8 @@ struct slice
}
/**
* Ensure that attribute vectors axes, starts, and ends are all the same size and values are in
* limits.
* Ensure that attribute vectors axes, starts, and ends are all the same size and values are
* within limits.
*/
value attributes() const
{
......@@ -70,6 +85,90 @@ struct slice
std::string name() const { return "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
* object's axes attribute. Assumes in_starts and in_ends are normalized; in_axes are valid.
*/
template <class A, class B>
std::vector<std::size_t>
lens_calc(const std::vector<std::size_t>& lengths, A in_starts, A in_ends, B in_axes) const
{
auto new_lens = lengths;
for(std::size_t i = 0; i < in_axes.size(); ++i)
{
auto axis = in_axes[i];
new_lens[axis] = in_ends[i] - in_starts[i];
}
return new_lens;
}
shape normalize_compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this, true}.has(1, 3, 4);
auto input_shape = inputs[0];
if(inputs.size() == 1)
{
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();
}))
{
MIGRAPHX_THROW("SLICE: slicing is not allowed on non-fixed dynamic input axis ");
}
if(input_shape.dynamic())
{
return shape{t,
lens_calc(input_shape.min_lens(), starts, ends, axes),
lens_calc(input_shape.max_lens(), starts, ends, axes),
{}};
}
else
{
return shape{
t, lens_calc(input_shape.lens(), starts, ends, axes), input_shape.strides()};
}
}
else
{
// 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(inputs[1].lens().at(0) != axes.size())
{
MIGRAPHX_THROW("SLICE: inputs starts and ends do not have the same dimension "
"as the axes attribute");
}
std::for_each(axes.cbegin(), axes.cend(), [&](const auto& axis) {
dds.at(axis) = {0, dds.at(axis).max};
});
}
else
{
// if axes is an input, then all the output dimensions could be 0 to the max value
std::transform(dds.begin(), dds.end(), dds.begin(), [](auto dd) {
return shape::dynamic_dimension{0, dd.max};
});
}
return shape{input_shape.type(), dds};
}
}
/**
* Calculates the starting offset for the sliced tensor.
* Used in compute when only data input and all other information are in the attributes.
*
* \param s static input shape
*/
auto compute_offset(const shape& s) const
{
const std::vector<std::size_t>& lens = s.lens();
......@@ -90,80 +189,131 @@ struct slice
offset += starts[axis] * strides[axis];
}
}
return offset;
return offset * s.type_size();
}
shape normalize_compute_shape(std::vector<shape> inputs) const
/**
* Calculates the starting offset for the sliced tensor (for aliasing).
* Used when the starts and/or the axes are inputs.
*
* \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
{
check_shapes{inputs, *this, true}.has(1);
auto input_shape = inputs[0];
auto t = input_shape.type();
// TODO: When support for dynamic shapes is added to normalize_attributes,
// remove this restriction.
if(input_shape.dynamic() and std::any_of(axes.begin(), axes.end(), [&](auto axis) {
return not input_shape.dyn_dims()[axis].is_fixed();
}))
auto ret = 0;
for(std::size_t i = 0; i < ax_vec.size(); ++i)
{
MIGRAPHX_THROW("SLICE: slicing is not allowed on non-fixed dynamic input axis ");
auto axis = ax_vec[i];
ret += input_starts[i] * s.strides().at(axis);
}
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.
*/
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
{
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}};
}
// For a static shape, old_lens will be adjusted to a new size
// for those axes that are sliced.
// For dynamic shape, the adjusted old_lens become the new max values,
// while updating the old mins and optimals if possible.
std::vector<std::size_t> new_mins;
std::vector<std::size_t> old_lens;
std::vector<std::size_t> old_strides;
// Doesn't handle optimals
if(input_shape.dynamic())
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())
{
old_lens = input_shape.max_lens();
new_mins = input_shape.min_lens();
case 1: {
std::size_t offset = compute_offset(input_shape);
return {dyn_out.computed_shape, [=] { return input.data() + offset; }};
}
else
{
old_lens = input_shape.lens();
// For static shape (including during eval step after a dynamic input) the strides are
// indexed into the pre-slice array, so they are larger than the apparent size of the
// resulting shape.
old_strides = input_shape.strides();
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; }};
}
std::vector<std::size_t> new_lens = old_lens;
for(std::size_t i = 0; i < axes.size(); i++)
{
auto axis = axes[i];
size_t sliced_length = ends[i] - starts[i];
// A Numpy indexing convention: a slice size larger than the actual dimension
// is legal and the "ends" value is clipped to the axis size
new_lens[axis] = std::min(new_lens[axis], sliced_length);
if(input_shape.dynamic())
{
// TODO: when non-fixed shape slicing is allowed, this will be different than
// sliced_length, making use of TBD start/end values.
std::size_t sliced_min_length = ends[i] - starts[i];
// if the slice size is smaller than maxes but larger than mins
new_mins[axis] = std::min(sliced_min_length, new_mins[axis]);
}
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()};
});
return {calc_shape, [=] { return input.data() + offset; }};
}
if(input_shape.dynamic())
{
return shape{t, new_mins, new_lens, {}};
default: {
// Should never get here; covering in case some code change occurs
MIGRAPHX_THROW("SLICE: invalid number of inputs");
}
else
{
return shape{t, new_lens, old_strides};
}
}
argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
{
auto input = args[0];
auto offset = compute_offset(input.get_shape()) * dyn_out.computed_shape.type_size();
return {dyn_out.computed_shape, [=] { return input.data() + offset; }};
}
std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
};
......
src/include/migraphx/operators.hpp
View file @ 8d32c6b8
......@@ -55,6 +55,7 @@
#include <migraphx/op/equal.hpp>
#include <migraphx/op/erf.hpp>
#include <migraphx/op/exp.hpp>
#include <migraphx/op/fill.hpp>
#include <migraphx/op/flatten.hpp>
#include <migraphx/op/floor.hpp>
#include <migraphx/op/fmod.hpp>
......
src/include/migraphx/optional.hpp
View file @ 8d32c6b8
......@@ -29,6 +29,17 @@
#if defined(CPPCHECK)
#define MIGRAPHX_HAS_OPTIONAL 1
#define MIGRAPHX_HAS_OPTIONAL_TS 1
#elif defined(_WIN32)
#if _MSC_VER >= 1920
#define MIGRAPHX_HAS_OPTIONAL 1
#define MIGRAPHX_HAS_OPTIONAL_TS 0
#elif _MSC_VER >= 1900
#define MIGRAPHX_HAS_OPTIONAL 0
#define MIGRAPHX_HAS_OPTIONAL_TS 1
#else
#define MIGRAPHX_HAS_OPTIONAL 0
#define MIGRAPHX_HAS_OPTIONAL_TS 0
#endif
#elif defined(__has_include)
#if __has_include(<optional>) && __cplusplus >= 201703L
#define MIGRAPHX_HAS_OPTIONAL 1
......
src/include/migraphx/pad_calc.hpp
View file @ 8d32c6b8
/*
* 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
......@@ -62,6 +62,15 @@ shape compute_padded_shape(const shape& input,
const std::vector<std::size_t>& stride,
const std::vector<std::size_t>& dilation);
// Used for dynamic auto padding of pooling operators where padding needs to be computed at
// evaulation time.
MIGRAPHX_EXPORT
shape compute_padded_pool_shape(const shape& input,
const shape& kernel,
const std::vector<std::size_t>& padding,
const std::vector<std::size_t>& stride,
const std::vector<std::size_t>& dilation);
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
......
src/include/migraphx/ranges.hpp
View file @ 8d32c6b8
......@@ -205,7 +205,7 @@ void transform(Range1&& r1, Range2&& r2, Iterator it, F f)
}
template <class Range>
auto reverse(Range& r)
auto reverse(Range&& r)
{
return range(std::make_reverse_iterator(r.end()), std::make_reverse_iterator(r.begin()));
}
......
src/include/migraphx/run_loop.hpp
View file @ 8d32c6b8
......@@ -31,6 +31,7 @@
#include <migraphx/module.hpp>
#include <migraphx/config.hpp>
#include <migraphx/ranges.hpp>
#include <array>
#include <string>
namespace migraphx {
......
src/include/migraphx/shape.hpp
View file @ 8d32c6b8
......@@ -263,7 +263,7 @@ struct MIGRAPHX_EXPORT shape
/// no padding
bool packed() const;
/// Returns true is the shape has been transposed. That is the strides are not in descending
/// Returns true if the shape has been transposed. That is the strides are not in descending
/// order
bool transposed() const;
......
src/include/migraphx/shape_for_each.hpp
View file @ 8d32c6b8
/*
* 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
......@@ -37,11 +37,11 @@ inline namespace MIGRAPHX_INLINE_NS {
template <class F>
void shape_for_each(const migraphx::shape& s, F f)
{
// Ensure calls to f use const ref to vector
auto call = [&f](const std::vector<std::size_t>& i) { f(i); };
std::vector<std::size_t> indices(s.lens().size());
const auto& index_const_ref = indices;
shape ss{s.type(), s.lens()};
for(std::size_t i = 0; i < ss.elements(); i++)
size_t max = ss.elements();
for(std::size_t i = 0; i < max; i++)
{
std::transform(ss.strides().begin(),
ss.strides().end(),
......@@ -51,9 +51,13 @@ void shape_for_each(const migraphx::shape& s, F f)
assert(len > 0 and stride > 0);
return (i / stride) % len;
});
call(indices);
if constexpr(std::is_invocable<F, decltype(index_const_ref), decltype(i)>{})
f(index_const_ref, i);
else
f(index_const_ref);
}
}
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
......
src/include/migraphx/simplify_dyn_ops.hpp 0 → 100644
View file @ 8d32c6b8
/*
* 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_RTGLIB_SIMPLIFY_DYN_OPS_HPP
#define MIGRAPHX_GUARD_RTGLIB_SIMPLIFY_DYN_OPS_HPP
#include <string>
#include <migraphx/instruction_ref.hpp>
#include <migraphx/config.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
struct module;
/**
* Convert dynamic ops to their static version if possible.
* Should be run after the split_single_dyn_dims pass.
*/
struct MIGRAPHX_EXPORT simplify_dyn_ops
{
std::string name() const { return "simplify_dyn_ops"; }
void apply(module& m) const;
};
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/simplify_reshapes.hpp
View file @ 8d32c6b8
......@@ -38,6 +38,7 @@ struct module;
*/
struct MIGRAPHX_EXPORT simplify_reshapes
{
size_t depth = 4;
std::string name() const { return "simplify_reshapes"; }
void apply(module& m) const;
};
......
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