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Commit 31065c7d authored by charlie's avatar charlie
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Merge branch 'dyn_squeeze' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_model_test

parents 6bec381f 6acbd4e4
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Showing with 619 additions and 335 deletions
src/pad_calc.cpp
View file @ 31065c7d
......@@ -52,19 +52,21 @@ void calculate_padding(int64_t idx,
}
}
std::vector<std::size_t> calc_dyn_auto_pad(std::vector<std::size_t> tensor_lens,
std::vector<std::size_t> k_lens,
std::vector<std::size_t> strides,
std::vector<std::size_t> dilations,
std::vector<std::size_t> calc_dyn_auto_pad(const std::vector<std::size_t>& input_lens,
const std::vector<std::size_t>& wei_lens,
const std::vector<std::size_t>& strides,
const std::vector<std::size_t>& dilations,
bool use_upper)
{
std::vector<std::size_t> padding;
padding.resize(2 * k_lens.size());
for(size_t i = 0; i < padding.size() / 2; i++)
assert(input_lens.size() >= 3);
std::size_t num_spatial_dims = input_lens.size() - 2;
padding.resize(2 * num_spatial_dims);
for(std::size_t i = 0; i < num_spatial_dims; i++)
{
std::ptrdiff_t input_dim = tensor_lens[i];
std::ptrdiff_t input_dim = input_lens[i + 2];
std::ptrdiff_t stride = strides[i];
std::ptrdiff_t weight_dim = k_lens[i];
std::ptrdiff_t weight_dim = wei_lens[i + 2];
std::ptrdiff_t dilation = dilations[i];
std::ptrdiff_t output_dim = (input_dim + stride - 1) / stride; // round up result
std::ptrdiff_t new_weight_dim = weight_dim + (weight_dim - 1) * (dilation - 1);
......@@ -86,5 +88,28 @@ std::vector<std::size_t> calc_dyn_auto_pad(std::vector<std::size_t> tensor_lens,
return padding;
}
shape compute_padded_shape(const shape& input,
const shape& weights,
const std::vector<std::size_t>& padding,
const std::vector<std::size_t>& stride,
const std::vector<std::size_t>& dilation)
{
const size_t num_spatial_dims = input.lens().size() - 2;
std::vector<size_t> output_lens{input.lens()[0], weights.lens()[0]};
// calculate the output shape of the convolution: ((W - K + 2P) / S) + 1
for(size_t i = 0; i < num_spatial_dims; ++i)
{
auto padding_factor = padding[i] + padding[i + num_spatial_dims];
output_lens.push_back(std::size_t(std::max<std::ptrdiff_t>(
1,
(input.lens()[i + 2] - (1 + dilation[i] * (weights.lens()[i + 2] - 1)) +
padding_factor) /
stride[i] +
1)));
}
return input.with_lens(output_lens);
}
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/process.cpp
View file @ 31065c7d
......@@ -50,7 +50,7 @@ int exec(const std::string& cmd, const std::function<void(const char*)>& std_out
{
// TODO: Use execve instead of popen
std::unique_ptr<FILE, decltype(closer)> pipe(popen(cmd.c_str(), "r"), closer); // NOLINT
if(!pipe)
if(not pipe)
MIGRAPHX_THROW("popen() failed: " + cmd);
std::array<char, 128> buffer;
while(fgets(buffer.data(), buffer.size(), pipe.get()) != nullptr)
......
src/program.cpp
View file @ 31065c7d
......@@ -37,6 +37,7 @@
#include <migraphx/output_iterator.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/marker.hpp>
#include <migraphx/supported_segments.hpp>
#include <iostream>
#include <sstream>
#include <algorithm>
......@@ -77,11 +78,11 @@ program& program::operator=(program p)
void program::assign(const program& p)
{
if(!impl)
if(not impl)
{
impl = std::make_unique<program_impl>();
}
else if(!impl->modules.empty())
else if(not impl->modules.empty())
{
impl->modules.clear();
}
......@@ -167,13 +168,37 @@ target_assignments program::get_target_assignments(const std::vector<target>& ta
target_assignments p;
const auto* mod = get_main_module();
for(auto it : iterator_for(*mod))
std::vector<std::pair<target, supported_segments>> target_subgraphs;
target_subgraphs.reserve(targets.size());
std::transform(targets.begin(),
targets.end(),
std::back_inserter(target_subgraphs),
[&](const auto& t) { return std::make_pair(t, t.find_supported(mod, m)); });
for(const auto ins : iterator_for(*mod))
{
auto t = std::max_element(
targets.begin(), targets.end(), [it, m](const target& lhs, const target& rhs) {
return lhs.is_supported(it, m) < rhs.is_supported(it, m);
});
p.add_assignment(it, t->name());
if(contains(p, ins))
{
continue;
}
for(const auto& [target, subgraph] : target_subgraphs)
{
// can't pass a structured binding into lambda in C++17 so create a variable for it
const auto& t = target;
for(const auto& segment : subgraph)
{
const auto& instructions = segment.instructions;
if(not contains(instructions, ins))
{
continue;
}
std::transform(instructions.begin(),
instructions.end(),
std::inserter(p, p.end()),
[&](auto instr) { return std::make_pair(instr, t.name()); });
}
}
}
return p;
}
......@@ -373,7 +398,7 @@ std::vector<argument> generic_eval(const program& p,
return generic_eval(mm, ctx, params, {}, make_trace);
}
std::vector<argument> program::eval(parameter_map params) const
std::vector<argument> program::eval(parameter_map params, execution_environment exec_env) const
{
auto& ctx = this->impl->ctx;
#ifndef NDEBUG
......@@ -398,6 +423,12 @@ std::vector<argument> program::eval(parameter_map params) const
#endif
auto trace_level = value_of(MIGRAPHX_TRACE_EVAL{});
std::vector<argument> ret;
if(exec_env.async)
{
ctx.wait_for(exec_env.queue);
}
if(trace_level > 0)
{
......@@ -409,49 +440,56 @@ std::vector<argument> program::eval(parameter_map params) const
ins_out[x] = ss.str();
});
return generic_eval(*this,
ctx,
std::move(params),
with_check_context([&](auto& ins, auto f, auto&& check_context) {
ctx.finish();
std::cout << "Run instruction: " << ins_out.at(ins) << std::endl;
timer t{};
auto result = check_context(f);
double t1 = t.record<milliseconds>();
ctx.finish();
double t2 = t.record<milliseconds>();
std::cout << "Time: " << t1 << "ms, " << t2 << "ms" << std::endl;
if(trace_level > 1 and ins->name().front() != '@' and
ins->name() != "load" and not result.empty())
{
target tgt = make_target(this->impl->target_name);
auto buffer = tgt.copy_from(result);
if(trace_level == 2)
{
std::cout << "Output has "
<< to_string_range(classify_argument(buffer))
<< std::endl;
std::cout << "Output: ";
preview_argument(std::cout, buffer);
std::cout << std::endl;
}
else
{
std::cout << "Output: " << buffer << std::endl;
}
}
return result;
}));
ret = generic_eval(*this,
ctx,
std::move(params),
with_check_context([&](auto& ins, auto f, auto&& check_context) {
ctx.finish();
std::cout << "Run instruction: " << ins_out.at(ins) << std::endl;
timer t{};
auto result = check_context(f);
double t1 = t.record<milliseconds>();
ctx.finish();
double t2 = t.record<milliseconds>();
std::cout << "Time: " << t1 << "ms, " << t2 << "ms" << std::endl;
if(trace_level > 1 and ins->name().front() != '@' and
ins->name() != "load" and not result.empty())
{
target tgt = make_target(this->impl->target_name);
auto buffer = tgt.copy_from(result);
if(trace_level == 2)
{
std::cout << "Output has "
<< to_string_range(classify_argument(buffer))
<< std::endl;
std::cout << "Output: ";
preview_argument(std::cout, buffer);
std::cout << std::endl;
}
else
{
std::cout << "Output: " << buffer << std::endl;
}
}
return result;
}));
}
else
{
return generic_eval(*this,
ctx,
std::move(params),
with_check_context([&](auto&, auto f, auto&& check_context) {
return check_context(f);
}));
ret = generic_eval(*this,
ctx,
std::move(params),
with_check_context([&](auto&, auto f, auto&& check_context) {
return check_context(f);
}));
}
if(exec_env.async)
{
ctx.finish_on(exec_env.queue);
}
return ret;
}
const int program_file_version = 5;
......
src/py/migraphx_py.cpp
View file @ 31065c7d
......@@ -40,6 +40,7 @@
#include <migraphx/register_target.hpp>
#include <migraphx/json.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/op/common.hpp>
#ifdef HAVE_GPU
#include <migraphx/gpu/hip.hpp>
......@@ -82,7 +83,7 @@ void visit_py(T x, F f)
{
f(x.template cast<bool>());
}
else if(py::isinstance<py::int_>(x))
else if(py::isinstance<py::int_>(x) or py::hasattr(x, "__index__"))
{
f(x.template cast<int>());
}
......@@ -263,12 +264,13 @@ MIGRAPHX_PYBIND11_MODULE(migraphx, m)
py::class_<migraphx::argument>(m, "argument", py::buffer_protocol())
.def_buffer([](migraphx::argument& x) -> py::buffer_info { return to_buffer_info(x); })
.def("__init__",
[](migraphx::argument& x, py::buffer b) {
py::buffer_info info = b.request();
new(&x) migraphx::argument(to_shape(info), info.ptr);
})
.def(py::init([](py::buffer b) {
py::buffer_info info = b.request();
return migraphx::argument(to_shape(info), info.ptr);
}))
.def("get_shape", &migraphx::argument::get_shape)
.def("data_ptr",
[](migraphx::argument& x) { return reinterpret_cast<std::uintptr_t>(x.data()); })
.def("tolist",
[](migraphx::argument& x) {
py::list l{x.get_shape().elements()};
......@@ -324,6 +326,7 @@ MIGRAPHX_PYBIND11_MODULE(migraphx, m)
.def("get_parameter_names", &migraphx::program::get_parameter_names)
.def("get_parameter_shapes", &migraphx::program::get_parameter_shapes)
.def("get_output_shapes", &migraphx::program::get_output_shapes)
.def("is_compiled", &migraphx::program::is_compiled)
.def(
"compile",
[](migraphx::program& p, const migraphx::target& t, bool offload_copy, bool fast_math) {
......@@ -352,24 +355,58 @@ MIGRAPHX_PYBIND11_MODULE(migraphx, m)
}
return p.eval(pm);
})
.def("run_async",
[](migraphx::program& p,
py::dict params,
std::uintptr_t stream,
std::string stream_name) {
migraphx::parameter_map pm;
for(auto x : params)
{
std::string key = x.first.cast<std::string>();
py::buffer b = x.second.cast<py::buffer>();
py::buffer_info info = b.request();
pm[key] = migraphx::argument(to_shape(info), info.ptr);
}
migraphx::execution_environment exec_env{
migraphx::any_ptr(reinterpret_cast<void*>(stream), stream_name), true};
return p.eval(pm, exec_env);
})
.def("sort", &migraphx::program::sort)
.def("print", [](const migraphx::program& p) { std::cout << p << std::endl; })
.def("__eq__", std::equal_to<migraphx::program>{})
.def("__ne__", std::not_equal_to<migraphx::program>{})
.def("__repr__", [](const migraphx::program& p) { return migraphx::to_string(p); });
py::class_<migraphx::operation>(m, "op")
.def(py::init([](const std::string& name, py::kwargs kwargs) {
migraphx::value v = migraphx::value::object{};
if(kwargs)
{
v = migraphx::to_value(kwargs);
}
return migraphx::make_op(name, v);
}))
py::class_<migraphx::operation> op(m, "op");
op.def(py::init([](const std::string& name, py::kwargs kwargs) {
migraphx::value v = migraphx::value::object{};
if(kwargs)
{
v = migraphx::to_value(kwargs);
}
return migraphx::make_op(name, v);
}))
.def("name", &migraphx::operation::name);
py::enum_<migraphx::op::pooling_mode>(op, "pooling_mode")
.value("average", migraphx::op::pooling_mode::average)
.value("max", migraphx::op::pooling_mode::max)
.value("lpnorm", migraphx::op::pooling_mode::lpnorm);
py::enum_<migraphx::op::rnn_direction>(op, "rnn_direction")
.value("forward", migraphx::op::rnn_direction::forward)
.value("reverse", migraphx::op::rnn_direction::reverse)
.value("bidirectional", migraphx::op::rnn_direction::bidirectional);
m.def(
"argument_from_pointer",
[](const migraphx::shape shape, const int64_t address) {
return migraphx::argument(shape, reinterpret_cast<void*>(address));
},
py::arg("shape"),
py::arg("address"));
m.def(
"parse_tf",
[](const std::string& filename,
......
src/quantization.cpp
View file @ 31065c7d
......@@ -70,7 +70,7 @@ void quantize_int8(program& prog,
{
std::set<std::string> op_names = {"convolution", "dot"};
std::set<std::string> input_ins_names(ins_names.begin(), ins_names.end());
if(!std::includes(
if(not std::includes(
op_names.begin(), op_names.end(), input_ins_names.begin(), input_ins_names.end()))
{
MIGRAPHX_THROW("QUANTIZE_INT8: only support DOT and CONVOLUTION operation");
......
src/replace_allocate.cpp
View file @ 31065c7d
......@@ -73,7 +73,7 @@ void insert_submod_allocations(instruction_ref ins, module& mod, const allocatio
name_shapes.insert(ps.begin(), ps.end());
}
for(auto& pn : name_shapes)
for(const auto& pn : name_shapes)
{
const auto& s = pn.second;
instruction_ref output{};
......
src/rewrite_batchnorm.cpp deleted 100644 → 0
View file @ 6bec381f
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 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/rewrite_batchnorm.hpp>
#include <migraphx/program.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/op/batch_norm_inference.hpp>
#include <migraphx/op/broadcast.hpp>
#include <migraphx/op/add.hpp>
#include <migraphx/op/mul.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/dfor.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
void rewrite_batchnorm::apply(module& m) const
{
for(auto ins : iterator_for(m))
{
if(ins->name() != "batch_norm_inference")
continue;
// Get scale, bias, mean, variance from inputs
auto gamma = ins->inputs()[1]->eval();
auto bias = ins->inputs()[2]->eval();
auto mean = ins->inputs()[3]->eval();
auto variance = ins->inputs()[4]->eval();
if(any_of({gamma, bias, mean, variance}, [](auto arg) { return arg.empty(); }))
continue;
std::vector<std::size_t> lens = ins->inputs()[1]->get_shape().lens();
shape s{ins->get_shape().type(), lens};
// Get epsilon
auto bn_op = any_cast<op::batch_norm_inference>(ins->get_operator());
auto epsilon = bn_op.epsilon;
argument a{s};
argument b{s};
visit_all(gamma, bias, mean, variance, a, b)(
[&](auto gamma2, auto bias2, auto mean2, auto variance2, auto a2, auto b2) {
dfor(a.get_shape().elements())(
[&](std::size_t c) { a2[c] = gamma2[c] / std::sqrt(variance2[c] + epsilon); });
dfor(b.get_shape().elements())([&](std::size_t c) {
b2[c] = bias2[c] - (gamma2[c] * mean2[c] / std::sqrt(variance2[c] + epsilon));
});
});
auto broadcast = op::broadcast{1, ins->get_shape().lens()};
auto a_ins = m.add_literal({a.get_shape(), a.data()});
auto a_broadcast = m.insert_instruction(ins, broadcast, a_ins);
auto mul = m.insert_instruction(ins, make_op("mul"), ins->inputs().front(), a_broadcast);
auto b_ins = m.add_literal({b.get_shape(), b.data()});
auto b_broadcast = m.insert_instruction(ins, broadcast, b_ins);
auto add = m.insert_instruction(ins, make_op("add"), mul, b_broadcast);
m.replace_instruction(ins, add);
}
}
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/include/migraphx/op/batch_norm_inference.hpp → src/rewrite_gelu.cpp
View file @ 31065c7d
......@@ -21,50 +21,39 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#ifndef MIGRAPHX_GUARD_OPERATORS_BATCH_NORM_HPP
#define MIGRAPHX_GUARD_OPERATORS_BATCH_NORM_HPP
#include <migraphx/check_shapes.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <migraphx/rewrite_gelu.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/matcher.hpp>
#include <migraphx/match/gelu_erf.hpp>
#include <migraphx/common.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct batch_norm_inference
struct find_gelu_erf
{
float epsilon = 1.0e-6f;
float momentum = 0.9f;
auto matcher() const { return match::gelu_erf(); }
std::string name() const { return "batch_norm_inference"; }
enum bn_infer_mode_t
void apply(module& m, const match::matcher_result& r) const
{
per_activation,
spatial,
};
bn_infer_mode_t bn_mode = spatial;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(
f(self.epsilon, "epsilon"), f(self.momentum, "momentum"), f(self.bn_mode, "bn_mode"));
}
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(5);
check_shapes{inputs.data(), inputs.data() + 1, *this}.same_ndims();
check_shapes{inputs.data() + 1, inputs.data() + inputs.size(), *this}.same_shape();
return inputs.front();
auto ins = r.result;
auto x = r.instructions["x"];
if(x->get_shape().type() != migraphx::shape::half_type)
return;
auto lit = m.add_literal(literal{shape{x->get_shape().type()}, {1.702f}});
auto mul = insert_common_op(m, ins, make_op("mul"), {x, lit});
auto sig = m.insert_instruction(ins, make_op("neg"), mul);
sig = m.insert_instruction(ins, make_op("exp"), sig);
auto one = m.add_literal(literal{shape{x->get_shape().type()}, {1.0f}});
sig = insert_common_op(m, ins, make_op("add"), {sig, one});
sig = m.insert_instruction(ins, make_op("div"), x, sig);
m.replace_instruction(ins, sig);
}
};
} // namespace op
void rewrite_gelu::apply(module& m) const { match::find_matches(m, find_gelu_erf{}); }
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/rewrite_pooling.cpp
View file @ 31065c7d
......@@ -47,12 +47,12 @@ void rewrite_pooling::apply(module& m) const
if(not s.standard())
continue;
auto&& op = any_cast<op::pooling>(ins->get_operator());
if(!std::all_of(op.padding.begin(), op.padding.end(), [](auto i) { return i == 0; }))
if(not std::all_of(op.padding.begin(), op.padding.end(), [](auto i) { return i == 0; }))
continue;
if(!std::all_of(op.stride.begin(), op.stride.end(), [](auto i) { return i == 1; }))
if(not std::all_of(op.stride.begin(), op.stride.end(), [](auto i) { return i == 1; }))
continue;
auto lens = s.lens();
if(!std::equal(lens.begin() + 2, lens.end(), op.lengths.begin(), op.lengths.end()))
if(not std::equal(lens.begin() + 2, lens.end(), op.lengths.begin(), op.lengths.end()))
continue;
std::int64_t n = s.lens()[0];
std::int64_t c = s.lens()[1];
......
src/rewrite_rnn.cpp
View file @ 31065c7d
......@@ -46,9 +46,6 @@
#include <migraphx/iterator_for.hpp>
#include <migraphx/dfor.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/op/common.hpp>
#include <migraphx/op/rnn_var_sl_last_output.hpp>
#include <migraphx/op/rnn_variable_seq_lens.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -214,7 +211,7 @@ void rewrite_rnn::apply_vanilla_rnn(module& m, instruction_ref ins) const
ih = m.add_literal(migraphx::literal{ih_shape, data});
}
if(!is_forward and variable_seq_len)
if(not is_forward and variable_seq_len)
{
args[0] =
m.insert_instruction(ins, make_op("rnn_var_sl_shift_sequence"), args[0], seq_lens);
......@@ -520,7 +517,7 @@ void rewrite_rnn::apply_gru(module& m, instruction_ref ins) const
ih = m.add_literal(migraphx::literal{ih_shape, data});
}
if(!is_forward and variable_seq_len)
if(not is_forward and variable_seq_len)
{
args[0] =
m.insert_instruction(ins, make_op("rnn_var_sl_shift_sequence"), args[0], seq_lens);
......@@ -977,7 +974,7 @@ void rewrite_rnn::apply_lstm(module& m, instruction_ref ins) const
pph = args[7];
}
if(!is_forward and variable_seq_len)
if(not is_forward and variable_seq_len)
{
args[0] =
m.insert_instruction(ins, make_op("rnn_var_sl_shift_sequence"), args[0], seq_lens);
......@@ -1294,11 +1291,11 @@ bool rewrite_rnn::is_variable_seq_lens(const module& m, instruction_ref seq_lens
std::vector<int64_t> vec_lens;
arg_lens.visit([&](auto l) { vec_lens.assign(l.begin(), l.end()); });
int64_t l = 0;
if(!vec_lens.empty())
if(not vec_lens.empty())
{
l = vec_lens[0];
}
if(!std::all_of(vec_lens.begin(), vec_lens.end(), [&](auto v) { return v == l; }))
if(not std::all_of(vec_lens.begin(), vec_lens.end(), [&](auto v) { return v == l; }))
{
is_var_lens = true;
}
......@@ -1318,7 +1315,7 @@ rewrite_rnn::get_seq_len(const module& m, instruction_ref input, instruction_ref
bool is_var_lens = is_variable_seq_lens(m, seq_lens);
auto input_shape = input->get_shape();
auto length = input_shape.lens()[0];
if(!is_var_lens and seq_lens != m.end())
if(not is_var_lens and seq_lens != m.end())
{
auto arg_len = seq_lens->eval();
std::vector<std::size_t> vec_lens;
......@@ -1387,7 +1384,7 @@ void rewrite_rnn::replace_last_cell_output(module& m,
if(variable_seq_len)
{
if(!ins_outputs.empty())
if(not ins_outputs.empty())
{
cell_outputs = m.insert_instruction(
std::next(ins),
......
src/shape.cpp
View file @ 31065c7d
......@@ -71,6 +71,19 @@ struct shape_impl
{
}
shape_impl(shape::type_t t,
std::vector<std::size_t> mins,
std::vector<std::size_t> maxes,
std::vector<std::size_t> opts)
: m_type(t)
{
assert(mins.size() == maxes.size() and maxes.size() == opts.size());
for(size_t i = 0; i < mins.size(); ++i)
{
m_dyn_dims.push_back(shape::dynamic_dimension{mins[i], maxes[i], opts[i]});
}
}
shape_impl(const std::vector<shape>& subs) : m_type(shape::tuple_type), m_shapes(subs) {}
shape::type_t m_type;
......@@ -224,6 +237,14 @@ shape::shape(type_t t, std::vector<shape::dynamic_dimension> dims)
{
}
shape::shape(type_t t,
std::vector<std::size_t> mins,
std::vector<std::size_t> maxes,
std::vector<std::size_t> opts)
: impl(std::make_shared<shape_impl>(t, std::move(mins), std::move(maxes), std::move(opts)))
{
}
shape::shape(const std::vector<shape>& subs) : impl(std::make_shared<shape_impl>(subs)) {}
shape::shape(std::shared_ptr<shape_impl> pimpl) : impl(std::move(pimpl)) {}
......@@ -244,6 +265,15 @@ const std::vector<std::size_t>& shape::lens() const { return impl->m_lens; }
const std::vector<std::size_t>& shape::strides() const { return impl->m_strides; }
std::size_t shape::ndim() const
{
if(this->dynamic())
{
return dyn_dims().size();
}
return lens().size();
}
std::size_t shape::elements() const { return impl->elements(); }
std::size_t shape::bytes() const
......@@ -437,6 +467,16 @@ shape shape::with_type(type_t t) const
return {c};
}
shape shape::to_dynamic() const
{
if(this->dynamic())
{
return *this;
}
std::vector<std::size_t> zeroes(this->ndim(), 0);
return {type(), lens(), lens(), zeroes};
}
std::size_t shape::element_space() const { return impl->element_space(); }
std::string shape::type_string() const { return name(this->type()); }
......@@ -464,20 +504,16 @@ bool shape::dynamic_dimension::is_fixed() const { return this->min == this->max;
bool shape::dynamic_dimension::has_optimal() const { return opt != 0; }
template <class Self, class F>
auto shape::dynamic_dimension::reflect(Self& self, F f)
{
return pack(f(self.min, "min"), f(self.max, "max"), f(self.opt, "opt"));
}
bool operator==(const shape::dynamic_dimension& x, const shape::dynamic_dimension& y)
{
return (x.min == y.min and x.max == y.max and x.opt == y.opt);
// don't check opt if both are fixed
return (x.min == y.min and x.max == y.max and
((x.is_fixed() and y.is_fixed()) or (x.opt == y.opt)));
}
bool operator!=(const shape::dynamic_dimension& x, const shape::dynamic_dimension& y)
{
return !(x == y);
return not(x == y);
}
std::ostream& operator<<(std::ostream& os, const shape::dynamic_dimension& x)
{
......@@ -497,7 +533,7 @@ bool operator==(const shape& x, const shape& y)
x.strides() == y.strides() and x.sub_shapes() == y.sub_shapes());
}
bool operator!=(const shape& x, const shape& y) { return !(x == y); }
bool operator!=(const shape& x, const shape& y) { return not(x == y); }
std::ostream& operator<<(std::ostream& os, const shape& x)
{
......
src/simplify_algebra.cpp
View file @ 31065c7d
......@@ -57,12 +57,14 @@ auto conv_const_weights()
auto reduction() { return match::name_contains("reduce"); }
// conv(x, w) * a => conv(x, a * w)
struct find_mul_conv
{
auto matcher() const
{
return match::name("mul")(match::either_arg(0, 1)(conv_const_weights().bind("conv"),
match::name("broadcast").bind("a")));
return match::name("mul")(
match::either_arg(0, 1)(conv_const_weights().bind("conv"),
match::name("broadcast", "multibroadcast").bind("a")));
}
void apply(module& m, const match::matcher_result& r) const
......@@ -72,14 +74,35 @@ struct find_mul_conv
auto a_ins = r.instructions["a"];
auto w_ins = r.instructions["w"];
auto broadcast_op = any_cast<op::broadcast>(a_ins->get_operator());
if(broadcast_op.axis != 1)
const auto& a_input_lens = a_ins->inputs().front()->get_shape().lens();
std::size_t num_not_one_dims = std::count_if(
a_input_lens.cbegin(), a_input_lens.cend(), [](auto dim) { return dim != 1; });
if(num_not_one_dims > 1)
return;
// check broadcasted along channels
const auto& a_lens = a_ins->get_shape().lens();
const auto& a_strides = a_ins->get_shape().strides();
auto is_broadcasted_axis = [](auto len, auto stride) { return len == 1 or stride == 0; };
if(a_strides.at(1) != 1)
return;
if(not is_broadcasted_axis(a_lens.front(), a_strides.front()))
return;
if(not std::equal(a_lens.begin() + 2,
a_lens.end(),
a_strides.begin() + 2,
a_strides.end(),
is_broadcasted_axis))
return;
auto sq = m.insert_instruction(ins, make_op("squeeze"), a_ins->inputs().front());
auto new_a = m.insert_instruction(
ins,
make_op("broadcast", {{"axis", 0}, {"out_lens", w_ins->get_shape().lens()}}),
a_ins->inputs().front());
ins, make_op("broadcast", {{"axis", 0}, {"out_lens", w_ins->get_shape().lens()}}), sq);
auto new_mul = m.insert_instruction(ins, make_op("mul"), new_a, w_ins);
auto new_conv = m.insert_instruction(
ins, conv_ins->get_operator(), conv_ins->inputs().front(), new_mul);
......@@ -208,6 +231,42 @@ struct find_mul_add
}
};
struct find_dot_add
{
auto matcher() const
{
return match::name("dot")(match::either_arg(0, 1)(
match::name("add")(
match::either_arg(0, 1)(match::any().bind("x"),
match::any_of(match::is_constant()).bind("b")),
match::none_of(match::args(match::is_constant(), match::is_constant())),
match::used_once()),
match::is_constant().bind("a")));
}
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto a_ins = r.instructions["a"];
auto b_ins = r.instructions["b"];
auto x_ins = r.instructions["x"];
assert(x_ins != b_ins);
const bool flipped = a_ins == ins->inputs().back();
auto insert_dot = [&](auto x, auto y) {
if(flipped)
return m.insert_instruction(ins, make_op("dot"), y, x);
else
return m.insert_instruction(ins, make_op("dot"), x, y);
};
auto ax_ins = insert_dot(a_ins, x_ins);
auto ab_ins = insert_dot(a_ins, b_ins);
m.replace_instruction(ins, make_op("add"), ax_ins, ab_ins);
}
};
struct find_add_lit_broadcast
{
auto matcher() const
......@@ -267,28 +326,26 @@ struct find_double_add_lit_broadcast
struct find_inner_broadcast
{
auto matcher() const
{
return pointwise(
match::nargs(2),
match::args(match::name("broadcast").bind("x"), match::name("broadcast").bind("y")));
}
auto matcher() const { return pointwise(match::all_of[match::inputs()](match::broadcast())); }
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto x_ins = r.instructions["x"];
auto y_ins = r.instructions["y"];
auto xbroadcast = any_cast<op::broadcast>(x_ins->get_operator());
auto ybroadcast = any_cast<op::broadcast>(y_ins->get_operator());
if(xbroadcast.axis != ybroadcast.axis)
auto ins = r.result;
auto broadcasts = ins->inputs();
if(broadcasts.empty())
return;
std::vector<instruction_ref> inputs;
std::transform(broadcasts.begin(),
broadcasts.end(),
std::back_inserter(inputs),
[](auto i) { return i->inputs().front(); });
if(std::any_of(inputs.begin(), inputs.end(), [&](auto i) {
return i->get_shape() != inputs.front()->get_shape();
}))
return;
auto op = m.insert_instruction(
ins, ins->get_operator(), x_ins->inputs().front(), y_ins->inputs().front());
m.replace_instruction(ins, xbroadcast, op);
auto op = m.insert_instruction(ins, ins->get_operator(), inputs);
m.replace_instruction(ins, broadcasts.front()->get_operator(), op);
}
};
......@@ -378,6 +435,24 @@ struct find_concat_op
}
};
void move_instructions_back(module& m, instruction_ref pos, std::vector<instruction_ref> inss)
{
auto start = range(m.begin(), pos);
for(auto ins : iterator_for(start))
{
auto it = std::find(inss.begin(), inss.end(), ins);
if(it != inss.end())
inss.erase(it);
}
for(auto ins : inss)
{
if(not m.has_instruction(ins))
continue;
move_instructions_back(m, pos, ins->inputs());
m.move_instruction(ins, pos);
}
}
std::vector<instruction_ref> get_splits(instruction_ref ins)
{
std::vector<instruction_ref> result;
......@@ -416,8 +491,9 @@ struct find_splits
{
auto matcher() const
{
return match::any(match::any_of[match::outputs()](match::name("slice")(
match::any_of[match::outputs()](match::pointwise(), reduction()))));
return match::any(
match::any_of[match::outputs()](match::name("slice")(match::any_of[match::outputs()](
match::pointwise(match::any_of(match::nargs(1), match::nargs(2))), reduction()))));
}
static bool is_dependent(const module& m, instruction_ref ins1, instruction_ref ins2)
......@@ -552,8 +628,7 @@ struct find_splits
}))
return;
for(auto data : data_args)
m.move_instructions(data, ins);
move_instructions_back(m, ins, data_args);
auto slice_op = any_cast<op::slice>(splits.front()->get_operator());
assert(not slice_op.axes.empty());
......@@ -580,10 +655,9 @@ struct find_splits
auto outputs = i->outputs();
for(auto output : outputs)
{
if(not contains({"reshape", "squeeze", "unsqueeze"}, output->name()))
if(output->name() != "reshape")
continue;
auto x =
m.insert_instruction(output, make_op("contiguous"), output->inputs());
auto x = m.insert_instruction(output, make_op("contiguous"), i);
m.replace_instruction(output, output->get_operator(), x);
}
......@@ -753,7 +827,7 @@ MIGRAPHX_PRED_MATCHER(horiz_conv_dot, instruction_ref ins)
};
auto dots = std::count_if(ins->outputs().begin(), ins->outputs().end(), pred("dot"));
auto convs = std::count_if(ins->outputs().begin(), ins->outputs().end(), pred("convolution"));
return !(dots < 2 and convs < 2);
return (dots >= 2 or convs >= 2);
}
struct find_conv_dot_horiz_fusion
......@@ -773,7 +847,7 @@ struct find_conv_dot_horiz_fusion
auto y = j->inputs()[1]->get_shape().lens();
if(x.size() != y.size())
return false;
// Check that non-axises match
// Check that non-axes match
int axis = 1;
if(i->name() == "dot")
{
......@@ -807,15 +881,23 @@ struct find_conv_dot_horiz_fusion
concat_axis = axis;
}
for(auto arg : args)
m.move_instructions(arg, input);
// TODO: Check if axises match
move_instructions_back(m, input, args);
// TODO: Check if axes match
auto concat =
m.insert_instruction(input, make_op("concat", {{"axis", concat_axis}}), args);
auto fused = m.insert_instruction(std::next(input), op, input, concat);
int64_t offset = 0;
for(auto arg : range(start, last))
{
auto outputs = arg->outputs();
for(auto output : outputs)
{
if(output->name() != "reshape")
continue;
auto x = m.insert_instruction(output, make_op("contiguous"), arg);
m.replace_instruction(output, output->get_operator(), x);
}
int64_t len = arg->get_shape().lens()[axis];
m.replace_instruction(
arg,
......@@ -851,6 +933,73 @@ struct find_div_const
}
};
struct find_unit_ops
{
auto matcher() const
{
auto mul_1 = match::name("mul")(
match::either_arg(0, 1)(match::has_value(1.0f), match::any().bind("x")));
auto div_1 =
match::name("div")(match::args(match::any().bind("x"), match::has_value(1.0f)));
auto add_0 = match::name("add")(
match::either_arg(0, 1)(match::has_value(0.0f, 1e-12), match::any().bind("x")));
auto sub_0 =
match::name("sub")(match::args(match::any().bind("x"), match::has_value(0.0f)));
return match::any_of(mul_1, div_1, add_0, sub_0);
}
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto c_in = r.instructions["x"];
m.replace_instruction(ins, c_in);
}
};
struct find_neg_unit_ops
{
auto matcher() const
{
auto mul_neg_1 = match::name("mul")(
match::either_arg(0, 1)(match::has_value(-1.0f), match::any().bind("x")));
auto div_neg_1 =
match::name("div")(match::args(match::any().bind("x"), match::has_value(-1.0f)));
auto sub_0 =
match::name("sub")(match::args(match::has_value(0.0f), match::any().bind("x")));
return match::any_of(mul_neg_1, div_neg_1, sub_0);
}
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto c_in = r.instructions["x"];
auto neg = m.add_instruction(make_op("neg"), c_in);
m.replace_instruction(ins, neg);
}
};
struct find_zero_ops
{
auto matcher() const
{
auto mul_zero = match::name("mul")(
match::either_arg(0, 1)(match::has_value(0.0f).bind("x"), match::any()));
auto div_zero =
match::name("div")(match::args(match::has_value(0.0f).bind("x"), match::any()));
return match::any_of(mul_zero, div_zero);
}
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto zero_ins = r.instructions["x"];
m.replace_instruction(ins, zero_ins);
}
};
struct find_sub_const
{
auto matcher() const
......@@ -926,7 +1075,7 @@ struct find_split_reshape
// all outputs are reshape and of the same shape
auto dims = any_cast<op::reshape>(rsp->get_operator()).dims;
if(!same_ops(vec_rsp))
if(not same_ops(vec_rsp))
{
return;
}
......@@ -942,23 +1091,42 @@ struct find_split_reshape
auto rsp_lens = rsp->get_shape().lens();
auto rsp_strides = rsp->get_shape().strides();
rsp_strides.insert(rsp_strides.begin(), rsp_strides[0] * rsp_lens[0]);
auto ait = std::find(rsp_strides.begin(), rsp_strides.end(), slc_dim_size);
auto ait = std::find(rsp_strides.begin(), rsp_strides.end(), slc_dim_size);
int rsp_axis = -1;
if(ait == rsp_strides.end())
{
return;
}
int rsp_axis = std::distance(rsp_strides.begin(), ait);
else if(ait == rsp_strides.end() - 1)
{
// edge case
// slice_dim == 1, in that case it could match with last stride of 1.
// it should accumulate lengths from last dim in that case. discount 1 to avoid going
// out of bounds.
assert(slc_dim_size == 1);
rsp_axis = std::distance(rsp_strides.begin(), ait) - 1;
}
else
{
rsp_axis = std::distance(rsp_strides.begin(), ait);
}
// calculate reshape output shape
std::vector<int64_t> vec_dims(vec_rsp.size());
std::transform(vec_rsp.begin(), vec_rsp.end(), vec_dims.begin(), [&](auto is) {
return is->get_shape().lens()[rsp_axis];
});
std::vector<int64_t> rsp_out_lens(rsp_lens.begin(), rsp_lens.end());
rsp_out_lens[rsp_axis] = std::accumulate(vec_dims.begin(), vec_dims.end(), std::int64_t{0});
// insert the reshape instruction
// insert the reshape instruction and add contiguous if needed
if(not input->get_shape().standard())
{
input = m.insert_instruction(std::next(input), make_op("contiguous"), input);
}
auto rsp_ins = m.insert_instruction(
std::next(input), make_op("reshape", {{"dims", rsp_out_lens}}), input);
......@@ -1005,7 +1173,7 @@ struct find_split_transpose
// all transpose are the same
auto perm = any_cast<op::transpose>(trans->get_operator()).dims;
if(!same_ops(vec_trans))
if(not same_ops(vec_trans))
{
return;
}
......@@ -1048,6 +1216,10 @@ void simplify_algebra::apply(module& m) const
find_mul_conv{},
find_mul_slice_conv{},
find_mul_add{},
find_unit_ops{},
find_neg_unit_ops{},
find_zero_ops{},
find_dot_add{},
find_div_const{},
find_sub_const{},
find_rsqrt{},
......
src/simplify_reshapes.cpp
View file @ 31065c7d
......@@ -99,7 +99,7 @@ struct find_reshaper
std::vector<instruction_ref> reshapes{ins};
while(is_reshaper(reshapes.back()))
{
assert(!reshapes.back()->inputs().empty());
assert(not reshapes.back()->inputs().empty());
assert(m.has_instruction(reshapes.back()->inputs().front()));
auto input = reshapes.back()->inputs().front();
reshapes.push_back(input);
......@@ -151,8 +151,11 @@ struct find_transpose
{
auto matcher() const
{
return match::name("transpose")(match::none_of(
match::skip_output(match::name("contiguous"))(match::name("transpose"))));
auto output_not_transpose =
match::none_of(match::skip_output(match::name("contiguous"))(match::name("transpose")));
auto input_has_transpose =
match::args(match::skip(match::name("contiguous"))(match::name("transpose")));
return match::name("transpose")(output_not_transpose, input_has_transpose);
}
void apply(module& m, const match::matcher_result& mr) const
......@@ -268,6 +271,44 @@ struct find_nested_slice
}
};
struct find_concat_multibroadcasts
{
auto matcher() const
{
return match::name("concat")(match::all_of[match::inputs()](match::name("multibroadcast")));
}
void apply(module& m, const match::matcher_result& mr) const
{
auto ins = mr.result;
auto op = any_cast<op::concat>(ins->get_operator());
auto out_lens = ins->get_shape().lens();
auto inputs = ins->inputs();
auto in_strides = inputs.front()->get_shape().strides();
// Only apply when concat axis is not a broadcasted dimension
if(std::any_of(inputs.begin(), inputs.end(), [&](auto i) {
return i->get_shape().strides()[op.axis] == 0;
}))
{
return;
}
// Use inputs of multibroadcast ops as inputs to new concat op
std::transform(inputs.begin(), inputs.end(), inputs.begin(), [](auto i) {
return i->inputs().front();
});
// Reduce axis by number of leading broadcasted dimensions
if(inputs.front()->get_shape().lens().size() < out_lens.size())
op.axis -= std::count(in_strides.begin(), in_strides.begin() + op.axis, 0);
auto concat = m.insert_instruction(ins, op, inputs);
m.replace_instruction(
ins, migraphx::make_op("multibroadcast", {{"out_lens", out_lens}}), concat);
}
};
struct find_concat_transpose
{
auto matcher() const
......@@ -285,7 +326,7 @@ struct find_concat_transpose
auto permutation = find_permutation(s);
// permutation should be the same for all inputs
if(!std::all_of(trans_inputs.begin(), trans_inputs.end(), [&](auto in) {
if(not std::all_of(trans_inputs.begin(), trans_inputs.end(), [&](auto in) {
return (find_permutation(in->get_shape()) == permutation);
}))
{
......@@ -664,9 +705,94 @@ struct find_slice_transpose
}
};
struct find_transpose_slice
{
auto matcher() const
{
return match::name("transpose")(match::all_of[match::outputs()](match::name("slice")));
}
static std::vector<int64_t> slice_distance(const op::slice& op)
{
assert(op.starts.size() == op.ends.size());
std::vector<int64_t> result(op.starts.size());
std::transform(
op.ends.begin(), op.ends.end(), op.starts.begin(), result.begin(), std::minus<>{});
return result;
}
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto slices = ins->outputs();
if(slices.empty())
return;
auto slice = any_cast<op::slice>(slices.front()->get_operator());
auto sdistance = slice_distance(slice);
// Check all distances and axes are the same
if(std::any_of(slices.begin(), slices.end(), [&](auto sins) {
auto s = any_cast<op::slice>(sins->get_operator());
return s.axes != slice.axes or slice_distance(s) != sdistance;
}))
return;
// Check distances are divisible by lens of corresponding axes
auto mod_by_distance = [&](const auto& v, auto f) {
return std::inner_product(v.begin(),
v.end(),
sdistance.begin(),
0,
std::plus<>{},
[&](auto x, auto d) -> uint64_t {
if(d == 0)
return 1;
return f(x) % d;
});
};
if(mod_by_distance(slice.axes, [&](auto x) { return ins->get_shape().lens()[x]; }) != 0 or
mod_by_distance(slice.starts, id{}) != 0 or mod_by_distance(slice.ends, id{}) != 0)
return;
// TODO: Handle multiple axes
if(sdistance.size() != 1)
return;
auto axis = slice.axes.front();
// Skip if axis would be packed
if(std::all_of(ins->get_shape().lens().begin(),
ins->get_shape().lens().begin() + axis,
[](auto x) { return x == 1; }))
return;
// Compute axis before transpose to use for unsqueeze
auto perm = ins->get_operator().to_value()["permutation"].to_vector<int64_t>();
auto preaxis = std::find(perm.begin(), perm.end(), axis) - perm.begin();
// Make unsqeeze
auto unsqueeze = m.insert_instruction(
ins, make_op("unsqueeze", {{"axes", {preaxis}}, {"steps", sdistance}}), ins->inputs());
// Make transpose
std::transform(perm.begin(), perm.end(), perm.begin(), [&](auto i) {
if(i > preaxis)
return i + 1;
return i;
});
perm.insert(perm.begin(), preaxis + 1);
auto transpose =
m.insert_instruction(ins, make_op("transpose", {{"permutation", perm}}), unsqueeze);
// Slice and squeeze
for(auto s : slices)
{
auto op = any_cast<op::slice>(s->get_operator());
op.axes = {0};
op.starts = {op.starts.front() / sdistance.front()};
op.ends = {op.ends.front() / sdistance.front()};
auto slice_ins = m.insert_instruction(ins, op, transpose);
auto squeeze =
m.insert_instruction(ins, make_op("squeeze", {{"axes", {0}}}), slice_ins);
m.replace_instruction(s, squeeze);
}
}
};
void simplify_reshapes::apply(module& m) const
{
for(int i = 0; i < 2; i++)
for(int i = 0; i < 4; i++)
{
match::find_matches(m,
find_where_op{},
......@@ -676,9 +802,11 @@ void simplify_reshapes::apply(module& m) const
find_reshaper{},
find_transpose{},
find_concat_transpose{},
find_concat_multibroadcasts{},
find_nested_convert{},
find_nested_slice{},
find_nested_concat{},
find_transpose_slice{},
find_slice_transpose{},
find_transpose_contiguous_reshaper_unary{});
dead_code_elimination{}.apply(m);
......
src/targets/cpu/CMakeLists.txt
View file @ 31065c7d
......@@ -35,6 +35,7 @@ add_library(migraphx_cpu
dnnl.cpp
eltwise.cpp
erf.cpp
fmod.cpp
fuse_ops.cpp
gather.cpp
gemm.cpp
......@@ -42,6 +43,7 @@ add_library(migraphx_cpu
logsoftmax.cpp
lowering.cpp
lrn.cpp
mod.cpp
preallocate.cpp
pooling.cpp
reduction.cpp
......
src/targets/cpu/binary.cpp
View file @ 31065c7d
......@@ -49,7 +49,7 @@ struct dnnl_binary : dnnl_op<dnnl_binary, dnnl::binary>
auto s0 = inputs.at(0);
auto s1 = inputs.at(1);
auto r = s0;
if(s0 != s1 or !s0.packed())
if(s0 != s1 or not s0.packed())
{
r = shape{s0.type(), s0.lens()};
}
......
src/target_assignments.cpp → src/targets/cpu/fmod.cpp
View file @ 31065c7d
......@@ -21,16 +21,16 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include <migraphx/target_assignments.hpp>
#include <migraphx/config.hpp>
#include <migraphx/cpu/pointwise.hpp>
#include <migraphx/op/fmod.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace cpu {
void target_assignments::add_assignment(instruction_ref ins, const std::string& target)
{
assignments.emplace(ins, target);
}
template struct cpu_binary<op::fmod>;
} // namespace cpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/cpu/lowering.cpp
View file @ 31065c7d
......@@ -26,7 +26,6 @@
#include <migraphx/instruction.hpp>
#include <migraphx/dfor.hpp>
#include <migraphx/op/identity.hpp>
#include <migraphx/op/batch_norm_inference.hpp>
#include <migraphx/op/convolution.hpp>
#include <migraphx/op/deconvolution.hpp>
#include <migraphx/op/quant_convolution.hpp>
......@@ -43,6 +42,8 @@
#include <migraphx/op/argmax.hpp>
#include <migraphx/op/argmin.hpp>
#include <migraphx/op/rnn_var_sl_last_output.hpp>
#include <migraphx/op/mod.hpp>
#include <migraphx/op/fmod.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/par_dfor.hpp>
......@@ -214,55 +215,6 @@ struct cpu_pad
};
MIGRAPHX_REGISTER_OP(cpu_pad)
struct leaky_relu_op
{
op::leaky_relu op;
std::string name() const { return "cpu::leaky_relu"; }
auto fcn() const
{
auto a = op.alpha;
return [a](auto x) { return x > 0 ? x : x * a; };
}
};
template <typename Op>
struct cpu_unary2 : auto_register_op<cpu_unary2<Op>>
{
cpu_unary2() = default;
template <class T>
cpu_unary2(T pop) : op(Op{std::move(pop)})
{
}
Op op;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return migraphx::reflect(self.op.op, f);
}
std::string name() const { return op.name(); }
shape compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(1);
const auto& s = inputs.at(0);
return {s.type(), s.lens()};
}
argument compute(context&, const shape& output_shape, std::vector<argument> args) const
{
argument result{output_shape};
visit_all(result, args[0])([&](auto output, auto input) {
assert(input.get_shape().standard());
std::transform(input.begin(), input.end(), output.begin(), op.fcn());
});
return result;
}
};
template struct cpu_unary2<leaky_relu_op>;
struct cpu_rnn_var_sl_last_output
{
op::rnn_var_sl_last_output op;
......
src/targets/gpu/include/migraphx/gpu/cos.hpp → src/targets/cpu/mod.cpp
View file @ 31065c7d
......@@ -21,22 +21,16 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#ifndef MIGRAPHX_GUARD_RTGLIB_COS_HPP
#define MIGRAPHX_GUARD_RTGLIB_COS_HPP
#include <migraphx/gpu/oper.hpp>
#include <migraphx/gpu/device/cos.hpp>
#include <migraphx/config.hpp>
#include <migraphx/cpu/pointwise.hpp>
#include <migraphx/op/mod.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace cpu {
struct hip_cos : unary_device<hip_cos, device::cos>
{
};
template struct cpu_binary<op::mod>;
} // namespace gpu
} // namespace cpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/targets/cpu/target.cpp
View file @ 31065c7d
......@@ -37,12 +37,10 @@
#include <migraphx/propagate_constant.hpp>
#include <migraphx/register_target.hpp>
#include <migraphx/replace_allocate.hpp>
#include <migraphx/rewrite_batchnorm.hpp>
#include <migraphx/rewrite_pooling.hpp>
#include <migraphx/rewrite_quantization.hpp>
#include <migraphx/rewrite_rnn.hpp>
#include <migraphx/schedule.hpp>
#include <migraphx/memory_coloring.hpp>
#include <migraphx/simplify_algebra.hpp>
#include <migraphx/simplify_qdq.hpp>
#include <migraphx/simplify_reshapes.hpp>
......@@ -78,8 +76,6 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
eliminate_identity{},
eliminate_pad{},
dead_code_elimination{},
rewrite_batchnorm{},
dead_code_elimination{},
rewrite_rnn{},
dead_code_elimination{},
eliminate_common_subexpression{},
......
src/targets/fpga/include/migraphx/fpga/target.hpp
View file @ 31065c7d
......@@ -30,6 +30,7 @@
#include <migraphx/compile_options.hpp>
#include <migraphx/fpga/context.hpp>
#include <migraphx/config.hpp>
#include <migraphx/supported_segments.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -41,7 +42,7 @@ struct target
std::string name() const;
std::vector<pass> get_passes(migraphx::context& ctx, const compile_options&) const;
migraphx::context get_context() const { return context{}; }
float is_supported(instruction_ref ins, support_metric m);
supported_segments find_supported(const_module_ref mod, support_metric m) const;
argument copy_to(const argument& arg) const { return arg; }
argument copy_from(const argument& arg) const { return arg; }
......
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