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Commit c4b1102e authored by charlie's avatar charlie
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Merge branch 'dyn_model_test' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_model_test

parents 5fc48e77 31065c7d
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src/targets/gpu/leaky_relu.cpp deleted 100644 → 0
View file @ 5fc48e77
/*
* 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/gpu/leaky_relu.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/gpu/miopen.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
shape miopen_leaky_relu::compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(2).not_broadcasted();
return inputs.at(1);
}
argument miopen_leaky_relu::compute(context& ctx,
const shape& output_shape,
const std::vector<argument>& args) const
{
float alpha = 1;
float beta = 0;
auto x_desc = make_tensor(args[0].get_shape());
auto y_desc = make_tensor(output_shape);
miopenActivationForward(ctx.get_stream().get_miopen(),
ad.get(),
&alpha,
x_desc.get(),
args[0].implicit(),
&beta,
y_desc.get(),
args[1].implicit());
return args[1];
}
void miopen_leaky_relu::finalize(context&, const shape&, const std::vector<shape>&)
{
ad = make_leaky_relu(op.alpha);
}
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/lowering.cpp
View file @ c4b1102e
......@@ -26,6 +26,8 @@
#include <migraphx/manage_ptr.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/instruction_ref.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/op/convolution.hpp>
#include <migraphx/op/deconvolution.hpp>
......@@ -35,15 +37,12 @@
#include <migraphx/op/quant_convolution.hpp>
#include <migraphx/op/quant_dot.hpp>
#include <migraphx/gpu/batch_norm_inference.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/gpu/convolution.hpp>
#include <migraphx/gpu/deconvolution.hpp>
#include <migraphx/gpu/device_name.hpp>
#include <migraphx/gpu/gemm.hpp>
#include <migraphx/gpu/int8_conv_pack.hpp>
#include <migraphx/gpu/miopen.hpp>
#include <migraphx/gpu/quant_convolution.hpp>
#include <migraphx/gpu/rocblas.hpp>
#include <migraphx/gpu/compiler.hpp>
#include <migraphx/iterator_for.hpp>
......@@ -96,14 +95,11 @@ struct miopen_apply
add_extend_op("argmax");
add_extend_op("argmin");
// add_extend_op("elu");
add_extend_op("gather");
// add_extend_op("leaky_relu");
add_extend_op("logsoftmax");
add_extend_op("lrn");
add_extend_op("multinomial");
add_extend_op("nonzero");
add_extend_op("pad");
add_extend_op("pooling");
add_extend_op("prefix_scan_sum");
add_extend_op("reverse");
......@@ -113,16 +109,15 @@ struct miopen_apply
add_extend_op("scatter_none");
add_extend_op("topk");
add_batch_norm_inference_op();
add_convolution_op();
add_deconvolution_op();
add_convolution_op<op::convolution>("convolution");
add_convolution_op<op::deconvolution>("deconvolution");
add_convolution_op<op::quant_convolution>("quant_convolution");
add_gemm_op<op::dot>("dot");
add_gemm_op<op::quant_dot>("quant_dot");
add_if_op();
add_loop_op();
add_neg_op();
add_nms_op();
add_quant_convolution_op();
}
void copy_params() const
......@@ -171,7 +166,8 @@ struct miopen_apply
init();
for(auto it = mod->begin(); it != mod->end(); it++)
{
auto s = it->get_shape();
auto s = it->get_shape();
auto attrs = it->get_operator().attributes();
if(apply_map.count(it->name()) > 0)
{
check_shape(s, apply_map.at(it->name())(it));
......@@ -180,11 +176,37 @@ struct miopen_apply
{
check_shape(s, insert_precompile_op(it));
}
else if(attrs.contains("target"))
{
check_shape(s, insert_custom_op(it, attrs));
}
}
copy_params();
}
instruction_ref insert_custom_op(instruction_ref ins, const value& attrs) const
{
const auto& custom_op = ins->get_operator();
if(attrs.at("target") == "cpu")
{
auto s = ins->get_shape();
std::vector<instruction_ref> cpu_inputs;
auto inputs = ins->inputs();
auto output = inputs.back();
std::transform(
inputs.begin(), inputs.end(), std::back_inserter(cpu_inputs), [&](auto in) {
return mod->insert_instruction(ins, make_op("hip::copy_from_gpu"), in);
});
cpu_inputs.front() =
mod->insert_instruction(ins, make_op("hip::sync_stream"), cpu_inputs);
auto cpu_out = mod->insert_instruction(ins, custom_op, cpu_inputs);
auto gpu_out =
mod->insert_instruction(ins, make_op("hip::copy_to_gpu"), cpu_out, output);
return mod->replace_instruction(ins, gpu_out);
}
return ins;
}
instruction_ref insert_precompile_op(instruction_ref ins) const
{
auto output = insert_allocation(ins, ins->get_shape());
......@@ -203,38 +225,6 @@ struct miopen_apply
return mod->insert_instruction(ins, make_op("allocate", {{"shape", to_value(s)}}));
}
void add_convolution_op()
{
apply_map.emplace("convolution", [=](instruction_ref ins) {
auto&& op = any_cast<op::convolution>(ins->get_operator());
auto conv = miopen_convolution{op, make_conv(op)};
auto ws = conv.find(get_context(), ins->get_shape(), to_shapes(ins->inputs()));
auto workspace = insert_allocation(ins, ws);
auto output = insert_allocation(ins, ins->get_shape());
return mod->replace_instruction(
ins, conv, ins->inputs().at(0), ins->inputs().at(1), workspace, output);
});
}
void add_deconvolution_op()
{
apply_map.emplace("deconvolution", [=](instruction_ref ins) {
auto&& op = any_cast<op::deconvolution>(ins->get_operator());
auto conv = miopen_deconvolution{op, make_deconv(op)};
auto ws = conv.find(get_context(), ins->get_shape(), to_shapes(ins->inputs()));
auto workspace = insert_allocation(ins, ws);
auto output = insert_allocation(ins, ins->get_shape());
return mod->replace_instruction(
ins, conv, ins->inputs().at(0), ins->inputs().at(1), workspace, output);
});
}
template <typename Op>
void add_gemm_op(const std::string& name)
{
......@@ -248,31 +238,33 @@ struct miopen_apply
});
}
void add_quant_convolution_op()
template <typename Op>
void add_convolution_op(const std::string& name)
{
apply_map.emplace("quant_convolution", [=](instruction_ref ins) {
auto&& op = any_cast<op::quant_convolution>(ins->get_operator());
shape ws;
miopen_quant_convolution conv;
auto compile_quant_conv_with_format = [&](bool format) {
conv = miopen_quant_convolution{op, format, make_conv(op)};
ws = conv.find(get_context(), ins->get_shape(), to_shapes(ins->inputs()));
apply_map.emplace(name, [=](instruction_ref ins) {
operation conv =
miopen_convolution<Op>{any_cast<Op>(ins->get_operator()), int8_x4_format};
migraphx::context ctx = get_context();
size_t ws_bytes = 0;
auto compile_conv_with_format = [&](bool format) {
conv = miopen_convolution<Op>{any_cast<Op>(ins->get_operator()), format};
auto ws = conv.compile(ctx, ins->get_shape(), to_shapes(ins->inputs()));
ws_bytes = ws.get("workspace", 0);
};
try
{
compile_quant_conv_with_format(int8_x4_format);
{ // for the regular convolution and deconvolution, this try would always succeed
compile_conv_with_format(int8_x4_format);
}
catch(migraphx::exception&)
{
// In case no solver supports the default format, retry using the other format.
compile_quant_conv_with_format(not int8_x4_format);
compile_conv_with_format(not int8_x4_format);
}
auto args = ins->inputs();
auto workspace = insert_allocation(ins, ws);
auto output = insert_allocation(ins, ins->get_shape());
auto workspace = insert_allocation(ins, shape{shape::int8_type, {ws_bytes}});
return mod->replace_instruction(ins, conv, args[0], args[1], workspace, output);
});
}
......@@ -307,43 +299,6 @@ struct miopen_apply
});
}
void add_batch_norm_inference_op()
{
apply_map.emplace("batch_norm_inference", [=](instruction_ref ins) {
auto&& op = any_cast<op::batch_norm_inference>(ins->get_operator());
auto output = insert_allocation(ins, ins->get_shape());
shape old_shape = ins->inputs().at(1)->get_shape();
auto input = ins->inputs()[0];
auto input_lens = input->get_shape().lens();
std::vector<int64_t> rsp_lens(input_lens.size(), 1);
// for per_activation case, also need to reshape input
if(op.bn_mode == op::batch_norm_inference::per_activation)
{
std::copy(input_lens.begin() + 1, input_lens.end(), rsp_lens.begin() + 1);
}
else
{
rsp_lens[1] = static_cast<int64_t>(old_shape.elements());
}
auto reshape_op = op::reshape{rsp_lens};
std::vector<instruction_ref> reshapes;
std::transform(ins->inputs().begin() + 1,
ins->inputs().end(),
std::back_inserter(reshapes),
[&](auto i) { return mod->insert_instruction(ins, reshape_op, i); });
return mod->replace_instruction(ins,
miopen_batch_norm_inference{op},
input,
reshapes[0],
reshapes[1],
reshapes[2],
reshapes[3],
output);
});
}
// use 0 - input to represent neg
void add_neg_op()
{
......
src/targets/gpu/mlir.cpp
View file @ c4b1102e
......@@ -21,6 +21,7 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include "migraphx/make_op.hpp"
#include <migraphx/gpu/mlir.hpp>
#ifdef MIGRAPHX_MLIR
......@@ -43,8 +44,9 @@
#include <migraphx/gpu/code_object_op.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/gpu/device_name.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/gpu/perfdb.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/permutation.hpp>
#include <deque>
#include <variant>
......@@ -370,7 +372,11 @@ struct mlir_program
mlir_operation_state& add_results(const std::vector<shape>& outputs)
{
auto x = prog->make_tensors(outputs);
std::vector<shape> reshaped(outputs.size());
std::transform(outputs.begin(), outputs.end(), reshaped.begin(), [](const shape& r) {
return shape{r.type(), r.lens()};
});
auto x = prog->make_tensors(reshaped);
mlirOperationStateAddResults(&op_state, x.size(), x.data());
return *this;
}
......@@ -502,11 +508,12 @@ struct mlir_program
{
pp =
problem_params{ins->get_operator(), to_shapes(ins->inputs()), ins->get_shape()};
std::string tuned = get_tune_params();
// check if HW supports xdlops
bool xdlops = contains(get_xdlops_archs(), target_name);
std::string tuned = get_tune_params(xdlops);
if(not tuned.empty())
ops.add_attributes({{"perf_config", tuned}});
// check if HW supports xdlops
if(contains(get_xdlops_archs(), target_name))
if(xdlops)
ops.add_attributes({{"xdlopsV2", true}});
}
......@@ -571,7 +578,7 @@ struct mlir_program
MIGRAPHX_THROW("Failed to compile mlir program");
}
std::string get_tune_params() { return get_mlir_perf_for_conv(pp); }
std::string get_tune_params(bool xdlops) { return get_mlir_perf_for_conv(pp, xdlops); }
mlir_context ctx;
MlirLocation location;
......@@ -589,8 +596,54 @@ std::string dump_mlir(const module& m)
return mlir_print(&mlirOperationPrint, mod_op);
}
code_object_op compile_mlir(const context&, const module& m)
void adjust_param_shapes(module& m, const std::vector<instruction_ref>& inputs)
{
auto names = m.get_parameter_names();
std::sort(names.begin(), names.end());
for(auto i : range(names.size()))
{
const auto& name = names[i];
const auto& input = inputs[i]->get_shape();
auto param = m.get_parameter(name);
if(input.standard())
continue;
auto lens = input.lens();
auto strides = input.strides();
std::vector<operation> ops;
if(input.transposed())
{
auto perm = find_permutation(input);
auto iperm = invert_permutation(perm);
lens = reorder_dims(lens, iperm);
strides = reorder_dims(strides, iperm);
ops.push_back(make_op("transpose", {{"permutation", perm}}));
}
if(input.broadcasted())
{
std::transform(lens.begin(),
lens.end(),
strides.begin(),
lens.begin(),
[](auto len, auto stride) -> std::size_t {
if(stride == 0)
return 1;
return len;
});
ops.push_back(make_op("multibroadcast", {{"out_lens", input.lens()}}));
}
auto new_param =
std::accumulate(ops.begin(),
ops.end(),
m.add_parameter(name + ".0", shape{input.type(), lens}),
[&](auto x, auto op) { return m.insert_instruction(param, op, x); });
m.replace_instruction(param, new_param);
m.remove_instruction(param);
}
}
code_object_op compile_mlir(const context&, module m, const std::vector<instruction_ref>& inputs)
{
adjust_param_shapes(m, inputs);
const bool trace = enabled(MIGRAPHX_TRACE_MLIR{});
if(trace)
std::cout << m << std::endl;
......@@ -662,13 +715,19 @@ instruction_ref insert_mlir(module& m,
std::string dump_mlir(const module&) { return {}; }
code_object_op compile_mlir(const context&, const module&) { return {}; }
template <class T>
void use(T&)
{
}
// Disabling clang-tidy warning on non-real useage.
// NOLINTBEGIN(performance-unnecessary-value-param)
code_object_op compile_mlir(const context&, module, const std::vector<instruction_ref>&)
{
return {};
}
// NOLINTEND(performance-unnecessary-value-param)
instruction_ref
// cppcheck-suppress funcArgNamesDifferent
insert_mlir(module& m, instruction_ref, code_object_op co, const std::vector<instruction_ref>&)
......
src/targets/gpu/perfdb.cpp
View file @ c4b1102e
......@@ -108,16 +108,17 @@ auto query_miopen_db(const std::string& query)
} // namespace
std::string get_mlir_perf_for_conv(const problem_params& pp)
std::string get_mlir_perf_for_conv(const problem_params& pp, bool xdlops)
{
std::string query = "select P.* \
std::string solver = xdlops ? "ConvMlirIgemmFwdXdlops" : "ConvMlirIgemmFwd";
std::string query = "select P.* \
from perf_db P, config C \
where P.config = C.id AND \
P.solver = 'ConvMlirIgemmFwdXdlops' AND \
P.solver = '${solver}' AND \
${config}";
auto results =
query_miopen_db(interpolate_string(query, {{"config", generate_miopen_config(pp)}}));
auto results = query_miopen_db(
interpolate_string(query, {{"config", generate_miopen_config(pp)}, {"solver", solver}}));
if(results.empty())
return "";
return results.front().at("params");
......
src/targets/gpu/quant_convolution.cpp deleted 100644 → 0
View file @ 5fc48e77
/*
* 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/gpu/quant_convolution.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/generate.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
shape miopen_quant_convolution::compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(4).standard();
return op.normalize_compute_shape({inputs.at(0), inputs.at(1)});
}
argument miopen_quant_convolution::compute(context& ctx,
const shape& output_shape,
const std::vector<argument>& args) const
{
auto x_desc = make_tensor(args[0].get_shape(), int8_x4_format);
auto w_desc = make_tensor(args[1].get_shape(), int8_x4_format);
auto y_desc = make_tensor(output_shape);
float alpha = 1;
float beta = 0;
auto status = miopenConvolutionForward(ctx.get_stream().get_miopen(),
&alpha,
x_desc.get(),
args[0].implicit(),
w_desc.get(),
args[1].implicit(),
cd.get(),
algo,
&beta,
y_desc.get(),
args[3].implicit(),
args[2].implicit(),
args[2].get_shape().bytes());
if(status != miopenStatusSuccess)
{
MIGRAPHX_THROW("QUANT_CONVOLUTION: run convolution forward failed");
}
return args[3];
}
shape miopen_quant_convolution::find(context& ctx,
const shape& output_shape,
std::vector<shape> inputs)
{
shape workspace_shape{};
auto x_desc = make_tensor(inputs[0], int8_x4_format);
auto w_desc = make_tensor(inputs[1], int8_x4_format);
auto y_desc = make_tensor(output_shape);
std::size_t workspace_size = 0;
miopenConvolutionForwardGetWorkSpaceSize(ctx.get_stream().get_miopen(),
w_desc.get(),
x_desc.get(),
cd.get(),
y_desc.get(),
&workspace_size);
workspace_shape = shape{shape::int8_type, {workspace_size}};
auto x_shape = inputs[0];
auto w_shape = inputs[1];
if(int8_x4_format)
{
x_shape = pack_int8_shape(x_shape);
w_shape = pack_int8_shape(w_shape);
}
auto x = to_gpu(generate_argument(x_shape));
auto w = to_gpu(generate_argument(w_shape));
auto y = allocate_gpu(output_shape);
auto workspace = allocate_gpu(workspace_shape);
int algo_count = 1;
miopenConvAlgoPerf_t perf;
auto status = miopenFindConvolutionForwardAlgorithm(ctx.get_stream().get_miopen(),
x_desc.get(),
x.implicit(),
w_desc.get(),
w.implicit(),
cd.get(),
y_desc.get(),
y.implicit(),
1,
&algo_count,
&perf,
workspace.implicit(),
workspace_size,
false);
if(status != miopenStatusSuccess)
MIGRAPHX_THROW("MIOpen Quant Convolution: find convolution failed");
algo = perf.fwd_algo;
size_t solution_count;
status = miopenConvolutionForwardGetSolutionCount(ctx.get_stream().get_miopen(),
w_desc.get(),
x_desc.get(),
cd.get(),
y_desc.get(),
&solution_count);
if(status != miopenStatusSuccess)
MIGRAPHX_THROW("MIOpen Quant Convolution: get solution count failed");
std::vector<miopenConvSolution_t> solutions(solution_count);
status = miopenConvolutionForwardGetSolution(ctx.get_stream().get_miopen(),
w_desc.get(),
x_desc.get(),
cd.get(),
y_desc.get(),
solution_count,
&solution_count,
solutions.data());
if(status != miopenStatusSuccess)
MIGRAPHX_THROW("MIOpen Quant Convolution: get solution failed");
solution_id = solutions.front().solution_id;
return shape{shape::int8_type, {perf.memory}};
}
void miopen_quant_convolution::finalize(context& ctx,
const shape& output_shape,
std::vector<shape> inputs)
{
if(cd == nullptr)
cd = make_conv(op);
if(solution_id == 0)
{
// Check that workspace hasn't changed
auto size = inputs.at(2).bytes();
auto ws = find(ctx, output_shape, inputs);
if(ws.bytes() > size)
MIGRAPHX_THROW("MIOpen Quant Convolution: workspace has changed during finalization.");
}
auto x_desc = make_tensor(inputs[0], int8_x4_format);
auto w_desc = make_tensor(inputs[1], int8_x4_format);
auto y_desc = make_tensor(output_shape);
auto status = miopenConvolutionForwardCompileSolution(ctx.get_stream().get_miopen(),
w_desc.get(),
x_desc.get(),
cd.get(),
y_desc.get(),
solution_id);
if(status != miopenStatusSuccess)
MIGRAPHX_THROW("MIOpen Quant Convolution: compile solution failed");
}
shape miopen_quant_convolution::pack_int8_shape(const shape& s) const
{
if(s.type() != shape::int8_type)
{
MIGRAPHX_THROW("PACK_INT8_SHAPE: only process int8_type");
}
auto lens = s.lens();
auto strides = s.strides();
lens[1] = (lens[1] + 3) / 4 * 4;
strides[0] = strides[1] * lens[1];
return {s.type(), lens, strides};
}
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/target.cpp
View file @ c4b1102e
......@@ -41,7 +41,6 @@
#include <migraphx/propagate_constant.hpp>
#include <migraphx/register_target.hpp>
#include <migraphx/replace_allocate.hpp>
#include <migraphx/rewrite_batchnorm.hpp>
#include <migraphx/rewrite_gelu.hpp>
#include <migraphx/rewrite_pooling.hpp>
#include <migraphx/rewrite_quantization.hpp>
......@@ -110,8 +109,6 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
dead_code_elimination{},
insert_pad{},
dead_code_elimination{},
rewrite_batchnorm{},
dead_code_elimination{},
rewrite_rnn{},
dead_code_elimination{},
inline_module{},
......@@ -141,12 +138,12 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
dead_code_elimination{},
pack_int8_args{},
dead_code_elimination{},
adjust_allocation{gpu_allocation_model{}},
dead_code_elimination{},
fuse_ops{&ctx, options.fast_math},
dead_code_elimination{},
replace_allocate{gpu_allocation_model{}, options.offload_copy},
dead_code_elimination{},
adjust_allocation{gpu_allocation_model{}},
dead_code_elimination{},
compile_ops{&ctx},
dead_code_elimination{},
write_literals{&ctx},
......
src/targets/ref/lowering.cpp
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src/tf/parse_batchnorm.cpp
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test/api/test_custom_op.cpp
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test/api/test_custom_op_gpu.cpp
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test/api/test_gpu.cpp
View file @ c4b1102e
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test/gpu/literal.cpp
View file @ c4b1102e
......@@ -48,4 +48,4 @@ void gpu_literal_test()
}
}
int main() { gpu_literal_test(); }
int main() { gpu_literal_test(); } // NOLINT (bugprone-exception-escape)
test/gpu/mlir.cpp
View file @ c4b1102e
......@@ -84,7 +84,7 @@ migraphx::program create_program_from_mlir(const migraphx::module& mmlir)
inputs.push_back(mm->add_parameter("output", mmlir.get_output_shapes().front()));
migraphx::gpu::context ctx;
migraphx::gpu::insert_mlir(*mm, mm->end(), compile_mlir(ctx, mmlir), inputs);
migraphx::gpu::insert_mlir(*mm, mm->end(), compile_mlir(ctx, mmlir, inputs), inputs);
return p;
}
......
test/gpu/quantization.cpp
View file @ c4b1102e
......@@ -30,7 +30,6 @@
#include <migraphx/ref/target.hpp>
#include <migraphx/gpu/target.hpp>
#include <migraphx/verify.hpp>
#include <migraphx/quantization.hpp>
#include <migraphx/dead_code_elimination.hpp>
#include <migraphx/propagate_constant.hpp>
#include <migraphx/pass_manager.hpp>
......
test/gpu/stream_sync.cpp 0 → 100644
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test/onnx/gen_onnx.py
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