Skip to content

GitLab

Commit c4b1102e authored by charlie's avatar charlie
Browse files

Merge branch 'dyn_model_test' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_model_test

parents 5fc48e77 31065c7d
Expand all Show whitespace changes
Inline Side-by-side
Showing with 765 additions and 517 deletions
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 @@ ...@@ -26,6 +26,8 @@
#include <migraphx/manage_ptr.hpp> #include <migraphx/manage_ptr.hpp>
#include <migraphx/instruction.hpp> #include <migraphx/instruction.hpp>
#include <migraphx/make_op.hpp> #include <migraphx/make_op.hpp>
#include <migraphx/instruction_ref.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/op/convolution.hpp> #include <migraphx/op/convolution.hpp>
#include <migraphx/op/deconvolution.hpp> #include <migraphx/op/deconvolution.hpp>
...@@ -35,15 +37,12 @@ ...@@ -35,15 +37,12 @@
#include <migraphx/op/quant_convolution.hpp> #include <migraphx/op/quant_convolution.hpp>
#include <migraphx/op/quant_dot.hpp> #include <migraphx/op/quant_dot.hpp>
#include <migraphx/gpu/batch_norm_inference.hpp>
#include <migraphx/gpu/context.hpp> #include <migraphx/gpu/context.hpp>
#include <migraphx/gpu/convolution.hpp> #include <migraphx/gpu/convolution.hpp>
#include <migraphx/gpu/deconvolution.hpp>
#include <migraphx/gpu/device_name.hpp> #include <migraphx/gpu/device_name.hpp>
#include <migraphx/gpu/gemm.hpp> #include <migraphx/gpu/gemm.hpp>
#include <migraphx/gpu/int8_conv_pack.hpp> #include <migraphx/gpu/int8_conv_pack.hpp>
#include <migraphx/gpu/miopen.hpp> #include <migraphx/gpu/miopen.hpp>
#include <migraphx/gpu/quant_convolution.hpp>
#include <migraphx/gpu/rocblas.hpp> #include <migraphx/gpu/rocblas.hpp>
#include <migraphx/gpu/compiler.hpp> #include <migraphx/gpu/compiler.hpp>
#include <migraphx/iterator_for.hpp> #include <migraphx/iterator_for.hpp>
...@@ -96,14 +95,11 @@ struct miopen_apply ...@@ -96,14 +95,11 @@ struct miopen_apply
add_extend_op("argmax"); add_extend_op("argmax");
add_extend_op("argmin"); add_extend_op("argmin");
// add_extend_op("elu");
add_extend_op("gather"); add_extend_op("gather");
// add_extend_op("leaky_relu");
add_extend_op("logsoftmax"); add_extend_op("logsoftmax");
add_extend_op("lrn"); add_extend_op("lrn");
add_extend_op("multinomial"); add_extend_op("multinomial");
add_extend_op("nonzero"); add_extend_op("nonzero");
add_extend_op("pad");
add_extend_op("pooling"); add_extend_op("pooling");
add_extend_op("prefix_scan_sum"); add_extend_op("prefix_scan_sum");
add_extend_op("reverse"); add_extend_op("reverse");
...@@ -113,16 +109,15 @@ struct miopen_apply ...@@ -113,16 +109,15 @@ struct miopen_apply
add_extend_op("scatter_none"); add_extend_op("scatter_none");
add_extend_op("topk"); add_extend_op("topk");
add_batch_norm_inference_op(); add_convolution_op<op::convolution>("convolution");
add_convolution_op(); add_convolution_op<op::deconvolution>("deconvolution");
add_deconvolution_op(); add_convolution_op<op::quant_convolution>("quant_convolution");
add_gemm_op<op::dot>("dot"); add_gemm_op<op::dot>("dot");
add_gemm_op<op::quant_dot>("quant_dot"); add_gemm_op<op::quant_dot>("quant_dot");
add_if_op(); add_if_op();
add_loop_op(); add_loop_op();
add_neg_op(); add_neg_op();
add_nms_op(); add_nms_op();
add_quant_convolution_op();
} }
void copy_params() const void copy_params() const
...@@ -172,6 +167,7 @@ struct miopen_apply ...@@ -172,6 +167,7 @@ struct miopen_apply
for(auto it = mod->begin(); it != mod->end(); it++) 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) if(apply_map.count(it->name()) > 0)
{ {
check_shape(s, apply_map.at(it->name())(it)); check_shape(s, apply_map.at(it->name())(it));
...@@ -180,11 +176,37 @@ struct miopen_apply ...@@ -180,11 +176,37 @@ struct miopen_apply
{ {
check_shape(s, insert_precompile_op(it)); check_shape(s, insert_precompile_op(it));
} }
else if(attrs.contains("target"))
{
check_shape(s, insert_custom_op(it, attrs));
}
} }
copy_params(); 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 instruction_ref insert_precompile_op(instruction_ref ins) const
{ {
auto output = insert_allocation(ins, ins->get_shape()); auto output = insert_allocation(ins, ins->get_shape());
...@@ -203,38 +225,6 @@ struct miopen_apply ...@@ -203,38 +225,6 @@ struct miopen_apply
return mod->insert_instruction(ins, make_op("allocate", {{"shape", to_value(s)}})); 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> template <typename Op>
void add_gemm_op(const std::string& name) void add_gemm_op(const std::string& name)
{ {
...@@ -248,31 +238,33 @@ struct miopen_apply ...@@ -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) { apply_map.emplace(name, [=](instruction_ref ins) {
auto&& op = any_cast<op::quant_convolution>(ins->get_operator()); operation conv =
shape ws; miopen_convolution<Op>{any_cast<Op>(ins->get_operator()), int8_x4_format};
miopen_quant_convolution conv; migraphx::context ctx = get_context();
auto compile_quant_conv_with_format = [&](bool format) { size_t ws_bytes = 0;
conv = miopen_quant_convolution{op, format, make_conv(op)}; auto compile_conv_with_format = [&](bool format) {
ws = conv.find(get_context(), ins->get_shape(), to_shapes(ins->inputs())); 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 try
{ { // for the regular convolution and deconvolution, this try would always succeed
compile_quant_conv_with_format(int8_x4_format); compile_conv_with_format(int8_x4_format);
} }
catch(migraphx::exception&) catch(migraphx::exception&)
{ {
// In case no solver supports the default format, retry using the other format. // 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 args = ins->inputs();
auto workspace = insert_allocation(ins, ws);
auto output = insert_allocation(ins, ins->get_shape()); 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); return mod->replace_instruction(ins, conv, args[0], args[1], workspace, output);
}); });
} }
...@@ -307,43 +299,6 @@ struct miopen_apply ...@@ -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 // use 0 - input to represent neg
void add_neg_op() void add_neg_op()
{ {
......
src/targets/gpu/mlir.cpp
View file @ c4b1102e
...@@ -21,6 +21,7 @@ ...@@ -21,6 +21,7 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE. * THE SOFTWARE.
*/ */
#include "migraphx/make_op.hpp"
#include <migraphx/gpu/mlir.hpp> #include <migraphx/gpu/mlir.hpp>
#ifdef MIGRAPHX_MLIR #ifdef MIGRAPHX_MLIR
...@@ -43,8 +44,9 @@ ...@@ -43,8 +44,9 @@
#include <migraphx/gpu/code_object_op.hpp> #include <migraphx/gpu/code_object_op.hpp>
#include <migraphx/gpu/context.hpp> #include <migraphx/gpu/context.hpp>
#include <migraphx/gpu/device_name.hpp> #include <migraphx/gpu/device_name.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/gpu/perfdb.hpp> #include <migraphx/gpu/perfdb.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/permutation.hpp>
#include <deque> #include <deque>
#include <variant> #include <variant>
...@@ -370,7 +372,11 @@ struct mlir_program ...@@ -370,7 +372,11 @@ struct mlir_program
mlir_operation_state& add_results(const std::vector<shape>& outputs) 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()); mlirOperationStateAddResults(&op_state, x.size(), x.data());
return *this; return *this;
} }
...@@ -502,11 +508,12 @@ struct mlir_program ...@@ -502,11 +508,12 @@ struct mlir_program
{ {
pp = pp =
problem_params{ins->get_operator(), to_shapes(ins->inputs()), ins->get_shape()}; 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()) if(not tuned.empty())
ops.add_attributes({{"perf_config", tuned}}); ops.add_attributes({{"perf_config", tuned}});
// check if HW supports xdlops if(xdlops)
if(contains(get_xdlops_archs(), target_name))
ops.add_attributes({{"xdlopsV2", true}}); ops.add_attributes({{"xdlopsV2", true}});
} }
...@@ -571,7 +578,7 @@ struct mlir_program ...@@ -571,7 +578,7 @@ struct mlir_program
MIGRAPHX_THROW("Failed to compile 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; mlir_context ctx;
MlirLocation location; MlirLocation location;
...@@ -589,8 +596,54 @@ std::string dump_mlir(const module& m) ...@@ -589,8 +596,54 @@ std::string dump_mlir(const module& m)
return mlir_print(&mlirOperationPrint, mod_op); 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{}); const bool trace = enabled(MIGRAPHX_TRACE_MLIR{});
if(trace) if(trace)
std::cout << m << std::endl; std::cout << m << std::endl;
...@@ -662,13 +715,19 @@ instruction_ref insert_mlir(module& m, ...@@ -662,13 +715,19 @@ instruction_ref insert_mlir(module& m,
std::string dump_mlir(const module&) { return {}; } std::string dump_mlir(const module&) { return {}; }
code_object_op compile_mlir(const context&, const module&) { return {}; }
template <class T> template <class T>
void use(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 instruction_ref
// cppcheck-suppress funcArgNamesDifferent // cppcheck-suppress funcArgNamesDifferent
insert_mlir(module& m, instruction_ref, code_object_op co, const std::vector<instruction_ref>&) 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) ...@@ -108,16 +108,17 @@ auto query_miopen_db(const std::string& query)
} // namespace } // 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 solver = xdlops ? "ConvMlirIgemmFwdXdlops" : "ConvMlirIgemmFwd";
std::string query = "select P.* \ std::string query = "select P.* \
from perf_db P, config C \ from perf_db P, config C \
where P.config = C.id AND \ where P.config = C.id AND \
P.solver = 'ConvMlirIgemmFwdXdlops' AND \ P.solver = '${solver}' AND \
${config}"; ${config}";
auto results = auto results = query_miopen_db(
query_miopen_db(interpolate_string(query, {{"config", generate_miopen_config(pp)}})); interpolate_string(query, {{"config", generate_miopen_config(pp)}, {"solver", solver}}));
if(results.empty()) if(results.empty())
return ""; return "";
return results.front().at("params"); 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 @@ ...@@ -41,7 +41,6 @@
#include <migraphx/propagate_constant.hpp> #include <migraphx/propagate_constant.hpp>
#include <migraphx/register_target.hpp> #include <migraphx/register_target.hpp>
#include <migraphx/replace_allocate.hpp> #include <migraphx/replace_allocate.hpp>
#include <migraphx/rewrite_batchnorm.hpp>
#include <migraphx/rewrite_gelu.hpp> #include <migraphx/rewrite_gelu.hpp>
#include <migraphx/rewrite_pooling.hpp> #include <migraphx/rewrite_pooling.hpp>
#include <migraphx/rewrite_quantization.hpp> #include <migraphx/rewrite_quantization.hpp>
...@@ -110,8 +109,6 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti ...@@ -110,8 +109,6 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
dead_code_elimination{}, dead_code_elimination{},
insert_pad{}, insert_pad{},
dead_code_elimination{}, dead_code_elimination{},
rewrite_batchnorm{},
dead_code_elimination{},
rewrite_rnn{}, rewrite_rnn{},
dead_code_elimination{}, dead_code_elimination{},
inline_module{}, inline_module{},
...@@ -141,12 +138,12 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti ...@@ -141,12 +138,12 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
dead_code_elimination{}, dead_code_elimination{},
pack_int8_args{}, pack_int8_args{},
dead_code_elimination{}, dead_code_elimination{},
adjust_allocation{gpu_allocation_model{}},
dead_code_elimination{},
fuse_ops{&ctx, options.fast_math}, fuse_ops{&ctx, options.fast_math},
dead_code_elimination{}, dead_code_elimination{},
replace_allocate{gpu_allocation_model{}, options.offload_copy}, replace_allocate{gpu_allocation_model{}, options.offload_copy},
dead_code_elimination{}, dead_code_elimination{},
adjust_allocation{gpu_allocation_model{}},
dead_code_elimination{},
compile_ops{&ctx}, compile_ops{&ctx},
dead_code_elimination{}, dead_code_elimination{},
write_literals{&ctx}, write_literals{&ctx},
......
src/targets/ref/lowering.cpp
View file @ c4b1102e
...@@ -26,7 +26,6 @@ ...@@ -26,7 +26,6 @@
#include <migraphx/instruction.hpp> #include <migraphx/instruction.hpp>
#include <migraphx/dfor.hpp> #include <migraphx/dfor.hpp>
#include <migraphx/op/identity.hpp> #include <migraphx/op/identity.hpp>
#include <migraphx/op/batch_norm_inference.hpp>
#include <migraphx/op/convolution.hpp> #include <migraphx/op/convolution.hpp>
#include <migraphx/op/deconvolution.hpp> #include <migraphx/op/deconvolution.hpp>
#include <migraphx/op/quant_convolution.hpp> #include <migraphx/op/quant_convolution.hpp>
...@@ -73,84 +72,6 @@ typename std::conditional_t<std::is_integral<T>{}, std::make_signed<T>, std::ena ...@@ -73,84 +72,6 @@ typename std::conditional_t<std::is_integral<T>{}, std::make_signed<T>, std::ena
return x; return x;
} }
//
// ref implemenataion of batch norm for inference
//
// inputs are:
// args[0] -> input data buffer
// args[1] -> mini batch mean
// args[2] -> mini batch variance
// args[3] -> gamma
// args[4] -> bias
//
// The equation to compute batch norm for inference is:
//
// output[i] = bias + gamma * (input[i] + mean) / sqrt(variance + epsilon)
//
// the input data format should be nchw
//
struct ref_batch_norm_inference
{
op::batch_norm_inference op;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return migraphx::reflect(self.op, f);
}
std::string name() const { return "ref::batch_norm_inference"; }
shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
argument compute(context&, const shape& output_shape, std::vector<argument> args) const
{
argument output{output_shape};
double epsilon = op.epsilon;
auto input = args[0];
auto arg_gamma = args[1];
auto arg_bias = args[2];
auto mini_batch_mean = args[3];
auto mini_batch_variance = args[4];
if(op.bn_mode == op::batch_norm_inference::spatial)
{
visit_all(output, input, mini_batch_mean, mini_batch_variance, arg_gamma, arg_bias)(
[&](auto result, auto buffer, auto mean, auto variance, auto gamma, auto bias) {
par_for(output_shape.elements(), [&](auto i) {
auto idx = output_shape.multi(i);
auto c = idx[1];
assert((variance[c] + epsilon) > 0);
result[i] =
gamma[c] * (buffer[i] - mean[c]) / std::sqrt(variance[c] + epsilon) +
bias[c];
});
});
}
if(op.bn_mode == op::batch_norm_inference::per_activation)
{
visit_all(output, input, mini_batch_mean, mini_batch_variance, arg_gamma, arg_bias)(
[&](auto result, auto buffer, auto mean, auto variance, auto gamma, auto bias) {
par_for(output_shape.elements(), [&](auto i) {
auto idx = output_shape.multi(i);
idx[0] = 0;
auto index = output_shape.index(idx);
assert((variance[index] + epsilon) > 0);
result[i] = gamma[index] * (buffer[i] - mean[index]) /
std::sqrt(variance[index] + epsilon) +
bias[index];
});
});
}
return output;
}
};
MIGRAPHX_REGISTER_OP(ref_batch_norm_inference)
struct ref_lrn struct ref_lrn
{ {
op::lrn op; op::lrn op;
...@@ -643,8 +564,6 @@ struct ref_apply ...@@ -643,8 +564,6 @@ struct ref_apply
void init() void init()
{ {
apply_map["batch_norm_inference"] =
extend_op<ref_batch_norm_inference, op::batch_norm_inference>();
apply_map["convolution"] = extend_op<ref_convolution<op::convolution>, op::convolution>(); apply_map["convolution"] = extend_op<ref_convolution<op::convolution>, op::convolution>();
apply_map["dot"] = extend_op<ref_gemm, op::dot>(); apply_map["dot"] = extend_op<ref_gemm, op::dot>();
apply_map["quant_dot"] = extend_op<ref_quant_gemm, op::quant_dot>(); apply_map["quant_dot"] = extend_op<ref_quant_gemm, op::quant_dot>();
......
src/tf/parse_batchnorm.cpp
View file @ c4b1102e
...@@ -23,6 +23,7 @@ ...@@ -23,6 +23,7 @@
*/ */
#include <migraphx/tf/op_parser.hpp> #include <migraphx/tf/op_parser.hpp>
#include <migraphx/tf/tf_parser.hpp> #include <migraphx/tf/tf_parser.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/ranges.hpp> #include <migraphx/ranges.hpp>
#include <migraphx/make_op.hpp> #include <migraphx/make_op.hpp>
...@@ -38,16 +39,37 @@ struct parse_batchnorm : op_parser<parse_batchnorm> ...@@ -38,16 +39,37 @@ struct parse_batchnorm : op_parser<parse_batchnorm>
instruction_ref parse(const op_desc& /*opd*/, instruction_ref parse(const op_desc& /*opd*/,
const tf_parser& /*parser*/, const tf_parser& /*parser*/,
tf_parser::node_info info, tf_parser::node_info info,
const std::vector<instruction_ref>& args) const std::vector<instruction_ref> args) const
{ {
float epsilon = 1e-5f; // different default epsilon than from ONNX
float momentum = 0.9f; float epsilon = 1e-4f;
if(contains(info.attributes, "epsilon")) if(contains(info.attributes, "epsilon"))
{ {
epsilon = info.attributes.at("epsilon").f(); epsilon = info.attributes.at("epsilon").f();
} }
auto op = make_op("batch_norm_inference", {{"epsilon", epsilon}, {"momentum", momentum}});
return info.add_instruction(op, args); auto x_lens = args[0]->get_shape().lens();
auto x_type = args[0]->get_shape().type();
// unsqueeze tensors of shape (C) to broadcast correctly
auto rt = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {0.5}});
auto eps = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {epsilon}});
auto scale_unsqueeze =
info.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), args[1]);
auto bias_unsqueeze =
info.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), args[2]);
auto mean_unsqueeze =
info.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), args[3]);
auto var_unsqueeze =
info.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), args[4]);
auto numer = info.add_broadcastable_binary_op("sub", args[0], mean_unsqueeze);
auto var_eps = info.add_broadcastable_binary_op("add", var_unsqueeze, eps);
auto denom = info.add_broadcastable_binary_op("pow", var_eps, rt);
auto div0 = info.add_broadcastable_binary_op("div", numer, denom);
auto r0 = info.add_broadcastable_binary_op("mul", div0, scale_unsqueeze);
return info.add_broadcastable_binary_op("add", r0, bias_unsqueeze);
} }
}; };
......
test/api/test_custom_op.cpp
View file @ c4b1102e
...@@ -43,6 +43,8 @@ struct sigmoid_custom_op final : migraphx::experimental_custom_op_base ...@@ -43,6 +43,8 @@ struct sigmoid_custom_op final : migraphx::experimental_custom_op_base
return inputs[1]; return inputs[1];
} }
virtual bool runs_on_offload_target() const override { return true; }
virtual migraphx::shape compute_shape(migraphx::shapes inputs) const override virtual migraphx::shape compute_shape(migraphx::shapes inputs) const override
{ {
if(inputs.size() != 2) if(inputs.size() != 2)
...@@ -111,4 +113,45 @@ TEST_CASE(run_sigmoid_with_incorrect_shape) ...@@ -111,4 +113,45 @@ TEST_CASE(run_sigmoid_with_incorrect_shape)
"Error in compute_shape of: sigmoid_custom_op: op must have two inputs")); "Error in compute_shape of: sigmoid_custom_op: op must have two inputs"));
} }
struct identity_custom_op final : migraphx::experimental_custom_op_base
{
virtual std::string name() const override { return "identity_custom_op"; }
virtual migraphx::argument
compute(migraphx::context, migraphx::shape, migraphx::arguments inputs) const override
{
return inputs[0];
}
virtual bool runs_on_offload_target() const override { return true; }
virtual migraphx::shape compute_shape(migraphx::shapes inputs) const override
{
if(inputs.size() != 1)
{
throw std::runtime_error("Identity op must have only one input");
}
return inputs.back();
}
virtual std::vector<size_t> output_alias(migraphx::shapes) const override { return {0, 1}; }
};
TEST_CASE(run_custom_op_with_invalid_output_alias)
{
identity_custom_op i_op;
migraphx::register_experimental_custom_op(i_op);
auto op = migraphx::operation("identity_custom_op");
EXPECT(op.name() == "identity_custom_op");
migraphx::program p;
migraphx::shape s{migraphx_shape_float_type, {12}};
migraphx::module m = p.get_main_module();
auto x = m.add_parameter("x", s);
auto i_ins = m.add_instruction(migraphx::operation("identity_custom_op"), {x});
migraphx_test_private_disable_exception_catch(true);
EXPECT(test::throws<std::exception>(
[&] { p.compile(migraphx::target("ref")); },
"Currently, CustomOps in MIGraphX only supports one output_alias"));
}
int main(int argc, const char* argv[]) { test::run(argc, argv); } int main(int argc, const char* argv[]) { test::run(argc, argv); }
test/api/test_custom_op_gpu.cpp
View file @ c4b1102e
This diff is collapsed.
test/api/test_gpu.cpp
View file @ c4b1102e
...@@ -25,6 +25,8 @@ ...@@ -25,6 +25,8 @@
#include <hip/hip_runtime_api.h> #include <hip/hip_runtime_api.h>
#include <migraphx/migraphx.h> #include <migraphx/migraphx.h>
#include <migraphx/migraphx.hpp> #include <migraphx/migraphx.hpp>
#include <migraphx/manage_ptr.hpp>
#include "test.hpp" #include "test.hpp"
TEST_CASE(load_and_run) TEST_CASE(load_and_run)
...@@ -44,11 +46,67 @@ TEST_CASE(load_and_run) ...@@ -44,11 +46,67 @@ TEST_CASE(load_and_run)
{ {
pp.add(name, migraphx::argument::generate(param_shapes[name])); pp.add(name, migraphx::argument::generate(param_shapes[name]));
} }
auto outputs = p.eval(pp); auto outputs = p.eval(pp);
CHECK(shapes_before.size() == outputs.size()); CHECK(shapes_before.size() == outputs.size());
CHECK(bool{shapes_before.front() == outputs.front().get_shape()}); CHECK(bool{shapes_before.front() == outputs.front().get_shape()});
} }
using hip_ptr = MIGRAPHX_MANAGE_PTR(void, hipFree);
using stream_ptr = MIGRAPHX_MANAGE_PTR(hipStream_t, hipStreamDestroy);
stream_ptr get_stream()
{
hipStream_t stream;
auto err = hipStreamCreateWithFlags(&stream, 0);
EXPECT(err == hipSuccess);
return stream_ptr{stream};
}
hip_ptr get_hip_buffer(size_t size)
{
void* ptr;
auto err = hipMalloc(&ptr, size);
EXPECT(err == hipSuccess);
return hip_ptr{ptr};
}
TEST_CASE(load_and_run_async)
{
auto p = migraphx::parse_onnx("conv_relu_maxpool_test.onnx");
auto shapes_before = p.get_output_shapes();
migraphx::compile_options options;
options.set_offload_copy(false);
p.compile(migraphx::target("gpu"), options);
auto shapes_after = p.get_output_shapes();
CHECK(shapes_before.size() == 1);
CHECK(shapes_before.size() == shapes_after.size());
CHECK(bool{shapes_before.front() == shapes_after.front()});
migraphx::program_parameters pp;
auto param_shapes = p.get_parameter_shapes();
stream_ptr stream = get_stream();
std::vector<hip_ptr> buffs;
std::vector<migraphx::argument> args;
for(auto&& name : param_shapes.names())
{
args.push_back(migraphx::argument::generate(param_shapes[name]));
buffs.push_back(get_hip_buffer(args.rbegin()->get_shape().bytes()));
auto err = hipMemcpy(buffs.rbegin()->get(),
args.rbegin()->data(),
args.rbegin()->get_shape().bytes(),
hipMemcpyHostToDevice);
EXPECT(err == hipSuccess);
pp.add(name, migraphx::argument(args.rbegin()->get_shape(), buffs.rbegin()->get()));
}
auto outputs = p.run_async(pp, stream.get());
CHECK(shapes_before.size() == outputs.size());
CHECK(bool{shapes_before.front() == outputs.front().get_shape()});
}
TEST_CASE(load_and_run_ctx) TEST_CASE(load_and_run_ctx)
{ {
auto p = migraphx::parse_onnx("conv_relu_maxpool_test.onnx"); auto p = migraphx::parse_onnx("conv_relu_maxpool_test.onnx");
...@@ -82,10 +140,10 @@ TEST_CASE(if_pl_test) ...@@ -82,10 +140,10 @@ TEST_CASE(if_pl_test)
migraphx::program_parameters pp; migraphx::program_parameters pp;
auto param_shapes = p.get_parameter_shapes(); auto param_shapes = p.get_parameter_shapes();
auto xs = param_shapes["x"]; auto xs = param_shapes["x"];
std::vector<float> xd(xs.bytes() / sizeof(float), 1.0); std::vector<float> xd(xs.elements(), 1.0);
pp.add("x", migraphx::argument(xs, xd.data())); pp.add("x", migraphx::argument(xs, xd.data()));
auto ys = param_shapes["y"]; auto ys = param_shapes["y"];
std::vector<float> yd(ys.bytes() / sizeof(float), 2.0); std::vector<float> yd(ys.elements(), 2.0);
pp.add("y", migraphx::argument(ys, yd.data())); pp.add("y", migraphx::argument(ys, yd.data()));
char ccond = cond; char ccond = cond;
pp.add("cond", migraphx::argument(param_shapes["cond"], &ccond)); pp.add("cond", migraphx::argument(param_shapes["cond"], &ccond));
......
test/gpu/literal.cpp
View file @ c4b1102e
...@@ -48,4 +48,4 @@ void gpu_literal_test() ...@@ -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
This diff is collapsed.
test/gpu/quantization.cpp
View file @ c4b1102e
This diff is collapsed.
test/gpu/stream_sync.cpp 0 → 100644
View file @ c4b1102e
This diff is collapsed.
test/onnx/gen_onnx.py
View file @ c4b1102e
This diff is collapsed.
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment