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Commit 41d4e92b authored by Khalique Ahmed's avatar Khalique Ahmed
Browse files

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

parents a8eb886b 32f6388c
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src/CMakeLists.txt
View file @ 41d4e92b
...@@ -81,7 +81,6 @@ add_library(migraphx ...@@ -81,7 +81,6 @@ add_library(migraphx
replace_allocate.cpp replace_allocate.cpp
simplify_qdq.cpp simplify_qdq.cpp
sqlite.cpp sqlite.cpp
rewrite_batchnorm.cpp
rewrite_gelu.cpp rewrite_gelu.cpp
rewrite_pooling.cpp rewrite_pooling.cpp
rewrite_quantization.cpp rewrite_quantization.cpp
...@@ -115,7 +114,6 @@ register_migraphx_ops( ...@@ -115,7 +114,6 @@ register_migraphx_ops(
as_shape as_shape
atanh atanh
atan atan
batch_norm_inference
broadcast broadcast
capture capture
ceil ceil
......
src/driver/alexnet.cpp
View file @ 41d4e92b
...@@ -25,6 +25,7 @@ ...@@ -25,6 +25,7 @@
#include <migraphx/make_op.hpp> #include <migraphx/make_op.hpp>
#include <migraphx/program.hpp> #include <migraphx/program.hpp>
#include <migraphx/generate.hpp> #include <migraphx/generate.hpp>
#include <migraphx/json.hpp>
#include "models.hpp" #include "models.hpp"
namespace migraphx { namespace migraphx {
namespace driver { namespace driver {
...@@ -39,153 +40,161 @@ migraphx::program alexnet(unsigned batch) // NOLINT(readability-function-size) ...@@ -39,153 +40,161 @@ migraphx::program alexnet(unsigned batch) // NOLINT(readability-function-size)
migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {1}}, 1))); migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {1}}, 1)));
auto x_main_module_2 = mmain->add_literal(migraphx::abs( auto x_main_module_2 = mmain->add_literal(migraphx::abs(
migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {1}}, 2))); migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {1}}, 2)));
auto x_0 = mmain->add_parameter( auto x_data_0 = mmain->add_parameter(
"0", migraphx::shape{migraphx::shape::float_type, {batch, 3, 224, 224}}); "data_0", migraphx::shape{migraphx::shape::float_type, {batch, 3, 224, 224}});
auto x_main_module_4 = mmain->add_literal( auto x_main_module_4 = mmain->add_literal(
migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {1000}}, 3)); migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {1000, 4096}}, 3));
auto x_main_module_5 = mmain->add_literal( auto x_main_module_5 = mmain->add_literal(
migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {1000, 4096}}, 4)); migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {1000}}, 4));
auto x_main_module_6 = mmain->add_literal( auto x_main_module_6 = mmain->add_literal(
migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {4096}}, 5)); migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {4096, 4096}}, 5));
auto x_main_module_7 = mmain->add_literal( auto x_main_module_7 = mmain->add_literal(migraphx::abs(
migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {4096, 4096}}, 6)); migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {4096}}, 6)));
auto x_main_module_8 = mmain->add_literal( auto x_main_module_8 = mmain->add_literal(
migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {4096}}, 7)); migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {4096, 9216}}, 7));
auto x_main_module_9 = mmain->add_literal( auto x_main_module_9 = mmain->add_literal(
migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {4096, 9216}}, 8)); migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {4096}}, 8));
auto x_main_module_10 = mmain->add_literal( auto x_main_module_10 = mmain->add_literal(migraphx::generate_literal(
migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {256}}, 9)); migraphx::shape{migraphx::shape::float_type, {256, 192, 3, 3}}, 9));
auto x_main_module_11 = mmain->add_literal(migraphx::generate_literal( auto x_main_module_11 = mmain->add_literal(
migraphx::shape{migraphx::shape::float_type, {256, 256, 3, 3}}, 10)); migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {256}}, 10));
auto x_main_module_12 = mmain->add_literal( auto x_main_module_12 = mmain->add_literal(migraphx::generate_literal(
migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {256}}, 11)); migraphx::shape{migraphx::shape::float_type, {384, 192, 3, 3}}, 11));
auto x_main_module_13 = mmain->add_literal(migraphx::generate_literal( auto x_main_module_13 = mmain->add_literal(
migraphx::shape{migraphx::shape::float_type, {256, 384, 3, 3}}, 12)); migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {384}}, 12));
auto x_main_module_14 = mmain->add_literal( auto x_main_module_14 = mmain->add_literal(migraphx::generate_literal(
migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {384}}, 13)); migraphx::shape{migraphx::shape::float_type, {384, 256, 3, 3}}, 13));
auto x_main_module_15 = mmain->add_literal(migraphx::generate_literal( auto x_main_module_15 = mmain->add_literal(
migraphx::shape{migraphx::shape::float_type, {384, 192, 3, 3}}, 14)); migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {384}}, 14));
auto x_main_module_16 = mmain->add_literal( auto x_main_module_16 = mmain->add_literal(migraphx::generate_literal(
migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {192}}, 15)); migraphx::shape{migraphx::shape::float_type, {256, 48, 5, 5}}, 15));
auto x_main_module_17 = mmain->add_literal(migraphx::generate_literal( auto x_main_module_17 = mmain->add_literal(migraphx::abs(
migraphx::shape{migraphx::shape::float_type, {192, 64, 5, 5}}, 16)); migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {256}}, 16)));
auto x_main_module_18 = mmain->add_literal( auto x_main_module_18 = mmain->add_literal(migraphx::generate_literal(
migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {64}}, 17)); migraphx::shape{migraphx::shape::float_type, {96, 3, 11, 11}}, 17));
auto x_main_module_19 = mmain->add_literal(migraphx::generate_literal( auto x_main_module_19 = mmain->add_literal(
migraphx::shape{migraphx::shape::float_type, {64, 3, 11, 11}}, 18)); migraphx::generate_literal(migraphx::shape{migraphx::shape::float_type, {96}}, 18));
auto x_main_module_20 = mmain->add_instruction( auto x_main_module_20 = mmain->add_instruction(
migraphx::make_json_op("convolution", migraphx::make_json_op("convolution",
"{dilation:[1,1],group:1,padding:[2,2,2,2],padding_mode:0,stride:[4," "{dilation:[1,1],group:1,padding:[0,0,0,0],padding_mode:0,stride:[4,"
"4],use_dynamic_same_auto_pad:0}"), "4],use_dynamic_same_auto_pad:0}"),
x_0, x_data_0,
x_main_module_19); x_main_module_18);
auto x_main_module_21 = mmain->add_instruction( auto x_main_module_21 = mmain->add_instruction(
migraphx::make_json_op("broadcast", "{axis:1,out_lens:[1,64,55,55]}"), x_main_module_18); migraphx::make_json_op("broadcast", "{axis:1,out_lens:[1,96,54,54]}"), x_main_module_19);
auto x_main_module_22 = auto x_main_module_22 =
mmain->add_instruction(migraphx::make_op("add"), x_main_module_20, x_main_module_21); mmain->add_instruction(migraphx::make_op("add"), x_main_module_20, x_main_module_21);
auto x_main_module_23 = mmain->add_instruction(migraphx::make_op("relu"), x_main_module_22); auto x_main_module_23 = mmain->add_instruction(migraphx::make_op("relu"), x_main_module_22);
auto x_main_module_24 = mmain->add_instruction( auto x_main_module_24 = mmain->add_instruction(
migraphx::make_json_op("lrn", "{alpha:9.999999747378752e-05,beta:0.75,bias:1.0,size:5}"),
x_main_module_23);
auto x_main_module_25 = mmain->add_instruction(
migraphx::make_json_op( migraphx::make_json_op(
"pooling", "pooling",
"{ceil_mode:0,lengths:[3,3],lp_order:2,mode:1,padding:[0,0,0,0],stride:[2,2]}"), "{ceil_mode:0,lengths:[3,3],lp_order:2,mode:1,padding:[0,0,0,0],stride:[2,2]}"),
x_main_module_23); x_main_module_24);
auto x_main_module_25 = mmain->add_instruction( auto x_main_module_26 = mmain->add_instruction(
migraphx::make_json_op("convolution", migraphx::make_json_op("convolution",
"{dilation:[1,1],group:1,padding:[2,2,2,2],padding_mode:0,stride:[1," "{dilation:[1,1],group:2,padding:[2,2,2,2],padding_mode:0,stride:[1,"
"1],use_dynamic_same_auto_pad:0}"), "1],use_dynamic_same_auto_pad:0}"),
x_main_module_24, x_main_module_25,
x_main_module_17); x_main_module_16);
auto x_main_module_26 = mmain->add_instruction( auto x_main_module_27 = mmain->add_instruction(
migraphx::make_json_op("broadcast", "{axis:1,out_lens:[1,192,27,27]}"), x_main_module_16); migraphx::make_json_op("broadcast", "{axis:1,out_lens:[1,256,26,26]}"), x_main_module_17);
auto x_main_module_27 = auto x_main_module_28 =
mmain->add_instruction(migraphx::make_op("add"), x_main_module_25, x_main_module_26); mmain->add_instruction(migraphx::make_op("add"), x_main_module_26, x_main_module_27);
auto x_main_module_28 = mmain->add_instruction(migraphx::make_op("relu"), x_main_module_27); auto x_main_module_29 = mmain->add_instruction(migraphx::make_op("relu"), x_main_module_28);
auto x_main_module_29 = mmain->add_instruction( auto x_main_module_30 = mmain->add_instruction(
migraphx::make_json_op("lrn", "{alpha:9.999999747378752e-05,beta:0.75,bias:1.0,size:5}"),
x_main_module_29);
auto x_main_module_31 = mmain->add_instruction(
migraphx::make_json_op( migraphx::make_json_op(
"pooling", "pooling",
"{ceil_mode:0,lengths:[3,3],lp_order:2,mode:1,padding:[0,0,0,0],stride:[2,2]}"), "{ceil_mode:0,lengths:[3,3],lp_order:2,mode:1,padding:[0,0,0,0],stride:[2,2]}"),
x_main_module_28); x_main_module_30);
auto x_main_module_30 = mmain->add_instruction( auto x_main_module_32 = mmain->add_instruction(
migraphx::make_json_op("convolution", migraphx::make_json_op("convolution",
"{dilation:[1,1],group:1,padding:[1,1,1,1],padding_mode:0,stride:[1," "{dilation:[1,1],group:1,padding:[1,1,1,1],padding_mode:0,stride:[1,"
"1],use_dynamic_same_auto_pad:0}"), "1],use_dynamic_same_auto_pad:0}"),
x_main_module_29, x_main_module_31,
x_main_module_15); x_main_module_14);
auto x_main_module_31 = mmain->add_instruction( auto x_main_module_33 = mmain->add_instruction(
migraphx::make_json_op("broadcast", "{axis:1,out_lens:[1,384,13,13]}"), x_main_module_14); migraphx::make_json_op("broadcast", "{axis:1,out_lens:[1,384,12,12]}"), x_main_module_15);
auto x_main_module_32 = auto x_main_module_34 =
mmain->add_instruction(migraphx::make_op("add"), x_main_module_30, x_main_module_31); mmain->add_instruction(migraphx::make_op("add"), x_main_module_32, x_main_module_33);
auto x_main_module_33 = mmain->add_instruction(migraphx::make_op("relu"), x_main_module_32); auto x_main_module_35 = mmain->add_instruction(migraphx::make_op("relu"), x_main_module_34);
auto x_main_module_34 = mmain->add_instruction( auto x_main_module_36 = mmain->add_instruction(
migraphx::make_json_op("convolution", migraphx::make_json_op("convolution",
"{dilation:[1,1],group:1,padding:[1,1,1,1],padding_mode:0,stride:[1," "{dilation:[1,1],group:2,padding:[1,1,1,1],padding_mode:0,stride:[1,"
"1],use_dynamic_same_auto_pad:0}"), "1],use_dynamic_same_auto_pad:0}"),
x_main_module_33, x_main_module_35,
x_main_module_13); x_main_module_12);
auto x_main_module_35 = mmain->add_instruction( auto x_main_module_37 = mmain->add_instruction(
migraphx::make_json_op("broadcast", "{axis:1,out_lens:[1,256,13,13]}"), x_main_module_12); migraphx::make_json_op("broadcast", "{axis:1,out_lens:[1,384,12,12]}"), x_main_module_13);
auto x_main_module_36 = auto x_main_module_38 =
mmain->add_instruction(migraphx::make_op("add"), x_main_module_34, x_main_module_35); mmain->add_instruction(migraphx::make_op("add"), x_main_module_36, x_main_module_37);
auto x_main_module_37 = mmain->add_instruction(migraphx::make_op("relu"), x_main_module_36); auto x_main_module_39 = mmain->add_instruction(migraphx::make_op("relu"), x_main_module_38);
auto x_main_module_38 = mmain->add_instruction( auto x_main_module_40 = mmain->add_instruction(
migraphx::make_json_op("convolution", migraphx::make_json_op("convolution",
"{dilation:[1,1],group:1,padding:[1,1,1,1],padding_mode:0,stride:[1," "{dilation:[1,1],group:2,padding:[1,1,1,1],padding_mode:0,stride:[1,"
"1],use_dynamic_same_auto_pad:0}"), "1],use_dynamic_same_auto_pad:0}"),
x_main_module_37, x_main_module_39,
x_main_module_11); x_main_module_10);
auto x_main_module_39 = mmain->add_instruction( auto x_main_module_41 = mmain->add_instruction(
migraphx::make_json_op("broadcast", "{axis:1,out_lens:[1,256,13,13]}"), x_main_module_10); migraphx::make_json_op("broadcast", "{axis:1,out_lens:[1,256,12,12]}"), x_main_module_11);
auto x_main_module_40 = auto x_main_module_42 =
mmain->add_instruction(migraphx::make_op("add"), x_main_module_38, x_main_module_39); mmain->add_instruction(migraphx::make_op("add"), x_main_module_40, x_main_module_41);
auto x_main_module_41 = mmain->add_instruction(migraphx::make_op("relu"), x_main_module_40); auto x_main_module_43 = mmain->add_instruction(migraphx::make_op("relu"), x_main_module_42);
auto x_main_module_42 = mmain->add_instruction( auto x_main_module_44 = mmain->add_instruction(
migraphx::make_json_op( migraphx::make_json_op(
"pooling", "pooling",
"{ceil_mode:0,lengths:[3,3],lp_order:2,mode:1,padding:[0,0,0,0],stride:[2,2]}"), "{ceil_mode:0,lengths:[3,3],lp_order:2,mode:1,padding:[0,0,1,1],stride:[2,2]}"),
x_main_module_41); x_main_module_43);
auto x_main_module_43 =
mmain->add_instruction(migraphx::make_json_op("flatten", "{axis:1}"), x_main_module_42);
auto x_main_module_44 = mmain->add_instruction(migraphx::make_op("identity"), x_main_module_43);
auto x_main_module_45 = mmain->add_instruction( auto x_main_module_45 = mmain->add_instruction(
migraphx::make_json_op("transpose", "{permutation:[1,0]}"), x_main_module_9); migraphx::make_json_op("reshape", "{dims:[1,9216]}"), x_main_module_44);
auto x_main_module_46 = auto x_main_module_46 = mmain->add_instruction(
mmain->add_instruction(migraphx::make_op("dot"), x_main_module_44, x_main_module_45); migraphx::make_json_op("transpose", "{permutation:[1,0]}"), x_main_module_8);
auto x_main_module_47 = mmain->add_instruction( auto x_main_module_47 =
migraphx::make_json_op("multibroadcast", "{out_lens:[1,4096]}"), x_main_module_8); mmain->add_instruction(migraphx::make_op("dot"), x_main_module_45, x_main_module_46);
auto x_main_module_48 = mmain->add_instruction( auto x_main_module_48 = mmain->add_instruction(
migraphx::make_json_op("multibroadcast", "{out_lens:[1,4096]}"), x_main_module_9);
auto x_main_module_49 = mmain->add_instruction(
migraphx::make_json_op("multibroadcast", "{out_lens:[1,4096]}"), x_main_module_2); migraphx::make_json_op("multibroadcast", "{out_lens:[1,4096]}"), x_main_module_2);
auto x_main_module_49 =
mmain->add_instruction(migraphx::make_op("mul"), x_main_module_47, x_main_module_48);
auto x_main_module_50 = auto x_main_module_50 =
mmain->add_instruction(migraphx::make_op("add"), x_main_module_46, x_main_module_49); mmain->add_instruction(migraphx::make_op("mul"), x_main_module_48, x_main_module_49);
auto x_main_module_51 = mmain->add_instruction(migraphx::make_op("relu"), x_main_module_50); auto x_main_module_51 =
auto x_main_module_52 = mmain->add_instruction(migraphx::make_op("identity"), x_main_module_51); mmain->add_instruction(migraphx::make_op("add"), x_main_module_47, x_main_module_50);
auto x_main_module_53 = mmain->add_instruction( auto x_main_module_52 = mmain->add_instruction(migraphx::make_op("relu"), x_main_module_51);
migraphx::make_json_op("transpose", "{permutation:[1,0]}"), x_main_module_7); auto x_main_module_53 = mmain->add_instruction(migraphx::make_op("identity"), x_main_module_52);
auto x_main_module_54 = auto x_main_module_54 = mmain->add_instruction(
mmain->add_instruction(migraphx::make_op("dot"), x_main_module_52, x_main_module_53); migraphx::make_json_op("transpose", "{permutation:[1,0]}"), x_main_module_6);
auto x_main_module_55 = mmain->add_instruction( auto x_main_module_55 =
migraphx::make_json_op("multibroadcast", "{out_lens:[1,4096]}"), x_main_module_6); mmain->add_instruction(migraphx::make_op("dot"), x_main_module_53, x_main_module_54);
auto x_main_module_56 = mmain->add_instruction( auto x_main_module_56 = mmain->add_instruction(
migraphx::make_json_op("multibroadcast", "{out_lens:[1,4096]}"), x_main_module_7);
auto x_main_module_57 = mmain->add_instruction(
migraphx::make_json_op("multibroadcast", "{out_lens:[1,4096]}"), x_main_module_1); migraphx::make_json_op("multibroadcast", "{out_lens:[1,4096]}"), x_main_module_1);
auto x_main_module_57 =
mmain->add_instruction(migraphx::make_op("mul"), x_main_module_55, x_main_module_56);
auto x_main_module_58 = auto x_main_module_58 =
mmain->add_instruction(migraphx::make_op("add"), x_main_module_54, x_main_module_57); mmain->add_instruction(migraphx::make_op("mul"), x_main_module_56, x_main_module_57);
auto x_main_module_59 = mmain->add_instruction(migraphx::make_op("relu"), x_main_module_58); auto x_main_module_59 =
auto x_main_module_60 = mmain->add_instruction( mmain->add_instruction(migraphx::make_op("add"), x_main_module_55, x_main_module_58);
migraphx::make_json_op("transpose", "{permutation:[1,0]}"), x_main_module_5); auto x_main_module_60 = mmain->add_instruction(migraphx::make_op("relu"), x_main_module_59);
auto x_main_module_61 = auto x_main_module_61 = mmain->add_instruction(migraphx::make_op("identity"), x_main_module_60);
mmain->add_instruction(migraphx::make_op("dot"), x_main_module_59, x_main_module_60);
auto x_main_module_62 = mmain->add_instruction( auto x_main_module_62 = mmain->add_instruction(
migraphx::make_json_op("multibroadcast", "{out_lens:[1,1000]}"), x_main_module_4); migraphx::make_json_op("transpose", "{permutation:[1,0]}"), x_main_module_4);
auto x_main_module_63 = mmain->add_instruction( auto x_main_module_63 =
mmain->add_instruction(migraphx::make_op("dot"), x_main_module_61, x_main_module_62);
auto x_main_module_64 = mmain->add_instruction(
migraphx::make_json_op("multibroadcast", "{out_lens:[1,1000]}"), x_main_module_5);
auto x_main_module_65 = mmain->add_instruction(
migraphx::make_json_op("multibroadcast", "{out_lens:[1,1000]}"), x_main_module_0); migraphx::make_json_op("multibroadcast", "{out_lens:[1,1000]}"), x_main_module_0);
auto x_main_module_64 = auto x_main_module_66 =
mmain->add_instruction(migraphx::make_op("mul"), x_main_module_62, x_main_module_63); mmain->add_instruction(migraphx::make_op("mul"), x_main_module_64, x_main_module_65);
auto x_main_module_65 = auto x_main_module_67 =
mmain->add_instruction(migraphx::make_op("add"), x_main_module_61, x_main_module_64); mmain->add_instruction(migraphx::make_op("add"), x_main_module_63, x_main_module_66);
mmain->add_return({x_main_module_65}); auto x_main_module_68 =
mmain->add_instruction(migraphx::make_json_op("softmax", "{axis:1}"), x_main_module_67);
mmain->add_return({x_main_module_68});
return p; return p;
} }
......
src/driver/inceptionv3.cpp
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src/driver/main.cpp
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...@@ -44,7 +44,6 @@ ...@@ -44,7 +44,6 @@
#include <migraphx/propagate_constant.hpp> #include <migraphx/propagate_constant.hpp>
#include <migraphx/quantization.hpp> #include <migraphx/quantization.hpp>
#include <migraphx/register_op.hpp> #include <migraphx/register_op.hpp>
#include <migraphx/rewrite_batchnorm.hpp>
#include <migraphx/simplify_algebra.hpp> #include <migraphx/simplify_algebra.hpp>
#include <migraphx/simplify_reshapes.hpp> #include <migraphx/simplify_reshapes.hpp>
#include <migraphx/register_target.hpp> #include <migraphx/register_target.hpp>
...@@ -221,7 +220,6 @@ struct loader ...@@ -221,7 +220,6 @@ struct loader
{ {
migraphx::run_passes(*p.get_main_module(), migraphx::run_passes(*p.get_main_module(),
{ {
migraphx::rewrite_batchnorm{},
migraphx::eliminate_identity{}, migraphx::eliminate_identity{},
migraphx::dead_code_elimination{}, migraphx::dead_code_elimination{},
migraphx::simplify_algebra{}, migraphx::simplify_algebra{},
......
src/driver/resnet50.cpp
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src/include/migraphx/op/batch_norm_inference.hpp deleted 100644 → 0
View file @ a8eb886b
/*
* 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.
*/
#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>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct batch_norm_inference
{
float epsilon = 1.0e-6f;
float momentum = 0.9f;
std::string name() const { return "batch_norm_inference"; }
enum bn_infer_mode_t
{
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();
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/common.hpp
View file @ 41d4e92b
...@@ -33,11 +33,11 @@ namespace migraphx { ...@@ -33,11 +33,11 @@ namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS { inline namespace MIGRAPHX_INLINE_NS {
namespace op { namespace op {
// Padding mode is default_ for fixed shape padding.
// same_lower and same_upper used for dynamic padding.
enum padding_mode_t enum padding_mode_t
{ {
default_, // NOLINT default_, // NOLINT
same,
valid,
same_lower, same_lower,
same_upper same_upper
}; };
......
src/include/migraphx/op/convolution.hpp
View file @ 41d4e92b
...@@ -41,9 +41,8 @@ struct convolution ...@@ -41,9 +41,8 @@ struct convolution
std::vector<std::size_t> stride = {1, 1}; std::vector<std::size_t> stride = {1, 1};
std::vector<std::size_t> dilation = {1, 1}; std::vector<std::size_t> dilation = {1, 1};
int group = 1; int group = 1;
padding_mode_t padding_mode = default_; padding_mode_t padding_mode = default_;
bool use_dynamic_same_auto_pad = false;
template <class Self, class F> template <class Self, class F>
static auto reflect(Self& self, F f) static auto reflect(Self& self, F f)
...@@ -52,8 +51,7 @@ struct convolution ...@@ -52,8 +51,7 @@ struct convolution
f(self.stride, "stride"), f(self.stride, "stride"),
f(self.dilation, "dilation"), f(self.dilation, "dilation"),
f(self.group, "group"), f(self.group, "group"),
f(self.padding_mode, "padding_mode"), f(self.padding_mode, "padding_mode"));
f(self.use_dynamic_same_auto_pad, "use_dynamic_same_auto_pad"));
} }
std::string name() const { return "convolution"; } std::string name() const { return "convolution"; }
...@@ -93,13 +91,6 @@ struct convolution ...@@ -93,13 +91,6 @@ struct convolution
x_shape.lens().at(1) != (w_shape.lens().at(1) * group)) x_shape.lens().at(1) != (w_shape.lens().at(1) * group))
MIGRAPHX_THROW("CONVOLUTION: mismatched channel numbers"); MIGRAPHX_THROW("CONVOLUTION: mismatched channel numbers");
std::vector<op::padding_mode_t> dyn_pad_modes = {op::padding_mode_t::same_upper,
op::padding_mode_t::same_lower};
if(use_dynamic_same_auto_pad and not contains(dyn_pad_modes, padding_mode))
{
MIGRAPHX_THROW("CONVOLUTION: use_dynamic_same_auto_pad set with invalid padding mode");
}
if(x_shape.dynamic() or w_shape.dynamic()) if(x_shape.dynamic() or w_shape.dynamic())
{ {
return dynamic_compute_shape(x_shape, w_shape); return dynamic_compute_shape(x_shape, w_shape);
...@@ -161,7 +152,7 @@ struct convolution ...@@ -161,7 +152,7 @@ struct convolution
dynamic_shape_push_back(w_shape); dynamic_shape_push_back(w_shape);
const size_t num_spatial_dims = x_shape.max_lens().size() - 2; const size_t num_spatial_dims = x_shape.max_lens().size() - 2;
if(use_dynamic_same_auto_pad) if(padding_mode != default_)
{ {
for(std::size_t i = 0; i < num_spatial_dims; ++i) for(std::size_t i = 0; i < num_spatial_dims; ++i)
{ {
......
src/include/migraphx/op/quant_convolution.hpp
View file @ 41d4e92b
...@@ -41,9 +41,8 @@ struct quant_convolution ...@@ -41,9 +41,8 @@ struct quant_convolution
std::vector<std::size_t> stride = {1, 1}; std::vector<std::size_t> stride = {1, 1};
std::vector<std::size_t> dilation = {1, 1}; std::vector<std::size_t> dilation = {1, 1};
padding_mode_t padding_mode = default_; padding_mode_t padding_mode = default_;
int group = 1; int group = 1;
bool use_dynamic_same_auto_pad = false;
template <class Self, class F> template <class Self, class F>
static auto reflect(Self& self, F f) static auto reflect(Self& self, F f)
...@@ -52,8 +51,7 @@ struct quant_convolution ...@@ -52,8 +51,7 @@ struct quant_convolution
f(self.stride, "stride"), f(self.stride, "stride"),
f(self.dilation, "dilation"), f(self.dilation, "dilation"),
f(self.padding_mode, "padding_mode"), f(self.padding_mode, "padding_mode"),
f(self.group, "group"), f(self.group, "group"));
f(self.use_dynamic_same_auto_pad, "use_dynamic_same_auto_pad"));
} }
value attributes() const value attributes() const
......
src/include/migraphx/operators.hpp
View file @ 41d4e92b
...@@ -35,7 +35,6 @@ ...@@ -35,7 +35,6 @@
#include <migraphx/op/as_shape.hpp> #include <migraphx/op/as_shape.hpp>
#include <migraphx/op/atan.hpp> #include <migraphx/op/atan.hpp>
#include <migraphx/op/atanh.hpp> #include <migraphx/op/atanh.hpp>
#include <migraphx/op/batch_norm_inference.hpp>
#include <migraphx/op/binary.hpp> #include <migraphx/op/binary.hpp>
#include <migraphx/op/broadcast.hpp> #include <migraphx/op/broadcast.hpp>
#include <migraphx/op/capture.hpp> #include <migraphx/op/capture.hpp>
......
src/include/migraphx/pad_calc.hpp
View file @ 41d4e92b
...@@ -24,9 +24,10 @@ ...@@ -24,9 +24,10 @@
#ifndef MIGRAPHX_GUARD_OPERATORS_PAD_CALC_HPP #ifndef MIGRAPHX_GUARD_OPERATORS_PAD_CALC_HPP
#define MIGRAPHX_GUARD_OPERATORS_PAD_CALC_HPP #define MIGRAPHX_GUARD_OPERATORS_PAD_CALC_HPP
#include <migraphx/config.hpp>
#include <cstdint> #include <cstdint>
#include <vector> #include <vector>
#include <migraphx/config.hpp>
#include <migraphx/shape.hpp>
namespace migraphx { namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS { inline namespace MIGRAPHX_INLINE_NS {
...@@ -42,18 +43,21 @@ void calculate_padding(int64_t idx, ...@@ -42,18 +43,21 @@ void calculate_padding(int64_t idx,
/*! /*!
* Calculate the padding for auto_padding. Used for dynamic shapes * Calculate the padding for auto_padding. Used for dynamic shapes
* where the padding calculation must be done at evaluation time. * where the padding calculation must be done at evaluation time.
* \param tensor_lens input tensor image shape
* \param k_lens weights kernel shape
* \param strides strides for the kernel
* \param dilations dilations for the kernel
* \param use_upper put odd padding on upper or lower side
* \return padding in the form of {x0_begin, x1_begin, ... x0_end , x1_end, ...} * \return padding in the form of {x0_begin, x1_begin, ... x0_end , x1_end, ...}
*/ */
std::vector<std::size_t> calc_dyn_auto_pad(std::vector<std::size_t> tensor_lens, std::vector<std::size_t> calc_dyn_auto_pad(const std::vector<std::size_t>& input_lens,
std::vector<std::size_t> k_lens, const std::vector<std::size_t>& wei_lens,
std::vector<std::size_t> strides, const std::vector<std::size_t>& strides,
std::vector<std::size_t> dilations, const std::vector<std::size_t>& dilations,
bool use_upper = true); bool use_upper);
// Used for dynamic auto padding of convolution operators since padding needs to be computed at
// evaulation time.
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);
} // namespace MIGRAPHX_INLINE_NS } // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx } // namespace migraphx
......
src/include/migraphx/rewrite_batchnorm.hpp deleted 100644 → 0
View file @ a8eb886b
/*
* 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.
*/
#ifndef MIGRAPHX_GUARD_RTGLIB_FWD_CONV_BATCHNORM_REWRITE_HPP
#define MIGRAPHX_GUARD_RTGLIB_FWD_CONV_BATCHNORM_REWRITE_HPP
#include <string>
#include <migraphx/instruction_ref.hpp>
#include <migraphx/config.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
struct module;
/**
* Rewrite batchnorm to a multiply and add.
*/
struct rewrite_batchnorm
{
std::string name() const { return "rewrite_batchnorm"; }
void apply(module& m) const;
};
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/onnx/parse_convolution.cpp
View file @ 41d4e92b
...@@ -125,11 +125,9 @@ struct parse_convolution : op_parser<parse_convolution> ...@@ -125,11 +125,9 @@ struct parse_convolution : op_parser<parse_convolution>
values["padding_mode"] = is_same_upper values["padding_mode"] = is_same_upper
? to_value(op::padding_mode_t::same_upper) ? to_value(op::padding_mode_t::same_upper)
: to_value(op::padding_mode_t::same_lower); : to_value(op::padding_mode_t::same_lower);
values["use_dynamic_same_auto_pad"] = true;
} }
else else
{ {
values["padding_mode"] = to_value(op::padding_mode_t::same);
// kernel shape will be fixed, so max_lens() == min_len() for kernel lengths // kernel shape will be fixed, so max_lens() == min_len() for kernel lengths
auto weight_lens = weights->get_shape().max_lens(); auto weight_lens = weights->get_shape().max_lens();
std::vector<std::size_t> k_lens(weight_lens.begin() + 2, weight_lens.end()); std::vector<std::size_t> k_lens(weight_lens.begin() + 2, weight_lens.end());
......
src/onnx/parse_deconvolution.cpp
View file @ 41d4e92b
...@@ -95,6 +95,8 @@ struct parse_deconvolution : op_parser<parse_deconvolution> ...@@ -95,6 +95,8 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
check_attr_sizes( check_attr_sizes(
kdims, values["dilation"].size(), "PARSE_CONV_TRANSPOSE: inconsistent dilations"); kdims, values["dilation"].size(), "PARSE_CONV_TRANSPOSE: inconsistent dilations");
} }
// TODO: auto padding needs to be implemented for this parser and operator
if(contains(info.attributes, "auto_pad")) if(contains(info.attributes, "auto_pad"))
{ {
auto s = info.attributes["auto_pad"].s(); auto s = info.attributes["auto_pad"].s();
...@@ -106,7 +108,9 @@ struct parse_deconvolution : op_parser<parse_deconvolution> ...@@ -106,7 +108,9 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
if(s.find("SAME") != std::string::npos) if(s.find("SAME") != std::string::npos)
{ {
values["padding_mode"] = to_value(op::padding_mode_t::same); bool is_same_upper = (s.find("SAME_UPPER") != std::string::npos);
values["padding_mode"] = is_same_upper ? to_value(op::padding_mode_t::same_upper)
: to_value(op::padding_mode_t::same_lower);
} }
} }
......
src/pad_calc.cpp
View file @ 41d4e92b
...@@ -52,19 +52,21 @@ void calculate_padding(int64_t idx, ...@@ -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> calc_dyn_auto_pad(const std::vector<std::size_t>& input_lens,
std::vector<std::size_t> k_lens, const std::vector<std::size_t>& wei_lens,
std::vector<std::size_t> strides, const std::vector<std::size_t>& strides,
std::vector<std::size_t> dilations, const std::vector<std::size_t>& dilations,
bool use_upper) bool use_upper)
{ {
std::vector<std::size_t> padding; std::vector<std::size_t> padding;
padding.resize(2 * k_lens.size()); assert(input_lens.size() >= 3);
for(std::size_t i = 0; i < padding.size() / 2; i++) 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 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 dilation = dilations[i];
std::ptrdiff_t output_dim = (input_dim + stride - 1) / stride; // round up result 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); 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, ...@@ -86,5 +88,28 @@ std::vector<std::size_t> calc_dyn_auto_pad(std::vector<std::size_t> tensor_lens,
return padding; 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_INLINE_NS
} // namespace migraphx } // namespace migraphx
src/rewrite_batchnorm.cpp deleted 100644 → 0
View file @ a8eb886b
/*
* 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/targets/cpu/lowering.cpp
View file @ 41d4e92b
...@@ -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>
......
src/targets/cpu/target.cpp
View file @ 41d4e92b
...@@ -37,7 +37,6 @@ ...@@ -37,7 +37,6 @@
#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_pooling.hpp> #include <migraphx/rewrite_pooling.hpp>
#include <migraphx/rewrite_quantization.hpp> #include <migraphx/rewrite_quantization.hpp>
#include <migraphx/rewrite_rnn.hpp> #include <migraphx/rewrite_rnn.hpp>
...@@ -78,8 +77,6 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti ...@@ -78,8 +77,6 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
eliminate_identity{}, eliminate_identity{},
eliminate_pad{}, eliminate_pad{},
dead_code_elimination{}, dead_code_elimination{},
rewrite_batchnorm{},
dead_code_elimination{},
rewrite_rnn{}, rewrite_rnn{},
dead_code_elimination{}, dead_code_elimination{},
eliminate_common_subexpression{}, eliminate_common_subexpression{},
......
src/targets/gpu/CMakeLists.txt 100755 → 100644
View file @ 41d4e92b
...@@ -78,7 +78,6 @@ add_library(migraphx_gpu ...@@ -78,7 +78,6 @@ add_library(migraphx_gpu
allocation_model.cpp allocation_model.cpp
argmax.cpp argmax.cpp
argmin.cpp argmin.cpp
batch_norm_inference.cpp
code_object_op.cpp code_object_op.cpp
compile_ops.cpp compile_ops.cpp
compile_gen.cpp compile_gen.cpp
...@@ -146,7 +145,6 @@ register_migraphx_gpu_ops(hip_ ...@@ -146,7 +145,6 @@ register_migraphx_gpu_ops(hip_
) )
register_migraphx_gpu_ops(miopen_ register_migraphx_gpu_ops(miopen_
abs abs
batch_norm_inference
contiguous contiguous
convolution convolution
deconvolution deconvolution
......
src/targets/gpu/batch_norm_inference.cpp deleted 100644 → 0
View file @ a8eb886b
/*
* 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/batch_norm_inference.hpp>
#include <migraphx/gpu/context.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
shape miopen_batch_norm_inference::compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(6);
check_shapes{inputs.data(), inputs.data() + 1, *this}.same_ndims().max_ndims(5);
return op.compute_shape({inputs.at(0), inputs.at(1), inputs.at(2), inputs.at(3), inputs.at(4)});
}
inline shape reshape_to_2d(const shape& input)
{
auto dims = input.lens();
if(dims.size() >= 4)
return input;
std::vector<size_t> new_dims(dims.begin(), dims.end());
std::size_t num = 4 - dims.size();
new_dims.insert(new_dims.end(), num, 1);
return {input.type(), new_dims};
}
argument miopen_batch_norm_inference::compute(context& ctx,
const shape& output_shape,
const std::vector<argument>& args) const
{
shape x_shape = args[0].get_shape();
shape y_shape = output_shape;
shape bn_shape = args[3].get_shape();
auto x_desc = make_tensor(reshape_to_2d(x_shape));
auto y_desc = make_tensor(reshape_to_2d(y_shape));
auto bn_desc = make_tensor(reshape_to_2d(bn_shape));
float alpha = 1.0;
float beta = 0.0f;
miopenBatchNormalizationForwardInference(ctx.get_stream().get_miopen(),
miopenBatchNormMode_t(op.bn_mode),
&alpha,
&beta,
x_desc.get(),
args[0].implicit(),
y_desc.get(),
args[5].implicit(),
bn_desc.get(),
args[1].implicit(),
args[2].implicit(),
args[3].implicit(),
args[4].implicit(),
op.epsilon);
return args[5];
}
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
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