experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Update testing hlo generated by hlo_converter.

Browse Source
This commit is contained in:
Yongfeng Gu 2019-08-05 12:02:39 -04:00
parent 8d7fe0a1e2
commit cc55b7f346
1 changed files with 23 additions and 25 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

48
tensorflow/compiler/xla/service/gpu/tests/gpu_kernel_tiling_test.cc
View File

@ -524,33 +524,31 @@ TEST_F(GpuKernelTilingTest, ColumnReductionSmallTileSizeX) {
const char *const kHloString = R"(
HloModule Test
%scalar_add_computation.1 {
%scalar_lhs.1 = f32[] parameter(0)
%scalar_rhs.1 = f32[] parameter(1)
ROOT %add.6 = f32[] add(f32[] %scalar_lhs.1, f32[] %scalar_rhs.1)
scalar_add_computation.1 {
scalar_lhs.1 = f32[] parameter(0)
scalar_rhs.1 = f32[] parameter(1)
ROOT add.6 = f32[] add(scalar_lhs.1, scalar_rhs.1)
}
ENTRY Test {
%param_3.241 = f16[512,2,9,9]{1,3,2,0} parameter(3)
%constant_661 = f16[] constant(0), metadata={op_type="Relu" op_name="Relu_19"}
%broadcast.695 = f16[512,2,9,9]{1,3,2,0} broadcast(f16[] %constant_661), dimensions={}, metadata={op_type="Relu" op_name="Relu_19"}
%compare.42 = pred[512,2,9,9]{1,3,2,0} compare(f16[512,2,9,9]{1,3,2,0} %param_3.241, f16[512,2,9,9]{1,3,2,0} %broadcast.695), direction=GT, metadata={op_type="ReluGrad" op_name="gradients/Relu_19_grad/ReluGrad"}
%param_2.401 = f16[512,2,9,9]{1,3,2,0} parameter(2)
%select.40 = f16[512,2,9,9]{1,3,2,0} select(pred[512,2,9,9]{1,3,2,0} %compare.42, f16[512,2,9,9]{1,3,2,0} %param_2.401, f16[512,2,9,9]{1,3,2,0} %broadcast.695), metadata={op_type="ReluGrad" op_name="gradients/Relu_19_grad/ReluGrad"}
%convert.196 = f32[512,2,9,9]{1,3,2,0} convert(f16[512,2,9,9]{1,3,2,0} %select.40), metadata={op_type="FusedBatchNormGradV2" op_name="gradients/batch_normalization_19/FusedBatchNormV2_grad/FusedBatchNormGradV2"}
%param_1.809 = f16[512,2,9,9]{1,3,2,0} parameter(1)
%copy.335 = f16[512,2,9,9]{1,3,2,0} copy(f16[512,2,9,9]{1,3,2,0} %param_1.809), metadata={op_name="XLA_Args"}
%convert.218 = f32[512,2,9,9]{1,3,2,0} convert(f16[512,2,9,9]{1,3,2,0} %copy.335), metadata={op_type="FusedBatchNormGradV2" op_name="gradients/batch_normalization_19/FusedBatchNormV2_grad/FusedBatchNormGradV2"}
%param_0.668 = f32[2]{0} parameter(0)
%broadcast.687 = f32[512,2,9,9]{1,3,2,0} broadcast(f32[2]{0} %param_0.668), dimensions={1}, metadata={op_type="FusedBatchNormGradV2" op_name="gradients/batch_normalization_19/FusedBatchNormV2_grad/FusedBatchNormGradV2"}
%subtract.136 = f32[512,2,9,9]{1,3,2,0} subtract(f32[512,2,9,9]{1,3,2,0} %convert.218, f32[512,2,9,9]{1,3,2,0} %broadcast.687), metadata={op_type="FusedBatchNormGradV2" op_name="gradients/batch_normalization_19/FusedBatchNormV2_grad/FusedBatchNormGradV2"}
%multiply.579 = f32[512,2,9,9]{1,3,2,0} multiply(f32[512,2,9,9]{1,3,2,0} %convert.196, f32[512,2,9,9]{1,3,2,0} %subtract.136), metadata={op_type="FusedBatchNormGradV2" op_name="gradients/batch_normalization_19/FusedBatchNormV2_grad/FusedBatchNormGradV2"}
%constant_485 = f32[] constant(0), metadata={op_type="L2Loss" op_name="L2Loss_21"}
%reduce.139 = f32[2]{0} reduce(f32[512,2,9,9]{1,3,2,0} %multiply.579, f32[] %constant_485), dimensions={0,2,3}, to_apply=%scalar_add_computation.1, metadata={op_type="FusedBatchNormGradV2" op_name="gradients/batch_normalization_19/FusedBatchNormV2_grad/FusedBatchNormGradV2"}
%reduce.140.clone.1 = f32[2]{0} reduce(f32[512,2,9,9]{1,3,2,0} %convert.196, f32[] %constant_485), dimensions={0,2,3}, to_apply=%scalar_add_computation.1, metadata={op_type="FusedBatchNormGradV2" op_name="gradients/batch_normalization_19/FusedBatchNormV2_grad/FusedBatchNormGradV2"}
ROOT %tuple.102 = (f32[2]{0}, f32[2]{0}) tuple(f32[2]{0} %reduce.139, f32[2]{0} %reduce.140.clone.1)
})";
param_3.241 = f16[512,2,9,9]{1,3,2,0} parameter(3)
constant_661 = f16[] constant(0)
broadcast.695 = f16[512,2,9,9]{1,3,2,0} broadcast(constant_661), dimensions={}
compare.42 = pred[512,2,9,9]{1,3,2,0} compare(param_3.241, broadcast.695), direction=GT
param_2.401 = f16[512,2,9,9]{1,3,2,0} parameter(2)
select.40 = f16[512,2,9,9]{1,3,2,0} select(compare.42, param_2.401, broadcast.695)
convert.196 = f32[512,2,9,9]{1,3,2,0} convert(select.40)
param_1.809 = f16[512,2,9,9]{1,3,2,0} parameter(1)
copy.335 = f16[512,2,9,9]{1,3,2,0} copy(param_1.809)
convert.218 = f32[512,2,9,9]{1,3,2,0} convert(copy.335)
param_0.668 = f32[2]{0} parameter(0)
broadcast.687 = f32[512,2,9,9]{1,3,2,0} broadcast(param_0.668), dimensions={1}
subtract.136 = f32[512,2,9,9]{1,3,2,0} subtract(convert.218, broadcast.687)
multiply.579 = f32[512,2,9,9]{1,3,2,0} multiply(convert.196, subtract.136)
constant_485 = f32[] constant(0)
reduce.139 = f32[2]{0} reduce(multiply.579, constant_485), dimensions={0,2,3}, to_apply=scalar_add_computation.1
reduce.140.clone.1 = f32[2]{0} reduce(convert.196, constant_485), dimensions={0,2,3}, to_apply=scalar_add_computation.1
ROOT tuple.102 = (f32[2]{0}, f32[2]{0}) tuple(reduce.139, reduce.140.clone.1)
})";
// Check that no loop is generated for reduction.
auto hlo_module =