Revert "Rework gemm_mm_reshaped_only_rhs_ kernels with new macros"
This reverts commit 10e88a7351 "Rework gemm_mm_reshaped_only_rhs_ kernels with new macros"
Resolves: COMPMID-5095
Signed-off-by: Ramy Elgammal<ramy.elgammal@arm.com>
Change-Id: I46e167882f072e7508b6101d295accb6e089e740
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7045
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
index 860082f..cfd98bd 100644
--- a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
@@ -107,12 +107,6 @@
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 10.f),
});
-/** Activation values to test */
-const auto act_identity = framework::dataset::make("Activation",
-{
- ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::IDENTITY),
-});
-
/** M0 values to test - precommit */
const auto m0_values_precommit = framework::dataset::make("M0", { 4 });
@@ -164,8 +158,8 @@
framework::dataset::make("export_to_cl_image_rhs", {true, false})),
// Only need to test F32 as F16 shares identical boundary handling logics
framework::dataset::make("DataType", DataType::F32)),
- framework::dataset::make("alpha", 1.0f )),
- framework::dataset::make("beta", 0.0f )),
+ framework::dataset::make("alpha", -0.75f )),
+ framework::dataset::make("beta", -0.35f )),
broadcast_bias_values),
framework::dataset::make("Activation", ActivationLayerInfo()));
@@ -176,7 +170,7 @@
experimental::PostOpList<PostOpArgBroadcast> post_ops{};
post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
- std::make_tuple(false, false, false),
+ std::make_tuple(true, true, false), // If broadcast in dims 0, 1 and 2
0,
ConvertPolicy::SATURATE);
post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
@@ -186,7 +180,7 @@
{
experimental::PostOpList<PostOpArgBroadcast> post_ops{};
post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
- std::make_tuple(false, false, false),
+ std::make_tuple(false, true, true), // If broadcast in dims 0, 1 and 2
1,
ConvertPolicy::SATURATE);
post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
@@ -195,18 +189,44 @@
experimental::PostOpList<PostOpArgBroadcast> post_ops_3()
{
experimental::PostOpList<PostOpArgBroadcast> post_ops{};
+ post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
- std::make_tuple(false, false, false),
+ std::make_tuple(false, false, true), // If broadcast in dims 0, 1 and 2
1,
ConvertPolicy::SATURATE);
return post_ops;
}
-
+// To test that the output of the main op is the first parameter in prelu post op
+experimental::PostOpList<PostOpArgBroadcast> post_ops_4()
+{
+ experimental::PostOpList<PostOpArgBroadcast> post_ops{};
+ post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
+ post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>(
+ std::make_tuple(false, false, true), // If true, broadcast in corresponding dim: 0, 1 or 2
+ 0,
+ ConvertPolicy::SATURATE);
+ post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
+ return post_ops;
+}
+// To test that the output of the main op is the second parameter in prelu post op i.e. it is the alpha_param
+experimental::PostOpList<PostOpArgBroadcast> post_ops_5()
+{
+ experimental::PostOpList<PostOpArgBroadcast> post_ops{};
+ post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
+ post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>(
+ std::make_tuple(false, false, false), // If true, broadcast in corresponding dim: 0, 1 or 2
+ 1,
+ ConvertPolicy::SATURATE);
+ post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
+ return post_ops;
+}
/** Different Post Op Lists */
const auto post_op_lists = framework::dataset::make("post_op_lists", {
post_ops_1(),
post_ops_2(),
- post_ops_3()
+ post_ops_3(),
+ post_ops_4(),
+ post_ops_5()
} );
bool is_post_op_list_valid(unsigned int m, unsigned int n, unsigned int k, unsigned int batch, DataType data_type, const experimental::PostOpList<ITensorInfo*>& post_ops)
@@ -446,7 +466,20 @@
ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
}
+TEST_CASE(BroadcastInAllDims, framework::DatasetMode::ALL)
+{
+ const auto data_type = DataType::F32;
+ const unsigned int m = 22;
+ const unsigned int n = 16;
+ const unsigned int k = 15;
+ const unsigned int batch = 3;
+ TensorShape post_op_arg_shape(1, 1, 1);
+ TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
+ experimental::PostOpList<ITensorInfo*> post_ops{};
+ post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
+ ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
+}
TEST_SUITE_END() // Valid
TEST_SUITE_END() // ValidateFusedPostOps
TEST_SUITE(Float)
@@ -600,7 +633,7 @@
i_values_rhs),
t_values_rhs),
framework::dataset::make("export_to_cl_image_rhs", {false, true})),
- framework::dataset::make("has_pad_y", {false})),
+ framework::dataset::make("has_pad_y", {false, true})),
framework::dataset::make("DataType", DataType::F32)),
a_values),
beta_values),
@@ -632,7 +665,7 @@
i_values_rhs),
t_values_rhs),
framework::dataset::make("export_to_cl_image_rhs", {false, true})),
- framework::dataset::make("has_pad_y", {false})),
+ framework::dataset::make("has_pad_y", {false, true})),
framework::dataset::make("DataType", DataType::F32)),
a_values),
beta_values),
@@ -669,7 +702,7 @@
a_values),
beta_values),
framework::dataset::make("broadcast_bias", { false } )),
- act_identity),
+ act_values),
post_op_lists)
)
{
@@ -766,7 +799,7 @@
i_values_rhs),
t_values_rhs),
framework::dataset::make("export_to_cl_image_rhs", true)),
- framework::dataset::make("has_pad_y", {false})),
+ framework::dataset::make("has_pad_y", {false, true})),
framework::dataset::make("DataType", DataType::F16)),
a_values),
beta_values),
@@ -798,7 +831,7 @@
i_values_rhs),
t_values_rhs),
framework::dataset::make("export_to_cl_image_rhs", true)),
- framework::dataset::make("has_pad_y", {false})),
+ framework::dataset::make("has_pad_y", {false, true})),
framework::dataset::make("DataType", DataType::F16)),
a_values),
beta_values),
@@ -834,7 +867,7 @@
a_values),
beta_values),
framework::dataset::make("broadcast_bias", { false } )),
- act_identity),
+ act_values),
post_op_lists)
)
{