Add post ops to ClGemmMatrixMultiplyReshapedOnlyRHSKernel and ClGemmMatrixMultiplyNativeKernel Part 3
Partially resolves: COMPMID-4435
Change-Id: Ifc5affa3a24a70942ca2d001380205df09b03ad7
Signed-off-by: SiCongLi <sicong.li@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6550
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
index dc5fbc3..e3f151a 100644
--- a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
@@ -53,6 +53,11 @@
template <typename T>
using CLGEMMMatrixMultiplyNativeFixture = GEMMMatrixMultiplyNativeValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>;
+// Fixture for CLGEMMMatrixMultiplyNative with post ops
+template <typename T>
+using CLGEMMMatrixMultiplyNativeWithPostOpsFixture =
+ GEMMMatrixMultiplyNativeWithPostOpsValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>;
+
// Fixture for CLGEMMMatrixMultiplyNative3D
template <typename T>
using CLGEMMMatrixMultiplyNative3DFixture = GEMMMatrixMultiplyNative3DValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>;
@@ -141,6 +146,80 @@
broadcast_bias_values),
framework::dataset::make("Activation", ActivationLayerInfo()));
+/** Post Ops */
+using PostOpArgBroadcast = CLGEMMMatrixMultiplyNativeWithPostOpsFixture<float>::PostOpArgBroadcast;
+experimental::PostOpList<PostOpArgBroadcast> post_ops_1()
+{
+ 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(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});
+ return post_ops;
+}
+experimental::PostOpList<PostOpArgBroadcast> post_ops_2()
+{
+ experimental::PostOpList<PostOpArgBroadcast> post_ops{};
+ post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
+ 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});
+ return post_ops;
+}
+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), // If broadcast in dims 0, 1 and 2
+ 1,
+ ConvertPolicy::SATURATE);
+ 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(),
+} );
+
+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)
+{
+ const auto lhs_info = GEMMLHSMatrixInfo(4,4,1,false,true);
+ const auto rhs_info = GEMMRHSMatrixInfo(4,4,1,true,true,false);
+
+ // Create TensorInfo for post op arguments
+ TensorInfo input0_info(TensorShape(k, m, batch), 1, data_type);
+ TensorInfo input1_info(TensorShape(n, k, batch), 1, data_type);
+ TensorInfo input2_info(TensorShape(n), 1, data_type);
+ TensorInfo output_info(TensorShape(n, m, batch), 1, data_type);
+
+ GEMMKernelInfo gemm_info(m, n, k, 0 /**< Depth of the output tensor in case is reinterpreted as 3D */,
+ false /**< reinterpret the input as 3D */,
+ true /**< Flag used to broadcast the bias addition */,
+ false /**< wider accumm */,
+ false /**< has pad y */,
+ ActivationLayerInfo::ActivationFunction::IDENTITY,
+ 1 /**< Multiplication factor for the width of the 1xW transposed block */,
+ 1 /**< Multiplication factor for the height of the 4x4 interleaved block */,
+ lhs_info,
+ rhs_info,
+ 0 /**< Offset to be added to each element of the matrix A */,
+ 0 /**< Offset to be added to each element of the matrix B */,
+ post_ops);
+ return bool(ClGemmMatrixMultiplyNativeKernel::validate(&input0_info.clone()->set_is_resizable(true),
+ &input1_info.clone()->set_is_resizable(true),
+ &input2_info.clone()->set_is_resizable(true),
+ &output_info.clone()->set_is_resizable(true),1.f,1.f,
+ lhs_info,
+ rhs_info,
+ gemm_info));
+}
+
/** Configuration test */
void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value, unsigned int m0_value, unsigned int n0_value, unsigned int k0_value, bool broadcast_bias, DataType data_type, const ActivationLayerInfo &act_info)
{
@@ -191,6 +270,119 @@
TEST_SUITE(CL)
TEST_SUITE(GEMMMatrixMultiplyNative)
+TEST_SUITE(ValidateFusedPostOpsConfigs)
+TEST_SUITE(Invalid)
+TEST_CASE(UnsupportedPostOpSequence, framework::DatasetMode::ALL)
+{
+ const auto data_type = DataType::F32;
+ const unsigned int m = 17;
+ const unsigned int n = 1;
+ const unsigned int k = 13;
+ const unsigned int batch = 2;
+ TensorShape post_op_arg0_shape(n, m, batch);
+ TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type);
+ auto post_op_arg1_info = post_op_arg_info.clone();
+
+ // Unsupported sequence of post ops
+ experimental::PostOpList<ITensorInfo*> post_ops{};
+ post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>(
+ &post_op_arg_info,
+ 1,
+ ConvertPolicy::SATURATE);
+ post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>(
+ post_op_arg1_info.get(),
+ 0,
+ ConvertPolicy::SATURATE);
+
+ ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
+}
+TEST_CASE(OutputWidened, framework::DatasetMode::ALL)
+{
+ // Invalid broadcast: post op tensors "widen" the output tensor
+ const auto data_type = DataType::F32;
+ const unsigned int m = 1;
+ const unsigned int n = 18;
+ const unsigned int k = 13;
+ const unsigned int batch = 2;
+ TensorShape post_op_arg_shape(n, m + 1, batch); // output's Y dimension (m) is "widened", which is not allowed
+ 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) == false, framework::LogLevel::ERRORS);
+}
+TEST_CASE(BroadcastInXDimOnly, framework::DatasetMode::ALL)
+{
+ // Invalid broadcast: post op tensors broadcast in the first dimension (X) only
+ 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, m, batch);
+ 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) == false, framework::LogLevel::ERRORS);
+}
+TEST_SUITE_END() // Invalid
+TEST_SUITE(Valid)
+TEST_CASE(EmptyPostOpList, 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;
+ experimental::PostOpList<ITensorInfo*> post_ops{};
+
+ ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
+}
+TEST_CASE(BroadcastInYDimOnly, 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(n, 1, batch);
+ 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_CASE(BroadcastInBothXandYDims, 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, batch);
+ 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_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)
TEST_SUITE(FP32)
DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine(
@@ -323,6 +515,32 @@
// Validate output
validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
}
+
+TEST_SUITE(FusedPostOps)
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeWithPostOpsFixture<float>, framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
+ m_values,
+ n_values),
+ k_values),
+ b_values),
+ framework::dataset::make("M0", { 4 })),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("alpha", {1.0f} )),
+ framework::dataset::make("beta", {1.0f} )),
+ framework::dataset::make("broadcast_bias", { false, true } )),
+ framework::dataset::make("Activation", { ActivationLayerInfo() })),
+ post_op_lists)
+ )
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
+}
+
+TEST_SUITE_END() // FusedPostOps
+
TEST_SUITE_END() // FP32
TEST_SUITE_END() // Float
TEST_SUITE_END() // GEMMMatrixMulipltyNative