COMPMID-3338 COMPMID-3336 COMPMID-3584

COMPMID-3338 Remove store padding in CLGEMMMatrixMultiplyReshapedOnlyRHSKernel
COMPMID-3336 Remove store padding in CLGEMMMatrixMultiplyNativeKernel
COMPMID-3584 Fix VSTORE to correctly deal with scalar case

* Implement STORE_BLOCK_BOUNDARY_AWARE, as part of the COMPMID-3332
investigation, with the following substantial changes:
    - Separate STORE_BLOCK_PARTIAL, STORE_ROW_PARTIAL and VSTORE_PARTIAL
    so that this change does not affect kernels not using STORE_BLOCK_BOUNDARY_AWARE.
    - Revamp vstore_ext_n to vstore_partial_n, and enhance
    VSTORE_PARTIAL to correctly handle both vector and scalar cases
* Remove the store padding (dst tensor) in CLGEMMMatrixMultiplyReshapedOnlyRHSKernel
and CLGEMMMatrixMultiplyNativeKernel
* Add configuration tests to check no padding is added by the
configuration.

Signed-off-by: SiCong Li <sicong.li@arm.com>
Change-Id: I4f0907867979d8dacedd03b4bcbd2fb19e4f1602
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3522
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

2 files changed, 160 insertions, 1 deletions

diff --git a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
index 5e4a12ddb7..bdf8248bb2 100644
--- a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019 Arm Limited.
+ * Copyright (c) 2019-2020 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -164,6 +164,54 @@ void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned
     CLGEMMMatrixMultiplyNative gemm;
     gemm.configure(&lhs, &rhs, &bias, &dst, 1.0f, 1.0f, lhs_info, rhs_info, kernel_info);
 }
+/** Zero padding test */
+bool validate_zero_padding(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)
+{
+    const unsigned int M = m_value;
+    const unsigned int N = n_value;
+    const unsigned int K = k_value;
+
+    GEMMLHSMatrixInfo lhs_info;
+    lhs_info.m0         = m0_value;
+    lhs_info.k0         = k0_value;
+
+    GEMMRHSMatrixInfo rhs_info;
+    rhs_info.n0         = n0_value;
+    rhs_info.k0         = k0_value;
+
+    GEMMKernelInfo kernel_info;
+    kernel_info.m               = M;
+    kernel_info.n               = N;
+    kernel_info.k               = K;
+    kernel_info.broadcast_bias  = broadcast_bias;
+    kernel_info.activation_info = act_info;
+
+    const TensorShape lhs_shape(K, M, b_value);
+    const TensorShape rhs_shape(N, K, b_value);
+    const TensorShape bias_shape(N,
+                                 broadcast_bias? 1 : M,
+                                 broadcast_bias? 1 : b_value);
+    const TensorShape dst_shape = compute_mm_shape(TensorInfo(lhs_shape, 1, data_type),
+                                                   TensorInfo(rhs_shape, 1, data_type),
+                                                   kernel_info);
+
+    // Create tensors
+    CLTensor lhs  = create_tensor<CLTensor>(lhs_shape, data_type);
+    CLTensor rhs  = create_tensor<CLTensor>(rhs_shape, data_type);
+    CLTensor bias = create_tensor<CLTensor>(bias_shape, data_type);
+    CLTensor dst  = create_tensor<CLTensor>(dst_shape, data_type);
+
+    ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+    // Create and configure function
+    CLGEMMMatrixMultiplyNative gemm;
+    gemm.configure(&lhs, &rhs, &bias, &dst, 1.0f, 1.0f, lhs_info, rhs_info, kernel_info);
+
+    return dst.info()->padding().empty();
+}
 } // namespace
 
 TEST_SUITE(CL)
@@ -185,6 +233,28 @@ m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, broadcast_bias
     validate_configuration(m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, broadcast_bias, DataType::F32, act_value);
 }
 
+/** Validate zero padding tests
+ *
+ * A series of validation tests to check that no padding is added as part of configuration for 4 different scenarios.
+ *
+ * Checks performed in order:
+ *     - No partial blocks in both x and y dimensions
+ *     - Partial blocks in x dimension
+ *     - Partial blocks in y dimension
+ *     - Partial blocks in both x and y dimensions
+ *     - No blocks in both x and y dimensions, scalar store (N0==1)
+ */
+DATA_TEST_CASE(ValidateZeroPadding, framework::DatasetMode::ALL, zip(zip(zip(
+framework::dataset::make("M",                   { 24, 64, 101, 1, 50 }),
+framework::dataset::make("N",                   { 48, 29, 16, 122, 20 })),
+framework::dataset::make("M0",                  { 4, 8, 7, 2, 1 })),
+framework::dataset::make("N0",                  { 4, 4, 16, 3, 1 })),
+m_value, n_value, m0_value, n0_value)
+{
+    bool status = validate_zero_padding(m_value, n_value, 23, 1, m0_value, n0_value, 4, false, DataType::F32, ActivationLayerInfo());
+    ARM_COMPUTE_EXPECT(status, framework::LogLevel::ERRORS);
+}
+
 FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL,
                 combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
                                                                    m_values,
diff --git a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
index 14b1c66e2c..0456ca2017 100644
--- a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
@@ -185,6 +185,69 @@ bool validate_configuration(unsigned int m_value, unsigned int n_value, unsigned
     CLGEMMMatrixMultiplyReshapedOnlyRHS gemm;
     return bool(gemm.validate(&lhs, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info));
 }
+
+/** Zero padding test */
+bool validate_zero_padding(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, unsigned int h0_value,
+                            bool i_value_rhs, bool t_value_rhs, bool export_to_cl_image, bool broadcast_bias, bool input_as_3d, unsigned int depth_output_gemm3d, const ActivationLayerInfo &act_info,
+                            DataType dt_input0, DataType dt_input1, DataType dt_input2, DataType dt_output, float alpha, float beta)
+{
+    const unsigned int M = m_value;
+    const unsigned int N = n_value;
+    const unsigned int K = k_value;
+
+    GEMMLHSMatrixInfo lhs_info;
+    lhs_info.m0         = m0_value;
+    lhs_info.k0         = k0_value;
+
+    GEMMRHSMatrixInfo rhs_info;
+    rhs_info.n0         = n0_value;
+    rhs_info.k0         = k0_value;
+    rhs_info.h0         = h0_value;
+    rhs_info.interleave = i_value_rhs;
+    rhs_info.transpose  = t_value_rhs;
+    rhs_info.export_to_cl_image = export_to_cl_image;
+
+    GEMMKernelInfo kernel_info;
+    kernel_info.m                       = M;
+    kernel_info.n                       = N;
+    kernel_info.k                       = K;
+    kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
+    kernel_info.reinterpret_input_as_3d = input_as_3d;
+    kernel_info.broadcast_bias          = broadcast_bias;
+    kernel_info.activation_info         = act_info;
+
+    const TensorShape lhs_shape(K, M, b_value);
+    const TensorShape rhs_shape(N, K, b_value);
+    const TensorShape rhs_shape_reshaped = compute_rhs_reshaped_shape(TensorInfo(rhs_shape, 1, dt_input1),
+                                                                      rhs_info);
+
+    const TensorShape dst_shape = compute_mm_shape(TensorInfo(lhs_shape, 1, dt_input0),
+                                                   TensorInfo(rhs_shape_reshaped, 1, dt_input1),
+                                                   kernel_info);
+
+    const TensorShape bias_shape(N,
+                                 M, // Correct calculation should be: broadcast_bias? 1 : M, it's wrong here on purpose just for validation test
+                                 broadcast_bias? 1 : b_value);
+
+    // Create tensors
+    CLTensor lhs  = create_tensor<CLTensor>(lhs_shape, dt_input0);
+    CLTensor rhs_reshaped  = create_tensor<CLTensor>(rhs_shape_reshaped, dt_input1);
+    CLTensor bias = create_tensor<CLTensor>(bias_shape, dt_input2);
+    CLTensor dst  = create_tensor<CLTensor>(dst_shape, dt_output);
+
+    ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+    // Validate zero-padding
+    CLGEMMMatrixMultiplyReshapedOnlyRHS gemm;
+
+    gemm.configure(&lhs, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
+
+    return dst.info()->padding().empty();
+}
 } // namespace
 
 TEST_SUITE(CL)
@@ -236,6 +299,32 @@ b_value, m0_value, n0_value, k0_value, broadcast_bias, input_as_3d, depth_output
     ARM_COMPUTE_EXPECT(status == expected_value, framework::LogLevel::ERRORS);
 }
 
+/** Validate zero padding tests
+ *
+ * A series of validation tests to check that no padding is added as part of configuration for 4 different scenarios.
+ *
+ * Checks performed in order:
+ *     - No partial blocks in both x and y dimensions
+ *     - Partial blocks in x dimension
+ *     - Partial blocks in y dimension
+ *     - Partial blocks in both x and y dimensions
+ */
+DATA_TEST_CASE(ValidateZeroPadding, framework::DatasetMode::ALL, zip(zip(zip(zip(
+framework::dataset::make("M",                   { 24, 64, 101, 1 }),
+framework::dataset::make("N",                   { 48, 29, 16, 122 })),
+framework::dataset::make("M0",                  { 4, 8, 7, 2 })),
+framework::dataset::make("N0",                  { 4, 4, 16, 3 })),
+framework::dataset::make("export_to_cl_image",  { false, true, true, false })),
+m_value, n_value, m0_value, n0_value, export_to_cl_image)
+{
+    constexpr DataType dt = DataType::F32;
+    // Disable export_to_cl_image if the target platform does not support the OpenCL cl_khr_image2d_from_buffer extension
+    bool actual_export_to_cl_image = image2d_from_buffer_supported(CLKernelLibrary::get().get_device()) && export_to_cl_image;
+
+    bool status = validate_zero_padding(m_value, n_value, 23, 1, m0_value, n0_value, 4, 1, false, false, actual_export_to_cl_image, false, 0, 0, ActivationLayerInfo(), dt, dt, dt, dt, 1.0f, 1.0f);
+    ARM_COMPUTE_EXPECT(status, framework::LogLevel::ERRORS);
+}
+
 TEST_SUITE(Float)
 TEST_SUITE(FP32)