COMPMID-1043: Rework GCGEMMMatrixMultiplyKernel interface and allow auto initialization of the tensors
This patch also:
- removes support for already reshaped weights in GCConvolutionLayer
- makes GCConvolutionLayer similar to CLGEMMConvolutionLayer
- enables usage of the GCGEMM function in GCConvolution instead of calling the
GEMM kernels directly
Change-Id: I3e4a64335555e86e18585d38d8fda4bfdb44e265
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/127696
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/arm_compute/core/CL/CLTypes.h b/arm_compute/core/CL/CLTypes.h
index ca48781..4a03cc9 100644
--- a/arm_compute/core/CL/CLTypes.h
+++ b/arm_compute/core/CL/CLTypes.h
@@ -24,6 +24,8 @@
#ifndef __ARM_COMPUTE_CL_TYPES_H__
#define __ARM_COMPUTE_CL_TYPES_H__
+#include "arm_compute/core/GPUTarget.h"
+
#include <string>
namespace arm_compute
@@ -31,26 +33,6 @@
/** Default string for the CLKernel configuration id */
static const std::string default_config_id = "no_config_id";
-/** Available GPU Targets */
-enum class GPUTarget
-{
- UNKNOWN = 0x101,
- GPU_ARCH_MASK = 0xF00,
- MIDGARD = 0x100,
- BIFROST = 0x200,
- T600 = 0x110,
- T700 = 0x120,
- T800 = 0x130,
- G71 = 0x210,
- G72 = 0x220,
- G51 = 0x230,
- G51BIG = 0x231,
- G51LIT = 0x232,
- TNOX = 0x240,
- TTRX = 0x250,
- TBOX = 0x260
-};
-
/** Available OpenCL Version */
enum class CLVersion
{
diff --git a/arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h b/arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h
index 3a0b22f..cea03a9 100644
--- a/arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h
+++ b/arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -25,6 +25,7 @@
#define __ARM_COMPUTE_GCGEMMMATRIXMULTIPLYKERNEL_H__
#include "arm_compute/core/GLES_COMPUTE/IGCKernel.h"
+#include "arm_compute/core/GPUTarget.h"
namespace arm_compute
{
@@ -32,9 +33,6 @@
/** GLES Compute kernel to multiply two input matrices "A" and "B" or to multiply a vector "A" by a matrix "B". All elements of the output matrix/vector will be multiplied by alpha
*
- * @note If the output tensor is a matrix, the implementation assumes that the input tensors @p input0 and @p input1 are both matrices and reshaped respectively with @ref GCGEMMInterleave4x4Kernel" and @ref GCGEMMTranspose1xWKernel
- * @note If the output tensor is a vector and the data type is F32, the implementation assumes that the first input tensor @p input0 is a vector and the second input tensor @p input1 a matrix. The implementation also assumes that both tensors have not been reshaped
- *
* @attention The second input tensor must have at least 2 dimensions (matrix)
*
*/
@@ -64,8 +62,23 @@
* @param[out] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0
* @param[in] alpha Weight of the matrix product
* @param[in] is_interleaved_transposed (Optional) True if input0 and input1 have been reshaped respectively using @ref GCGEMMInterleave4x4Kernel and @ref GCGEMMTranspose1xWKernel
+ * @param[in] reshape_info (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped
*/
- void configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed = true);
+ void configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed = true, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo());
+ /** Static function to check if given info will lead to a valid configuration of @ref GCGEMMMatrixMultiplyKernel
+ *
+ * @param[in] input0 Input tensor containing the Matrix A. Data types supported: F16/F32
+ * @param[in] input1 Input tensor containing the Matrix B. Data type supported: same as @p input0
+ * @param[in] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0
+ * @param[in] alpha Weight of the matrix product
+ * @param[in] is_interleaved_transposed True if input0 and input1 have been reshaped respectively using @ref GCGEMMInterleave4x4Kernel and @ref GCGEMMTranspose1xWKernel
+ * @param[in] reshape_info GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped
+ * @param[in] gpu_target GPU Target
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info,
+ GPUTarget gpu_target);
// Inherited methods overridden:
void run(const Window &window) override;
diff --git a/arm_compute/core/GPUTarget.h b/arm_compute/core/GPUTarget.h
new file mode 100644
index 0000000..8a5ca80
--- /dev/null
+++ b/arm_compute/core/GPUTarget.h
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * 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 __ARM_COMPUTE_GPUTARGET_H__
+#define __ARM_COMPUTE_GPUTARGET_H__
+
+namespace arm_compute
+{
+/** Available GPU Targets */
+enum class GPUTarget
+{
+ UNKNOWN = 0x101,
+ GPU_ARCH_MASK = 0xF00,
+ MIDGARD = 0x100,
+ BIFROST = 0x200,
+ T600 = 0x110,
+ T700 = 0x120,
+ T800 = 0x130,
+ G71 = 0x210,
+ G72 = 0x220,
+ G51 = 0x230,
+ G51BIG = 0x231,
+ G51LIT = 0x232,
+ TNOX = 0x240,
+ TTRX = 0x250,
+ TBOX = 0x260
+};
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_GPUTARGET_H__ */
diff --git a/arm_compute/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.h b/arm_compute/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.h
index 54b17b4..fa29f44 100644
--- a/arm_compute/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.h
+++ b/arm_compute/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.h
@@ -27,15 +27,13 @@
#include "arm_compute/core/GLES_COMPUTE/kernels/GCCol2ImKernel.h"
#include "arm_compute/core/GLES_COMPUTE/kernels/GCFillBorderKernel.h"
-#include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMInterleave4x4Kernel.h"
-#include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h"
-#include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMTranspose1xWKernel.h"
#include "arm_compute/core/GLES_COMPUTE/kernels/GCIm2ColKernel.h"
#include "arm_compute/core/GLES_COMPUTE/kernels/GCWeightsReshapeKernel.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/GLES_COMPUTE/GCMemoryGroup.h"
#include "arm_compute/runtime/GLES_COMPUTE/GCTensor.h"
#include "arm_compute/runtime/GLES_COMPUTE/functions/GCActivationLayer.h"
+#include "arm_compute/runtime/GLES_COMPUTE/functions/GCGEMM.h"
#include "arm_compute/runtime/IFunction.h"
#include <memory>
@@ -46,7 +44,6 @@
/** Function to reshape and transpose the weights. This function calls the following kernels:
* -# @ref GCWeightsReshapeKernel
- * -# @ref GCGEMMTranspose1xWKernel
*/
class GCConvolutionLayerReshapeWeights : public IFunction
{
@@ -55,22 +52,18 @@
GCConvolutionLayerReshapeWeights();
/** Set the input and output tensors.
*
- * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
- * Data type supported: F16/F32.
- * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
- * @param[out] output Destination tensor. Data types supported: Same as @p weights.
- * @param[in] transpose1xW True if the weights are to undergo a 1xW transposition after reshaping (in case of GEMM operation), false otherwise.
- * Data types supported: Same as @p weights.
+ * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
+ * Data type supported: F16/F32.
+ * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
+ * @param[out] output Destination tensor. Data types supported: Same as @p weights.
*/
- void configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, bool transpose1xW);
+ void configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output);
// Inherited methods overridden:
void run() override;
private:
- GCWeightsReshapeKernel _weights_reshape_kernel;
- GCGEMMTranspose1xWKernel _weights_transposed_kernel;
- GCTensor _weights_reshaped;
- bool _transpose1xW;
+ GCWeightsReshapeKernel _weights_reshape_kernel;
+ GCTensor _weights_reshaped;
};
/** Basic function to compute the convolution layer. This function calls the following GLES kernels:
@@ -86,7 +79,14 @@
public:
/** Default constructor */
GCConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
-
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ GCConvolutionLayer(const GCConvolutionLayer &) = delete;
+ /** Default move constructor */
+ GCConvolutionLayer(GCConvolutionLayer &&) = default;
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ GCConvolutionLayer &operator=(const GCConvolutionLayer &) = delete;
+ /** Default move assignment operator */
+ GCConvolutionLayer &operator=(GCConvolutionLayer &&) = default;
/** Set the input and output tensors.
*
* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
@@ -105,6 +105,26 @@
*/
void configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info,
const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo());
+ /** Static function to check if given info will lead to a valid configuration of @ref GCConvolutionLayer.
+ *
+ * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
+ * while every optional dimension from 4 and above represent a batch of inputs.
+ * Data types supported: QS8/QASYMM8/QS16/F16/F32.
+ * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
+ * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
+ * Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
+ * @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
+ * Data types supported: Same as @p input.
+ * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
+ * @param[in] weights_info Specifies if the weights tensor has been reshaped with GCWeightsReshapeKernel. If this is not part of the fully connected layer the weights
+ * tensor has also been transposed with GCGEMMTranspose1xWKernel. Data type supported: Same as @p input.
+ * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
+ * @param[in] act_info (Optional) Activation layer information in case of a fused activation.
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+ const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo());
// Inherited methods overridden:
void run() override;
@@ -115,20 +135,30 @@
* @param input Input tensor. Data types supported: F16/F32.
* @param weights Weights tensor. Data type supported: Same as @p input.
* @param output Output tensor. Data types supported: Same as @p input,
- * @param is_interleaved_transposed Flag that signals if matrix is interleaved transposed
*/
- void configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output, bool is_interleaved_transposed = true);
+ void configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output);
+ /** Static function to check if given info will lead to a valid configuration of @ref GCGEMMConvolutionLayer matrix multiply routines
+ *
+ * @param[in] input Input tensor. Data types supported: QS8/QASYMM8/QS16/F16/F32.
+ * @param[in] weights Weights tensor. Data type supported: Same as @p input.
+ * @param[in] output Output tensor. Data types supported: Same as @p input,
+ * except for input of QASYMM8 type where output should be of S32 type.
+ *
+ * @return a status
+ */
+ static Status validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output);
private:
GCMemoryGroup _memory_group;
GCConvolutionLayerReshapeWeights _reshape_weights;
GCIm2ColKernel _input_im2col_kernel;
- GCGEMMInterleave4x4Kernel _input_interleave_kernel;
- GCGEMMMatrixMultiplyKernel _mm_kernel;
+ GCGEMM _mm_gemm;
GCCol2ImKernel _output_col2im_kernel;
GCFillBorderKernel _fill_border;
GCActivationLayer _activationlayer_function;
+ const IGCTensor *_original_weights;
+
GCTensor _input_im2col_reshaped;
GCTensor _input_interleaved_reshaped;
GCTensor _weights_reshaped;
@@ -136,9 +166,7 @@
GCTensor _gemm_output;
GCTensor _tmp_output;
- bool _append_bias;
- bool _is_fully_connected_convolution;
- bool _are_weights_reshaped;
+ bool _is_first_run;
bool _is_activationlayer_enabled;
};
}
diff --git a/arm_compute/runtime/GLES_COMPUTE/functions/GCGEMM.h b/arm_compute/runtime/GLES_COMPUTE/functions/GCGEMM.h
index 31ad0ab..a1d6c8a 100644
--- a/arm_compute/runtime/GLES_COMPUTE/functions/GCGEMM.h
+++ b/arm_compute/runtime/GLES_COMPUTE/functions/GCGEMM.h
@@ -69,6 +69,20 @@
* if the reshape of matrix B should happen only for the first run
*/
void configure(const IGCTensor *a, const IGCTensor *b, const IGCTensor *c, IGCTensor *output, float alpha, float beta, const GEMMInfo &gemm_info = GEMMInfo());
+ /** Static function to check if given info will lead to a valid configuration of @ref GCGEMM.
+ *
+ * @param[in] a First input tensor (Matrix or Vector A). Data types supported: F16/F32
+ * @param[in] b Second input tensor (Matrix B). Data type supported: same as @p a.
+ * @param[in] c Third input tensor (Matrix C). It can be a nullptr if just the multiplication between @p a and @p b is needed. Data type supported: same as @p a.
+ * @param[out] output Output tensor. Data type supported: same as @p a
+ * @param[in] alpha Weight of the matrix product
+ * @param[in] beta Weight of matrix C
+ * @param[in] gemm_info (Optional) Specifies if the matrix A and/or matrix B have been reshaped and
+ * if the reshape of matrix B should happen only for the first run
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *a, const ITensorInfo *b, const IGCTensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info = GEMMInfo());
// Inherited methods overridden:
void run() override;
@@ -83,6 +97,8 @@
GCTensor _tmp_b;
bool _is_interleaved_transposed;
bool _run_addition;
+ bool _is_first_run;
+ bool _reshape_b_only_on_first_run;
};
}
diff --git a/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp
index a5f09e8..b4bb547 100644
--- a/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017, 2018 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -31,37 +31,180 @@
#include "arm_compute/core/GLES_COMPUTE/IGCTensor.h"
#include "arm_compute/core/GLES_COMPUTE/OpenGLES.h"
#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include <set>
#include <string>
using namespace arm_compute;
using namespace arm_compute::gles_compute;
+using namespace arm_compute::misc::shape_calculator;
+
+namespace
+{
+using ElementsProcessed = Steps;
+
+inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info)
+{
+ ARM_COMPUTE_UNUSED(reshape_info);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
+
+ if(!is_interleaved_transposed)
+ {
+ ARM_COMPUTE_ERROR_ON(input0->dimension(0) != input1->dimension(1));
+
+ if(output->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != output->dimension(0));
+ ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != output->dimension(1));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
+ }
+ }
+ else
+ {
+ const int m = reshape_info.m();
+ const int n = reshape_info.n();
+ const int k = reshape_info.k();
+ const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width();
+ const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
+
+ TensorShape tensor_shape0{ input0->tensor_shape() };
+ tensor_shape0.set(0, k);
+ tensor_shape0.set(1, m);
+
+ TensorShape tensor_shape1{ input1->tensor_shape() };
+ tensor_shape1.set(0, n);
+ tensor_shape1.set(1, k);
+
+ const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0);
+ const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1);
+
+ const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(compute_interleaved_shape(tensor_info0, mult_interleave4x4_height));
+ const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_transpose1xW_with_element_size_shape(tensor_info1, mult_transpose1xW_width));
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);
+
+ if(output->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != static_cast<size_t>(n));
+ ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != static_cast<size_t>(m));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, output);
+ }
+ }
+
+ return Status{};
+}
+
+inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output,
+ bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info,
+ GPUTarget gpu_target, ElementsProcessed &num_elements_processed)
+{
+ ARM_COMPUTE_UNUSED(gpu_target);
+
+ // Output tensor auto inizialitation if not yet initialized
+ TensorShape tensor_shape{ input0->tensor_shape() };
+ tensor_shape.set(0, is_interleaved_transposed ? reshape_info.n() : input1->dimension(0));
+ tensor_shape.set(1, is_interleaved_transposed ? reshape_info.m() : input0->dimension(1));
+
+ auto_init_if_empty(*output, input0->clone()->set_tensor_shape(tensor_shape));
+
+ bool window_changed = false;
+ Window win{};
+
+ const DataType data_type = input0->data_type();
+ unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
+ unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
+
+ if(is_interleaved_transposed)
+ {
+ // Configure window kernel
+ num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(data_type);
+ num_elems_processed_per_iteration_y = 4;
+
+ win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+ AccessWindowRectangle input0_access(input0, 0, 0, num_elems_processed_per_iteration_y, 1, 1.f, 0.25f);
+ AccessWindowTranspose input1_access(input1, 0, 0, num_elems_processed_per_iteration_x, 1, 0.f, 0.25f);
+ AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+
+ update_window_and_padding(win, input0_access, input1_access, output_access);
+
+ output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
+ }
+ else // The input tensors have not been reshaped
+ {
+ // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor
+
+ switch(data_type)
+ {
+ case DataType::F16:
+ num_elems_processed_per_iteration_x = 4;
+ num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 4);
+ break;
+
+ case DataType::F32:
+ num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(data_type);
+ num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 4);
+ break;
+
+ default:
+ ARM_COMPUTE_ERROR("Current data type is not supported");
+ break;
+ }
+
+ win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+ AccessWindowStatic input0_access(input0, 0, 0, ceil_to_multiple(input0->dimension(0), 8), ceil_to_multiple(input0->dimension(1), num_elems_processed_per_iteration_y));
+ AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1));
+ AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+
+ update_window_and_padding(win, input0_access, input1_access, output_access);
+
+ Coordinates coord;
+ coord.set_num_dimensions(output->num_dimensions());
+ output_access.set_valid_region(win, ValidRegion(coord, output->tensor_shape()));
+ }
+
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, win);
+}
+} // namespace
GCGEMMMatrixMultiplyKernel::GCGEMMMatrixMultiplyKernel()
: _input0(nullptr), _input1(nullptr), _output(nullptr)
{
}
-void GCGEMMMatrixMultiplyKernel::configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed)
+void GCGEMMMatrixMultiplyKernel::configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F32, DataType::F16);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
- if(!is_interleaved_transposed)
- {
- ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1));
- }
+ // Perform validate step
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info));
_input0 = input0;
_input1 = input1;
_output = output;
+ ElementsProcessed num_elements_processed{};
+
+ // Configure kernel window
+ auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info, GPUTarget::UNKNOWN, num_elements_processed);
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ IGCKernel::configure(win_config.second);
+
+ // Create build options
std::set<std::string> build_opts;
+ std::string kernel_name;
Window win;
build_opts.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(1));
@@ -74,6 +217,12 @@
// Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
if(is_interleaved_transposed)
{
+ const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width();
+ const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
+
+ build_opts.emplace("#define MULT_TRANSPOSE1XW_WIDTH " + support::cpp11::to_string(mult_transpose1xW_width));
+ build_opts.emplace("#define MULT_INTERLEAVE4X4_HEIGHT " + support::cpp11::to_string(mult_interleave4x4_height));
+
switch(input0->info()->data_type())
{
case DataType::F16:
@@ -91,56 +240,20 @@
build_opts.emplace("#define GEMM_MM_INTERLEAVED_TRANSPOSED");
- // Create kernel
- _kernel = GCKernelLibrary::get().create_kernel(("gemm_mm_interleaved_transposed"), build_opts);
-
- // Configure window kernel
- const unsigned int num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(input0->info()->data_type());
- constexpr unsigned int num_elems_processed_per_iteration_y = 4;
-
- win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-
- AccessWindowRectangle input0_access(input0->info(), 0, 0, num_elems_processed_per_iteration_y, 1, 1.f, 0.25f);
- AccessWindowTranspose input1_access(input1->info(), 0, 0, num_elems_processed_per_iteration_x, 1, 0.f, 0.25f);
- AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
-
- update_window_and_padding(win, input0_access, input1_access, output_access);
-
- output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+ kernel_name = "gemm_mm_interleaved_transposed";
}
else
{
- ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1));
-
// Special case for 1xN, 2xN, 3xN and 4xN input0 tensor
- unsigned int num_elems_processed_per_iteration_x;
- unsigned int num_elems_processed_per_iteration_y;
switch(input0->info()->data_type())
{
case DataType::F16:
build_opts.emplace("#define DATA_TYPE_FP16");
-
-#define MM_PROCESS_4X_OPTIMIZED
-
-#if defined(MM_PROCESS_4X)
- num_elems_processed_per_iteration_x = 4;
- num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->info()->dimension(1)), 4);
- build_opts.emplace("#define MM_PROCESS_4X");
-#elif defined(MM_PROCESS_4X_OPTIMIZED) /* MM_PROCESS_4X */
- num_elems_processed_per_iteration_x = 4;
- num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->info()->dimension(1)), 4);
build_opts.emplace("#define MM_PROCESS_4X_OPTIMIZED");
-#elif defined(MM_PROCESS_8X) /* MM_PROCESS_4X */
- num_elems_processed_per_iteration_x = 8;
- num_elems_processed_per_iteration_y = 1;
- build_opts.emplace("#define MM_PROCESS_8X");
-#endif /* MM_PROCESS_4X */
break;
case DataType::F32:
- num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(input0->info()->data_type());
- num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->info()->dimension(1)), 4);
build_opts.emplace("#define DATA_TYPE_FP32");
break;
@@ -150,31 +263,31 @@
}
build_opts.emplace("#define GEMM_MM_FLOATING_POINT");
- build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_X " + support::cpp11::to_string(num_elems_processed_per_iteration_x));
- build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_Y " + support::cpp11::to_string(num_elems_processed_per_iteration_y));
+ build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_X " + support::cpp11::to_string(num_elements_processed.x()));
+ build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_Y " + support::cpp11::to_string(num_elements_processed.y()));
- // Create kernel
- _kernel = GCKernelLibrary::get().create_kernel("gemm_mm_floating_point", build_opts);
-
- win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-
-#if defined(MM_PROCESS_4X_OPTIMIZED)
- AccessWindowStatic input0_access(input0->info(), 0, 0, ceil_to_multiple(input0->info()->dimension(0), 8), ceil_to_multiple(input0->info()->dimension(1), num_elems_processed_per_iteration_y));
-#else /* MM_PROCESS_4X_OPTIMIZED */
- AccessWindowStatic input0_access(input0->info(), 0, 0, ceil_to_multiple(input0->info()->dimension(0), num_elems_processed_per_iteration_x), ceil_to_multiple(input0->info()->dimension(1),
- num_elems_processed_per_iteration_y));
-#endif /* MM_PROCESS_4X_OPTIMIZED */
- AccessWindowStatic input1_access(input1->info(), 0, 0, ceil_to_multiple(input1->info()->dimension(0), num_elems_processed_per_iteration_x), input1->info()->dimension(1));
- AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
-
- update_window_and_padding(win, input0_access, input1_access, output_access);
-
- Coordinates coord;
- coord.set_num_dimensions(output->info()->num_dimensions());
- output_access.set_valid_region(win, ValidRegion(coord, output->info()->tensor_shape()));
+ kernel_name = "gemm_mm_floating_point";
}
- IGCKernel::configure(win);
+ // Create kernel
+ _kernel = GCKernelLibrary::get().create_kernel(kernel_name, build_opts);
+}
+
+Status GCGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved_transposed,
+ const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target)
+{
+ ARM_COMPUTE_UNUSED(alpha);
+ ElementsProcessed num_elements_processed{};
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, is_interleaved_transposed, reshape_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
+ input1->clone().get(),
+ output->clone().get(),
+ is_interleaved_transposed,
+ reshape_info,
+ gpu_target,
+ num_elements_processed)
+ .first);
+ return Status{};
}
void GCGEMMMatrixMultiplyKernel::run(const Window &window)
diff --git a/src/core/GLES_COMPUTE/kernels/GCWeightsReshapeKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCWeightsReshapeKernel.cpp
index 4c08873..55bf9b7 100644
--- a/src/core/GLES_COMPUTE/kernels/GCWeightsReshapeKernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCWeightsReshapeKernel.cpp
@@ -31,11 +31,13 @@
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/GLES_COMPUTE/GCHelpers.h"
using namespace arm_compute;
using namespace arm_compute::gles_compute;
+using namespace arm_compute::misc::shape_calculator;
GCWeightsReshapeKernel::GCWeightsReshapeKernel()
: _input(nullptr), _biases(nullptr), _output(nullptr)
@@ -47,15 +49,8 @@
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_NULLPTR(output);
- // Calculate output shape
- TensorShape output_shape{ input->info()->tensor_shape() };
- output_shape.collapse(3);
- const size_t tmp_dim = output_shape[0];
- output_shape.set(0, output_shape[1]);
- output_shape.set(1, tmp_dim + (biases != nullptr ? 1 : 0));
-
// Output tensor auto inizialitation if not yet initialized
- auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));
+ auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(compute_weights_reshaped_shape(*input->info(), (biases != nullptr))));
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
diff --git a/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp b/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp
index b1c8665..dc73eb8 100644
--- a/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp
+++ b/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp
@@ -37,14 +37,14 @@
using namespace arm_compute;
GCConvolutionLayerReshapeWeights::GCConvolutionLayerReshapeWeights()
- : _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
+ : _weights_reshape_kernel(), _weights_reshaped()
{
}
-void GCConvolutionLayerReshapeWeights::configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, bool transpose1xW)
+void GCConvolutionLayerReshapeWeights::configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output)
{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
if(biases != nullptr)
@@ -56,75 +56,62 @@
}
const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
- const unsigned bias_element = (append_biases) ? 1 : 0;
const IGCTensor *biases_to_use = (append_biases) ? biases : nullptr;
- _transpose1xW = transpose1xW;
-
- if(transpose1xW)
- {
- // Create tensor to store the reshaped weights
- const unsigned int mat_weights_cols = weights->info()->dimension(3);
- const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;
- TensorShape shape_wr(mat_weights_cols, mat_weights_rows);
- const DataType dt = weights->info()->data_type();
- const int fixed_point_position = weights->info()->fixed_point_position();
- TensorInfo info_wr(shape_wr, 1, dt, fixed_point_position);
-
- _weights_reshaped.allocator()->init(info_wr);
- _weights_reshape_kernel.configure(weights, biases_to_use, &_weights_reshaped);
- _weights_transposed_kernel.configure(&_weights_reshaped, output);
- _weights_reshaped.allocator()->allocate();
- }
- else
- {
- _weights_reshape_kernel.configure(weights, biases_to_use, output);
- }
+ _weights_reshape_kernel.configure(weights, biases_to_use, output);
}
void GCConvolutionLayerReshapeWeights::run()
{
GCScheduler::get().dispatch(_weights_reshape_kernel);
- if(_transpose1xW)
- {
- GCScheduler::get().dispatch(_weights_transposed_kernel);
- }
}
GCConvolutionLayer::GCConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _fill_border(), _activationlayer_function(),
- _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false),
- _are_weights_reshaped(false), _is_activationlayer_enabled(false)
+ : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _mm_gemm(), _output_col2im_kernel(), _fill_border(), _activationlayer_function(), _original_weights(nullptr),
+ _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _is_first_run(true), _is_activationlayer_enabled(false)
{
}
-void GCConvolutionLayer::configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output, bool is_interleaved_transposed)
+void GCConvolutionLayer::configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output)
{
- _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info()));
+
+ _mm_gemm.configure(input, weights, nullptr, output, 1.f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */));
+}
+
+Status GCConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output)
+{
+ // Perform validation step on Matrix multiply function
+ GCGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */));
+ return Status{};
}
void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
const Size2D &dilation, const ActivationLayerInfo &act_info)
{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
- ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2));
+ ARM_COMPUTE_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!");
+ ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2));
ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
+ _is_first_run = true;
+ _original_weights = weights;
+
if(biases != nullptr)
{
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
- ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3));
+ ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
}
const DataType dt = input->info()->data_type();
- _append_bias = (biases != nullptr);
- _are_weights_reshaped = weights_info.are_reshaped();
-
- const unsigned bias_element = (_append_bias) ? 1 : 0;
- const IGCTensor *biases_to_use = (_append_bias) ? biases : nullptr;
+ const bool append_bias = (biases != nullptr);
+ const unsigned bias_element = (append_bias) ? 1 : 0;
+ const IGCTensor *biases_to_use = (append_bias) ? biases : nullptr;
// Get parameters from conv_info
unsigned int stride_x = 0;
@@ -135,57 +122,19 @@
unsigned int conv_w = 0;
unsigned int conv_h = 0;
- const unsigned int kernel_width = (_are_weights_reshaped) ? weights_info.kernel_size().first : weights->info()->dimension(0);
- const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : weights->info()->dimension(1);
+ const unsigned int kernel_width = weights->info()->dimension(0);
+ const unsigned int kernel_height = weights->info()->dimension(1);
std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
conv_info, dilation);
- // Check if its a "fully connected" convolution
- _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
- const bool run_interleaved = (!_is_fully_connected_convolution);
-
unsigned int mat_weights_cols = weights->info()->dimension(3);
unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;
- // Reshape weights if needed
- if(_are_weights_reshaped)
- {
- if(_is_fully_connected_convolution)
- {
- mat_weights_cols = weights->info()->dimension(0);
- mat_weights_rows = weights->info()->dimension(1);
- }
- else
- {
- mat_weights_cols = weights_info.num_kernels();
- const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4;
- mat_weights_rows = quarter_reshaped_cols + bias_element;
- }
- }
- else
- {
- if(_is_fully_connected_convolution)
- {
- // Create tensor to store the reshaped weights
- int num_elems_read_per_iteration_x = 1;
- if(dt == DataType::F16)
- {
- num_elems_read_per_iteration_x = 2;
- }
- TensorShape shape_wr((ceil_to_multiple(mat_weights_cols, num_elems_read_per_iteration_x)), mat_weights_rows);
- _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wr));
- _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false /* 1xW transpose */);
- }
- else
- {
- // Create tensor to store transposed weights
- const float transpose_width = 16.0f / input->info()->element_size();
- TensorShape shape_wt(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
- _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wt));
- _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, true /* 1xW transpose */);
- }
- weights = &_weights_reshaped;
- }
+ // _weights_reshaped will be auto configured in the kernel.
+ // Just append biases and do not transpose 1xW as it will be reshaped in GCGEMM
+ _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped);
+
+ weights = &_weights_reshaped;
// Create tensor to store im2col reshaped inputs
const unsigned int mat_input_cols = mat_weights_rows;
@@ -200,19 +149,6 @@
_input_im2col_reshaped.allocator()->init(im2col_reshaped_info);
_memory_group.manage(&_input_im2col_reshaped);
- // Create tensor (interleave) to prepare input tensor for GEMM
- if(run_interleaved)
- {
- TensorShape shape_interleaved = shape_im2col;
- shape_interleaved.set(0, shape_interleaved.x() * 4);
- shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
-
- // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
- TensorInfo interleaved_info(shape_interleaved, 1, dt, input->info()->fixed_point_position());
- _input_interleaved_reshaped.allocator()->init(interleaved_info);
- _memory_group.manage(&_input_interleaved_reshaped);
- }
-
// Create GEMM output tensor
TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape();
shape_gemm.set(0, mat_weights_cols);
@@ -224,26 +160,18 @@
_gemm_output.allocator()->init(info_gemm);
_memory_group.manage(&_gemm_output);
- // Configure kernels
if(dt == DataType::F16)
{
BorderSize border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left());
input->info()->extend_padding(border_size);
_fill_border.configure(input, border_size, BorderMode::CONSTANT, PixelValue(0)); // for PAD of im2col fp16: consider it as border
}
- _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias, dilation);
+ // Configure im2col
+ _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation);
- // Configure matrix multiply
- if(run_interleaved)
- {
- _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
- configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output);
- _input_interleaved_reshaped.allocator()->allocate();
- }
- else
- {
- configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, false);
- }
+ // Configure GEMM
+ configure_mm(&_input_im2col_reshaped, weights, &_gemm_output);
+
_input_im2col_reshaped.allocator()->allocate();
// Configure Col2Im
@@ -253,10 +181,7 @@
ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
// Allocate intermediate tensor
- if(!_are_weights_reshaped)
- {
- _weights_reshaped.allocator()->allocate();
- }
+ _weights_reshaped.allocator()->allocate();
//Configure Activation Layer
_is_activationlayer_enabled = act_info.enabled();
@@ -265,15 +190,22 @@
{
_activationlayer_function.configure(output, nullptr, act_info);
}
+
+ ARM_COMPUTE_UNUSED(weights_info);
}
void GCConvolutionLayer::run()
{
// Run weights reshaping (Runs once for every configure)
- if(!_are_weights_reshaped)
+ if(_is_first_run)
{
- _are_weights_reshaped = true;
+ ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
+
_reshape_weights.run();
+ _is_first_run = false;
+
+ // Mark original weights tensor as unused
+ _original_weights->mark_as_unused();
}
_memory_group.acquire();
@@ -283,16 +215,8 @@
GCScheduler::get().memory_barrier();
GCScheduler::get().dispatch(_input_im2col_kernel);
- if(!_is_fully_connected_convolution)
- {
- GCScheduler::get().memory_barrier();
- // Run interleave4x4
- GCScheduler::get().dispatch(_input_interleave_kernel);
- }
-
- GCScheduler::get().memory_barrier();
- // Runs matrix multiply on reshaped matrices
- GCScheduler::get().dispatch(_mm_kernel);
+ // Run gemm on reshaped matrices
+ _mm_gemm.run();
GCScheduler::get().memory_barrier();
// Reshape output matrix
diff --git a/src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp b/src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp
index 9c8568a..0a75a38 100644
--- a/src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp
+++ b/src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp
@@ -40,62 +40,82 @@
using namespace arm_compute;
using namespace arm_compute::gles_compute;
+namespace
+{
+Status validate_arguments(const ITensorInfo *a, const ITensorInfo *b, const IGCTensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info = GEMMInfo())
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
+
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F16, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output);
+ ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
+ ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
+
+ if(c != nullptr)
+ {
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, c->info());
+ ARM_COMPUTE_ERROR_ON_MSG(a->dimension(1) != c->info()->dimension(1), "The C matrix must have the same number of rows as the matrix A");
+ ARM_COMPUTE_ERROR_ON_MSG(b->dimension(0) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix B");
+ }
+
+ if(output->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(b->dimension(0) != output->dimension(0), "The output matrix must have the same number of columns as the matrix B");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(1) != output->dimension(1), "The output matrix must have the same number of rows as the matrix A");
+ }
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(0) != b->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
+
+ ARM_COMPUTE_UNUSED(alpha);
+ ARM_COMPUTE_UNUSED(beta);
+ ARM_COMPUTE_UNUSED(gemm_info);
+ return Status{};
+}
+} // namespace
+
GCGEMM::GCGEMM(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _is_interleaved_transposed(false), _run_addition(false)
+ : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _is_interleaved_transposed(false), _run_addition(false),
+ _is_first_run(true), _reshape_b_only_on_first_run(false)
{
}
void GCGEMM::configure(const IGCTensor *a, const IGCTensor *b, const IGCTensor *c, IGCTensor *output, float alpha, float beta, const GEMMInfo &gemm_info)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F32);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output);
- ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
- ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
- ARM_COMPUTE_ERROR_ON_MSG(gemm_info.reshape_b_only_on_first_run(), "Reshape matrix B only on first run is not supported");
- ARM_COMPUTE_UNUSED(gemm_info);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
- if(c != nullptr)
- {
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, c);
- ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(1) != c->info()->dimension(1), "The C matrix must have the same number of rows as the matrix A");
- ARM_COMPUTE_ERROR_ON_MSG(b->info()->dimension(0) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix C");
- ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(0) != output->info()->dimension(0), "The C matrix must have the same number of rows as the output matrix");
- ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(1) != output->info()->dimension(1), "The C matrix must have the same number of columns as the output matrix");
- }
+ // Perform validation step
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(a->info(), b->info(), c, output->info(), alpha, beta, gemm_info));
- ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(0) != b->info()->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
-
- // If the input tensor has less than 16 rows, we run a special version of GEMM without reshaping the input tensors
- _is_interleaved_transposed = a->info()->dimension(1) > 16;
+ // Check if we need to reshape the matrix B only on the first run
+ _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
const IGCTensor *matrix_a = a;
const IGCTensor *matrix_b = b;
+ // Arguments used by GEMMReshapeInfo
+ // If we pass the matrix A and matrix B reshaped to GCGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to GCGEMMReshapeInfo
+ // in order to know how the matrices have been reshaped
+ const int m = a->info()->dimension(1);
+ const int n = b->info()->dimension(0);
+ const int k = a->info()->dimension(0);
+ int mult_transpose1xW_width = 1;
+ int mult_interleave4x4_height = 1;
+
+ // If the input tensor has less than 16 rows, we run a special version of GEMM without reshaping the input tensors
+ _is_interleaved_transposed = a->info()->dimension(1) > 16;
+
if(_is_interleaved_transposed)
{
matrix_a = &_tmp_a;
matrix_b = &_tmp_b;
- TensorShape shape_tmp_a = a->info()->tensor_shape();
- TensorShape shape_tmp_b = b->info()->tensor_shape();
-
- shape_tmp_a.set(0, a->info()->dimension(0) * 4);
- shape_tmp_a.set(1, std::ceil(a->info()->dimension(1) / 4.0f));
-
- const unsigned int transpose_w = max_gc_vector_width / data_size_from_type(b->info()->data_type());
- shape_tmp_b.set(0, b->info()->dimension(1) * transpose_w);
- shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / static_cast<float>(transpose_w)));
-
- TensorInfo info_a(shape_tmp_a, 1, a->info()->data_type(), a->info()->fixed_point_position());
- _tmp_a.allocator()->init(info_a);
+ // Manage intermediate buffers
_memory_group.manage(&_tmp_a);
-
- TensorInfo info_b(shape_tmp_b, 1, b->info()->data_type(), b->info()->fixed_point_position());
- _tmp_b.allocator()->init(info_b);
- if(!gemm_info.reshape_b_only_on_first_run())
+ if(!_reshape_b_only_on_first_run)
{
_memory_group.manage(&_tmp_b);
}
+ // _tmp_a and _tmp_b will be auto configured in _interleave_kernel and in _transpose_kernel
// Configure interleave kernel
_interleave_kernel.configure(a, &_tmp_a);
@@ -104,7 +124,7 @@
_transpose_kernel.configure(b, &_tmp_b);
}
- _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed);
+ _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height));
if(_is_interleaved_transposed)
{
@@ -121,6 +141,12 @@
}
}
+Status GCGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const IGCTensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info)
+{
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(a, b, c, output, alpha, beta, gemm_info));
+ return Status{};
+}
+
void GCGEMM::run()
{
_memory_group.acquire();
@@ -129,8 +155,17 @@
// Run interleave kernel
GCScheduler::get().dispatch(_interleave_kernel, false);
- // Run transpose kernel
- GCScheduler::get().dispatch(_transpose_kernel, false);
+ if(_is_first_run)
+ {
+ // Run transpose kernel
+ GCScheduler::get().dispatch(_transpose_kernel, false);
+ _is_first_run = false;
+ }
+ else if(!_reshape_b_only_on_first_run)
+ {
+ // Run transpose kernel
+ GCScheduler::get().dispatch(_transpose_kernel, false);
+ }
GCScheduler::get().memory_barrier();
}
diff --git a/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp b/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp
index a23c3ec..bc0170f 100644
--- a/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp
+++ b/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp
@@ -118,7 +118,7 @@
TEST_SUITE(Float)
TEST_SUITE(FP16)
FIXTURE_DATA_TEST_CASE(RunSmall, GCConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType",
DataType::F16)),
ActivationFunctionsDataset))
@@ -127,7 +127,7 @@
validate(GCAccessor(_target), _reference, tolerance_f16, tolerance_num);
}
FIXTURE_DATA_TEST_CASE(RunLarge, GCConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType",
DataType::F16)),
ActivationFunctionsDataset))
@@ -139,7 +139,7 @@
TEST_SUITE(FP32)
FIXTURE_DATA_TEST_CASE(RunSmall, GCConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F32)),
ActivationFunctionsDataset))
{
@@ -147,7 +147,7 @@
validate(GCAccessor(_target), _reference, tolerance_f32, tolerance_num);
}
FIXTURE_DATA_TEST_CASE(RunLarge, GCConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F32)),
ActivationFunctionsDataset))
{