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;
};
}