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-rw-r--r--arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h117
1 files changed, 58 insertions, 59 deletions
diff --git a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h
index 9727e108a5..885f8430cf 100644
--- a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2021 Arm Limited.
+ * Copyright (c) 2017-2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -24,16 +24,15 @@
#ifndef ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H
#define ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H
+#include "arm_compute/function_info/FullyConnectedLayerInfo.h"
#include "arm_compute/runtime/IFunction.h"
-
-#include "arm_compute/runtime/MemoryGroup.h"
-#include "arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h"
-#include "arm_compute/runtime/NEON/functions/NEFlattenLayer.h"
-#include "arm_compute/runtime/NEON/functions/NEGEMM.h"
-#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
+#include "arm_compute/runtime/IMemoryManager.h"
+#include "arm_compute/runtime/IWeightsManager.h"
#include "arm_compute/runtime/NEON/functions/NETranspose.h"
#include "arm_compute/runtime/Tensor.h"
+#include <memory>
+
namespace arm_compute
{
namespace weights_transformations
@@ -77,10 +76,10 @@ private:
} // namespace weights_transformations
/** Basic function to compute a Fully Connected layer. This function calls the following kernels:
- * -# @ref NEIm2ColKernel (called when the input comes from a convolutional layer)
+ * -# @ref cpu::kernels::CpuIm2ColKernel (called when the input comes from a convolutional layer)
* -# @ref NETranspose (if @p are_weights_reshaped is set to false and transpose_weights is set to true ) (called once)
- * -# @ref NEGEMMMatrixMultiplyKernel or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
- * -# @ref NEGEMMMatrixAdditionKernel or @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if quantized asymmetric) (if @p biases is not equal to nullptr)
+ * -# @ref NEGEMM or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
+ * -# @ref cpu::kernels::CpuGemmMatrixAdditionKernel or @ref NEGEMMLowpOutputStage (if quantized asymmetric) (if @p biases is not equal to nullptr)
*
* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
*/
@@ -88,7 +87,8 @@ class NEFullyConnectedLayer : public IFunction
{
public:
/** Constructor */
- NEFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr, IWeightsManager *weights_manager = nullptr);
+ NEFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr,
+ IWeightsManager *weights_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEFullyConnectedLayer(const NEFullyConnectedLayer &) = delete;
/** Prevent instances of this class from being moved (As this class contains pointers) */
@@ -113,66 +113,65 @@ public:
* |QASYMM8 |QASYMM8 |S32 |QASYMM8 |
* |QASYMM8_SIGNED |QASYMM8_SIGNED |S32 |QASYMM8_SIGNED |
*
- * @param[in] input Source tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
- * @param[in] weights Weights tensor. The weights must be 2 dimensional.
- * If this function is called after a Convolution Layer, the (transposed) weights will have as many rows as the product of the first 3 input's dimensions.
- * If it is called after another FullyConnected Layer, the (transposed) weights will have as many rows as the input's first dimension.
- * Data type supported: Same as @p input.
- * @param[in] biases Bias tensor. Can be nullptr. Data type supported: Same as @p weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED.
- * @param[out] output Destination tensor. Its shape should be equal to the output of a matrix multiplication between:
- * - The output of im2col on the input and the (transposed) 2D weights, if the function is called after a Convolution Layer
- * - The input tensor and the (transposed) 2D weights, if the function is called after another FullyConnected Layer.
- * Data type supported: Same as @p input.
- * @param[in] fc_info (Optional) Fully connected layer additional info
+ * @param[in] input Source tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
+ * @param[in] weights Weights tensor. The weights must be 2 dimensional.
+ * If this function is called after a Convolution Layer, the (transposed) weights will have as many rows as the product of the first 3 input's dimensions.
+ * If it is called after another FullyConnected Layer, the (transposed) weights will have as many rows as the input's first dimension.
+ * Data type supported: Same as @p input.
+ * @param[in] biases Bias tensor. Can be nullptr. Data type supported: Same as @p weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED.
+ * @param[out] output Destination tensor. Its shape should be equal to the output of a matrix multiplication between:
+ * - The output of im2col on the input and the (transposed) 2D weights, if the function is called after a Convolution Layer
+ * - The input tensor and the (transposed) 2D weights, if the function is called after another FullyConnected Layer.
+ * Data type supported: Same as @p input.
+ * @param[in] fc_info (Optional) Fully connected layer additional info
+ * @param[in] weights_info (Optional) Stores neccessary compute information when weights are already reshaped
*/
- void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output,
- FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo());
+ void configure(const ITensor *input,
+ const ITensor *weights,
+ const ITensor *biases,
+ ITensor *output,
+ FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo(),
+ const WeightsInfo &weights_info = WeightsInfo());
/** Static function to check if given info will lead to a valid configuration of @ref NEFullyConnectedLayer
*
- * @param[in] input Source tensor info. Data type supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
- * @param[in] weights Weights tensor info. The weights must be 2 dimensional.
- * If this function is called after a Convolution Layer, the (transposed) weights will have as many rows as the product of the first 3 input's dimensions.
- * If it is called after another FullyConnected Layer, the (transposed) weights will have as many rows as the input's first dimension.
- * Data type supported: Same as @p input.
- * @param[in] biases Bias tensor. Can be nullptr. Data type supported: Same as @p weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED.
- * @param[in] output Destination tensor info. Its shape should be equal to the output of a matrix multiplication between:
- * - The output of im2col on the input and the (transposed) 2D weights, if the function is called after a Convolution Layer
- * - The input tensor and the (transposed) 2D weights, if the function is called after another FullyConnected Layer.
- * Data type supported: Same as @p input.
- * @param[in] fc_info (Optional) Fully connected layer additional info
+ * Similar to @ref NEFullyConnectedLayer::configure()
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *input,
+ const ITensorInfo *weights,
+ const ITensorInfo *biases,
+ const ITensorInfo *output,
+ FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo(),
+ const WeightsInfo &weights_info = WeightsInfo());
+
+ /** Static function that queries whether fixed-format kernel exists for a given problem description
+ *
+ * @param[out] expected_weight_format Format in which weights should be for found fixed format kernel
+ * @param[in] input Source tensor
+ * @param[in] weights Weights tensor.
+ * @param[in] biases Bias tensor. Can be nullptr. Data type supported: Same as @p weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED.
+ * @param[in] output Destination tensor
+ * @param[in] fc_info Fully connected layer additional info
+ * @param[in] weights_info Describes weights shape
*
* @return a status
*/
- static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
- FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo());
+ static Status has_opt_impl(arm_compute::WeightFormat &expected_weight_format,
+ const ITensorInfo *input,
+ const ITensorInfo *weights,
+ const ITensorInfo *biases,
+ const ITensorInfo *output,
+ const FullyConnectedLayerInfo &fc_info,
+ const WeightsInfo &weights_info);
//Inherited methods override
void run() override;
void prepare() override;
private:
- void configure_fc_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act);
- void configure_conv_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act);
- void configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act);
-
- MemoryGroup _memory_group;
- IWeightsManager *_weights_manager;
- NEFlattenLayer _flatten;
- NEConvertFullyConnectedWeights _convert_weights;
- weights_transformations::NEConvertFullyConnectedWeightsManaged _convert_weights_managed;
- NETranspose _reshape_weights_function;
- weights_transformations::NEFullyConnectedLayerReshapeWeightsManaged _reshape_weights_managed_function;
- NEGEMM _mm_gemm;
- NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
- Tensor _flatten_output;
- Tensor _converted_weights_output;
- Tensor _reshape_weights_output;
- const ITensor *_original_weights;
- bool _are_weights_converted;
- bool _are_weights_reshaped;
- bool _is_fc_after_conv;
- bool _is_quantized_asymmetric;
- bool _is_prepared;
+ struct Impl;
+ std::unique_ptr<Impl> _impl;
};
} // namespace arm_compute
#endif /* ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H */
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-04 03:03:46 +0000