diff options
| author | Gian Marco Iodice <gianmarco.iodice@arm.com> | 2021-04-16 15:08:59 +0100 |
|---|---|---|
| committer | Gian Marco Iodice <gianmarco.iodice@arm.com> | 2021-07-02 15:56:45 +0000 |
| commit | 8155c0253c00aa9e26651361460c66feb39829a6 (patch) | |
| tree | 41dacc432d4d1f1daa32d20d15e5120c11b9fa56 | |
| parent | 2eb5d16b839cbc28c6cb7f0de7a0bf15290b425a (diff) | |
| download | ComputeLibrary-8155c0253c00aa9e26651361460c66feb39829a6.tar.gz | |
Rework OpenCL Depthwise Convolution
- Remove dedicated kernels for NCHW. Now we only use NHWC with permute
- Remove specialized kernels for 3x3 NHWC
- Simplify CLDepthwiseConvolutionLayer.cpp to call just the native
implementation for both floating-point and quantized data types
- Develop two parametric opencl kernels for depthwise convolution layer NHWC
(floating-point and quantized)
- Add support to export the weights to cl_image
- Extend test for depthwise convolution on opencl
Resolves COMPMID-4417
Change-Id: Ibe533f79c2860f9cac8e921895d5a8f947753a5c
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5893
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
28 files changed, 1446 insertions, 4587 deletions
diff --git a/Android.bp b/Android.bp index 5943b56450..670f0697d7 100644 --- a/Android.bp +++ b/Android.bp @@ -90,8 +90,6 @@ cc_library_static { "src/core/CL/kernels/CLDeconvolutionLayerUpsampleKernel.cpp", "src/core/CL/kernels/CLDeconvolutionReshapeOutputKernel.cpp", "src/core/CL/kernels/CLDepthToSpaceLayerKernel.cpp", - "src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp", - "src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp", "src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp", "src/core/CL/kernels/CLFFTDigitReverseKernel.cpp", "src/core/CL/kernels/CLFFTRadixStageKernel.cpp", diff --git a/arm_compute/core/CL/CLHelpers.h b/arm_compute/core/CL/CLHelpers.h index a9ac6a5933..180211c558 100644 --- a/arm_compute/core/CL/CLHelpers.h +++ b/arm_compute/core/CL/CLHelpers.h @@ -236,5 +236,13 @@ bool get_wbsm_support_info(const cl::Device &device); */ void set_wbsm(cl::Kernel &kernel, cl_int wbsm_hint); +/* Helper function to check if we can export the weights to cl_image + * + * @param[in] tensor Weights tensor + * + * @return true if we can export the weights to cl_image + */ +bool export_weights_to_cl_image(const ITensorInfo *tensor); + } // namespace arm_compute #endif /* ARM_COMPUTE_CLHELPERS_H */ diff --git a/arm_compute/core/KernelDescriptors.h b/arm_compute/core/KernelDescriptors.h index 6c1fc74b1e..a6e5c3372e 100644 --- a/arm_compute/core/KernelDescriptors.h +++ b/arm_compute/core/KernelDescriptors.h @@ -96,16 +96,12 @@ struct GEMMKernelInfo GEMMLowpOutputStageInfo output_stage{}; /**< GEMMLowp output stage information */ }; -/** Descriptor used by the depthwise convolution kernels */ -struct DWCKernelInfo +/** Compute descriptor used by the depthwise convolution native kernel */ +struct DWCComputeKernelInfo { - ActivationLayerInfo activation_info{}; /**< Activation function to perform after the depthwise convolution */ -}; - -/** Descriptor used by the depthwise convolution kernels to retrieve the number of output elements processed by each thread */ -struct DWCWeightsKernelInfo -{ - unsigned int n0{ 0 }; /**< Number of columns processed by each thread */ + unsigned int n0{ 0 }; /**< Number of columns processed by each thread */ + unsigned int m0{ 0 }; /**< Number of rows processed by each thread */ + bool export_weights_to_cl_image{ false }; /**< Export the weights to cl_image */ }; /** Descriptor used by the softmax kernels */ diff --git a/arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h index bbb00a1ebc..01ddae12bb 100644 --- a/arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h +++ b/arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h @@ -33,13 +33,14 @@ namespace arm_compute { class CLCompileContext; -class CLFillBorderKernel; class CLDepthwiseConvolutionLayerNativeKernel; -class CLDepthwiseConvolutionLayer3x3NCHWKernel; -class CLDepthwiseConvolutionLayer3x3NHWCKernel; class ICLTensor; /** Function to execute a depthwise convolution + * + * -# @ref CLDepthwiseConvolutionLayerNativeKernel + * -# @ref CLPermute (if the data layout is NCHW) + * */ class CLDepthwiseConvolutionLayer : public IFunction { @@ -102,239 +103,38 @@ public: * * @return a status */ - static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1, - ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); + static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, + unsigned int depth_multiplier = 1, ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); // Inherited methods overriden: void run() override; void prepare() override; -private: - /** Static function to choose the best depthwise convolution function for @ref CLDepthwiseConvolutionLayer - * - * @param[in] input Source tensor info. Data type supported: QASYMM8/FP16/FP32. Data layout supported: NHWC, NCHW - * @param[in] weights Weights tensor info. These are 3D tensors with shape [kernel_x, kernel_y, IFM]. - * Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8. - * @param[in] biases Biases tensor info. A 1D tensor with shape [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input, S32 when input is QASYMM8. - * @param[in] output Destination tensor. Data type supported: same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU for 3x3 QASYMM8 supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - * - * @return a Depthwise Convolution Function - */ - static DepthwiseConvolutionFunction get_depthwiseconvolution_function(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1, - ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); - - /** Basic function to execute a depthwise convolution for kernel size 3x3xC (when data layout NCHW) or Cx3x3 (when data layout NHWC). This function calls the following OpenCL kernels: - * - * -# @ref CLDepthwiseConvolutionLayer3x3NCHWKernel (if data_layout == NCHW) - * -# @ref CLDepthwiseConvolutionLayer3x3NHWCKernel (if data_layout == NHWC) - * -# @ref CLFillBorderKernel (if pad_x or pad_y > 0) - * - */ - class CLDepthwiseConvolutionLayerInternal3x3 : public IFunction - { - public: - /** Default constructor */ - CLDepthwiseConvolutionLayerInternal3x3(std::shared_ptr<IMemoryManager> memory_manager = nullptr); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLDepthwiseConvolutionLayerInternal3x3(const CLDepthwiseConvolutionLayerInternal3x3 &) = delete; - /** Default move constructor */ - CLDepthwiseConvolutionLayerInternal3x3(CLDepthwiseConvolutionLayerInternal3x3 &&) = default; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLDepthwiseConvolutionLayerInternal3x3 &operator=(const CLDepthwiseConvolutionLayerInternal3x3 &) = delete; - /** Default move assignment operator */ - CLDepthwiseConvolutionLayerInternal3x3 &operator=(CLDepthwiseConvolutionLayerInternal3x3 &&) = default; - /** Initialize the function's source, destination, conv and border_size. - * - * @param[in, out] input Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling). - * @param[in] weights Weights tensor. A 3D tensor with shape [3, 3, IFM]. - * Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8. - * @param[in] biases Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input. - * @param[out] output Destination tensor. Data type supported: same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU for 3x3 QASYMM8 supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - */ - void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1, - ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); - /** Initialize the function's source, destination, conv and border_size. - * - * @param[in] compile_context The compile context to be used. - * @param[in, out] input Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling). - * @param[in] weights Weights tensor. A 3D tensor with shape [3, 3, IFM]. - * Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8. - * @param[in] biases Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input. - * @param[out] output Destination tensor. Data type supported: same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU for 3x3 QASYMM8 supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - */ - void configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); - - /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayer3x3 - * - * @param[in] input Source tensor info. Data type supported: QASYMM8 for all layouts, F16/F32 for NCHW. - * @param[in] weights Weights tensor info. A 3D tensor with shape [3, 3, IFM]. - * Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8. - * @param[in] biases Biases tensor info. A 1D tensor with shape [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input, S32 when input is QASYMM8. - * @param[in] output Destination tensor. Data type supported: same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU for 3x3 QASYMM8 supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1, - ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); - - // Inherited methods overriden: - void run() override; - void prepare() override; - - void set_memory_group(std::shared_ptr<IMemoryManager> memory_manager) - { - _memory_group = MemoryGroup(std::move(memory_manager)); - }; - - private: - MemoryGroup _memory_group; - std::unique_ptr<CLDepthwiseConvolutionLayer3x3NCHWKernel> _kernel_nchw; - std::unique_ptr<CLDepthwiseConvolutionLayer3x3NHWCKernel> _kernel_nhwc; - std::unique_ptr<CLFillBorderKernel> _border_handler; - CLPermute _permute_input_to_nchw; - CLPermute _permute_weights_to_nchw; - CLPermute _permute_output_to_nhwc; - CLTensor _permuted_input; - CLTensor _permuted_weights; - CLTensor _permuted_output; - CLTensor _output_multipliers; - CLTensor _output_shifts; - const ITensor *_original_weights; - const ITensor *_input; - const ITensor *_output; - bool _needs_permute; - bool _is_prepared; - bool _is_quantized; - bool _is_nhwc; - }; - - /** Basic function to execute a generic depthwise convolution. This function calls the following OpenCL kernels: - * - * -# @ref CLDepthwiseConvolutionLayerNativeKernel - * -# @ref CLPermute (x 3) if the data layout is NCHW - * - */ - class CLDepthwiseConvolutionLayerGeneric : public IFunction + void set_memory_group(std::shared_ptr<IMemoryManager> memory_manager) { - public: - /** Default constructor */ - CLDepthwiseConvolutionLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager = nullptr); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLDepthwiseConvolutionLayerGeneric(const CLDepthwiseConvolutionLayerGeneric &) = delete; - /** Default move constructor */ - CLDepthwiseConvolutionLayerGeneric(CLDepthwiseConvolutionLayerGeneric &&) = default; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLDepthwiseConvolutionLayerGeneric &operator=(const CLDepthwiseConvolutionLayerGeneric &) = delete; - /** Default move assignment operator */ - CLDepthwiseConvolutionLayerGeneric &operator=(CLDepthwiseConvolutionLayerGeneric &&) = default; - /** Initialize the function's source, destination, weights and convolution information. - * - * @param[in, out] input Source tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/F32. (Written to only for border filling). - * @param[in] weights Weights tensor. These are 3D tensors with shape [kernel_x, kernel_y, IFM]. - * Data type supported: Same as @p input or QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL when @p input is QASYMM8. - * @param[in] biases Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input, S32 when input is QASYMM8/QASYMM8_SIGNED. - * @param[out] output Destination tensor. Data type supported: same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - */ - void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); - /** Initialize the function's source, destination, weights and convolution information. - * - * @param[in] compile_context The compile context to be used. - * @param[in, out] input Source tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/F32. (Written to only for border filling). - * @param[in] weights Weights tensor. These are 3D tensors with shape [kernel_x, kernel_y, IFM]. - * Data type supported: Same as @p input or QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL when @p input is QASYMM8. - * @param[in] biases Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input, S32 when input is QASYMM8/QASYMM8_SIGNED. - * @param[out] output Destination tensor. Data type supported: same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - */ - void configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); - - /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayerGeneric - * - * @param[in] input Source tensor info. Data type supported: QASYMM8/QASYMM8_SIGNED/F32. - * @param[in] weights Weights tensor info. These are 3D tensors with shape [kernel_x, kernel_y, IFM]. - * Data type supported: Same as @p input or QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL when @p input is QASYMM8. - * @param[in] biases Biases tensor info. A 1D tensor with shape [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input, S32 when input is QASYMM8/QASYMM8_SIGNED. - * @param[in] output Destination tensor. Data type supported: same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); - - // Inherited methods overriden: - void run() override; - void prepare() override; - - void set_memory_group(std::shared_ptr<IMemoryManager> memory_manager) - { - _memory_group = MemoryGroup(std::move(memory_manager)); - }; - - private: - MemoryGroup _memory_group; - - std::unique_ptr<CLDepthwiseConvolutionLayerNativeKernel> _dwc_native_kernel; - CLPermute _permute_input_to_nhwc; - CLPermute _permute_weights_to_nhwc; - CLPermute _permute_output_to_nchw; - - CLTensor _permuted_input; - CLTensor _permuted_weights; - CLTensor _permuted_output; - CLTensor _output_multipliers; - CLTensor _output_shifts; - const ITensor *_original_weights; - const ITensor *_input; - const ITensor *_output; - - bool _needs_permute; - bool _is_prepared; - bool _is_quantized; + _memory_group = MemoryGroup(std::move(memory_manager)); }; - std::shared_ptr<IMemoryManager> _memory_manager; - - DepthwiseConvolutionFunction _depth_conv_func; - CLDepthwiseConvolutionLayerInternal3x3 _func_3x3; - CLDepthwiseConvolutionLayerGeneric _func_generic; +private: + MemoryGroup _memory_group; + + std::unique_ptr<CLDepthwiseConvolutionLayerNativeKernel> _dwc_native_kernel; + CLPermute _permute_input_to_nhwc; + CLPermute _permute_weights_to_nhwc; + CLPermute _permute_output_to_nchw; + + CLTensor _permuted_input; + CLTensor _permuted_weights; + CLTensor _permuted_output; + CLTensor _output_multipliers; + CLTensor _output_shifts; + const ITensor *_original_weights; + const ITensor *_input; + const ITensor *_output; + + bool _needs_permute; + bool _is_prepared; + bool _is_quantized; }; } // namespace arm_compute #endif /*ARM_COMPUTE_CLDEPTHWISECONVOLUTION_H */ diff --git a/docs/user_guide/release_version_and_change_log.dox b/docs/user_guide/release_version_and_change_log.dox index 2d4d0358f2..0c8b57ff9f 100644 --- a/docs/user_guide/release_version_and_change_log.dox +++ b/docs/user_guide/release_version_and_change_log.dox @@ -62,7 +62,7 @@ v21.05 Public major release - @ref NEDeconvolutionLayer - Remove padding from OpenCL kernels: - @ref CLL2NormalizeLayerKernel - - @ref CLDepthwiseConvolutionLayer3x3NHWCKernel + - CLDepthwiseConvolutionLayer3x3NHWCKernel - @ref CLNormalizationLayerKernel - @ref CLNormalizePlanarYUVLayerKernel - @ref opencl::kernels::ClMulKernel @@ -271,7 +271,7 @@ v20.11 Public major release - @ref CLDepthwiseConvolutionLayerNativeKernel - CLDepthConvertLayerKernel - CLCopyKernel - - @ref CLDepthwiseConvolutionLayer3x3NHWCKernel + - CLDepthwiseConvolutionLayer3x3NHWCKernel - CLActivationLayerKernel - CLWinogradFilterTransformKernel - CLWidthConcatenateLayerKernel @@ -1032,10 +1032,10 @@ v18.11 Public major release - CLWidthConcatenateLayer - CLFlattenLayer - @ref CLSoftmaxLayer - - Add dot product support for @ref CLDepthwiseConvolutionLayer3x3NHWCKernel non-unit stride + - Add dot product support for CLDepthwiseConvolutionLayer3x3NHWCKernel non-unit stride - Add SVE support - Fused batch normalization into convolution layer weights in @ref CLFuseBatchNormalization - - Fuses activation in @ref CLDepthwiseConvolutionLayer3x3NCHWKernel, @ref CLDepthwiseConvolutionLayer3x3NHWCKernel and @ref NEGEMMConvolutionLayer + - Fuses activation in CLDepthwiseConvolutionLayer3x3NCHWKernel, CLDepthwiseConvolutionLayer3x3NHWCKernel and @ref NEGEMMConvolutionLayer - Added NHWC data layout support to: - @ref CLChannelShuffleLayer - @ref CLDeconvolutionLayer @@ -1045,7 +1045,7 @@ v18.11 Public major release - NEDepthwiseConvolutionLayer3x3Kernel - CLPixelWiseMultiplicationKernel - Added FP16 support to the following kernels: - - @ref CLDepthwiseConvolutionLayer3x3NHWCKernel + - CLDepthwiseConvolutionLayer3x3NHWCKernel - NEDepthwiseConvolutionLayer3x3Kernel - @ref CLNormalizePlanarYUVLayerKernel - @ref CLWinogradConvolutionLayer (5x5 kernel) @@ -1286,7 +1286,7 @@ v17.09 Public major release - NEReshapeLayerKernel / @ref NEReshapeLayer - New OpenCL kernels / functions: - - @ref CLDepthwiseConvolutionLayer3x3NCHWKernel @ref CLDepthwiseConvolutionLayer3x3NHWCKernel CLDepthwiseIm2ColKernel CLDepthwiseVectorToTensorKernel CLDepthwiseWeightsReshapeKernel / CLDepthwiseConvolutionLayer3x3 @ref CLDepthwiseConvolutionLayer CLDepthwiseSeparableConvolutionLayer + - CLDepthwiseConvolutionLayer3x3NCHWKernel CLDepthwiseConvolutionLayer3x3NHWCKernel CLDepthwiseIm2ColKernel CLDepthwiseVectorToTensorKernel CLDepthwiseWeightsReshapeKernel / CLDepthwiseConvolutionLayer3x3 @ref CLDepthwiseConvolutionLayer CLDepthwiseSeparableConvolutionLayer - CLDequantizationLayerKernel / CLDequantizationLayer - CLDirectConvolutionLayerKernel / @ref CLDirectConvolutionLayer - CLFlattenLayer diff --git a/filelist.json b/filelist.json index 7129efbf21..7512ac12bd 100644 --- a/filelist.json +++ b/filelist.json @@ -389,20 +389,6 @@ ] } }, - "DepthwiseConvolutionLayer3x3NCHW": { - "files": { - "kernel": [ - "src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp" - ] - } - }, - "DepthwiseConvolutionLayer3x3NHWC": { - "files": { - "kernel": [ - "src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp" - ] - } - }, "DepthwiseConvolutionLayerNative": { "files": { "kernel": [ diff --git a/src/core/CL/CLHelpers.cpp b/src/core/CL/CLHelpers.cpp index 6af378c7ab..3323929742 100644 --- a/src/core/CL/CLHelpers.cpp +++ b/src/core/CL/CLHelpers.cpp @@ -22,6 +22,7 @@ * SOFTWARE. */ #include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/CL/CLTypes.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/Log.h" @@ -427,4 +428,42 @@ void set_wbsm(cl::Kernel &kernel, cl_int wbsm_hint) ARM_COMPUTE_ERROR_ON(err != CL_SUCCESS); } +bool export_weights_to_cl_image(const ITensorInfo *tensor) +{ + if(tensor->tensor_shape()[0] % 4) + { + return false; + } + + // If not floating point + if(!is_data_type_float(tensor->data_type())) + { + return false; + } + + // Check if the cl_khr_image2d_from_buffer extension is supported on the target platform + if(!image2d_from_buffer_supported(CLKernelLibrary::get().get_device())) + { + return false; + } + + // Check cl image pitch alignment + if(get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()) == 0) + { + return false; + } + + const size_t image_w = tensor->tensor_shape()[0] / 4; + const size_t image_h = tensor->tensor_shape()[1] * tensor->tensor_shape()[2] * tensor->tensor_shape()[3]; + const size_t max_image_w = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_WIDTH>(); + const size_t max_image_h = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_HEIGHT>(); + + if(image_w > max_image_w || image_h > max_image_h) + { + return false; + } + + return true; +} + } // namespace arm_compute diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index bbd4009389..d8983fcae9 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -22,16 +22,13 @@ * SOFTWARE. */ #include "arm_compute/core/CL/CLKernelLibrary.h" - #include "arm_compute/core/Error.h" #include "src/core/gpu/cl/ClKernelLibrary.h" - #include <algorithm> #include <array> #include <fstream> #include <utility> #include <vector> - namespace arm_compute { CLKernelLibrary::CLKernelLibrary() @@ -39,123 +36,99 @@ CLKernelLibrary::CLKernelLibrary() { opencl_is_available(); // Make sure the OpenCL symbols are initialised *before* the CLKernelLibrary is built } - CLKernelLibrary &CLKernelLibrary::get() { static CLKernelLibrary _kernel_library; return _kernel_library; } - Kernel CLKernelLibrary::create_kernel(const std::string &kernel_name, const std::set<std::string> &build_options_set) const { - const opencl::ClKernelLibrary &klib = opencl::ClKernelLibrary::get(); - - const std::string program_name = klib.program_name(kernel_name); - auto program = klib.program(program_name); - const std::string &kernel_path = CLKernelLibrary::get().get_kernel_path(); - + const opencl::ClKernelLibrary &klib = opencl::ClKernelLibrary::get(); + const std::string program_name = klib.program_name(kernel_name); + auto program = klib.program(program_name); + const std::string &kernel_path = CLKernelLibrary::get().get_kernel_path(); return _compile_context.create_kernel(kernel_name, program_name, program.program, kernel_path, build_options_set, program.is_binary); } - std::string CLKernelLibrary::get_program_name(const std::string &kernel_name) const { return opencl::ClKernelLibrary::get().program_name(kernel_name); } - void CLKernelLibrary::init(std::string kernel_path, cl::Context context, cl::Device device) { _compile_context = CLCompileContext(context, device); opencl::ClKernelLibrary::get().set_kernel_path(kernel_path); } - void CLKernelLibrary::set_kernel_path(const std::string &kernel_path) { opencl::ClKernelLibrary::get().set_kernel_path(kernel_path); } - cl::Context &CLKernelLibrary::context() { return _compile_context.context(); } - const cl::Device &CLKernelLibrary::get_device() { return _compile_context.get_device(); } - void CLKernelLibrary::set_device(cl::Device device) { _compile_context.set_device(device); } - void CLKernelLibrary::set_context(cl::Context context) { _compile_context.set_context(context); } - std::string CLKernelLibrary::get_kernel_path() { return opencl::ClKernelLibrary::get().kernel_path(); } - void CLKernelLibrary::clear_programs_cache() { _compile_context.clear_programs_cache(); } - const std::map<std::string, cl::Program> &CLKernelLibrary::get_built_programs() const { return _compile_context.get_built_programs(); } - void CLKernelLibrary::add_built_program(const std::string &built_program_name, const cl::Program &program) { _compile_context.add_built_program(built_program_name, program); } - bool CLKernelLibrary::fp16_supported() const { return _compile_context.fp16_supported(); } - bool CLKernelLibrary::int64_base_atomics_supported() const { return _compile_context.int64_base_atomics_supported(); } - bool CLKernelLibrary::is_wbsm_supported() { return _compile_context.is_wbsm_supported(); } - std::pair<std::string, bool> CLKernelLibrary::get_program(const std::string &program_name) const { auto program_info = opencl::ClKernelLibrary::get().program(program_name); return std::make_pair(std::move(program_info.program), program_info.is_binary); } - size_t CLKernelLibrary::max_local_workgroup_size(const cl::Kernel &kernel) const { return _compile_context.max_local_workgroup_size(kernel); } - cl::NDRange CLKernelLibrary::default_ndrange() const { return _compile_context.default_ndrange(); } - std::string CLKernelLibrary::get_device_version() { return _compile_context.get_device_version(); } - cl_uint CLKernelLibrary::get_num_compute_units() { return _compile_context.get_num_compute_units(); } - CLCompileContext &CLKernelLibrary::get_compile_context() { return _compile_context; } -} // namespace arm_compute +} // namespace arm_compute
\ No newline at end of file diff --git a/src/core/CL/CLKernels.h b/src/core/CL/CLKernels.h index ea40239c6f..09f8109445 100644 --- a/src/core/CL/CLKernels.h +++ b/src/core/CL/CLKernels.h @@ -36,8 +36,6 @@ #include "src/core/CL/kernels/CLDeconvolutionLayerUpsampleKernel.h" #include "src/core/CL/kernels/CLDeconvolutionReshapeOutputKernel.h" #include "src/core/CL/kernels/CLDepthToSpaceLayerKernel.h" -#include "src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h" -#include "src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h" #include "src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h" #include "src/core/CL/kernels/CLFFTDigitReverseKernel.h" #include "src/core/CL/kernels/CLFFTRadixStageKernel.h" diff --git a/src/core/CL/cl_kernels/activation_quant_helpers.h b/src/core/CL/cl_kernels/activation_quant_helpers.h index a32e4e94a3..c420578546 100644 --- a/src/core/CL/cl_kernels/activation_quant_helpers.h +++ b/src/core/CL/cl_kernels/activation_quant_helpers.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2020 Arm Limited. + * Copyright (c) 2019-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -51,7 +51,12 @@ inline TYPE lu_brelu_op(TYPE x) // Hard Swish Activation inline TYPE hard_swish_op(TYPE x) { - return (x * ((min(max((TYPE)(x + (TYPE)3.f), (TYPE)0.f), (TYPE)6.f)) * (TYPE)0.166666667f)); + return (x * ((min(max((TYPE)(x + (TYPE)3.f), (TYPE)0.f), (TYPE)6.f)) * (TYPE)0.166666667f)); +} + +inline TYPE identiy_op(TYPE x) +{ + return x; } #define ACTIVATION_OP2(op, x) op##_op(x) diff --git a/src/core/CL/cl_kernels/depthwise_convolution.cl b/src/core/CL/cl_kernels/depthwise_convolution.cl deleted file mode 100644 index 22a38e7094..0000000000 --- a/src/core/CL/cl_kernels/depthwise_convolution.cl +++ /dev/null @@ -1,1781 +0,0 @@ -/* - * Copyright (c) 2017-2021 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. - */ -#include "helpers.h" - -#include "activation_float_helpers.h" - -/** Get the pointer position at a certain offset in x and y direction. - * - * @param[in] ptr Pointer to the starting position of the buffer - * @param[in] x Relative X position - * @param[in] y Relative Y position - * @param[in] stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] stride_y Stride of the source tensor in Y dimension (in bytes) - * - * @return a uchar - */ -inline __global uchar *ptr_offset(__global uchar *ptr, const int x, const int y, const int stride_x, const int stride_y) -{ - return ptr + x * stride_x + y * stride_y; -} - -#if(DILATION_X == 1 && DILATION_Y == 1) - -#define CONVOLUTION1x3_2X1_STRIDE1(acc, src0, weights_row0) \ - ({ \ - acc.s0 = fma(src0.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0.s1, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0.s2, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0.s1, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0.s2, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src0.s3, weights_row0.s2, acc.s1); \ - }) - -#define CONVOLUTION1x3_4X1_STRIDE1(acc, src0, weights_row0) \ - ({ \ - acc.s0 = fma(src0.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0.s1, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0.s2, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0.s1, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0.s2, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src0.s3, weights_row0.s2, acc.s1); \ - acc.s2 = fma(src0.s2, weights_row0.s0, acc.s2); \ - acc.s2 = fma(src0.s3, weights_row0.s1, acc.s2); \ - acc.s2 = fma(src0.s4, weights_row0.s2, acc.s2); \ - acc.s3 = fma(src0.s3, weights_row0.s0, acc.s3); \ - acc.s3 = fma(src0.s4, weights_row0.s1, acc.s3); \ - acc.s3 = fma(src0.s5, weights_row0.s2, acc.s3); \ - }) - -#define CONVOLUTION1x3_2X1_STRIDE2(acc, src0, src1, weights_row0) \ - ({ \ - acc.s0 = fma(src0.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0.s1, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0.s2, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0.s2, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0.s3, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src1.s0, weights_row0.s2, acc.s1); \ - }) - -#define CONVOLUTION1x3_4X1_STRIDE2(acc, src0, src1, weights_row0) \ - ({ \ - acc.s0 = fma(src0.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0.s1, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0.s2, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0.s2, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0.s3, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src0.s4, weights_row0.s2, acc.s1); \ - acc.s2 = fma(src0.s4, weights_row0.s0, acc.s2); \ - acc.s2 = fma(src0.s5, weights_row0.s1, acc.s2); \ - acc.s2 = fma(src0.s6, weights_row0.s2, acc.s2); \ - acc.s3 = fma(src0.s6, weights_row0.s0, acc.s3); \ - acc.s3 = fma(src0.s7, weights_row0.s1, acc.s3); \ - acc.s3 = fma(src1.s0, weights_row0.s2, acc.s3); \ - }) - -#else /* DILATION_X==1 && DILATION_Y==1 */ - -#define CONVOLUTION1x3_2X1_STRIDE1(acc, src0_left, src0_mid, src0_right, weights_row0) \ - ({ \ - acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0_left.s1, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0_mid.s1, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src0_right.s1, weights_row0.s2, acc.s1); \ - }) - -#define CONVOLUTION1x3_2X1_STRIDE2(acc, src0_left, src0_mid, src0_right, weights_row0) \ - ({ \ - acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0_left.s2, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0_mid.s2, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src0_right.s2, weights_row0.s2, acc.s1); \ - }) - -#define CONVOLUTION1x3_4X1_STRIDE1(acc, src0_left, src0_mid, src0_right, weights_row0) \ - ({ \ - acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0_left.s1, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0_mid.s1, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src0_right.s1, weights_row0.s2, acc.s1); \ - acc.s2 = fma(src0_left.s2, weights_row0.s0, acc.s2); \ - acc.s2 = fma(src0_mid.s2, weights_row0.s1, acc.s2); \ - acc.s2 = fma(src0_right.s2, weights_row0.s2, acc.s2); \ - acc.s3 = fma(src0_left.s3, weights_row0.s0, acc.s3); \ - acc.s3 = fma(src0_mid.s3, weights_row0.s1, acc.s3); \ - acc.s3 = fma(src0_right.s3, weights_row0.s2, acc.s3); \ - }) - -#define CONVOLUTION1x3_4X1_STRIDE2(acc, src0_left, src0_mid, src0_right, weights_row0) \ - ({ \ - acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0_left.s2, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0_mid.s2, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src0_right.s2, weights_row0.s2, acc.s1); \ - acc.s2 = fma(src0_left.s4, weights_row0.s0, acc.s2); \ - acc.s2 = fma(src0_mid.s4, weights_row0.s1, acc.s2); \ - acc.s2 = fma(src0_right.s4, weights_row0.s2, acc.s2); \ - acc.s3 = fma(src0_left.s6, weights_row0.s0, acc.s3); \ - acc.s3 = fma(src0_mid.s6, weights_row0.s1, acc.s3); \ - acc.s3 = fma(src0_right.s6, weights_row0.s2, acc.s3); \ - }) - -#endif /* DILATION_X==1 && DILATION_Y==1 */ - -#if defined(DEPTH_MULTIPLIER) && defined(DST_CHANNELS) && defined(IS_F32) -#if defined(CONV_STRIDE_X) - -#if CONV_STRIDE_X == 1 -#define convolution1x3 convolution1x3_stride_1 -#elif CONV_STRIDE_X == 2 -#define convolution1x3 convolution1x3_stride_2 -#elif CONV_STRIDE_X == 3 -#define convolution1x3 convolution1x3_stride_3 -#else /* CONV_STRIDE_X */ -#error "Stride not supported" -#endif /* CONV_STRIDE_X */ - -/** Compute a 1D horizontal convolution of size 3 and stride 1 for floating point type. - * - * @param[in] left_pixel Pointer to the left pixel. - * @param[in] left_coeff Weight of the left pixel - * @param[in] middle_coeff Weight of the middle pixel - * @param[in] right_coeff Weight of the right pixel - * - * @return a float2 containing 2 convoluted values. - */ -inline float2 convolution1x3_stride_1(__global const uchar *left_pixel, - const float left_coeff, - const float middle_coeff, - const float right_coeff) -{ -#if(DILATION_X == 1 && DILATION_Y == 1) - float4 temp = vload4(0, (__global float *)left_pixel); - - float2 left = CONVERT(temp.s01, float2); - float2 middle = CONVERT(temp.s12, float2); - float2 right = CONVERT(temp.s23, float2); - return left * (float2)left_coeff + middle * (float2)middle_coeff + right * (float2)right_coeff; -#else /* DILATION_X==1 && DILATION_Y==1 */ - return vload2(0, (__global float *)left_pixel) * (float2)left_coeff - + vload2(0, (__global float *)(left_pixel) + DILATION_X) * (float2)middle_coeff - + vload2(0, (__global float *)(left_pixel) + 2 * DILATION_X) * (float2)right_coeff; -#endif /* DILATION_X==1 && DILATION_Y==1 */ -} - -/** Compute a 1D horizontal convolution of size 3 and stride 2 for floating point type. - * - * @param[in] left_pixel Pointer to the left pixel. - * @param[in] left_coeff Weight of the left pixel - * @param[in] middle_coeff Weight of the middle pixel - * @param[in] right_coeff Weight of the right pixel - * - * @return a float2 containing 2 convoluted values. - */ -inline float2 convolution1x3_stride_2(__global const uchar *left_pixel, - const float left_coeff, - const float middle_coeff, - const float right_coeff) -{ -#if(DILATION_X == 1 && DILATION_Y == 1) - float4 temp0 = vload4(0, (__global float *)left_pixel); - float temp1 = *((__global float *)(left_pixel + 4 * sizeof(float))); - - float2 left = CONVERT(temp0.s02, float2); - float2 middle = CONVERT(temp0.s13, float2); - float2 right = CONVERT((float2)(temp0.s2, temp1), float2); - - return left * (float2)left_coeff + middle * (float2)middle_coeff + right * (float2)right_coeff; -#else /* DILATION_X==1 && DILATION_Y==1 */ - __global float *left_pixel_float = (__global float *)left_pixel; - - return vload4(0, left_pixel_float).s02 * (float2)left_coeff - + vload4(0, left_pixel_float + DILATION_X).s02 * (float2)middle_coeff - + vload4(0, left_pixel_float + DILATION_X * 2).s02 * (float2)right_coeff; - -#endif /* DILATION_X==1 && DILATION_Y==1 */ -} - -/** Compute a 1D horizontal convolution of size 3 and stride 3 for floating point type. - * - * @param[in] left_pixel Pointer to the left pixel. - * @param[in] left_coeff Weight of the left pixel - * @param[in] middle_coeff Weight of the middle pixel - * @param[in] right_coeff Weight of the right pixel - * - * @return a float2 containing 2 convoluted values. - */ -inline float2 convolution1x3_stride_3(__global const uchar *left_pixel, - const float left_coeff, - const float middle_coeff, - const float right_coeff) -{ -#if(DILATION_X == 1 && DILATION_Y == 1) - float4 temp0 = vload4(0, (__global float *)left_pixel); - float2 temp1 = vload2(0, (__global float *)(left_pixel + 4 * sizeof(float))); - - float2 left = CONVERT(temp0.s03, float2); - float2 middle = CONVERT((float2)(temp0.s1, temp1.s0), float2); - float2 right = CONVERT((float2)(temp0.s2, temp1.s1), float2); - - return left * (float2)left_coeff + middle * (float2)middle_coeff + right * (float2)right_coeff; -#else /* DILATION_X==1 && DILATION_Y==1 */ - __global float *left_pixel_float = (__global float *)left_pixel; - - return (float2)(*left_pixel_float, *(left_pixel_float + 3)) * (float2)left_coeff - + (float2)(*(left_pixel_float + DILATION_X), *(left_pixel_float + DILATION_X + 3)) * (float2)middle_coeff - + (float2)(*(left_pixel_float + DILATION_X * 2), *(left_pixel_float + DILATION_X * 2 + 3)) * (float2)right_coeff; -#endif /* DILATION_X==1 && DILATION_Y==1 */ -} - -/** Apply a 3x3 convolution matrix to a single channel F32 input image and return the result. - * - * Convolution matrix layout: - * - * [ mat0, mat1, mat2 ]\n - * [ mat3, mat4, mat5 ]\n - * [ mat6, mat7, mat8 ]\n - * - * @param[in] src A pointer to source Image structure - * @param[in] mat0 Coefficient from the convolution matrix - * @param[in] mat1 Coefficient from the convolution matrix - * @param[in] mat2 Coefficient from the convolution matrix - * @param[in] mat3 Coefficient from the convolution matrix - * @param[in] mat4 Coefficient from the convolution matrix - * @param[in] mat5 Coefficient from the convolution matrix - * @param[in] mat6 Coefficient from the convolution matrix - * @param[in] mat0 Coefficient from the convolution matrix - * @param[in] mat7 Coefficient from the convolution matrix - * @param[in] mat8 Coefficient from the convolution matrix - * - * @return a float2 containing 2 convoluted values. - */ -inline float2 convolution3x3( - __global const uchar *src, - unsigned int src_stride_y, - const float mat0, const float mat1, const float mat2, - const float mat3, const float mat4, const float mat5, - const float mat6, const float mat7, const float mat8) -{ - float2 pixels; - - pixels = convolution1x3((src + 0 * DILATION_Y * src_stride_y), mat0, mat1, mat2); - pixels += convolution1x3((src + 1 * DILATION_Y * src_stride_y), mat3, mat4, mat5); - pixels += convolution1x3((src + 2 * DILATION_Y * src_stride_y), mat6, mat7, mat8); - - return pixels; -} - -/** This OpenCL kernel computes the depthwise convolution 3x3 - * - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32 - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: F32 - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F32 - * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) - * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) - * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) - * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the biases vector - * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: F16/F32 - * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) - * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector - */ -__kernel void depthwise_convolution_3x3( - TENSOR3D_DECLARATION(src), - TENSOR3D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights) -#if defined(HAS_BIAS) - , - VECTOR_DECLARATION(biases) -#endif //defined(HAS_BIAS) -) -{ - Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst); - Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); - - float2 pixels = 0.0f; - - // Extract channel and linearized batch indices - const int channel = get_global_id(2) % DST_CHANNELS; - const int batch = get_global_id(2) / DST_CHANNELS; - // Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER) - - __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; - - __global uchar *src_addr = src_ptr + get_global_id(0) * src_step_x + get_global_id(1) * src_step_y + get_global_id(2) * src_step_z - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * - (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; - - // Load the weights - float3 weights_values0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); - float3 weights_values1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); - float3 weights_values2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y)); - - pixels = convolution3x3(src_addr, src_stride_y, - weights_values0.s0, weights_values0.s1, weights_values0.s2, - weights_values1.s0, weights_values1.s1, weights_values1.s2, - weights_values2.s0, weights_values2.s1, weights_values2.s2); -#if defined(HAS_BIAS) - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - - float bias = *((__global float *)(vector_offset(&biases, channel))); - - pixels += (float2)bias; -#endif //defined(HAS_BIAS) - - vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, pixels, A_VAL, B_VAL), 0, (__global float *)dst.ptr); -} -#endif //defined(CONV_STRIDE_X) - -#if(DILATION_X > 1 || DILATION_Y > 1) - -/** Perform 3x3 convolution for stride_x=1 and stride_y=1 when DILATION_X>1 or DILATION_Y>1 for F32 - * - * @param[in] src_addr Pointer to the starting position of where to perform the convolution - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] y_offset Offset from the source tensor from which to start convolution - * @param[in] weights_addr Pointer from where to get weights - * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension - */ -inline float2 convolution_3x3_dilation_stridex1_stridey1_f32(__global uchar *src_addr, const int stride_x_bytes, const int stride_y_bytes, - const int y_offset, __global uchar *weights_addr, const int weights_stride_y) -{ - // Load the weights - float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); - float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); - float3 weights_row2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y)); - - float2 pixels0 = 0.0f; - - float2 src00_left = vload2(0, (__global float *)ptr_offset(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); // Row0 - float2 src00_mid = vload2(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - float2 src00_right = vload2(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - - float2 src10_left = vload2(0, (__global float *)ptr_offset(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); // Row1 - float2 src10_mid = vload2(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - float2 src10_right = vload2(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - - float2 src20_left = vload2(0, (__global float *)ptr_offset(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); // Row2 - float2 src20_mid = vload2(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - float2 src20_right = vload2(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - - CONVOLUTION1x3_2X1_STRIDE1(pixels0, src00_left, src00_mid, src00_right, weights_row0); - CONVOLUTION1x3_2X1_STRIDE1(pixels0, src10_left, src10_mid, src10_right, weights_row1); - CONVOLUTION1x3_2X1_STRIDE1(pixels0, src20_left, src20_mid, src20_right, weights_row2); - - return pixels0; -} - -/** Perform 3x3 convolution for stride_x=2 and stride_y=2 when DILATION_X>1 or DILATION_Y>1 for F32 - * - * @param[in] src_addr Pointer to the starting position of where to perform the convolution - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] y_offset Offset from the source tensor from which to start convolution - * @param[in] weights_addr Pointer from where to get weights - * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension - */ -inline float2 convolution_3x3_dilation_stridex2_stridey2_f32(__global uchar *src_addr, const int stride_x_bytes, const int stride_y_bytes, - const int y_offset, __global uchar *weights_addr, const int weights_stride_y) -{ - // Load the weights - float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); - float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); - float3 weights_row2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y)); - - float2 pixels0 = 0.0f; - - float3 src00_left = vload3(0, (__global float *)ptr_offset(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); // Row0 - float3 src00_mid = vload3(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - float3 src00_right = vload3(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - - float3 src10_left = vload3(0, (__global float *)ptr_offset(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); // Row1 - float3 src10_mid = vload3(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - float3 src10_right = vload3(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - - float3 src20_left = vload3(0, (__global float *)ptr_offset(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); // Row2 - float3 src20_mid = vload3(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - float3 src20_right = vload3(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - - CONVOLUTION1x3_2X1_STRIDE2(pixels0, src00_left, src00_mid, src00_right, weights_row0); - CONVOLUTION1x3_2X1_STRIDE2(pixels0, src10_left, src10_mid, src10_right, weights_row1); - CONVOLUTION1x3_2X1_STRIDE2(pixels0, src20_left, src20_mid, src20_right, weights_row2); - - return pixels0; -} - -#endif /* (DILATION_X > 1 || DILATION_Y > 1) */ - -/** This OpenCL kernel is optimized for Bifrost architectures and computes the depthwise convolution 3x3 when both - * stride_x and stride_y are equal to 1 - * - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note If activation function is enabled, the data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float. - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32 - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: F32 - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F32 - * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) - * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) - * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) - * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the biases vector - * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: F32 - * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) - * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector - */ -__kernel void depthwise_convolution_3x3_stridex1_stridey1_f32( - TENSOR3D_DECLARATION(src), - TENSOR3D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights) -#if defined(HAS_BIAS) - , - VECTOR_DECLARATION(biases) -#endif //defined(HAS_BIAS) -) -{ - Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst); - Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); - - float2 pixels0 = 0.0f; - float2 pixels1 = 0.0f; - float2 pixels2 = 0.0f; - float2 pixels3 = 0.0f; - - // Extract channel and linearized batch indices - const int channel = get_global_id(2) % DST_CHANNELS; - const int batch = get_global_id(2) / DST_CHANNELS; - // Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER) - __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; - __global uchar *src_addr = src_ptr + get_global_id(0) * src_step_x + get_global_id(1) * src_step_y + get_global_id(2) * src_step_z - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * - (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; - -#if(DILATION_X == 1 && DILATION_Y == 1) - // Load the weights - float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); - float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); - float3 weights_row2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y)); - - // Note: Since each work-item computes 4x2 elements, we need to load 6 rows from the input tensor - float4 src00 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y)); // Row0 - float4 src10 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y)); // Row1 - float4 src20 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y)); // Row2 - float4 src30 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y)); // Row3 - float4 src40 = vload4(0, (__global float *)(src_addr + 4 * src_stride_y)); // Row4 - float4 src50 = vload4(0, (__global float *)(src_addr + 5 * src_stride_y)); // Row5 - - CONVOLUTION1x3_2X1_STRIDE1(pixels0, src00, weights_row0); - CONVOLUTION1x3_2X1_STRIDE1(pixels0, src10, weights_row1); - CONVOLUTION1x3_2X1_STRIDE1(pixels0, src20, weights_row2); - CONVOLUTION1x3_2X1_STRIDE1(pixels1, src10, weights_row0); - CONVOLUTION1x3_2X1_STRIDE1(pixels1, src20, weights_row1); - CONVOLUTION1x3_2X1_STRIDE1(pixels1, src30, weights_row2); - CONVOLUTION1x3_2X1_STRIDE1(pixels2, src20, weights_row0); - CONVOLUTION1x3_2X1_STRIDE1(pixels2, src30, weights_row1); - CONVOLUTION1x3_2X1_STRIDE1(pixels2, src40, weights_row2); - CONVOLUTION1x3_2X1_STRIDE1(pixels3, src30, weights_row0); - CONVOLUTION1x3_2X1_STRIDE1(pixels3, src40, weights_row1); - CONVOLUTION1x3_2X1_STRIDE1(pixels3, src50, weights_row2); - -#else /* DILATION_X==1 && DILATION_Y==1 */ - - //3x3 Convolution of elements starting in 0th row - pixels0 = convolution_3x3_dilation_stridex1_stridey1_f32(src_addr, src_stride_x, src_stride_y, 0, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 1st row - pixels1 = convolution_3x3_dilation_stridex1_stridey1_f32(src_addr, src_stride_x, src_stride_y, 1, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 2nd row - pixels2 = convolution_3x3_dilation_stridex1_stridey1_f32(src_addr, src_stride_x, src_stride_y, 2, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 3rd row - pixels3 = convolution_3x3_dilation_stridex1_stridey1_f32(src_addr, src_stride_x, src_stride_y, 3, weights_addr, weights_stride_y); - -#endif /* DILATION_X==1 && DILATION_Y==1 */ - -#ifdef HAS_BIAS - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - - float bias = *((__global float *)(vector_offset(&biases, channel))); - - pixels0 += (float2)bias; - pixels1 += (float2)bias; - pixels2 += (float2)bias; - pixels3 += (float2)bias; -#endif /* defined(HAS_BIAS) */ - - vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, pixels0, A_VAL, B_VAL), 0, (__global float *)(dst.ptr + 0 * dst_stride_y)); - vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, pixels1, A_VAL, B_VAL), 0, (__global float *)(dst.ptr + 1 * dst_stride_y)); - vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, pixels2, A_VAL, B_VAL), 0, (__global float *)(dst.ptr + 2 * dst_stride_y)); - vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, pixels3, A_VAL, B_VAL), 0, (__global float *)(dst.ptr + 3 * dst_stride_y)); -} - -/** This OpenCL kernel is optimized for Bifrost architectures and computes the depthwise convolution 3x3 when both - * stride_x and stride_y are equal to 2 - * - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note If activation function is enabled, the data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float. - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32 - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: F32 - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F32 - * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) - * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) - * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) - * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the biases vector - * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: F32 - * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) - * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector - */ -__kernel void depthwise_convolution_3x3_stridex2_stridey2_f32( - TENSOR3D_DECLARATION(src), - TENSOR3D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights) -#if defined(HAS_BIAS) - , - VECTOR_DECLARATION(biases) -#endif //defined(HAS_BIAS) -) -{ - Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst); - Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); - - float2 pixels0 = 0.0f; - float2 pixels1 = 0.0f; - - // Extract channel and linearized batch indices - const int channel = get_global_id(2) % DST_CHANNELS; - const int batch = get_global_id(2) / DST_CHANNELS; - // Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER) - __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; - __global uchar *src_addr = src_ptr + get_global_id(0) * src_step_x + get_global_id(1) * src_step_y + get_global_id(2) * src_step_z - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * - (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; - -#if(DILATION_X == 1 && DILATION_Y == 1) - - // Load the weights - float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); - float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); - float3 weights_row2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y)); - - // Note: Since each work-item computes 4x2 elements, we need to load 5 rows from the input tensor - float4 src00 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y)); // Row0 - float2 src01 = vload2(2, (__global float *)(src_addr + 0 * src_stride_y)); // Row0 - float4 src10 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y)); // Row1 - float2 src11 = vload2(2, (__global float *)(src_addr + 1 * src_stride_y)); // Row1 - float4 src20 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y)); // Row2 - float2 src21 = vload2(2, (__global float *)(src_addr + 2 * src_stride_y)); // Row2 - float4 src30 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y)); // Row3 - float2 src31 = vload2(2, (__global float *)(src_addr + 3 * src_stride_y)); // Row3 - float4 src40 = vload4(0, (__global float *)(src_addr + 4 * src_stride_y)); // Row4 - float2 src41 = vload2(2, (__global float *)(src_addr + 4 * src_stride_y)); // Row4 - - CONVOLUTION1x3_2X1_STRIDE2(pixels0, src00, src01, weights_row0); - CONVOLUTION1x3_2X1_STRIDE2(pixels0, src10, src11, weights_row1); - CONVOLUTION1x3_2X1_STRIDE2(pixels0, src20, src21, weights_row2); - CONVOLUTION1x3_2X1_STRIDE2(pixels1, src20, src21, weights_row0); - CONVOLUTION1x3_2X1_STRIDE2(pixels1, src30, src31, weights_row1); - CONVOLUTION1x3_2X1_STRIDE2(pixels1, src40, src41, weights_row2); - -#else /* DILATION_X==1 && DILATION_Y==1 */ - - //3x3 Convolution of elements starting in 0th row - pixels0 = convolution_3x3_dilation_stridex2_stridey2_f32(src_addr, src_stride_x, src_stride_y, 0, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 2nd row - pixels1 = convolution_3x3_dilation_stridex2_stridey2_f32(src_addr, src_stride_x, src_stride_y, 2, weights_addr, weights_stride_y); -#endif /* DILATION_X==1 && DILATION_Y==1 */ - -#ifdef HAS_BIAS - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - - float bias = *((__global float *)(vector_offset(&biases, channel))); - - pixels0 += (float2)bias; - pixels1 += (float2)bias; -#endif /* defined(HAS_BIAS) */ - - vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, pixels0, A_VAL, B_VAL), 0, (__global float *)(dst.ptr + 0 * dst_stride_y)); - vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, pixels1, A_VAL, B_VAL), 0, (__global float *)(dst.ptr + 1 * dst_stride_y)); -} - -#endif // defined(DEPTH_MULTIPLIER) && defined(DST_CHANNELS) && defined(IS_F32) - -#if defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) && defined(DEPTH_MULTIPLIER) && defined(DST_CHANNELS) && defined(IS_F16) -#if defined(CONV_STRIDE_X) -#if CONV_STRIDE_X == 1 -#define convolution1x3_f16 convolution1x3_stride_1_f16 -#elif CONV_STRIDE_X == 2 -#define convolution1x3_f16 convolution1x3_stride_2_f16 -#elif CONV_STRIDE_X == 3 -#define convolution1x3_f16 convolution1x3_stride_3_f16 -#else /* CONV_STRIDE_X */ -#error "Stride not supported" -#endif /* CONV_STRIDE_X */ - -#if(DILATION_X > 1 || DILATION_Y > 1) - -/** Perform 3x3 convolution for stride_x=1 and stride_y=1 when DILATION_X>1 or DILATION_Y>1 for f16 - * - * @param[in] src_addr Pointer to the starting position of where to perform the convolution - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] y_offset Offset from the source tensor from which to start convolution - * @param[in] weights_addr Pointer from where to get weights - * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension - */ -inline half4 convolution_3x3_dilation_stridex1_stridey1_f16(__global uchar *src_addr, const int stride_x_bytes, const int stride_y_bytes, - const int y_offset, __global uchar *weights_addr, const int weights_stride_y) -{ - // Load the weights - half3 weights_row0 = vload3(0, (__global half *)(weights_addr + 0 * weights_stride_y)); - half3 weights_row1 = vload3(0, (__global half *)(weights_addr + 1 * weights_stride_y)); - half3 weights_row2 = vload3(0, (__global half *)(weights_addr + 2 * weights_stride_y)); - - half4 pixels0 = 0.0f; - - half4 src00_left = vload4(0, (__global half *)ptr_offset(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); // Row0 - half4 src00_mid = vload4(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - half4 src00_right = vload4(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - - half4 src10_left = vload4(0, (__global half *)ptr_offset(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); // Row1 - half4 src10_mid = vload4(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - half4 src10_right = vload4(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - - half4 src20_left = vload4(0, (__global half *)ptr_offset(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); // Row2 - half4 src20_mid = vload4(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - half4 src20_right = vload4(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - - CONVOLUTION1x3_4X1_STRIDE1(pixels0, src00_left, src00_mid, src00_right, weights_row0); - CONVOLUTION1x3_4X1_STRIDE1(pixels0, src10_left, src10_mid, src10_right, weights_row1); - CONVOLUTION1x3_4X1_STRIDE1(pixels0, src20_left, src20_mid, src20_right, weights_row2); - - return pixels0; -} - -/** Perform 3x3 convolution for stride_x=2 and stride_y=2 when DILATION_X>1 or DILATION_Y>1 for F16 - * - * @param[in] src_addr Pointer to the starting position of where to perform the convolution - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] y_offset Offset from the source tensor from which to start convolution - * @param[in] weights_addr Pointer from where to get weights - * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension - */ -inline half4 convolution_3x3_dilation_stridex2_stridey2_f16(__global uchar *src_addr, const int stride_x_bytes, const int stride_y_bytes, - const int y_offset, __global uchar *weights_addr, const int weights_stride_y) -{ - // Load the weights - half3 weights_row0 = vload3(0, (__global half *)(weights_addr + 0 * weights_stride_y)); - half3 weights_row1 = vload3(0, (__global half *)(weights_addr + 1 * weights_stride_y)); - half3 weights_row2 = vload3(0, (__global half *)(weights_addr + 2 * weights_stride_y)); - - half4 pixels0 = 0.0f; - - half8 src00_left = vload8(0, (__global half *)ptr_offset(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); // Row0 - half8 src00_mid = vload8(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - half8 src00_right = vload8(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - - half8 src10_left = vload8(0, (__global half *)ptr_offset(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); // Row1 - half8 src10_mid = vload8(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - half8 src10_right = vload8(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - - half8 src20_left = vload8(0, (__global half *)ptr_offset(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); // Row2 - half8 src20_mid = vload8(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - half8 src20_right = vload8(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - - CONVOLUTION1x3_4X1_STRIDE2(pixels0, src00_left, src00_mid, src00_right, weights_row0); - CONVOLUTION1x3_4X1_STRIDE2(pixels0, src10_left, src10_mid, src10_right, weights_row1); - CONVOLUTION1x3_4X1_STRIDE2(pixels0, src20_left, src20_mid, src20_right, weights_row2); - - return pixels0; -} - -#endif // (DILATION_X > 1 && DILATION_Y > 1) - -/** Compute a 1D horizontal convolution of size 3 and stride 1 for 16bit floating point type. - * - * @param[in] left_pixel Pointer to the left pixel. - * @param[in] left_coeff Weight of the left pixel - * @param[in] middle_coeff Weight of the middle pixel - * @param[in] right_coeff Weight of the right pixel - * - * @return a half4 containing 4 convoluted values. - */ -inline half4 convolution1x3_stride_1_f16(__global const uchar *left_pixel, - const half left_coeff, - const half middle_coeff, - const half right_coeff) -{ -#if(DILATION_X == 1 && DILATION_Y == 1) - - half8 temp = vload8(0, (__global half *)left_pixel); - - half4 left = CONVERT(temp.s0123, half4); - half4 middle = CONVERT(temp.s1234, half4); - half4 right = CONVERT(temp.s2345, half4); - - return left * (half4)left_coeff + middle * (half4)middle_coeff + right * (half4)right_coeff; -#else /* DILATION_X==1 && DILATION_Y==1 */ - return vload4(0, (__global half *)left_pixel) * (half4)left_coeff - + vload4(0, (__global half *)(left_pixel) + DILATION_X) * (half4)middle_coeff - + vload4(0, (__global half *)(left_pixel) + 2 * DILATION_X) * (half4)right_coeff; - -#endif /* DILATION_X==1 && DILATION_Y==1 */ -} - -/** Compute a 1D horizontal convolution of size 3 and stride 2 for 16bit floating point type. - * - * @param[in] left_pixel Pointer to the left pixel. - * @param[in] left_coeff Weight of the left pixel - * @param[in] middle_coeff Weight of the middle pixel - * @param[in] right_coeff Weight of the right pixel - * - * @return a half4 containing 4 convoluted values. - */ -inline half4 convolution1x3_stride_2_f16(__global const uchar *left_pixel, - const half left_coeff, - const half middle_coeff, - const half right_coeff) -{ -#if(DILATION_X == 1 && DILATION_Y == 1) - - half8 temp0 = vload8(0, (__global half *)left_pixel); - half temp1 = *((__global half *)(left_pixel + 8 * sizeof(half))); - - half4 left = CONVERT(temp0.s0246, half4); - half4 middle = CONVERT(temp0.s1357, half4); - half4 right = CONVERT((half4)(temp0.s246, temp1), half4); - - return left * (half4)left_coeff + middle * (half4)middle_coeff + right * (half4)right_coeff; -#else /* DILATION_X==1 && DILATION_Y==1 */ - - __global half *left_pixel_float = (__global half *)left_pixel; - - return (half4)(*left_pixel_float, *(left_pixel_float + 2), *(left_pixel_float + 4), *(left_pixel_float + 6)) * (half4)left_coeff - + (half4)(*(left_pixel_float + DILATION_X), *(left_pixel_float + DILATION_X + 2), *(left_pixel_float + DILATION_X + 4), *(left_pixel_float + DILATION_X + 6)) * (half4)middle_coeff - + (half4)(*(left_pixel_float + DILATION_X * 2), *(left_pixel_float + DILATION_X * 2 + 2), *(left_pixel_float + DILATION_X * 2 + 4), *(left_pixel_float + DILATION_X * 2 + 6)) * (half4)right_coeff; - -#endif /* DILATION_X==1 && DILATION_Y==1 */ -} - -/** Compute a 1D horizontal convolution of size 3 and stride 3 for 16bit floating point type. - * - * @param[in] left_pixel Pointer to the left pixel. - * @param[in] left_coeff Weight of the left pixel - * @param[in] middle_coeff Weight of the middle pixel - * @param[in] right_coeff Weight of the right pixel - * - * @return a half4 containing 4 convoluted values. - */ -inline half4 convolution1x3_stride_3_f16(__global const uchar *left_pixel, - const half left_coeff, - const half middle_coeff, - const half right_coeff) -{ -#if(DILATION_X == 1 && DILATION_Y == 1) - - half16 temp0 = vload16(0, (__global half *)left_pixel); - - half4 left = CONVERT(temp0.s0369, half4); - half4 middle = CONVERT(temp0.s147A, half4); - half4 right = CONVERT(temp0.s258B, half4); - - return left * (half4)left_coeff + middle * (half4)middle_coeff + right * (half4)right_coeff; -#else /* DILATION_X==1 && DILATION_Y==1 */ - - __global half *left_pixel_float = (__global half *)left_pixel; - - return (half4)(*left_pixel_float, *(left_pixel_float + 3), *(left_pixel_float + 6), *(left_pixel_float + 9)) * (half4)left_coeff - + (half4)(*(left_pixel_float + DILATION_X), *(left_pixel_float + DILATION_X + 3), *(left_pixel_float + DILATION_X + 6), *(left_pixel_float + DILATION_X + 9)) * (half4)middle_coeff - + (half4)(*(left_pixel_float + DILATION_X * 2), *(left_pixel_float + DILATION_X * 2 + 3), *(left_pixel_float + DILATION_X * 2 + 6), *(left_pixel_float + DILATION_X * 2 + 9)) * (half4)right_coeff; - -#endif /* DILATION_X==1 && DILATION_Y==1 */ -} - -/** Apply a 3x3 convolution matrix to a single channel F16 input image and return the result. - * - * Convolution matrix layout: - * - * [ mat0, mat1, mat2 ]\n - * [ mat3, mat4, mat5 ]\n - * [ mat6, mat7, mat8 ]\n - * - * @param[in] src A pointer to source Image structure - * @param[in] mat0 Coefficient from the convolution matrix - * @param[in] mat1 Coefficient from the convolution matrix - * @param[in] mat2 Coefficient from the convolution matrix - * @param[in] mat3 Coefficient from the convolution matrix - * @param[in] mat4 Coefficient from the convolution matrix - * @param[in] mat5 Coefficient from the convolution matrix - * @param[in] mat6 Coefficient from the convolution matrix - * @param[in] mat0 Coefficient from the convolution matrix - * @param[in] mat7 Coefficient from the convolution matrix - * @param[in] mat8 Coefficient from the convolution matrix - * - * @return a half4 containing 4 convoluted values. - */ -inline half4 convolution3x3_f16( - __global uchar *src, uint src_stride_y, - const half mat0, const half mat1, const half mat2, - const half mat3, const half mat4, const half mat5, - const half mat6, const half mat7, const half mat8) -{ - half4 pixels; - - pixels = convolution1x3_f16(src, mat0, mat1, mat2); - pixels += convolution1x3_f16(src + DILATION_Y * src_stride_y, mat3, mat4, mat5); - pixels += convolution1x3_f16(src + DILATION_Y * 2 * src_stride_y, mat6, mat7, mat8); - - return pixels; -} - -#if defined(DEPTH_MULTIPLIER) - -/** This OpenCL kernel computes the depthwise convolution 3x3 - * - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note If activation function is enabled, the data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types: half. - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16 - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr - * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) - * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) - * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) - * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the biases vector - * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: F16 - * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) - * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector - */ -__kernel void depthwise_convolution_3x3_f16( - TENSOR3D_DECLARATION(src), - TENSOR3D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights) -#if defined(HAS_BIAS) - , - VECTOR_DECLARATION(biases) -#endif //defined(HAS_BIAS) -) -{ - Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst); - Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); -#if defined(HAS_BIAS) - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); -#endif //defined(HAS_BIAS) - - // Extract channel and linearized batch indices - const int channel = get_global_id(2) % DST_CHANNELS; - const int batch = get_global_id(2) / DST_CHANNELS; - // Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER) - __global uchar *src_addr = src_ptr + get_global_id(0) * src_step_x + get_global_id(1) * src_step_y + get_global_id(2) * src_step_z - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * - (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; - __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; - - uchar3 offset = (uchar3)(0, 1, 2) * (uchar3)weights_stride_y; - half3 weights_values0 = vload3(0, (__global half *)(weights_addr + offset.s0)); - half3 weights_values1 = vload3(0, (__global half *)(weights_addr + offset.s1)); - half3 weights_values2 = vload3(0, (__global half *)(weights_addr + offset.s2)); - - half4 pixels = convolution3x3_f16(src_addr, src_stride_y, weights_values0.s0, weights_values0.s1, weights_values0.s2, - weights_values1.s0, weights_values1.s1, weights_values1.s2, - weights_values2.s0, weights_values2.s1, weights_values2.s2); -#if defined(HAS_BIAS) - pixels += (half4)(*((__global half *)(biases.ptr + channel * biases_stride_x))); -#endif //defined(HAS_BIAS) - - vstore4(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, pixels, A_VAL, B_VAL), 0, (__global half *)dst.ptr); -} -#endif // defined(DEPTH_MULTIPLIER) -#endif // defined(CONV_STRIDE_X) - -/** This OpenCL kernel is optimized for Bifrost architectures and computes the 16bit floating point depthwise convolution 3x3 - * when both stride_x and stride_y are equal to 1 - * - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note If activation function is enabled, the data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types: half. - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16 - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr - * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) - * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) - * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) - * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the biases vector - * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as @p src_ptr - * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) - * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector - */ -__kernel void depthwise_convolution_3x3_stridex1_stridey1_f16( - TENSOR3D_DECLARATION(src), - TENSOR3D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights) -#if defined(HAS_BIAS) - , - VECTOR_DECLARATION(biases) -#endif //defined(HAS_BIAS) -) -{ - Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst); - Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); - - // Extract channel and linearized batch indices - const int channel = get_global_id(2) % DST_CHANNELS; - const int batch = get_global_id(2) / DST_CHANNELS; - -#ifdef HAS_BIAS - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - - half bias = *((__global half *)(vector_offset(&biases, channel))); -#endif /* defined(HAS_BIAS) */ - - half4 pixels0 = 0.0f; - half4 pixels1 = 0.0f; - half4 pixels2 = 0.0f; - half4 pixels3 = 0.0f; - - // Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER) - __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; - __global uchar *src_addr = src_ptr + get_global_id(0) * src_step_x + get_global_id(1) * src_step_y + get_global_id(2) * src_step_z - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * - (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; - -#if(DILATION_X == 1 && DILATION_Y == 1) - // Load the weights - half3 weights_row0 = vload3(0, (__global half *)(weights_addr + 0 * weights_stride_y)); - half3 weights_row1 = vload3(0, (__global half *)(weights_addr + 1 * weights_stride_y)); - half3 weights_row2 = vload3(0, (__global half *)(weights_addr + 2 * weights_stride_y)); - - // Note: Since each work-item computes 4x4 elements, we need to load 6 rows from the input tensor - half8 src00 = vload8(0, (__global half *)(src_addr + 0 * src_stride_y)); // Row0 - half8 src10 = vload8(0, (__global half *)(src_addr + 1 * src_stride_y)); // Row1 - half8 src20 = vload8(0, (__global half *)(src_addr + 2 * src_stride_y)); // Row2 - half8 src30 = vload8(0, (__global half *)(src_addr + 3 * src_stride_y)); // Row3 - half8 src40 = vload8(0, (__global half *)(src_addr + 4 * src_stride_y)); // Row4 - half8 src50 = vload8(0, (__global half *)(src_addr + 5 * src_stride_y)); // Row5 - - CONVOLUTION1x3_4X1_STRIDE1(pixels0, src00, weights_row0); - CONVOLUTION1x3_4X1_STRIDE1(pixels0, src10, weights_row1); - CONVOLUTION1x3_4X1_STRIDE1(pixels0, src20, weights_row2); - CONVOLUTION1x3_4X1_STRIDE1(pixels1, src10, weights_row0); - CONVOLUTION1x3_4X1_STRIDE1(pixels1, src20, weights_row1); - CONVOLUTION1x3_4X1_STRIDE1(pixels1, src30, weights_row2); - CONVOLUTION1x3_4X1_STRIDE1(pixels2, src20, weights_row0); - CONVOLUTION1x3_4X1_STRIDE1(pixels2, src30, weights_row1); - CONVOLUTION1x3_4X1_STRIDE1(pixels2, src40, weights_row2); - CONVOLUTION1x3_4X1_STRIDE1(pixels3, src30, weights_row0); - CONVOLUTION1x3_4X1_STRIDE1(pixels3, src40, weights_row1); - CONVOLUTION1x3_4X1_STRIDE1(pixels3, src50, weights_row2); - -#else /* DILATION_X==1 && DILATION_Y==1 */ - - //3x3 Convolution of elements starting in 0th row - pixels0 = convolution_3x3_dilation_stridex1_stridey1_f16(src_addr, src_stride_x, src_stride_y, 0, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 1st row - pixels1 = convolution_3x3_dilation_stridex1_stridey1_f16(src_addr, src_stride_x, src_stride_y, 1, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 2nd row - pixels2 = convolution_3x3_dilation_stridex1_stridey1_f16(src_addr, src_stride_x, src_stride_y, 2, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 3rd row - pixels3 = convolution_3x3_dilation_stridex1_stridey1_f16(src_addr, src_stride_x, src_stride_y, 3, weights_addr, weights_stride_y); - -#endif /* DILATION_X==1 && DILATION_Y==1 */ - -#ifdef HAS_BIAS - pixels0 += (half4)bias; - pixels1 += (half4)bias; - pixels2 += (half4)bias; - pixels3 += (half4)bias; -#endif /* defined(HAS_BIAS) */ - - vstore4(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, pixels0, A_VAL, B_VAL), 0, (__global half *)(dst.ptr + 0 * dst_stride_y)); - vstore4(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, pixels1, A_VAL, B_VAL), 0, (__global half *)(dst.ptr + 1 * dst_stride_y)); - vstore4(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, pixels2, A_VAL, B_VAL), 0, (__global half *)(dst.ptr + 2 * dst_stride_y)); - vstore4(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, pixels3, A_VAL, B_VAL), 0, (__global half *)(dst.ptr + 3 * dst_stride_y)); -} - -/** This OpenCL kernel is optimized for Bifrost architectures and computes 16bit floating point the depthwise convolution 3x3 - * when both stride_x and stride_y are equal to 2 - * - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note If activation function is enabled, the data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types: half. - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16 - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_y * number of elements along Z processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr - * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) - * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) - * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) - * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the biases vector - * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as @p src_ptr - * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) - * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector - */ -__kernel void depthwise_convolution_3x3_stridex2_stridey2_f16( - TENSOR3D_DECLARATION(src), - TENSOR3D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights) -#if defined(HAS_BIAS) - , - VECTOR_DECLARATION(biases) -#endif //defined(HAS_BIAS) -) -{ - Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst); - Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); - - // Extract channel and linearized batch indices - const int channel = get_global_id(2) % DST_CHANNELS; - const int batch = get_global_id(2) / DST_CHANNELS; - -#ifdef HAS_BIAS - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - - half bias = *((__global half *)(vector_offset(&biases, channel))); -#endif /* defined(HAS_BIAS) */ - - half4 pixels0 = 0.0f; - half4 pixels1 = 0.0f; - - // Load relevant input and weights data ( Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER) - __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; - __global uchar *src_addr = src_ptr + get_global_id(0) * src_step_x + get_global_id(1) * src_step_y + get_global_id(2) * src_step_z - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * - (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; - -#if(DILATION_X == 1 && DILATION_Y == 1) - - // Load the weights - half3 weights_row0 = vload3(0, (__global half *)(weights_addr + 0 * weights_stride_y)); - half3 weights_row1 = vload3(0, (__global half *)(weights_addr + 1 * weights_stride_y)); - half3 weights_row2 = vload3(0, (__global half *)(weights_addr + 2 * weights_stride_y)); - - // Note: Since each work-item computes 2x4 elements, we need to load 5 rows from the input tensor - half8 src00 = vload8(0, (__global half *)(src_addr + 0 * src_stride_y)); // Row0 - half2 src01 = vload2(4, (__global half *)(src_addr + 0 * src_stride_y)); // Row0 - half8 src10 = vload8(0, (__global half *)(src_addr + 1 * src_stride_y)); // Row1 - half2 src11 = vload2(4, (__global half *)(src_addr + 1 * src_stride_y)); // Row1 - half8 src20 = vload8(0, (__global half *)(src_addr + 2 * src_stride_y)); // Row2 - half2 src21 = vload2(4, (__global half *)(src_addr + 2 * src_stride_y)); // Row2 - half8 src30 = vload8(0, (__global half *)(src_addr + 3 * src_stride_y)); // Row3 - half2 src31 = vload2(4, (__global half *)(src_addr + 3 * src_stride_y)); // Row3 - half8 src40 = vload8(0, (__global half *)(src_addr + 4 * src_stride_y)); // Row4 - half2 src41 = vload2(4, (__global half *)(src_addr + 4 * src_stride_y)); // Row4 - - CONVOLUTION1x3_4X1_STRIDE2(pixels0, src00, src01, weights_row0); - CONVOLUTION1x3_4X1_STRIDE2(pixels0, src10, src11, weights_row1); - CONVOLUTION1x3_4X1_STRIDE2(pixels0, src20, src21, weights_row2); - CONVOLUTION1x3_4X1_STRIDE2(pixels1, src20, src21, weights_row0); - CONVOLUTION1x3_4X1_STRIDE2(pixels1, src30, src31, weights_row1); - CONVOLUTION1x3_4X1_STRIDE2(pixels1, src40, src41, weights_row2); - -#else /* DILATION_X==1 && DILATION_Y==1 */ - //3x3 Convolution of elements starting in 0th row - pixels0 = convolution_3x3_dilation_stridex2_stridey2_f16(src_addr, src_stride_x, src_stride_y, 0, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 2nd row - pixels1 = convolution_3x3_dilation_stridex2_stridey2_f16(src_addr, src_stride_x, src_stride_y, 2, weights_addr, weights_stride_y); -#endif /* DILATION_X==1 && DILATION_Y==1 */ - -#ifdef HAS_BIAS - pixels0 += (half4)bias; - pixels1 += (half4)bias; -#endif /* defined(HAS_BIAS) */ - - vstore4(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, pixels0, A_VAL, B_VAL), 0, (__global half *)(dst.ptr + 0 * dst_stride_y)); - vstore4(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, pixels1, A_VAL, B_VAL), 0, (__global half *)(dst.ptr + 1 * dst_stride_y)); -} -#endif // defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) && defined(DEPTH_MULTIPLIER) && defined(DST_CHANNELS) && defined(IS_F16) - -#if defined(SRC_DIM1) && defined(SRC_DIM2) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(N0) && defined(DATA_TYPE) && defined(DILATION_X) && defined(DILATION_Y) && defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) && defined(CONV_PAD_LEFT) && defined(CONV_PAD_TOP) && defined(VEC_SIZE_LEFTOVER) -/** This function computes the depthwise convolution for NHWC data layout. This kernel assumes that the weights tensor is NOT reshaped - * - * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float - * @note The number of elements processed must be passed at compile time using -DN0 (e.g. -DN0=2) - * @note The depth multiplier must be passed at compile time using -DDEPTH_MULTIPLIER (e.g. -DDEPTH_MULTIPLIER=1) - * @note The first dimension of the input tensor must be passed at compile time using -DSRC_DIM1 (e.g. -DSRC_DIM1=112) - * @note The second dimension of the input tensor must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM2=80) - * @note The kernel width must be passed at compile time using -DKERNEL_WIDTH (e.g. -DKERNEL_WIDTH=5) - * @note The kernel height must be passed at compile time using -DKERNEL_HEIGHT (e.g. -DKERNEL_HEIGHT=5) - * @note The convolution pad top must be passed at compile time using -DCONV_PAD_TOP (e.g. -DCONV_PAD_TOP=1) - * @note The convolution pad top must be passed at compile time using -DCONV_PAD_LEFT (e.g. -DCONV_PAD_LEFT=1) - * @note The convolution stride along the width must be passed at compile time using -DCONV_STRIDE_X (e.g. -DCONV_STRIDE_Y=X) - * @note The convolution stride along the height must be passed at compile time using -DCONV_STRIDE_Y (e.g. -DCONV_STRIDE_Y=1) - * @note Leftover vector size has to be passed at compile time using -DVEC_SIZE_LEFTOVER. e.g. -DVEC_SIZE=3. It is defined as the remainder between the input's first dimension and VEC_SIZE - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32 - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_y * number of elements along Z processed per workitem(in bytes) - * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) - * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as src_ptr - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes) - * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F16/F32 - * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) - * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) - * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) - * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor - * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as src_ptr - * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) - * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector - */ -__kernel void dwc_MxN_native_fp_nhwc( - TENSOR4D_DECLARATION(src), - TENSOR4D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights) -#if defined(HAS_BIAS) - , - VECTOR_DECLARATION(biases) -#endif // defined(HAS_BIAS) -) -{ - int x_offs = max((int)(get_global_id(0) * N0 - (N0 - VEC_SIZE_LEFTOVER) % N0), 0) * sizeof(DATA_TYPE); - - int x = get_global_id(0); // channels - int y = get_global_id(1); // spatial coordinate x -#if defined(DST_DEPTH) - int z = get_global_id(2) % (int)DST_DEPTH; // spatial coordinate y - int b = get_global_id(2) / (int)DST_DEPTH; // batch -#else // defined(DST_DEPTH) - int z = get_global_id(2); // spatial coordinate y -#endif // defined(DST_DEPTH) - - __global uchar *s_addr = src_ptr + src_offset_first_element_in_bytes + x_offs; - - __global uchar *d_addr = dst_ptr + dst_offset_first_element_in_bytes + x_offs * (int)DEPTH_MULTIPLIER + y * dst_stride_y + z * dst_stride_z; - - __global uchar *w_addr = weights_ptr + weights_offset_first_element_in_bytes + x_offs * (int)DEPTH_MULTIPLIER; - -#if defined(HAS_BIAS) - __global uchar *b_addr = biases_ptr + biases_offset_first_element_in_bytes + x_offs * (int)DEPTH_MULTIPLIER; -#endif // defined(HAS_BIAS) - -#if defined(DST_DEPTH) - s_addr += b * src_stride_w; - d_addr += b * dst_stride_w; -#endif // defined(DST_DEPTH) - - for(int d = 0; d < (int)DEPTH_MULTIPLIER; ++d) - { - // Each work-item computes N0x1x1 elements - VEC_DATA_TYPE(DATA_TYPE, N0) - res0 = 0; - - int x_coord = y * CONV_STRIDE_X - (int)CONV_PAD_LEFT; - int y_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP; - - for(int yk = 0; yk < KERNEL_HEIGHT; ++yk) - { - if(y_coord >= 0 && y_coord < SRC_DIM2) - { - int x_coord_tmp = x_coord; - - for(int xk = 0; xk < KERNEL_WIDTH; ++xk) - { - if(x_coord_tmp >= 0 && x_coord_tmp < SRC_DIM1) - { - int s_offset = x_coord_tmp * (int)src_stride_y + y_coord * (int)src_stride_z; - int w_offset = xk * weights_stride_y + yk * weights_stride_z; - - // Load input and weights values - VEC_DATA_TYPE(DATA_TYPE, N0) - i = VLOAD(N0)(0, (__global DATA_TYPE *)(s_addr + s_offset)); - VEC_DATA_TYPE(DATA_TYPE, N0) - w = VLOAD(N0)(0, (__global DATA_TYPE *)(w_addr + w_offset)); - -#if GPU_ARCH == GPU_ARCH_MIDGARD - res0 += i * w; -#else // GPU_ARCH == GPU_ARCH_MIDGARD - res0 = fma(i, w, res0); -#endif // GPU_ARCH == GPU_ARCH_MIDGARD - } - x_coord_tmp += DILATION_X; - } - } - y_coord += DILATION_Y; - } - -#if defined(HAS_BIAS) - res0 += VLOAD(N0)(0, (__global DATA_TYPE *)(b_addr)); -#endif // defined(HAS_BIAS) - - res0 = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, N0, res0, A_VAL, B_VAL); - - STORE_VECTOR_SELECT(res, DATA_TYPE, d_addr, N0, VEC_SIZE_LEFTOVER, VEC_SIZE_LEFTOVER != 0 && get_global_id(0) == 0) - - w_addr += sizeof(DATA_TYPE); - d_addr += sizeof(DATA_TYPE); -#if defined(HAS_BIAS) - b_addr += sizeof(DATA_TYPE); -#endif // defined(HAS_BIAS) - } -} -#endif // defined(SRC_DIM1) && defined(SRC_DIM2) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defiend(N0) && defined(DATA_TYPE) && defined(DILATION_X) && defined(DILATION_Y) && defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) && defined(CONV_PAD_LEFT) && defined(CONV_PAD_TOP) && defined(VEC_SIZE_LEFTOVER) - -#if defined(VEC_SIZE) && defined(SRC_DIM_2) && defined(CONV_PAD_TOP) && defined(CONV_PAD_LEFT) && defined(DATA_TYPE) - -#if DATA_TYPE != float || DATA_TYPE != half -#error "Unsupported data type" -#endif // DATA_TYPE != float || DATA_TYPE != half - -#define VEC_FLOAT VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - -#define FILL_ZERO_OUT_OF_BOUND_3(data_type, vec_size, basename, cond) \ - ({ \ - basename##0 = select(basename##0, (VEC_DATA_TYPE(data_type, vec_size))0, (SELECT_VEC_DATA_TYPE(data_type, vec_size))((cond).s0)); \ - basename##1 = select(basename##1, (VEC_DATA_TYPE(data_type, vec_size))0, (SELECT_VEC_DATA_TYPE(data_type, vec_size))((cond).s1)); \ - basename##2 = select(basename##2, (VEC_DATA_TYPE(data_type, vec_size))0, (SELECT_VEC_DATA_TYPE(data_type, vec_size))((cond).s2)); \ - }) - -#define FILL_ZERO_OUT_OF_BOUND_4(data_type, vec_size, basename, cond) \ - ({ \ - FILL_ZERO_OUT_OF_BOUND_3(data_type, vec_size, basename, cond); \ - basename##3 = select(basename##3, (VEC_DATA_TYPE(data_type, vec_size))0, (SELECT_VEC_DATA_TYPE(data_type, vec_size))((cond).s3)); \ - }) - -#if defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) - -/** This function computes the depthwise convolution for NHWC data layout when the stride along the width or height is not 1. - * - * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float - * @note The number of elements read per thread must be passed at compile time using -DVEC_SIZE (e.g. -DVEC_SIZE=2) - * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112) - * @note The convolution pad top must be passed at compile time using -DCONV_PAD_TOP (e.g. -DCONV_PAD_TOP=1) - * @note The convolution pad top must be passed at compile time using -DCONV_PAD_LEFT (e.g. -DCONV_PAD_LEFT=1) - * @note The convolution stride along the width must be passed at compile time using -DCONV_STRIDE_X (e.g. -DCONV_STRIDE_Y=X) - * @note The convolution stride along the height must be passed at compile time using -DCONV_STRIDE_Y (e.g. -DCONV_STRIDE_Y=1) - * @note The dilation_x and dilation_y must be passed at compile time using -DDILATION_X and -DDILATION_Y: e.g. -DDILATION_X=1, -DDILATION_Y=1 - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size - * @note The size of the partial store block in x must be passed at compile time using -DPARTIAL_STORE_N0 (e.g. -DPARTIAL_STORE_N0=1) - * @note In case of biases, -DHAS_BIAS must to be passed at compile - * @note If the output tensor has more than three dimensions, its third dimension must be passed at compile time using -DDST_DEPTH (e.g. -DDST_DEPTH=32) - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32 - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_y * number of elements along Z processed per workitem(in bytes) - * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) - * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as src_ptr - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes) - * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F16/F32 - * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) - * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) - * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) - * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor - * @param[in] max_offset Max offset for the input tensor - * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as src_ptr - * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) - * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector - */ -__kernel void depthwise_convolution_3x3_nhwc( - TENSOR4D_DECLARATION(src), - TENSOR4D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights) -#if defined(HAS_BIAS) - , - VECTOR_DECLARATION(biases) -#endif /* defined(HAS_BIAS) */ -) -{ - int x_offset = max((int)(get_global_id(0) * VEC_SIZE - (VEC_SIZE - PARTIAL_STORE_N0) % VEC_SIZE), 0) * sizeof(DATA_TYPE); - int y = get_global_id(1); // spatial coordinate x -#if defined(DST_DEPTH) - int z = get_global_id(2) % (int)DST_DEPTH; // spatial coordinate y - int b = get_global_id(2) / (int)DST_DEPTH; // batch -#else // defined(DST_DEPTH) - int z = get_global_id(2); // spatial coordinate y -#endif // defined(DST_DEPTH) - - __global uchar *weights_addr = weights_ptr + weights_offset_first_element_in_bytes + x_offset; - -#if defined(DST_DEPTH) - __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x_offset + b * src_stride_w; -#else /* defined(DST_DEPTH) */ - __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x_offset; -#endif /* defined(DST_DEPTH) */ - - int3 src_coord_y = (int3)(y * CONV_STRIDE_X - CONV_PAD_LEFT) + (int3)(0, DILATION_X, 2 * DILATION_X); - int3 src_coord_z = (int3)(z * CONV_STRIDE_Y - CONV_PAD_TOP) + (int3)(0, DILATION_Y, 2 * DILATION_Y); - - int3 src_offset_y = clamp(src_coord_y, (int3)0, (int3)(SRC_DIM_1 - 1)); - int3 src_offset_z = clamp(src_coord_z, (int3)0, (int3)(SRC_DIM_2 - 1)); - - // Use these vectors to check whether the unclamped load would have been out of bounds - src_coord_y = (src_offset_y != src_coord_y); - src_coord_z = (src_offset_z != src_coord_z); - - src_offset_y *= (int3)src_stride_y; - src_offset_z *= (int3)src_stride_z; - - // We compute VEC_SIZEx1x1 [C,W,H] elements - VEC_FLOAT acc0 = 0; - - // Load weights - VEC_FLOAT w0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y + 0 * weights_stride_z)); - VEC_FLOAT w1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y + 0 * weights_stride_z)); - VEC_FLOAT w2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y + 0 * weights_stride_z)); - VEC_FLOAT w3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y + 1 * weights_stride_z)); - VEC_FLOAT w4 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y + 1 * weights_stride_z)); - VEC_FLOAT w5 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y + 1 * weights_stride_z)); - VEC_FLOAT w6 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y + 2 * weights_stride_z)); - VEC_FLOAT w7 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y + 2 * weights_stride_z)); - VEC_FLOAT w8 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y + 2 * weights_stride_z)); - - // Load input values - // z == 0 - VEC_FLOAT values0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s0 + src_offset_y.s0)); - VEC_FLOAT values1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s0 + src_offset_y.s1)); - VEC_FLOAT values2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s0 + src_offset_y.s2)); - - FILL_ZERO_OUT_OF_BOUND_3(DATA_TYPE, VEC_SIZE, values, src_coord_y | (int3)src_coord_z.s0); - - acc0 = fma(values0, w0, acc0); - acc0 = fma(values1, w1, acc0); - acc0 = fma(values2, w2, acc0); - - // z == 1 - values0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s1 + src_offset_y.s0)); - values1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s1 + src_offset_y.s1)); - values2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s1 + src_offset_y.s2)); - - FILL_ZERO_OUT_OF_BOUND_3(DATA_TYPE, VEC_SIZE, values, src_coord_y | (int3)src_coord_z.s1); - - acc0 = fma(values0, w3, acc0); - acc0 = fma(values1, w4, acc0); - acc0 = fma(values2, w5, acc0); - - // z == 2 - values0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s2 + src_offset_y.s0)); - values1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s2 + src_offset_y.s1)); - values2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s2 + src_offset_y.s2)); - - FILL_ZERO_OUT_OF_BOUND_3(DATA_TYPE, VEC_SIZE, values, src_coord_y | (int3)src_coord_z.s2); - - acc0 = fma(values0, w6, acc0); - acc0 = fma(values1, w7, acc0); - acc0 = fma(values2, w8, acc0); - -#if defined(HAS_BIAS) - __global uchar *biases_addr = biases_ptr + biases_offset_first_element_in_bytes + x_offset; - VEC_FLOAT bias_values = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)biases_addr); - acc0 += bias_values; -#endif // defined(HAS_BIAS) - -#if defined(DST_DEPTH) - __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_offset + y * dst_step_y + z * dst_step_z + b * dst_stride_w; -#else /* defined(DST_DEPTH) */ - __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_offset + y * dst_step_y + z * dst_step_z; -#endif /* defined(DST_DEPTH) */ - - acc0 = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, acc0, A_VAL, B_VAL); - STORE_VECTOR_SELECT(acc, DATA_TYPE, dst_addr, VEC_SIZE, PARTIAL_STORE_N0, PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0) -} -#endif // defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) - -#if defined(NUM_ROWS_PROCESSED) && defined(NUM_PLANES_PROCESSED) -/** This function computes the depthwise convolution for NHWC data layout when the stride along the width and height is 1. - * - * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float - * @note The number of elements read per thread must be passed at compile time using -DVEC_SIZE (e.g. -DVEC_SIZE=2) - * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112) - * @note The number of rows processed per thread must be passed at compile time using -DNUM_ROWS_PROCESSED (i.e. -DNUM_ROWS_PROCESSED=2) - * @note The number of planes processed per thread must be passed at compile time using -DNUM_PLANES_PROCESSED (i.e. -DNUM_PLANES_PROCESSED=2) - * @note The convolution pad top must be passed at compile time using -DCONV_PAD_TOP (e.g. -DCONV_PAD_TOP=1) - * @note The convolution pad top must be passed at compile time using -DCONV_PAD_LEFT (e.g. -DCONV_PAD_LEFT=1) - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size - * @note The size of the partial store block in y must be passed at compile time using -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_M0=1) - * @note The size of the partial store block in x must be passed at compile time using -DPARTIAL_STORE_N0 (e.g. -DPARTIAL_STORE_N0=1) - * @note The size of the output's second dimension must be passed at compile time using -DDST_DIM_1 (e.g. -DDST_DIM_1=64) - * @note The size of the output's third dimension must be passed at compile time using -DDST_DIM_2 (e.g. -DDST_DIM_2=32) - * @note In case of biases, -DHAS_BIAS must to be passed at compile - * @note If the output tensor has more than three dimensions, its third dimension must be passed at compile time using -DDST_DEPTH (e.g. -DDST_DEPTH=32) - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32 - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_y * number of elements along Z processed per workitem(in bytes) - * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) - * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as src_ptr - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes) - * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F16/F32 - * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) - * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) - * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) - * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor - * @param[in] max_offset Max offset for the input tensor - * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as src_ptr - * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) - * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector - */ -__kernel void depthwise_convolution_3x3_nhwc_stride1( - TENSOR4D_DECLARATION(src), - TENSOR4D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights) -#if defined(HAS_BIAS) - , - VECTOR_DECLARATION(biases) -#endif /* defined(HAS_BIAS) */ -) -{ - int x_offset = max((int)(get_global_id(0) * VEC_SIZE - (VEC_SIZE - PARTIAL_STORE_N0) % VEC_SIZE), 0) * sizeof(DATA_TYPE); - int y = get_global_id(1); // spatial coordinate x -#if defined(DST_DEPTH) - int z = get_global_id(2) % (int)DST_DEPTH; // spatial coordinate y - int b = get_global_id(2) / (int)DST_DEPTH; // batch -#else // defined(DST_DEPTH) - int z = get_global_id(2); // spatial coordinate y -#endif // defined(DST_DEPTH) - - __global uchar *weights_addr = weights_ptr + weights_offset_first_element_in_bytes + x_offset; - -#if defined(DST_DEPTH) - __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x_offset + b * src_stride_w; -#else /* defined(DST_DEPTH) */ - __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x_offset; -#endif /* defined(DST_DEPTH) */ - - int4 src_coord_y = (int4)(y * NUM_ROWS_PROCESSED - CONV_PAD_LEFT) + V_OFFS4(int); - int4 src_coord_z = (int4)(z * NUM_PLANES_PROCESSED - CONV_PAD_TOP) + V_OFFS4(int); - - int4 src_offset_y = clamp(src_coord_y, (int4)0, (int4)(SRC_DIM_1 - 1)); - int4 src_offset_z = clamp(src_coord_z, (int4)0, (int4)(SRC_DIM_2 - 1)); - - // Use these vectors to check whether the unclamped load would have been out of bounds - src_coord_y = (src_offset_y != src_coord_y); - src_coord_z = (src_offset_z != src_coord_z); - - src_offset_y *= (int4)src_stride_y; - src_offset_z *= (int4)src_stride_z; - - // We compute VEC_SIZEx2x2 [C,W,H] elements - VEC_FLOAT acc0 = 0; - VEC_FLOAT acc1 = 0; - VEC_FLOAT acc2 = 0; - VEC_FLOAT acc3 = 0; - - // Load weights - VEC_FLOAT w0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y + 0 * weights_stride_z)); - VEC_FLOAT w1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y + 0 * weights_stride_z)); - VEC_FLOAT w2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y + 0 * weights_stride_z)); - VEC_FLOAT w3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y + 1 * weights_stride_z)); - VEC_FLOAT w4 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y + 1 * weights_stride_z)); - VEC_FLOAT w5 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y + 1 * weights_stride_z)); - VEC_FLOAT w6 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y + 2 * weights_stride_z)); - VEC_FLOAT w7 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y + 2 * weights_stride_z)); - VEC_FLOAT w8 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y + 2 * weights_stride_z)); - - // Load input values - // z == 0 - VEC_FLOAT values0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s0 + src_offset_y.s0)); - VEC_FLOAT values1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s0 + src_offset_y.s1)); - VEC_FLOAT values2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s0 + src_offset_y.s2)); - VEC_FLOAT values3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s0 + src_offset_y.s3)); - - FILL_ZERO_OUT_OF_BOUND_4(DATA_TYPE, VEC_SIZE, values, src_coord_y | (int4)src_coord_z.s0); - - acc0 = fma(values0, w0, acc0); - acc0 = fma(values1, w1, acc0); - acc0 = fma(values2, w2, acc0); - acc1 = fma(values1, w0, acc1); - acc1 = fma(values2, w1, acc1); - acc1 = fma(values3, w2, acc1); - - // z == 1 - values0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s1 + src_offset_y.s0)); - values1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s1 + src_offset_y.s1)); - values2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s1 + src_offset_y.s2)); - values3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s1 + src_offset_y.s3)); - - FILL_ZERO_OUT_OF_BOUND_4(DATA_TYPE, VEC_SIZE, values, src_coord_y | (int4)src_coord_z.s1); - - acc0 = fma(values0, w3, acc0); - acc0 = fma(values1, w4, acc0); - acc0 = fma(values2, w5, acc0); - acc1 = fma(values1, w3, acc1); - acc1 = fma(values2, w4, acc1); - acc1 = fma(values3, w5, acc1); - - acc2 = fma(values0, w0, acc2); - acc2 = fma(values1, w1, acc2); - acc2 = fma(values2, w2, acc2); - acc3 = fma(values1, w0, acc3); - acc3 = fma(values2, w1, acc3); - acc3 = fma(values3, w2, acc3); - - // z == 2 - values0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s2 + src_offset_y.s0)); - values1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s2 + src_offset_y.s1)); - values2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s2 + src_offset_y.s2)); - values3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s2 + src_offset_y.s3)); - - FILL_ZERO_OUT_OF_BOUND_4(DATA_TYPE, VEC_SIZE, values, src_coord_y | (int4)src_coord_z.s2); - - acc0 = fma(values0, w6, acc0); - acc0 = fma(values1, w7, acc0); - acc0 = fma(values2, w8, acc0); - acc1 = fma(values1, w6, acc1); - acc1 = fma(values2, w7, acc1); - acc1 = fma(values3, w8, acc1); - - acc2 = fma(values0, w3, acc2); - acc2 = fma(values1, w4, acc2); - acc2 = fma(values2, w5, acc2); - acc3 = fma(values1, w3, acc3); - acc3 = fma(values2, w4, acc3); - acc3 = fma(values3, w5, acc3); - - // z == 3 - values0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s3 + src_offset_y.s0)); - values1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s3 + src_offset_y.s1)); - values2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s3 + src_offset_y.s2)); - values3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + src_offset_z.s3 + src_offset_y.s3)); - - FILL_ZERO_OUT_OF_BOUND_4(DATA_TYPE, VEC_SIZE, values, src_coord_y | (int4)src_coord_z.s3); - - acc2 = fma(values0, w6, acc2); - acc2 = fma(values1, w7, acc2); - acc2 = fma(values2, w8, acc2); - acc3 = fma(values1, w6, acc3); - acc3 = fma(values2, w7, acc3); - acc3 = fma(values3, w8, acc3); - -#if defined(HAS_BIAS) - __global uchar *biases_addr = biases_ptr + biases_offset_first_element_in_bytes + x_offset; - - VEC_FLOAT bias_values = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)biases_addr); - - acc0 += bias_values; - acc1 += bias_values; - acc2 += bias_values; - acc3 += bias_values; -#endif // defined(HAS_BIAS) - - int2 dst_offset_y = min((int2)(y * NUM_ROWS_PROCESSED) + V_OFFS2(int), (int2)(DST_DIM_1 - 1)) * (int2)dst_stride_y; - int dst_coord_z = z * NUM_PLANES_PROCESSED; - -#if defined(DST_DEPTH) - __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_offset + dst_coord_z * dst_stride_z + b * dst_stride_w; -#else // defined(DST_DEPTH) - __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_offset + dst_coord_z * dst_stride_z; -#endif // defined(DST_DEPTH) - - /* Store vectors in reverse order along the Y. The Y offsets are calculated so that they are forced to be in bound. - * If only the first address is in bound, the Y offset of the second address will be brought back and there will be 2 writes in the same location for the same thread. - * Since the last vector to be written is always the valid one for that location, it overwrites the wrong values. - */ - values0 = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, acc1, A_VAL, B_VAL); - STORE_VECTOR_SELECT(values, DATA_TYPE, dst_addr + dst_offset_y.s1, VEC_SIZE, PARTIAL_STORE_N0, PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0) - - values0 = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, acc0, A_VAL, B_VAL); - STORE_VECTOR_SELECT(values, DATA_TYPE, dst_addr + dst_offset_y.s0, VEC_SIZE, PARTIAL_STORE_N0, PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0) - -#if((DST_DIM_2 % NUM_PLANES_PROCESSED) != 0) - if((dst_coord_z + 1) < DST_DIM_2) -#endif // ((DST_DIM_2 % NUM_PLANES_PROCESSED) != 0) - { - values0 = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, acc3, A_VAL, B_VAL); - STORE_VECTOR_SELECT(values, DATA_TYPE, dst_addr + dst_stride_z + dst_offset_y.s1, VEC_SIZE, PARTIAL_STORE_N0, PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0) - - values0 = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, acc2, A_VAL, B_VAL); - STORE_VECTOR_SELECT(values, DATA_TYPE, dst_addr + dst_stride_z + dst_offset_y.s0, VEC_SIZE, PARTIAL_STORE_N0, PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0) - } -} - -#endif // defined(NUM_ROWS_PROCESSED) && defined(NUM_PLANES_PROCESSED) -#endif // defined(VEC_SIZE) && defined(SRC_DIM_2) && defined(CONV_PAD_TOP) && defined(CONV_PAD_LEFT) && defined(DATA_TYPE)
\ No newline at end of file diff --git a/src/core/CL/cl_kernels/depthwise_convolution_quantized.cl b/src/core/CL/cl_kernels/depthwise_convolution_quantized.cl deleted file mode 100644 index 000dce1590..0000000000 --- a/src/core/CL/cl_kernels/depthwise_convolution_quantized.cl +++ /dev/null @@ -1,961 +0,0 @@ -/* - * Copyright (c) 2017-2021 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. - */ - -#include "helpers_asymm.h" - -#ifndef VEC_SIZE -#if defined(N0) -#define VEC_SIZE N0 -#else /* defined(N0) */ -#define VEC_SIZE 8 -#endif /* defined(N0) */ -#endif /* VEC_SIZE */ - -#if defined(ACTIVATION_TYPE) && defined(CONST_0) -#include "activation_layer_quant.cl" -#define ACTIVATION_FUNC(x) PERFORM_ACTIVATION_QUANT(ACTIVATION_TYPE, x) -#else /* defined(ACTIVATION_TYPE) && defined(CONST_0) */ -#define ACTIVATION_FUNC(x) (x) -#endif /* defined(ACTIVATION_TYPE) && defined(CONST_0) */ - -#define VEC_INT VEC_DATA_TYPE(int, VEC_SIZE) -#define VEC_FLOAT VEC_DATA_TYPE(float, VEC_SIZE) -#define VEC_SHORT VEC_DATA_TYPE(short, VEC_SIZE) - -#if defined(DATA_TYPE) && defined(WEIGHTS_TYPE) - -#define VEC_TYPE(size) VEC_DATA_TYPE(DATA_TYPE, size) - -#if defined(WEIGHTS_OFFSET) && defined(INPUT_OFFSET) && defined(K_OFFSET) && ((defined(OUTPUT_OFFSET) && defined(OUTPUT_MULTIPLIER) && defined(OUTPUT_SHIFT)) || defined(REAL_MULTIPLIER)) - -#if defined(WEIGHTS_PROMOTED_TYPE) -#define VEC_WEIGHTS_PROMOTED_TYPE(size) VEC_DATA_TYPE(WEIGHTS_PROMOTED_TYPE, size) - -#if defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) -#if defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) -#define ARM_DOT(x, y, val) val = arm_dot_acc((x), (y), val); -#else // defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) -#define ARM_DOT(x, y, val) val += arm_dot((x), (y)); -#endif // defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) -#endif // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) - -#if defined(CONV_STRIDE_Y) && defined(CONV_STRIDE_X) && defined(DEPTH_MULTIPLIER) && defined(DST_CHANNELS) - -#if CONV_STRIDE_X > 3 -#error "Stride X not supported" -#endif /* CONV_STRIDE_X > 3 */ - -#if !defined(IS_DOT8) - -#if DILATION_X == 1 - -#if CONV_STRIDE_X == 1 -#define GET_VALUES(first_value, left, middle, right) \ - ({ \ - int8 temp0 = CONVERT(vload8(0, (__global DATA_TYPE *)(first_value)), int8); \ - int2 temp1 = CONVERT(vload2(0, (__global DATA_TYPE *)(first_value + 8 * sizeof(DATA_TYPE))), int2); \ - \ - left = CONVERT(temp0.s01234567, int8); \ - middle = CONVERT((int8)(temp0.s1234, temp0.s567, temp1.s0), int8); \ - right = CONVERT((int8)(temp0.s2345, temp0.s67, temp1.s01), int8); \ - }) -#elif CONV_STRIDE_X == 2 -#define GET_VALUES(first_value, left, middle, right) \ - ({ \ - int16 temp0 = CONVERT(vload16(0, (__global DATA_TYPE *)(first_value)), int16); \ - int temp1 = CONVERT(*((__global DATA_TYPE *)(first_value + 16 * sizeof(DATA_TYPE))), int); \ - \ - left = CONVERT(temp0.s02468ace, int8); \ - middle = CONVERT(temp0.s13579bdf, int8); \ - right = CONVERT((int8)(temp0.s2468, temp0.sace, temp1), int8); \ - }) -#else /* CONV_STRIDE_X */ -#define GET_VALUES(first_value, left, middle, right) \ - ({ \ - int16 temp0 = CONVERT(vload16(0, (__global DATA_TYPE *)(first_value)), int16); \ - int8 temp1 = CONVERT(vload8(0, (__global DATA_TYPE *)(first_value + 16 * sizeof(DATA_TYPE))), int8); \ - \ - left = CONVERT((int8)(temp0.s0369, temp0.scf, temp1.s25), int8); \ - middle = CONVERT((int8)(temp0.s147a, temp0.sd, temp1.s036), int8); \ - right = CONVERT((int8)(temp0.s258b, temp0.se, temp1.s147), int8); \ - }) -#endif /* CONV_STRIDE_X */ - -#else /* DILATION_X == 1 */ - -#if CONV_STRIDE_X == 1 -#define GET_VALUES(first_value, left, middle, right) \ - ({ \ - left = CONVERT(vload8(0, (__global DATA_TYPE *)(first_value)), int8); \ - middle = CONVERT(vload8(0, (__global DATA_TYPE *)(first_value + DILATION_X * sizeof(DATA_TYPE))), int8); \ - right = CONVERT(vload8(0, (__global DATA_TYPE *)(first_value + 2 * DILATION_X * sizeof(DATA_TYPE))), int8); \ - }) -#elif CONV_STRIDE_X == 2 -#define GET_VALUES(first_value, left, middle, right) \ - ({ \ - int16 temp0 = CONVERT(vload16(0, (__global DATA_TYPE *)(first_value)), int16); \ - left = CONVERT(temp0.s02468ace, int8); \ - \ - temp0 = CONVERT(vload16(0, (__global DATA_TYPE *)(first_value + DILATION_X * sizeof(DATA_TYPE))), int16); \ - middle = CONVERT(temp0.s02468ace, int8); \ - \ - temp0 = CONVERT(vload16(0, (__global DATA_TYPE *)(first_value + 2 * DILATION_X * sizeof(DATA_TYPE))), int16); \ - right = CONVERT(temp0.s02468ace, int8); \ - }) -#else /* CONV_STRIDE_X */ -#define GET_VALUES(first_value, left, middle, right) \ - ({ \ - int16 temp0 = CONVERT(vload16(0, (__global DATA_TYPE *)(first_value)), int16); \ - int8 temp1 = CONVERT(vload8(0, (__global DATA_TYPE *)(first_value + 16 * sizeof(DATA_TYPE))), int8); \ - left = CONVERT((int8)(temp0.s0369, temp0.scf, temp1.s25), int8); \ - \ - temp0 = CONVERT(vload16(0, (__global DATA_TYPE *)(first_value + DILATION_X * sizeof(DATA_TYPE))), int16); \ - temp1 = CONVERT(vload8(0, (__global DATA_TYPE *)(first_value + (16 + DILATION_X) * sizeof(DATA_TYPE))), int8); \ - middle = CONVERT((int8)(temp0.s0369, temp0.scf, temp1.s25), int8); \ - \ - temp0 = CONVERT(vload16(0, (__global DATA_TYPE *)(first_value + 2 * DILATION_X * sizeof(DATA_TYPE))), int16); \ - temp1 = CONVERT(vload8(0, (__global DATA_TYPE *)(first_value + (16 + 2 * DILATION_X) * sizeof(DATA_TYPE))), int8); \ - right = CONVERT((int8)(temp0.s0369, temp0.scf, temp1.s25), int8); \ - }) - -#endif /* CONV_STRIDE_X */ -#endif /* DILATION_X==1 */ - -/** This function computes the depthwise convolution quantized. - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/QASYMM8_SIGNED - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL - * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) - * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) - * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) - * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor - * @param[in] output_multipliers_ptr Pointer to the output multipliers vector. Supported data types: S32 - * @param[in] output_multipliers_stride_x Stride of the output multipliers vector in X dimension (in bytes) - * @param[in] output_multipliers_step_x output_multipliers_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_multipliers_offset_first_element_in_bytes The offset of the first element in the output multipliers vector - * @param[in] output_shifts_ptr Pointer to the output shifts vector. Supported data types: S32 - * @param[in] output_shifts_stride_x Stride of the output shifts vector in X dimension (in bytes) - * @param[in] output_shifts_step_x output_shifts_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_shifts_offset_first_element_in_bytes The offset of the first element in the output shifts vector - * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: S32 - * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) - * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector - */ - -__kernel void dwc_3x3_native_quantized8_nchw( - TENSOR3D_DECLARATION(src), - TENSOR3D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights), - VECTOR_DECLARATION(output_multipliers), - VECTOR_DECLARATION(output_shifts) -#if defined(HAS_BIAS) - , - VECTOR_DECLARATION(biases) -#endif //defined(HAS_BIAS) -) -{ - __global uchar *src_addr = src_ptr + get_global_id(0) * src_step_x + get_global_id(1) * src_step_y + get_global_id(2) * src_step_z; - Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst); - Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); - Vector output_multipliers = CONVERT_TO_VECTOR_STRUCT_NO_STEP(output_multipliers); - Vector output_shifts = CONVERT_TO_VECTOR_STRUCT_NO_STEP(output_shifts); - - // Extract channel and linearized batch indices - const int channel = get_global_id(2) % DST_CHANNELS; - const int batch = get_global_id(2) / DST_CHANNELS; - -#if defined(HAS_BIAS) - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - - int bias_value = *((__global int *)(vector_offset(&biases, channel))); -#endif //defined(HAS_BIAS) - - // Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER) - src_addr -= batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z + (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; - __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; - - VEC_DATA_TYPE(WEIGHTS_TYPE, 3) - w0 = vload3(0, (__global WEIGHTS_TYPE *)(weights_addr + 0 * weights_stride_y)); - VEC_DATA_TYPE(WEIGHTS_TYPE, 3) - w1 = vload3(0, (__global WEIGHTS_TYPE *)(weights_addr + 1 * weights_stride_y)); - VEC_DATA_TYPE(WEIGHTS_TYPE, 3) - w2 = vload3(0, (__global WEIGHTS_TYPE *)(weights_addr + 2 * weights_stride_y)); - -#if defined(PER_CHANNEL_QUANTIZATION) - const int output_multiplier = *((__global int *)vector_offset(&output_multipliers, channel)); - const int output_shift = *((__global int *)vector_offset(&output_shifts, channel)); -#endif // defined(PER_CHANNEL_QUANTIZATION) - - int8 values0 = 0; - int8 sum0 = 0; -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - int8 values1 = 0; - int8 sum1 = 0; -#endif /* CONV_STRIDE_Y &&DILATION_Y==1 */ - - // Row0 - int8 left, middle, right; - GET_VALUES(src_addr + 0 * src_stride_y, left, middle, right); - values0 += left * (int8)(w0.s0); - values0 += middle * (int8)(w0.s1); - values0 += right * (int8)(w0.s2); - -#if WEIGHTS_OFFSET != 0 - sum0 += left + middle + right; -#endif /* WEIGHTS_OFFSET != 0 */ - - // Row1 - GET_VALUES(src_addr + DILATION_Y * src_stride_y, left, middle, right); - values0 += left * (int8)(w1.s0); - values0 += middle * (int8)(w1.s1); - values0 += right * (int8)(w1.s2); - -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - values1 += left * (int8)(w0.s0); - values1 += middle * (int8)(w0.s1); - values1 += right * (int8)(w0.s2); -#endif /* CONV_STRIDE_Y && DILATION_Y== 1 */ - -#if WEIGHTS_OFFSET != 0 - int8 tmp = left + middle + right; - sum0 += tmp; -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - sum1 += tmp; -#endif /* CONV_STRIDE_Y &&DILATION_Y== 1 */ -#endif /* WEIGHTS_OFFSET != 0 */ - - // Row2 - GET_VALUES(src_addr + 2 * DILATION_Y * src_stride_y, left, middle, right); - values0 += left * (int8)(w2.s0); - values0 += middle * (int8)(w2.s1); - values0 += right * (int8)(w2.s2); -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - values1 += left * (int8)(w1.s0); - values1 += middle * (int8)(w1.s1); - values1 += right * (int8)(w1.s2); -#endif /* CONV_STRIDE_Y &&DILATION_Y == 1 */ - -#if WEIGHTS_OFFSET != 0 - tmp = left + middle + right; - sum0 += tmp; -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - sum1 += tmp; -#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1 */ -#endif /* WEIGHTS_OFFSET != 0 */ - -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - // Row3 - GET_VALUES(src_addr + 3 * src_stride_y, left, middle, right); - values1 += left * (int8)(w2.s0); - values1 += middle * (int8)(w2.s1); - values1 += right * (int8)(w2.s2); - -#if WEIGHTS_OFFSET != 0 - sum1 += left + middle + right; -#endif /* WEIGHTS_OFFSET != 0 */ -#endif /* CONV_STRIDE_Y && DILATION_Y == 1 */ - -#if defined(HAS_BIAS) - values0 += (int8)(bias_value); -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - values1 += (int8)(bias_value); -#endif /* CONV_STRIDE_Y & &DILATION_Y == 1 */ -#endif //defined(HAS_BIAS) - -#if WEIGHTS_OFFSET != 0 - values0 += sum0 * (int8)(WEIGHTS_OFFSET); -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - values1 += sum1 * (int8)(WEIGHTS_OFFSET); -#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1 */ -#endif /* WEIGHTS_OFFSET != 0 */ - -#if INPUT_OFFSET != 0 - VEC_WEIGHTS_PROMOTED_TYPE(3) - tmp_we = CONVERT(w0, VEC_WEIGHTS_PROMOTED_TYPE(3)) + CONVERT(w1, VEC_WEIGHTS_PROMOTED_TYPE(3)) + CONVERT(w2, VEC_WEIGHTS_PROMOTED_TYPE(3)); - - WEIGHTS_PROMOTED_TYPE sum_weights = tmp_we.s0 + tmp_we.s1 + tmp_we.s2; - values0 += sum_weights * (int8)(INPUT_OFFSET); -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - values1 += sum_weights * (int8)(INPUT_OFFSET); -#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1 */ -#endif /* INPUT_OFFSET != 0 */ - -#if K_OFFSET != 0 - values0 += (int8)(K_OFFSET); -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - values1 += (int8)(K_OFFSET); -#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1*/ -#endif /* K_OFFSET != 0 */ - -#if defined(REAL_MULTIPLIER) - - values0 = CONVERT(round(CONVERT(values0, float8) * (float8)REAL_MULTIPLIER), int8); - -#else // defined(REAL_MULTIPLIER) - -#if defined(PER_CHANNEL_QUANTIZATION) - int8 res0_shift_lt0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(values0, output_multiplier, output_shift, 8); - int8 res0_shift_gt0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(values0, output_multiplier, output_shift, 8); - values0 = select(res0_shift_lt0, res0_shift_gt0, (int8)(output_shift) >= 0); -#else // defined(PER_CHANNEL_QUANTIZATION) -#if OUTPUT_SHIFT < 0 - values0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(values0, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8); -#else // OUTPUT_SHIFT < 0 - values0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(values0, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8); -#endif // OUTPUT_OFFSET < 0 -#endif // defined(PER_CHANNEL_QUANTIZATION) - -#endif // defined(REAL_MULTIPLIER) - - values0 += (int8)OUTPUT_OFFSET; - VEC_TYPE(8) - res0 = CONVERT_SAT(values0, VEC_TYPE(8)); - - vstore8(ACTIVATION_FUNC(res0), 0, dst.ptr); -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 -#if defined(REAL_MULTIPLIER) - - values1 = CONVERT(round(CONVERT(values1, float8) * (float8)REAL_MULTIPLIER), int8); - -#else // defined(REAL_MULTIPLIER) - -#if defined(PER_CHANNEL_QUANTIZATION) - int8 res1_shift_lt0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(values1, output_multiplier, output_shift, 8); - int8 res1_shift_gt0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(values1, output_multiplier, output_shift, 8); - values1 = select(res1_shift_lt0, res1_shift_gt0, (int8)(output_shift) >= 0); -#else // defined(PER_CHANNEL_QUANTIZATION) -#if OUTPUT_SHIFT < 0 - values1 = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(values1, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8); -#else // OUTPUT_SHIFT < 0 - values1 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(values1, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8); -#endif // OUTPUT_OFFSET < 0 -#endif // defined(PER_CHANNEL_QUANTIZATION) - -#endif // defined(REAL_MULTIPLIER) - - values1 += (int8)OUTPUT_OFFSET; - VEC_TYPE(8) - res1 = CONVERT_SAT(values1, VEC_TYPE(8)); - - vstore8(ACTIVATION_FUNC(res1), 0, dst.ptr + dst_stride_y); -#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1*/ -} - -#else // !defined(IS_DOT8) - -#if DILATION_X == 1 -#if CONV_STRIDE_X == 1 -#define GET_VALUES(first_value, left, middle, right) \ - ({ \ - VEC_TYPE(8) \ - temp0 = vload8(0, (__global DATA_TYPE *)(first_value)); \ - VEC_TYPE(2) \ - temp1 = vload2(0, (__global DATA_TYPE *)(first_value + 8 * sizeof(DATA_TYPE))); \ - \ - left = temp0.s01234567; \ - middle = (VEC_TYPE(8))(temp0.s1234, temp0.s567, temp1.s0); \ - right = (VEC_TYPE(8))(temp0.s2345, temp0.s67, temp1.s01); \ - }) -#elif CONV_STRIDE_X == 2 -#define GET_VALUES(first_value, left, middle, right) \ - ({ \ - VEC_TYPE(16) \ - temp0 = vload16(0, (__global DATA_TYPE *)(first_value)); \ - DATA_TYPE temp1 = *((__global DATA_TYPE *)(first_value + 16 * sizeof(DATA_TYPE))); \ - \ - left = temp0.s02468ace; \ - middle = temp0.s13579bdf; \ - right = (VEC_TYPE(8))(temp0.s2468, temp0.sace, temp1); \ - }) -#else /* CONV_STRIDE_X */ -#define GET_VALUES(first_value, left, middle, right) \ - ({ \ - VEC_TYPE(16) \ - temp0 = vload16(0, (__global DATA_TYPE *)(first_value)); \ - VEC_TYPE(8) \ - temp1 = vload8(0, (__global DATA_TYPE *)(first_value + 16 * sizeof(DATA_TYPE))); \ - \ - left = (VEC_TYPE(8))(temp0.s0369, temp0.scf, temp1.s25); \ - middle = (VEC_TYPE(8))(temp0.s147a, temp0.sd, temp1.s036); \ - right = (VEC_TYPE(8))(temp0.s258b, temp0.se, temp1.s147); \ - }) -#endif /* CONV_STRIDE_X */ -#else /*DILATION_X==1*/ - -#if CONV_STRIDE_X == 1 -#define GET_VALUES(first_value, left, middle, right) \ - ({ \ - left = vload8(0, (__global DATA_TYPE *)(first_value)); \ - middle = vload8(0, (__global DATA_TYPE *)(first_value + DILATION_X * sizeof(DATA_TYPE))); \ - right = vload8(0, (__global DATA_TYPE *)(first_value + 2 * DILATION_X * sizeof(DATA_TYPE))); \ - }) -#elif CONV_STRIDE_X == 2 -#define GET_VALUES(first_value, left, middle, right) \ - ({ \ - VEC_TYPE(16) \ - temp0 = vload16(0, (__global DATA_TYPE *)(first_value)); \ - left = temp0.s02468ace; \ - temp0 = vload16(0, (__global DATA_TYPE *)(first_value + DILATION_X * sizeof(DATA_TYPE))); \ - middle = temp0.s02468ace; \ - temp0 = vload16(0, (__global DATA_TYPE *)(first_value + 2 * DILATION_X * sizeof(DATA_TYPE))); \ - right = temp0.s02468ace; \ - }) -#else /* CONV_STRIDE_X */ -#define GET_VALUES(first_value, left, middle, right) \ - ({ \ - VEC_TYPE(16) \ - temp0 = vload16(0, (__global DATA_TYPE *)(first_value)); \ - VEC_TYPE(8) \ - temp1 = vload8(0, (__global DATA_TYPE *)(first_value + 16 * sizeof(DATA_TYPE))); \ - left = (VEC_TYPE(8))(temp0.s0369, temp0.scf, temp1.s25); \ - \ - temp0 = vload16(0, (__global DATA_TYPE *)(first_value + DILATION_X * sizeof(DATA_TYPE))); \ - temp1 = vload8(0, (__global DATA_TYPE *)(first_value + (16 + DILATION_X) * sizeof(DATA_TYPE))); \ - middle = (VEC_TYPE(8))(temp0.s0369, temp0.scf, temp1.s25); \ - \ - temp0 = vload16(0, (__global DATA_TYPE *)(first_value + 2 * DILATION_X * sizeof(DATA_TYPE))); \ - temp1 = vload8(0, (__global DATA_TYPE *)(first_value + (16 + 2 * DILATION_X) * sizeof(DATA_TYPE))); \ - right = (VEC_TYPE(8))(temp0.s0369, temp0.scf, temp1.s25); \ - }) - -#endif /* CONV_STRIDE_X */ -#endif /*DILATION_X==1*/ -/** This function computes the depthwise convolution quantized using dot product when the data layout is NCHW. - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/QASYMM8_SIGNED - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL - * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) - * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) - * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) - * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor - * @param[in] output_multipliers_ptr Pointer to the output multipliers vector. Supported data types: S32 - * @param[in] output_multipliers_stride_x Stride of the output multipliers vector in X dimension (in bytes) - * @param[in] output_multipliers_step_x output_multipliers_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_multipliers_offset_first_element_in_bytes The offset of the first element in the output multipliers vector - * @param[in] output_shifts_ptr Pointer to the output shifts vector. Supported data types: S32 - * @param[in] output_shifts_stride_x Stride of the output shifts vector in X dimension (in bytes) - * @param[in] output_shifts_step_x output_shifts_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_shifts_offset_first_element_in_bytes The offset of the first element in the output shifts vector - * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: S32 - * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) - * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector - */ - -__kernel void dwc_3x3_native_quantized8_dot8_nchw( - TENSOR3D_DECLARATION(src), - TENSOR3D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights), - VECTOR_DECLARATION(output_multipliers), - VECTOR_DECLARATION(output_shifts) -#if defined(HAS_BIAS) - , - VECTOR_DECLARATION(biases) -#endif //defined(HAS_BIAS) -) -{ - __global uchar *src_addr = src_ptr + get_global_id(0) * src_step_x + get_global_id(1) * src_step_y + get_global_id(2) * src_step_z; - Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst); - Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); - Vector output_multipliers = CONVERT_TO_VECTOR_STRUCT_NO_STEP(output_multipliers); - Vector output_shifts = CONVERT_TO_VECTOR_STRUCT_NO_STEP(output_shifts); - - // Extract channel and linearized batch indices - const int channel = get_global_id(2) % DST_CHANNELS; - const int batch = get_global_id(2) / DST_CHANNELS; - -#if defined(HAS_BIAS) - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - - const int bias_value = *((__global int *)(vector_offset(&biases, channel))); -#endif //defined(HAS_BIAS) - - // Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER) - src_addr -= batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z + (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; - __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; - - VEC_TYPE(3) - w0 = vload3(0, (__global WEIGHTS_TYPE *)(weights_addr + 0 * weights_stride_y)); - VEC_TYPE(3) - w1 = vload3(0, (__global WEIGHTS_TYPE *)(weights_addr + 1 * weights_stride_y)); - VEC_TYPE(3) - w2 = vload3(0, (__global WEIGHTS_TYPE *)(weights_addr + 2 * weights_stride_y)); - - const int output_multiplier = *((__global int *)vector_offset(&output_multipliers, 0)); - const int output_shift = *((__global int *)vector_offset(&output_shifts, 0)); - - VEC_TYPE(8) - left0, middle0, right0; - VEC_TYPE(8) - left1, middle1, right1; - VEC_TYPE(8) - left2, middle2, right2; - - int8 values0 = 0; - int8 sum0 = 0; - - GET_VALUES(src_addr + 0 * src_stride_y, left0, middle0, right0); - GET_VALUES(src_addr + DILATION_Y * src_stride_y, left1, middle1, right1); - GET_VALUES(src_addr + 2 * DILATION_Y * src_stride_y, left2, middle2, right2); - -#if WEIGHTS_OFFSET != 0 - sum0 += convert_int8(left0) + convert_int8(middle0) + convert_int8(right0); - sum0 += convert_int8(left1) + convert_int8(middle1) + convert_int8(right1); - sum0 += convert_int8(left2) + convert_int8(middle2) + convert_int8(right2); -#endif /* WEIGHTS_OFFSET != 0 */ - -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - // If conv_stride_y is equals to 1, we compute two output rows - - VEC_TYPE(8) - left3, middle3, right3; - int8 values1 = 0; - int8 sum1 = 0; - - GET_VALUES(src_addr + 3 * src_stride_y, left3, middle3, right3); - -#if WEIGHTS_OFFSET != 0 - sum1 += convert_int8(left1) + convert_int8(middle1) + convert_int8(right1); - sum1 += convert_int8(left2) + convert_int8(middle2) + convert_int8(right2); - sum1 += convert_int8(left3) + convert_int8(middle3) + convert_int8(right3); -#endif /* WEIGHTS_OFFSET != 0 */ -#endif // CONV_STRIDE_Y == 1 && DILATION_Y==1 - - ARM_DOT((VEC_TYPE(4))(left0.s0, middle0.s0, right0.s0, left1.s0), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values0.s0); - ARM_DOT((VEC_TYPE(4))(middle1.s0, right1.s0, left2.s0, middle2.s0), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values0.s0); - values0.s0 += right2.s0 * w2.s2; - - ARM_DOT((VEC_TYPE(4))(left0.s1, middle0.s1, right0.s1, left1.s1), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values0.s1); - ARM_DOT((VEC_TYPE(4))(middle1.s1, right1.s1, left2.s1, middle2.s1), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values0.s1); - values0.s1 += right2.s1 * w2.s2; - - ARM_DOT((VEC_TYPE(4))(left0.s2, middle0.s2, right0.s2, left1.s2), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values0.s2); - ARM_DOT((VEC_TYPE(4))(middle1.s2, right1.s2, left2.s2, middle2.s2), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values0.s2); - values0.s2 += right2.s2 * w2.s2; - - ARM_DOT((VEC_TYPE(4))(left0.s3, middle0.s3, right0.s3, left1.s3), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values0.s3); - ARM_DOT((VEC_TYPE(4))(middle1.s3, right1.s3, left2.s3, middle2.s3), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values0.s3); - values0.s3 += right2.s3 * w2.s2; - - ARM_DOT((VEC_TYPE(4))(left0.s4, middle0.s4, right0.s4, left1.s4), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values0.s4); - ARM_DOT((VEC_TYPE(4))(middle1.s4, right1.s4, left2.s4, middle2.s4), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values0.s4); - values0.s4 += right2.s4 * w2.s2; - - ARM_DOT((VEC_TYPE(4))(left0.s5, middle0.s5, right0.s5, left1.s5), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values0.s5); - ARM_DOT((VEC_TYPE(4))(middle1.s5, right1.s5, left2.s5, middle2.s5), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values0.s5); - values0.s5 += right2.s5 * w2.s2; - - ARM_DOT((VEC_TYPE(4))(left0.s6, middle0.s6, right0.s6, left1.s6), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values0.s6); - ARM_DOT((VEC_TYPE(4))(middle1.s6, right1.s6, left2.s6, middle2.s6), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values0.s6); - values0.s6 += right2.s6 * w2.s2; - - ARM_DOT((VEC_TYPE(4))(left0.s7, middle0.s7, right0.s7, left1.s7), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values0.s7); - ARM_DOT((VEC_TYPE(4))(middle1.s7, right1.s7, left2.s7, middle2.s7), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values0.s7); - values0.s7 += right2.s7 * w2.s2; - -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - ARM_DOT((VEC_TYPE(4))(left1.s0, middle1.s0, right1.s0, left2.s0), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values1.s0); - ARM_DOT((VEC_TYPE(4))(middle2.s0, right2.s0, left3.s0, middle3.s0), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values1.s0); - values1.s0 += right3.s0 * w2.s2; - - ARM_DOT((VEC_TYPE(4))(left1.s1, middle1.s1, right1.s1, left2.s1), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values1.s1); - ARM_DOT((VEC_TYPE(4))(middle2.s1, right2.s1, left3.s1, middle3.s1), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values1.s1); - values1.s1 += right3.s1 * w2.s2; - - ARM_DOT((VEC_TYPE(4))(left1.s2, middle1.s2, right1.s2, left2.s2), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values1.s2); - ARM_DOT((VEC_TYPE(4))(middle2.s2, right2.s2, left3.s2, middle3.s2), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values1.s2); - values1.s2 += right3.s2 * w2.s2; - - ARM_DOT((VEC_TYPE(4))(left1.s3, middle1.s3, right1.s3, left2.s3), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values1.s3); - ARM_DOT((VEC_TYPE(4))(middle2.s3, right2.s3, left3.s3, middle3.s3), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values1.s3); - values1.s3 += right3.s3 * w2.s2; - - ARM_DOT((VEC_TYPE(4))(left1.s4, middle1.s4, right1.s4, left2.s4), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values1.s4); - ARM_DOT((VEC_TYPE(4))(middle2.s4, right2.s4, left3.s4, middle3.s4), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values1.s4); - values1.s4 += right3.s4 * w2.s2; - - ARM_DOT((VEC_TYPE(4))(left1.s5, middle1.s5, right1.s5, left2.s5), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values1.s5); - ARM_DOT((VEC_TYPE(4))(middle2.s5, right2.s5, left3.s5, middle3.s5), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values1.s5); - values1.s5 += right3.s5 * w2.s2; - - ARM_DOT((VEC_TYPE(4))(left1.s6, middle1.s6, right1.s6, left2.s6), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values1.s6); - ARM_DOT((VEC_TYPE(4))(middle2.s6, right2.s6, left3.s6, middle3.s6), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values1.s6); - values1.s6 += right3.s6 * w2.s2; - - ARM_DOT((VEC_TYPE(4))(left1.s7, middle1.s7, right1.s7, left2.s7), (VEC_TYPE(4))(w0.s0, w0.s1, w0.s2, w1.s0), values1.s7); - ARM_DOT((VEC_TYPE(4))(middle2.s7, right2.s7, left3.s7, middle3.s7), (VEC_TYPE(4))(w1.s1, w1.s2, w2.s0, w2.s1), values1.s7); - values1.s7 += right3.s7 * w2.s2; -#endif // CONV_STRIDE_Y == 1 && DILATION_Y==1 - -#if defined(HAS_BIAS) - values0 += (int8)(bias_value); -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - values1 += (int8)(bias_value); -#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1 */ -#endif //defined(HAS_BIAS) - -#if WEIGHTS_OFFSET != 0 - values0 += sum0 * (int8)(WEIGHTS_OFFSET); -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - values1 += sum1 * (int8)(WEIGHTS_OFFSET); -#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1 */ -#endif /* WEIGHTS_OFFSET != 0 */ - -#if INPUT_OFFSET != 0 - WEIGHTS_PROMOTED_TYPE sum_weights = 0; - VEC_WEIGHTS_PROMOTED_TYPE(3) - tmp_we = CONVERT(w0, VEC_WEIGHTS_PROMOTED_TYPE(3)) + CONVERT(w1, VEC_WEIGHTS_PROMOTED_TYPE(3)) + CONVERT(w2, VEC_WEIGHTS_PROMOTED_TYPE(3)); - sum_weights += tmp_we.s0 + tmp_we.s1 + tmp_we.s2; - values0 += sum_weights * (int8)(INPUT_OFFSET); -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - values1 += sum_weights * (int8)(INPUT_OFFSET); -#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1*/ -#endif /* INPUT_OFFSET != 0 */ - -#if K_OFFSET != 0 - values0 += (int8)(K_OFFSET); -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - values1 += (int8)(K_OFFSET); -#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1*/ -#endif /* K_OFFSET != 0 */ - -#if defined(REAL_MULTIPLIER) - - values0 = CONVERT(round(CONVERT(values0, float8) * (float8)REAL_MULTIPLIER), int8); - -#else // defined(REAL_MULTIPLIER) - -#if defined(PER_CHANNEL_QUANTIZATION) - int8 res0_shift_lt0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(values0, output_multiplier, output_shift, 8); - int8 res0_shift_gt0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(values0, output_multiplier, output_shift, 8); - values0 = select(res0_shift_lt0, res0_shift_gt0, (int8)(output_shift) >= 0); -#else // defined(PER_CHANNEL_QUANTIZATION) -#if OUTPUT_SHIFT < 0 - values0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(values0, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8); -#else // OUTPUT_SHIFT < 0 - values0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(values0, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8); -#endif // OUTPUT_OFFSET < 0 -#endif // defined(PER_CHANNEL_QUANTIZATION) - -#endif // defined(REAL_MULTIPLIER) - - values0 += (int8)OUTPUT_OFFSET; - VEC_TYPE(8) - res0 = CONVERT_SAT(values0, VEC_TYPE(8)); - - vstore8(ACTIVATION_FUNC(res0), 0, dst.ptr); -#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 - -#if defined(REAL_MULTIPLIER) - - values1 = CONVERT(round(CONVERT(values1, float8) * (float8)REAL_MULTIPLIER), int8); - -#else // defined(REAL_MULTIPLIER) - -#if defined(PER_CHANNEL_QUANTIZATION) - int8 res1_shift_lt0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(values1, output_multiplier, output_shift, 8); - int8 res1_shift_gt0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(values1, output_multiplier, output_shift, 8); - values1 = select(res1_shift_lt0, res1_shift_gt0, (int8)(output_shift) >= 0); -#else // defined(PER_CHANNEL_QUANTIZATION) -#if OUTPUT_SHIFT < 0 - values1 = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(values1, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8); -#else // OUTPUT_SHIFT < 0 - values1 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(values1, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8); -#endif // OUTPUT_OFFSET < 0 -#endif // defined(PER_CHANNEL_QUANTIZATION) - -#endif // defined(REAL_MULTIPLIER) - - values1 += (int8)OUTPUT_OFFSET; - VEC_TYPE(8) - res1 = CONVERT_SAT(values1, VEC_TYPE(8)); - - vstore8(ACTIVATION_FUNC(res1), 0, dst.ptr + dst_stride_y); -#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1*/ -} - -#endif // !defined(IS_DOT8) - -#endif /* defined(CONV_STRIDE_Y) && defined(CONV_STRIDE_X) && defined(DEPTH_MULTIPLIER) && defined(DST_CHANNELS) */ - -#if defined(VEC_SIZE) && defined(SRC_DIM_1) && defined(SRC_DIM_2) && defined(CONV_PAD_TOP) && defined(CONV_PAD_LEFT) - -#define asymm_mult_by_quant_multiplier_less_than_one(x, y, z) ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(x, y, z, VEC_SIZE) - -#define MULTIPLY_ADD(x, y, acc) acc += CONVERT(CONVERT(x, VEC_WEIGHTS_PROMOTED_TYPE(VEC_SIZE)) * CONVERT(y, VEC_WEIGHTS_PROMOTED_TYPE(VEC_SIZE)), VEC_INT) - -#if WEIGHTS_OFFSET != 0 -#define MULTIPLY_ADD_ACCUMULATE(x, y, acc, sum) \ - ({ \ - sum += CONVERT(x, VEC_INT); \ - MULTIPLY_ADD(x, y, acc); \ - }) -#else /* WEIGHTS_OFFSET != 0 */ -#define MULTIPLY_ADD_ACCUMULATE(x, y, acc, sum) MULTIPLY_ADD(x, y, acc) -#endif /* WEIGHTS_OFFSET != 0 */ - -#if defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) -#define DOT_PRODUCT(acc, val0, val1, val2, val3, val4, val5, val6, val7, val8, w0, w1) \ - ({ \ - ARM_DOT((VEC_TYPE(4))(val0, val1, val2, val3), w0.s0123, acc); \ - ARM_DOT((VEC_TYPE(4))(val4, val5, val6, val7), w0.s4567, acc); \ - acc += val8 * w1; \ - }) - -#define DOT_PRODUCT_REDUCTION(sum, val0, val1, val2, val3, val4, val5, val6, val7, val8) \ - ({ \ - sum = val0; \ - ARM_DOT((VEC_TYPE(4))(val1, val2, val3, val4), (VEC_TYPE(4))1, sum); \ - ARM_DOT((VEC_TYPE(4))(val5, val6, val7, val8), (VEC_TYPE(4))1, sum); \ - }) - -#define DOT_PRODUCT_REDUCTION_WEIGHTS(sum, w0, w1) \ - ({ \ - sum = w1; \ - ARM_DOT(w0.s0123, (VEC_TYPE(4))1, sum); \ - ARM_DOT(w0.s4567, (VEC_TYPE(4))1, sum); \ - }) - -#endif // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) - -#endif // defined(VEC_SIZE) && defined(SRC_DIM_1) && defined(SRC_DIM_2) && defined(CONV_PAD_TOP) && defined(CONV_PAD_LEFT) - -#endif // defined(WEIGHTS_PROMOTED_TYPE) - -#endif // defined(WEIGHTS_OFFSET) && defined(INPUT_OFFSET) && defined(K_OFFSET) && ((defined(OUTPUT_OFFSET) && defined(OUTPUT_MULTIPLIER) && defined(OUTPUT_SHIFT)) || defined(REAL_MULTIPLIER)) - -#if defined(SRC_DIM1) && defined(SRC_DIM2) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(N0) && defined(DILATION_X) && defined(DILATION_Y) && defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) && defined(CONV_PAD_LEFT) && defined(CONV_PAD_TOP) && defined(INPUT_OFFSET) && defined(WEIGHTS_OFFSET) && defined(OUTPUT_OFFSET) && defined(OUTPUT_SHIFT) && defined(OUTPUT_MULTIPLIER) && defined(VEC_SIZE_LEFTOVER) -/** This function computes the depthwise convolution for NHWC data layout. - * - * @note The number of elements processed must be passed at compile time using -DN0 (e.g. -DN0=2) - * @note The depth multiplier must be passed at compile time using -DDEPTH_MULTIPLIER (e.g. -DDEPTH_MULTIPLIER=1) - * @note The first dimension of the input tensor must be passed at compile time using -DSRC_DIM1 (e.g. -DSRC_DIM1=112) - * @note The second dimension of the input tensor must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM2=80) - * @note The kernel width must be passed at compile time using -DKERNEL_WIDTH (e.g. -DKERNEL_WIDTH=5) - * @note The kernel height must be passed at compile time using -DKERNEL_HEIGHT (e.g. -DKERNEL_HEIGHT=5) - * @note The convolution pad top must be passed at compile time using -DCONV_PAD_TOP (e.g. -DCONV_PAD_TOP=1) - * @note The convolution pad top must be passed at compile time using -DCONV_PAD_LEFT (e.g. -DCONV_PAD_LEFT=1) - * @note The convolution stride along the width must be passed at compile time using -DCONV_STRIDE_X (e.g. -DCONV_STRIDE_Y=X) - * @note The convolution stride along the height must be passed at compile time using -DCONV_STRIDE_Y (e.g. -DCONV_STRIDE_Y=1) - * @note Leftover vector size has to be passed at compile time using -DVEC_SIZE_LEFTOVER. e.g. -DVEC_SIZE=3. It is defined as the remainder between the input's first dimension and VEC_SIZE - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/QASYMM8_SIGNED - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_y * number of elements along Z processed per workitem(in bytes) - * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) - * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes) - * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL - * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) - * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) - * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) - * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor - * @param[in] output_multipliers_ptr Pointer to the output multipliers vector. Supported data types: S32 - * @param[in] output_multipliers_stride_x Stride of the output multipliers vector in X dimension (in bytes) - * @param[in] output_multipliers_step_x output_multipliers_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_multipliers_offset_first_element_in_bytes The offset of the first element in the output multipliers vector - * @param[in] output_shifts_ptr Pointer to the output shifts vector. Supported data types: S32 - * @param[in] output_shifts_stride_x Stride of the output shifts vector in X dimension (in bytes) - * @param[in] output_shifts_step_x output_shifts_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_shifts_offset_first_element_in_bytes The offset of the first element in the output shifts vector - * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: S32 - * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) - * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector - */ -__kernel void dwc_MxN_native_quantized8_nhwc( - TENSOR4D_DECLARATION(src), - TENSOR4D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights), - VECTOR_DECLARATION(output_multipliers), - VECTOR_DECLARATION(output_shifts) -#if defined(HAS_BIAS) - , - VECTOR_DECLARATION(biases) -#endif // defined(HAS_BIAS) -) -{ - int x_offs = max((int)(get_global_id(0) * N0 - (N0 - VEC_SIZE_LEFTOVER) % N0), 0); - int y = get_global_id(1); // spatial coordinate x -#if defined(DST_DEPTH) - int z = get_global_id(2) % (int)DST_DEPTH; // spatial coordinate y - int b = get_global_id(2) / (int)DST_DEPTH; // batch -#else // defined(DST_DEPTH) - int z = get_global_id(2); // spatial coordinate y -#endif // defined(DST_DEPTH) - - __global uchar *s_addr = src_ptr + src_offset_first_element_in_bytes + x_offs * sizeof(DATA_TYPE); - - __global uchar *d_addr = dst_ptr + dst_offset_first_element_in_bytes + x_offs * sizeof(DATA_TYPE) * (int)DEPTH_MULTIPLIER + y * dst_stride_y + z * dst_stride_z; - - __global uchar *w_addr = weights_ptr + weights_offset_first_element_in_bytes + x_offs * sizeof(WEIGHTS_TYPE) * (int)DEPTH_MULTIPLIER; - -#if defined(HAS_BIAS) - __global uchar *b_addr = biases_ptr + biases_offset_first_element_in_bytes + x_offs * sizeof(int) * (int)DEPTH_MULTIPLIER; -#endif // defined(HAS_BIAS) - -#if defined(PER_CHANNEL_QUANTIZATION) - __global uchar *out_mul_addr = output_multipliers_ptr + output_multipliers_offset_first_element_in_bytes + x_offs * sizeof(int) * (int)DEPTH_MULTIPLIER; - __global uchar *out_shift_addr = output_shifts_ptr + output_shifts_offset_first_element_in_bytes + x_offs * sizeof(int) * (int)DEPTH_MULTIPLIER; -#endif // defined(PER_CHANNEL_QUANTIZATION) - -#if defined(DST_DEPTH) - s_addr += b * src_stride_w; - d_addr += b * dst_stride_w; -#endif // defined(DST_DEPTH) - -#if DEPTH_MULTIPLIER > 1 - for(int d = 0; d < (int)DEPTH_MULTIPLIER; ++d) - { -#endif // DEPTH_MULTIPLIER > 1 - // Each work-item computes N0x1x1 elements - VEC_INT res = 0; - - int x_coord = y * CONV_STRIDE_X - (int)CONV_PAD_LEFT; - int y_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP; - - for(int yk = 0; yk < KERNEL_HEIGHT; ++yk) - { - if(y_coord >= 0 && y_coord < SRC_DIM2) - { - int x_coord_tmp = x_coord; - - for(int xk = 0; xk < KERNEL_WIDTH; ++xk) - { - if(x_coord_tmp >= 0 && x_coord_tmp < SRC_DIM1) - { - int s_offset = x_coord_tmp * (int)src_stride_y + y_coord * (int)src_stride_z; - int w_offset = xk * weights_stride_y + yk * weights_stride_z; - - // Load input and weights values - VEC_INT i = CONVERT(VLOAD(N0)(0, (__global DATA_TYPE *)(s_addr + s_offset)), VEC_INT); - VEC_INT w = CONVERT(VLOAD(N0)(0, (__global WEIGHTS_TYPE *)(w_addr + w_offset)), VEC_INT); - - res += (i + (VEC_INT)INPUT_OFFSET) * (w + (VEC_INT)WEIGHTS_OFFSET); - } - x_coord_tmp += DILATION_X; - } - } - y_coord += DILATION_Y; - } - -#if defined(HAS_BIAS) - VEC_INT bias = VLOAD(N0)(0, (__global int *)(b_addr)); - res += bias; -#endif // defined(HAS_BIAS) - -#if defined(PER_CHANNEL_QUANTIZATION) - VEC_INT output_multiplier = VLOAD(N0)(0, (__global int *)(out_mul_addr)); - VEC_INT output_shift = VLOAD(N0)(0, (__global int *)(out_shift_addr)); - - VEC_INT res_shift_lt0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(res, output_multiplier, output_shift, N0); - VEC_INT res_shift_gt0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(res, output_multiplier, output_shift, N0); - res = select(res_shift_lt0, res_shift_gt0, (VEC_INT)(output_shift) >= 0); -#else // defined(PER_CHANNEL_QUANTIZATION) -#if OUTPUT_SHIFT < 0 - res = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(res, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, N0); -#else // OUTPUT_SHIFT < 0 - res = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(res, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, N0); -#endif // OUTPUT_OFFSET < 0 -#endif // defined(PER_CHANNEL_QUANTIZATION) - - res += (VEC_INT)OUTPUT_OFFSET; - - VEC_TYPE(VEC_SIZE) - res0 = CONVERT_SAT(res, VEC_TYPE(VEC_SIZE)); - res0 = ACTIVATION_FUNC(res0); - - STORE_VECTOR_SELECT(res, DATA_TYPE, d_addr, N0, VEC_SIZE_LEFTOVER, VEC_SIZE_LEFTOVER != 0 && get_global_id(0) == 0) - -#if DEPTH_MULTIPLIER > 1 - w_addr += sizeof(WEIGHTS_TYPE); - d_addr += sizeof(DATA_TYPE); -#if defined(PER_CHANNEL_QUANTIZATION) - out_mul_addr += sizeof(int); - out_shift_addr += sizeof(int); -#endif // defined(PER_CHANNEL_QUANTIZATION) -#if defined(HAS_BIAS) - b_addr += sizeof(int); -#endif // defined(HAS_BIAS) - } -#endif // DEPTH_MULTIPLIER > 1 -} -#endif // defined(SRC_DIM1) && defined(SRC_DIM2) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defiend(N0) && defined(DILATION_X) && defined(DILATION_Y) && defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) && defined(CONV_PAD_LEFT) && defined(CONV_PAD_TOP) && defined(INPUT_OFFSET) && defined(WEIGHTS_OFFSET) && defined(OUTPUT_OFFSET) && defined(OUTPUT_SHIFT) && defined(OUTPUT_MULTIPLIER) && defined(VEC_SIZE_LEFTOVER) -#endif // defined(DATA_TYPE) && defined(WEIGHTS_TYPE) diff --git a/src/core/CL/cl_kernels/direct_convolution.cl b/src/core/CL/cl_kernels/direct_convolution.cl index c5444cd7cc..75a7a0f004 100644 --- a/src/core/CL/cl_kernels/direct_convolution.cl +++ b/src/core/CL/cl_kernels/direct_convolution.cl @@ -32,10 +32,9 @@ * * @note Data layout supported: NHWC * @note Data type supported: F32/F16/QASYMM8/QASYMM8_SIGNED - * @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=half) * @note The accumulation data type must be passed at compile time using -DACC_DATA_TYPE (e.g. -DDATA_TYPE_PROMOTED=half) * @note The convolution padding (left and top) must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (e.g. -DPAD_LEFT=2, -DPAD_TOP=2) - * @note The convolution strides must be passed at compile time using -DSTRIDE and -DPAD_TOP (e.g. -DPAD_LEFT=2, -DPAD_TOP=2) + * @note The convolution strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y (e.g. -DSTRIDE_X=2, -DSTRIDE_Y=2) * @note The spatial dimensions of the weights must be passed at compile time using -DWEI_WIDTH and -DWEI_HEIGHT (e.g. -DWEI_WIDTH=9, -DWEI_HEIGHT=9) * @note The spatial dimensions of the source tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT (e.g. -DSRC_WIDTH=96, -DSRC_HEIGHT=64) * @note The spatial dimensions of the destination tensor must be passed at compile time using -DDST_WIDTH and -DDST_HEIGHT (e.g. -DDST_WIDTH=96, -DDST_HEIGHT=64) diff --git a/src/core/CL/cl_kernels/dwc_native_fp_nhwc.cl b/src/core/CL/cl_kernels/dwc_native_fp_nhwc.cl new file mode 100644 index 0000000000..1ec85f37d3 --- /dev/null +++ b/src/core/CL/cl_kernels/dwc_native_fp_nhwc.cl @@ -0,0 +1,212 @@ +/* + * Copyright (c) 2021 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. + */ + +#include "activation_float_helpers.h" +#include "helpers.h" +#include "helpers_asymm.h" +#include "tile_helpers.h" + +#if defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DST_WIDTH) && defined(DST_HEIGHT) && defined(WEI_WIDTH) && defined(WEI_HEIGHT) && defined(N0) && defined(M0) && defined(DILATION_X) && defined(DILATION_Y) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) +//! @cond Doxygen_Suppress +/** OpenCL kernel to compute the depthwise convolution for floating-point data types (F32/F16) + * + * @note Data layout supported: NHWC + * @note Data type supported: F32/F16 + * @note The accumulation data type must be passed at compile time using -DACC_DATA_TYPE (e.g. -DDATA_TYPE_PROMOTED=half) + * @note The convolution padding (left and top) must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (e.g. -DPAD_LEFT=2, -DPAD_TOP=2) + * @note The convolution strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y (e.g. -DSTRIDE_X=2, -DSTRIDE_Y=2) + * @note The convolution dilations must be passed at compile time using -DDILATION_X and -DDILATION_Y (e.g. -DDILATION_X=2, -DDILATION_Y=2) + * @note The spatial dimensions of the weights must be passed at compile time using -DWEI_WIDTH and -DWEI_HEIGHT (e.g. -DWEI_WIDTH=9, -DWEI_HEIGHT=9) + * @note The spatial dimensions of the source tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT (e.g. -DSRC_WIDTH=96, -DSRC_HEIGHT=64) + * @note The spatial dimensions of the destination tensor must be passed at compile time using -DDST_WIDTH and -DDST_HEIGHT (e.g. -DDST_WIDTH=96, -DDST_HEIGHT=64) + * @note The channels of the source tensor must be passed at compile time using -DSRC_CHANNELS (e.g. -DSRC_CHANNELS=64) + * @note The channels of the destination tensor must be passed at compile time using -DDST_CHANNELS (e.g. -DDDST_CHANNELS=64) + * @note The tensor type ("BUFFER" or "IMAGE") of the source tensor must be passed at compile time using -DSRC_TENSOR_TYPE (e.g. -DSRC_TENSOR_TYPE=BUFFER) + * @note The tensor type ("BUFFER" or "IMAGE") of the weights tensor must be passed at compile time using -DWEI_TENSOR_TYPE (e.g. -DWEI_TENSOR_TYPE=BUFFER) + * @note The tensor type ("BUFFER" or "IMAGE") of the destination tensor must be passed at compile time using -DDST_TENSOR_TYPE (e.g. -DDST_TENSOR_TYPE=BUFFER) + * @note The data type of the source tensor must be passed at compile time using -DSRC_DATA_TYPE (e.g. -DSRC_DATA_TYPE=float) + * @note The data type of the weights tensor must be passed at compile time using -DWEI_DATA_TYPE (e.g. -DWEI_DATA_TYPE=float) + * @note The data type of the destination tensor must be passed at compile time using -DDST_DATA_TYPE (e.g. -DDST_DATA_TYPE=float) + * @note The data type of the accumulators must be passed at compile time using -DACC_DATA_TYPE (e.g. -DACC_DATA_TYPE=float) + * @note The number of M0 rows (width) to process must be passed at compile time using -DM0 (e.g. -DM0=2) + * @note The number of N0 output channels to process must be passed at compile time using -DN0 (e.g. -DN0=2) + * @note The size of the partial store block in the first dimension must be passed at compile time using -DPARTIAL_N0 (e.g. -DPARTIAL_N0=1) + * @note Only the following configurations of M0 and N0 are currently supported: + * - M0 = 1, 2, 3, 4, 5, .... n (M0 != 1 with STRIDE_X == 1 && DILATION_X == 1 only) + * - N0 = 2, 3, 4, 8, 16 (only 4, 8 and 16 if WEI_TENSOR_TYPE=IMAGE) + * @note The number of rows to read from the src tensor must be passed at compile time using -DM0_A (e.g., -DM0_A=3). M0_A must be equal to WEI_WIDTH + (M0 - 1) + * + * @param[in] src_ptr Pointer to the source tensor. Supported data type: F16/F32 + * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] dst_ptr Pointer to the destination tensor. Supported data type: same as @p src_ptr + * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes) + * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor + * @param[in] wei_ptr Pointer to the weights tensor. Supported data type: same as @p src_ptr + * @param[in] wei_stride_x Stride of the weights tensor in X dimension (in bytes) + * @param[in] wei_step_x wei_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] wei_stride_y Stride of the weights tensor in Y dimension (in bytes) + * @param[in] wei_step_y wei_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] wei_stride_z Stride of the weights tensor in Z dimension (in bytes) + * @param[in] wei_step_z wei_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] wei_stride_w Stride of the weights tensor in W dimension (in bytes) + * @param[in] wei_step_w wei_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] wei_offset_first_element_in_bytes The offset of the first element in the bias matrix + * @param[in] bia_ptr (Optional) Pointer to the bias tensor Supported data type: same as @p src_ptr (if F32/F16) or S32 (if QASYMM8/QASYMM8_SIGNED) + * @param[in] bia_stride_x (Optional) Stride of the bias tensor in X dimension (in bytes) + * @param[in] bia_step_x (Optional) bia_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] bia_offset_first_element_in_bytes (Optional) The offset of the first element in the bias matrix + */ +//! @endcond +__kernel void dwc_native_fp_nhwc( + TENSOR4D(src, SRC_TENSOR_TYPE), + TENSOR4D(dst, DST_TENSOR_TYPE), + TENSOR4D(wei, WEI_TENSOR_TYPE) +#if defined(HAS_BIAS) + , + VECTOR_DECLARATION(bia) +#endif // defined(HAS_BIAS) +) +{ + // All the tensor dimensions are passed at compile time. + // In case of dynamic tensor support, the following dimensions should be passed as function argument. +#define _IWEI_WIDTH WEI_WIDTH +#define _IWEI_HEIGHT WEI_HEIGHT +#define _ISRC_WIDTH SRC_WIDTH +#define _ISRC_HEIGHT SRC_HEIGHT +#define _IDST_WIDTH DST_WIDTH +#define _IDST_HEIGHT DST_HEIGHT +#define _IDST_CHANNELS DST_CHANNELS +#define _IM0_A M0_A // _IWEI_WIDTH + (M0 - 1) Rows tile A (If M0 != 1, the tiles overlap of 1 element on the X dimension) +#define _IN0_A N0 // Cols tile A +#define _IM0_B _IWEI_WIDTH // Rows tile B +#define _IN0_B N0 // Cols tile B +#define _IBOUNDARY_CHECK (!((WEI_WIDTH == 1 && WEI_HEIGHT == 1 && PAD_LEFT == 0 && PAD_TOP == 0 && M0 == 1))) + + const int cout = GET_SPATIAL_IDX(0, N0, PARTIAL_N0); // OFM + const int xo = GET_SPATIAL_IDX(1, M0, 0); // WIDTH +#if defined(BATCHED_EXECUTION) + const int yo = GET_SPATIAL_IDX(2, 1, 0) % _IDST_HEIGHT; // HEIGHT + const int bout = GET_SPATIAL_IDX(2, 1, 0) / _IDST_HEIGHT; // BATCH SIZE IDX +#else // defined(BATCHED_EXECUTION) + const int yo = GET_SPATIAL_IDX(2, 1, 0); // HEIGHT + const int bout = 0; // BATCH SIZE IDX +#endif // defined(BATCHED_EXECUTION) + + int xi = xo * STRIDE_X; + int yi = yo * STRIDE_Y; + xi -= PAD_LEFT; + yi -= PAD_TOP; + + int d = 0; +#if DEPTH_MULTIPLIER != 1 + for(; d < DEPTH_MULTIPLIER; d++) +#endif // DEPTH_MULTIPLIER != 1 + { + TILE(ACC_DATA_TYPE, M0, N0, c); + + // Reset accumulators + LOOP_UNROLLING(int, i, 0, 1, M0, + { + c[i].v = 0; + }) + + LOOP_UNROLLING(int, yk, 0, 1, _IWEI_HEIGHT, + { + TILE(SRC_DATA_TYPE, _IM0_A, _IN0_A, a); + + LOOP_UNROLLING(int, i, 0, 1, _IM0_A, + { + a[i].v = 0; + }) + + // Load tile from the src tensor (TILE A) + T_LOAD_NHWC_WITH_DILATION(SRC_DATA_TYPE, 1, _IM0_A, _IN0_A, SRC_TENSOR_TYPE, src, bout, yi + yk * DILATION_Y, xi, cout, _ISRC_WIDTH, _ISRC_HEIGHT, DILATION_X, 1, _IBOUNDARY_CHECK, a); + + TILE(WEI_DATA_TYPE, _IM0_B, _IN0_B, b); + + // Load tile from the weights tensor (TILE B) + T_LOAD(WEI_DATA_TYPE, _IM0_B, _IN0_B, WEI_TENSOR_TYPE, wei, (cout * DEPTH_MULTIPLIER) + d, yk * _IM0_B, 1, wei_stride_y, b); + + // Optimized path for STRIDE_X == 1 + // If M0 != 1, we can skip the common loads between the two applied kernels on the X (WIDTH) dimension + LOOP_UNROLLING(int, m0, 0, 1, M0, + { + LOOP_UNROLLING(int, xk, 0, 1, _IWEI_WIDTH, + { + c[m0].v += a[xk + m0].v *b[xk].v; + }) + }) + }) + +#if defined(HAS_BIAS) + TILE(BIA_DATA_TYPE, 1, N0, bias0); + + T_LOAD(BIA_DATA_TYPE, 1, N0, BUFFER, bia, (cout * DEPTH_MULTIPLIER) + d, 0, 0, 0, bias0); + + // c = c + bias[broadcasted] + T_ADD_BROADCAST_X(ACC_DATA_TYPE, M0, N0, c, bias0, c); +#endif // HAS_BIAS + + T_ACTIVATION(ACC_DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, c, c); + + TILE(uint, M0, 1, dst_indirect_y); + + bool x_cond = PARTIAL_N0 != 0 && get_global_id(0) == 0; + + if(x_cond) + { + LOOP_UNROLLING(int, m0, 0, 1, M0, + { + int xi_out = min(xo + M0 - 1 - m0, (int)(_IDST_WIDTH) - 1); + VSTORE_PARTIAL(N0, PARTIAL_N0) + (c[M0 - 1 - m0].v, 0, (__global DST_DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + ((cout * DEPTH_MULTIPLIER) + d) * sizeof(DST_DATA_TYPE) + xi_out * dst_stride_y + yo * dst_stride_z + bout * dst_stride_w)); + }) + } + else + { + LOOP_UNROLLING(int, m0, 0, 1, M0, + { + int xi_out = min(xo + M0 - 1 - m0, (int)(_IDST_WIDTH) - 1); + VSTORE(N0) + (c[M0 - 1 - m0].v, 0, (__global DST_DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + ((cout * DEPTH_MULTIPLIER) + d) * sizeof(DST_DATA_TYPE) + xi_out * dst_stride_y + yo * dst_stride_z + bout * dst_stride_w)); + }) + } + } +} +#endif // defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DST_WIDTH) && defined(DST_HEIGHT) && defined(WEI_WIDTH) && defined(WEI_HEIGHT) && defined(N0) && defined(M0) && defined(DILATION_X) && defined(DILATION_Y) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP)
\ No newline at end of file diff --git a/src/core/CL/cl_kernels/dwc_native_quantized_nhwc.cl b/src/core/CL/cl_kernels/dwc_native_quantized_nhwc.cl new file mode 100644 index 0000000000..cd0f29230d --- /dev/null +++ b/src/core/CL/cl_kernels/dwc_native_quantized_nhwc.cl @@ -0,0 +1,277 @@ +/* + * Copyright (c) 2021 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. + */ + +#include "helpers.h" +#include "tile_helpers.h" + +#define CALCULATE_WEIGHTS_OFFSET_CORRECTION(A_DATA_TYPE, B_DATA_TYPE) CALCULATE_WEIGHTS_OFFSET_CORRECTION_STR(A_DATA_TYPE, B_DATA_TYPE) +#define CALCULATE_WEIGHTS_OFFSET_CORRECTION_STR(A_DATA_TYPE, B_DATA_TYPE) CALCULATE_WEIGHTS_OFFSET_CORRECTION_##A_DATA_TYPE##_##B_DATA_TYPE +#define CALCULATE_WEIGHTS_OFFSET_CORRECTION_char_char (0) +#define CALCULATE_WEIGHTS_OFFSET_CORRECTION_uchar_uchar (0) +#define CALCULATE_WEIGHTS_OFFSET_CORRECTION_uchar_char (128) +#define CALCULATE_WEIGHTS_OFFSET_CORRECTION_char_uchar (-128) + +#define T_LOAD_MULTIPLIERS_SHIFT_PER_TENSOR() \ + ({}) + +#define T_LOAD_MULTIPLIERS_SHIFT_PER_CHANNEL() \ + TILE(DST_MULTIPLIERS_DATA_TYPE, 1, N0, multipliers); \ + TILE(DST_SHIFTS_DATA_TYPE, 1, N0, shifts); \ + T_LOAD(DST_MULTIPLIERS_DATA_TYPE, 1, N0, BUFFER, dst_multipliers, cout *DEPTH_MULTIPLIER + d, 0, 0, 0, multipliers); \ + T_LOAD(DST_SHIFTS_DATA_TYPE, 1, N0, BUFFER, dst_shifts, cout *DEPTH_MULTIPLIER + d, 0, 0, 0, shifts); + +#define T_LOAD_MULTIPLIERS_SHIFT(QUANTIZATION_TYPE) T_LOAD_MULTIPLIERS_SHIFT_STR(QUANTIZATION_TYPE) +#define T_LOAD_MULTIPLIERS_SHIFT_STR(QUANTIZATION_TYPE) T_LOAD_MULTIPLIERS_SHIFT_##QUANTIZATION_TYPE() + +#if defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DST_WIDTH) && defined(DST_HEIGHT) && defined(WEI_WIDTH) && defined(WEI_HEIGHT) && defined(N0) && defined(M0) && defined(DILATION_X) && defined(DILATION_Y) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) +//! @cond Doxygen_Suppress +/** OpenCL kernel to compute the depthwise convolution for quantized data types + * + * @note Data layout supported: NHWC + * @note Data type supported: QSYMM8/QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL + * @note The convolution padding (left and top) must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (e.g. -DPAD_LEFT=2, -DPAD_TOP=2) + * @note The convolution strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y (e.g. -DSTRIDE_X=2, -DSTRIDE_Y=2) + * @note The convolution dilations must be passed at compile time using -DDILATION_X and -DDILATION_Y (e.g. -DDILATION_X=2, -DDILATION_Y=2) + * @note The spatial dimensions of the weights must be passed at compile time using -DWEI_WIDTH and -DWEI_HEIGHT (e.g. -DWEI_WIDTH=9, -DWEI_HEIGHT=9) + * @note The spatial dimensions of the source tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT (e.g. -DSRC_WIDTH=96, -DSRC_HEIGHT=64) + * @note The spatial dimensions of the destination tensor must be passed at compile time using -DDST_WIDTH and -DDST_HEIGHT (e.g. -DDST_WIDTH=96, -DDST_HEIGHT=64) + * @note The channels of the source tensor must be passed at compile time using -DSRC_CHANNELS (e.g. -DSRC_CHANNELS=64) + * @note The channels of the destination tensor must be passed at compile time using -DDST_CHANNELS (e.g. -DDDST_CHANNELS=64) + * @note The tensor type ("BUFFER" or "IMAGE") of the source tensor must be passed at compile time using -DSRC_TENSOR_TYPE (e.g. -DSRC_TENSOR_TYPE=BUFFER) + * @note The tensor type ("BUFFER" or "IMAGE") of the weights tensor must be passed at compile time using -DWEI_TENSOR_TYPE (e.g. -DWEI_TENSOR_TYPE=BUFFER) + * @note The tensor type ("BUFFER" or "IMAGE") of the destination tensor must be passed at compile time using -DDST_TENSOR_TYPE (e.g. -DDST_TENSOR_TYPE=BUFFER) + * @note The data type of the source tensor must be passed at compile time using -DSRC_DATA_TYPE (e.g. -DSRC_DATA_TYPE=int8) + * @note The data type of the weights tensor must be passed at compile time using -DWEI_DATA_TYPE (e.g. -DWEI_DATA_TYPE=int8) + * @note The data type of the destination tensor must be passed at compile time using -DDST_DATA_TYPE (e.g. -DDST_DATA_TYPE=int8) + * @note The data type of the accumulators must be passed at compile time using -DACC_DATA_TYPE (e.g. -DACC_DATA_TYPE=int) + * @note The number of M0 rows (width) to process must be passed at compile time using -DM0 (e.g. -DM0=2) + * @note The number of N0 output channels to process must be passed at compile time using -DN0 (e.g. -DN0=2) + * @note The size of the partial store block in the first dimension must be passed at compile time using -DPARTIAL_N0 (e.g. -DPARTIAL_N0=1) + * @note The activation type must be passed at compile using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu + * @note The A and B variables required by some activation functions must be passed at compile time using -DA_VAL= and -DB_VAL= respectively + * @note The quantization offset used for both the per-tensor and per-channel quantization must be passed at compile using -DDST_OFFSET (e.g., -DDST_OFFSET=3) + * @note The quantization shift for the per-tensor quantization must be passed at compile time using -DDST_SHIFT (e.g., -DDST_SHIFT=1) + * @note The quantization multiplier for the per-tensor quantization must be passed at compile using -DDST_MULTIPLIER (e.g., -DDST_MULTIPLER=121432) + * @note Only the following configurations of M0 and N0 are currently supported: + * - M0 = 1, 2, 3, 4, 5, .... n (M0 != 1 with STRIDE_X == 1 && DILATION_X == 1 only) + * - N0 = 2, 3, 4, 8, 16 + * @note The number of rows to read from the src tensor must be passed at compile time using -DM0_A (e.g., -DM0_A=3). M0_A must be equal to WEI_WIDTH + (M0 - 1) + * + * @param[in] src_ptr Pointer to the source tensor. Supported data type: QSYMM8/QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL + * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] dst_ptr Pointer to the destination tensor. Supported data type: same as @p src_ptr + * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes) + * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor + * @param[in] wei_ptr Pointer to the weights tensor. Supported data type: same as @p src_ptr + * @param[in] wei_stride_x Stride of the weights tensor in X dimension (in bytes) + * @param[in] wei_step_x wei_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] wei_stride_y Stride of the weights tensor in Y dimension (in bytes) + * @param[in] wei_step_y wei_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] wei_stride_z Stride of the weights tensor in Z dimension (in bytes) + * @param[in] wei_step_z wei_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] wei_stride_w Stride of the weights tensor in W dimension (in bytes) + * @param[in] wei_step_w wei_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] wei_offset_first_element_in_bytes The offset of the first element in the weights tensor + * @param[in] dst_multipliers_ptr Pointer to the destination multipliers tensor for the per-channel quantization. Supported data type: S32 + * @param[in] dst_multipliers_stride_x Stride of the destination multipliers tensor in X dimension (in bytes) + * @param[in] dst_multipliers_step_x dst_multipliers_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] dst_multipliers_offset_first_element_in_bytes The offset of the first element in the destination multipliers tensor + * @param[in] dst_shifts_ptr Pointer to the destination shifts tensor for the per-channel quantization. Supported data type: S32 + * @param[in] dst_shifts_stride_x Stride of the destination shifts tensor in X dimension (in bytes) + * @param[in] dst_shifts_step_x dst_shifts_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] dst_shifts_offset_first_element_in_bytes The offset of the first element in the destination shifts tensor + * @param[in] bia_ptr (Optional) Pointer to the bias tensor Supported data type: S32 + * @param[in] bia_stride_x (Optional) Stride of the bias tensor in X dimension (in bytes) + * @param[in] bia_step_x (Optional) bia_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] bia_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor + */ +//! @endcond +__kernel void dwc_native_quantized_nhwc( + TENSOR4D(src, SRC_TENSOR_TYPE), + TENSOR4D(dst, DST_TENSOR_TYPE), + TENSOR4D(wei, WEI_TENSOR_TYPE), + VECTOR_DECLARATION(dst_multipliers), + VECTOR_DECLARATION(dst_shifts) +#if defined(HAS_BIAS) + , + VECTOR_DECLARATION(bia) +#endif // defined(HAS_BIAS) +) +{ + // All the tensor dimensions are passed at compile time. + // In case of dynamic tensor support, the following dimensions should be passed as function argument. +#define _IWEI_WIDTH WEI_WIDTH +#define _IWEI_HEIGHT WEI_HEIGHT +#define _ISRC_WIDTH SRC_WIDTH +#define _ISRC_HEIGHT SRC_HEIGHT +#define _IDST_WIDTH DST_WIDTH +#define _IDST_HEIGHT DST_HEIGHT +#define _IDST_CHANNELS DST_CHANNELS +#define _IM0_A M0_A // _IWEI_WIDTH + (M0 - 1) Rows tile A (If M0 != 1, the tiles overlap of 1 element on the X dimension) +#define _IN0_A N0 // Cols tile A +#define _IM0_B _IWEI_WIDTH // Rows tile B +#define _IN0_B N0 // Cols tile B +#define _IBOUNDARY_CHECK (!((WEI_WIDTH == 1 && WEI_HEIGHT == 1 && PAD_LEFT == 0 && PAD_TOP == 0 && M0 == 1))) + + const int cout = GET_SPATIAL_IDX(0, N0, PARTIAL_N0); // OFM + const int xo = GET_SPATIAL_IDX(1, M0, 0); // WIDTH +#if defined(BATCHED_EXECUTION) + const int yo = GET_SPATIAL_IDX(2, 1, 0) % _IDST_HEIGHT; // HEIGHT + const int bout = GET_SPATIAL_IDX(2, 1, 0) / _IDST_HEIGHT; // BATCH SIZE IDX +#else // defined(BATCHED_EXECUTION) + const int yo = GET_SPATIAL_IDX(2, 1, 0); // HEIGHT + const int bout = 0; // BATCH SIZE IDX +#endif // defined(BATCHED_EXECUTION) + + int xi = xo * STRIDE_X; + int yi = yo * STRIDE_Y; + xi -= PAD_LEFT; + yi -= PAD_TOP; + + int d = 0; +#if DEPTH_MULTIPLIER != 1 + for(; d < DEPTH_MULTIPLIER; d++) +#endif // DEPTH_MULTIPLIER != 1 + { + TILE(ACC_DATA_TYPE, M0, N0, c); + + // Reset accumulators + LOOP_UNROLLING(int, i, 0, 1, M0, + { + c[i].v = 0; + }) + + LOOP_UNROLLING(int, yk, 0, 1, _IWEI_HEIGHT, + { + TILE(SRC_DATA_TYPE, _IM0_A, _IN0_A, a); + + LOOP_UNROLLING(int, i, 0, 1, _IM0_A, + { + a[i].v = ZERO_VALUE; + }) + + // Load tile from the src tensor (TILE A) + T_LOAD_NHWC_WITH_DILATION(SRC_DATA_TYPE, 1, _IM0_A, _IN0_A, SRC_TENSOR_TYPE, src, bout, yi + yk * DILATION_Y, xi, cout, _ISRC_WIDTH, _ISRC_HEIGHT, DILATION_X, 1, _IBOUNDARY_CHECK, a); + + TILE(WEI_DATA_TYPE, _IM0_B, _IN0_B, b); + + // Load tile from the weights tensor (TILE B) + T_LOAD(WEI_DATA_TYPE, _IM0_B, _IN0_B, WEI_TENSOR_TYPE, wei, cout * DEPTH_MULTIPLIER + d, yk * _IM0_B, 1, wei_stride_y, b); + + // Optimized path for STRIDE_X == 1 + // If M0 != 1, we can skip the common loads between the two applied kernels on the X (WIDTH) dimension + LOOP_UNROLLING(int, m0, 0, 1, M0, + { + LOOP_UNROLLING(int, n0, 0, 1, N0, + { +#if _IWEI_WIDTH <= 16 +#define DOT_DATA_TYPE SRC_DATA_TYPE +#define WEI_OFFSET_CORRECTION (CALCULATE_WEIGHTS_OFFSET_CORRECTION(SRC_DATA_TYPE, WEI_DATA_TYPE)) + + // Optimized path for the dot instruction + TILE(DOT_DATA_TYPE, 1, _IWEI_WIDTH, x0); + TILE(DOT_DATA_TYPE, 1, _IWEI_WIDTH, y0); + ACC_DATA_TYPE offset_a = 0; + ACC_DATA_TYPE offset_b = 0; + + LOOP_UNROLLING(int, xk, 0, 1, _IWEI_WIDTH, + { + x0[0].s[xk] = a[xk + m0].s[n0]; + y0[0].s[xk] = b[xk].s[n0] + (int)WEI_OFFSET_CORRECTION; + }) + DOT_PRODUCT_INTEGER8(DOT_DATA_TYPE, DOT_DATA_TYPE, ACC_DATA_TYPE, _IWEI_WIDTH, x0[0].v, y0[0].v, c[m0].s[n0]); + REDUCE_INTEGER8(DOT_DATA_TYPE, DOT_DATA_TYPE, ACC_DATA_TYPE, _IWEI_WIDTH, x0[0].v, offset_a); + REDUCE_INTEGER8(DOT_DATA_TYPE, DOT_DATA_TYPE, ACC_DATA_TYPE, _IWEI_WIDTH, y0[0].v, offset_b); + c[m0].s[n0] += offset_a * (ACC_DATA_TYPE)(WEI_OFFSET - (ACC_DATA_TYPE)WEI_OFFSET_CORRECTION) + offset_b * (ACC_DATA_TYPE)SRC_OFFSET; +#else // _IWEI_WIDTH <= 16 + LOOP_UNROLLING(int, xk, 0, 1, _IWEI_WIDTH, + { + c[m0].s[n0] += ((ACC_DATA_TYPE)a[xk + m0].s[n0] + (ACC_DATA_TYPE)(SRC_OFFSET)) * ((ACC_DATA_TYPE)b[xk].s[n0] + (ACC_DATA_TYPE)(WEI_OFFSET)); + }) +#endif // _IWEI_WIDTH <= 16 + }) + }) + }) + +#if _IWEI_WIDTH <= 16 + T_ADD_CONSTANT(ACC_DATA_TYPE, M0, N0, c, (_IWEI_WIDTH * _IWEI_HEIGHT * SRC_OFFSET * (ACC_DATA_TYPE)(WEI_OFFSET - (ACC_DATA_TYPE)WEI_OFFSET_CORRECTION)), c); +#endif // _IWEI_WIDTH <= 16 + +#if defined(HAS_BIAS) + TILE(BIA_DATA_TYPE, 1, N0, bias0); + + // Load bias + T_LOAD(BIA_DATA_TYPE, 1, N0, BUFFER, bia, cout * DEPTH_MULTIPLIER + d, 0, 0, 0, bias0); + + // c = c + bias[broadcasted] + T_ADD_BROADCAST_X(ACC_DATA_TYPE, M0, N0, c, bias0, c); +#endif // HAS_BIAS + + T_LOAD_MULTIPLIERS_SHIFT(QUANTIZATION_TYPE); + + // Quantize the tile + TILE(DST_DATA_TYPE, M0, N0, cq); + T_QUANTIZE8(ACC_DATA_TYPE, DST_DATA_TYPE, QUANTIZATION_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, c, multipliers, shifts, cq); + + // Perform activation + T_ACTIVATION_QUANTIZED(DST_DATA_TYPE, M0, N0, ACTIVATION_TYPE, DST_OFFSET, A_VAL, B_VAL, cq, cq); + + bool x_cond = PARTIAL_N0 != 0 && get_global_id(0) == 0; + + if(x_cond) + { + LOOP_UNROLLING(int, m0, 0, 1, M0, + { + int xi_out = min(xo + M0 - 1 - m0, (int)(_IDST_WIDTH) - 1); + VSTORE_PARTIAL(N0, PARTIAL_N0) + (cq[M0 - 1 - m0].v, 0, (__global DST_DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + ((cout * DEPTH_MULTIPLIER) + d) * sizeof(DST_DATA_TYPE) + xi_out * dst_stride_y + yo * dst_stride_z + bout * dst_stride_w)); + }) + } + else + { + LOOP_UNROLLING(int, m0, 0, 1, M0, + { + int xi_out = min(xo + M0 - 1 - m0, (int)(_IDST_WIDTH) - 1); + VSTORE(N0) + (cq[M0 - 1 - m0].v, 0, (__global DST_DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + ((cout * DEPTH_MULTIPLIER) + d) * sizeof(DST_DATA_TYPE) + xi_out * dst_stride_y + yo * dst_stride_z + bout * dst_stride_w)); + }) + } + } +} +#endif // defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DST_WIDTH) && defined(DST_HEIGHT) && defined(WEI_WIDTH) && defined(WEI_HEIGHT) && defined(N0) && defined(M0) && defined(DILATION_X) && defined(DILATION_Y) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP)
\ No newline at end of file diff --git a/src/core/CL/cl_kernels/tile_helpers.h b/src/core/CL/cl_kernels/tile_helpers.h index 7910b4ce0e..4bff7e665c 100644 --- a/src/core/CL/cl_kernels/tile_helpers.h +++ b/src/core/CL/cl_kernels/tile_helpers.h @@ -25,6 +25,40 @@ // *INDENT-OFF* // clang-format off +#define TILE_VECTOR_SIZE1 1 +#define TILE_VECTOR_SIZE2 2 +#define TILE_VECTOR_SIZE3 3 +#define TILE_VECTOR_SIZE4 4 +#define TILE_VECTOR_SIZE5 8 +#define TILE_VECTOR_SIZE6 8 +#define TILE_VECTOR_SIZE7 8 +#define TILE_VECTOR_SIZE8 8 +#define TILE_VECTOR_SIZE9 16 +#define TILE_VECTOR_SIZE10 16 +#define TILE_VECTOR_SIZE11 16 +#define TILE_VECTOR_SIZE12 16 +#define TILE_VECTOR_SIZE13 16 +#define TILE_VECTOR_SIZE14 16 +#define TILE_VECTOR_SIZE15 16 +#define TILE_VECTOR_SIZE16 16 + +#define TILE_VECTOR_TYPE1(DATA_TYPE) DATA_TYPE##1 +#define TILE_VECTOR_TYPE2(DATA_TYPE) DATA_TYPE##2 +#define TILE_VECTOR_TYPE3(DATA_TYPE) DATA_TYPE##3 +#define TILE_VECTOR_TYPE4(DATA_TYPE) DATA_TYPE##4 +#define TILE_VECTOR_TYPE5(DATA_TYPE) DATA_TYPE##8 +#define TILE_VECTOR_TYPE6(DATA_TYPE) DATA_TYPE##8 +#define TILE_VECTOR_TYPE7(DATA_TYPE) DATA_TYPE##8 +#define TILE_VECTOR_TYPE8(DATA_TYPE) DATA_TYPE##8 +#define TILE_VECTOR_TYPE9(DATA_TYPE) DATA_TYPE##16 +#define TILE_VECTOR_TYPE10(DATA_TYPE) DATA_TYPE##16 +#define TILE_VECTOR_TYPE11(DATA_TYPE) DATA_TYPE##16 +#define TILE_VECTOR_TYPE12(DATA_TYPE) DATA_TYPE##16 +#define TILE_VECTOR_TYPE13(DATA_TYPE) DATA_TYPE##16 +#define TILE_VECTOR_TYPE14(DATA_TYPE) DATA_TYPE##16 +#define TILE_VECTOR_TYPE15(DATA_TYPE) DATA_TYPE##16 +#define TILE_VECTOR_TYPE16(DATA_TYPE) DATA_TYPE##16 + /** Tile object * A tile object is a 2D memory block and can be accessed using the following syntax: * -# a[m0].v = access the the vector at row "m0" (OpenCL vector) @@ -38,8 +72,8 @@ #define TILE(DATA_TYPE, H, W, BASENAME) TILE_STR(DATA_TYPE, H, W, BASENAME) #define TILE_STR(DATA_TYPE, H, W, BASENAME) \ union { \ - DATA_TYPE s[W]; \ - DATA_TYPE##W v; \ + DATA_TYPE s[TILE_VECTOR_SIZE##W]; \ + TILE_VECTOR_TYPE##W(DATA_TYPE) v; \ } BASENAME[H] #define TENSOR4D_IMAGE(name) \ @@ -235,52 +269,122 @@ * * @note Performs: c += dot(a, b) * - * @param[in] DST_DATA_TYPE Accumulator data type - * @param[in] K0 Number of accumulations - * @param[in] a OpenCL vector a - * @param[in] b OpenCL vector b - * @param[in] c Scalar variable c + * @param[in] A_DATA_TYPE A (lhs) data type + * @param[in] B_DATA_TYPE B (rhs) data type + * @param[in] C_DATA_TYPE C (accumulator) data type + * @param[in] K0 Number of accumulations + * @param[in] a OpenCL vector a + * @param[in] b OpenCL vector b + * @param[in] c Scalar variable c */ -#define DOT_PRODUCT_INTEGER8(DST_DATA_TYPE, K0, a, b, c) DOT_PRODUCT_INTEGER8_STR(DST_DATA_TYPE, K0, a, b, c) -#define DOT_PRODUCT_INTEGER8_STR(DST_DATA_TYPE, K0, a, b, c) DOT_PRODUCT##K0##_INTEGER8(DST_DATA_TYPE, a, b, c) -#define DOT_PRODUCT1_INTEGER8(DST_DATA_TYPE, a, b, c) \ +#define DOT_PRODUCT_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, K0, a, b, c) DOT_PRODUCT_INTEGER8_STR(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, K0, a, b, c) +#define DOT_PRODUCT_INTEGER8_STR(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, K0, a, b, c) DOT_PRODUCT##K0##_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) +#define DOT_PRODUCT1_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ - c += (DST_DATA_TYPE)a * (DST_DATA_TYPE)b; \ + c += (C_DATA_TYPE)(a) * (C_DATA_TYPE)(b); \ }) -#define DOT_PRODUCT2_INTEGER8(DST_DATA_TYPE, a, b, c) \ +#if defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) +#define DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c = arm_dot_acc((A_DATA_TYPE##4)((a).s01, (A_DATA_TYPE##2)(0)), (B_DATA_TYPE##4)(((b).s01), (B_DATA_TYPE##2)(0)), (c)); +#define DOT_PRODUCT3_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c = arm_dot_acc((A_DATA_TYPE##4)((a).s012, (A_DATA_TYPE)0), (B_DATA_TYPE##4)(((b).s012), (B_DATA_TYPE)0), (c)); +#define DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c = arm_dot_acc((a), (b), (c)); +#elif defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) +#define DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c += arm_dot((A_DATA_TYPE##4)((a).s01, (A_DATA_TYPE##2)(0), ), (B_DATA_TYPE##4)(((b).s01), (B_DATA_TYPE##2)(0))); +#define DOT_PRODUCT3_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c += arm_dot((A_DATA_TYPE##4)((a).s012, (A_DATA_TYPE)0), (B_DATA_TYPE##4)(((b).s012), (B_DATA_TYPE)0)); +#define DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c += arm_dot((a), (b)); +#else // defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) +#define DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ - c += (DST_DATA_TYPE)a.s0 * (DST_DATA_TYPE)b.s0; \ - c += (DST_DATA_TYPE)a.s1 * (DST_DATA_TYPE)b.s1; \ + c += (C_DATA_TYPE)(a).s0 * (C_DATA_TYPE)(b).s0; \ + c += (C_DATA_TYPE)(a).s1 * (C_DATA_TYPE)(b).s1; \ }) -#define DOT_PRODUCT3_INTEGER8(DST_DATA_TYPE, a, b, c) \ +#define DOT_PRODUCT3_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ - DOT_PRODUCT2_INTEGER8(DST_DATA_TYPE, a, b, c); \ - c += (DST_DATA_TYPE)a.s2 * (DST_DATA_TYPE)b.s2; \ + DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c); \ + c += (C_DATA_TYPE)(a).s2 * (C_DATA_TYPE)(b).s2; \ }) -#if defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) -#define DOT_PRODUCT4_INTEGER8(DST_DATA_TYPE, x, y, val) val = arm_dot_acc((x), (y), (val)); -#elif defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) -#define DOT_PRODUCT4_INTEGER8(DST_DATA_TYPE, x, y, val) val += arm_dot((x), (y)); -#else // defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) -#define DOT_PRODUCT4_INTEGER8(DST_DATA_TYPE, x, y, val) \ +#define DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, x, y, val) \ ({ \ - val += (DST_DATA_TYPE)x.s0 * (DST_DATA_TYPE)y.s0; \ - val += (DST_DATA_TYPE)x.s1 * (DST_DATA_TYPE)y.s1; \ - val += (DST_DATA_TYPE)x.s2 * (DST_DATA_TYPE)y.s2; \ - val += (DST_DATA_TYPE)x.s3 * (DST_DATA_TYPE)y.s3; \ + val += (C_DATA_TYPE)(x).s0 * (C_DATA_TYPE)(y).s0; \ + val += (C_DATA_TYPE)(x).s1 * (C_DATA_TYPE)(y).s1; \ + val += (C_DATA_TYPE)(x).s2 * (C_DATA_TYPE)(y).s2; \ + val += (C_DATA_TYPE)(x).s3 * (C_DATA_TYPE)(y).s3; \ }) #endif // defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) -#define DOT_PRODUCT8_INTEGER8(DST_DATA_TYPE, a, b, c) \ - ({ \ - DOT_PRODUCT4_INTEGER8(DST_DATA_TYPE, (a.lo), (b.lo), c); \ - DOT_PRODUCT4_INTEGER8(DST_DATA_TYPE, (a.hi), (b.hi), c); \ +#define DOT_PRODUCT5_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ + ({ \ + DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s0123), ((b).s0123), c); \ + DOT_PRODUCT1_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s4), ((b).s4), c); \ + }) +#define DOT_PRODUCT6_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ + ({ \ + DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s0123), ((b).s0123), c); \ + DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s45), ((b).s45), c); \ + }) +#define DOT_PRODUCT7_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ + ({ \ + DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s0123), ((b).s0123), c); \ + DOT_PRODUCT3_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s456), ((b).s456), c); \ + }) +#define DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ + ({ \ + DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).lo), ((b).lo), c); \ + DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).hi), ((b).hi), c); \ + }) +#define DOT_PRODUCT9_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ + ({ \ + DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \ + DOT_PRODUCT1_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s8), ((b).s8), c); \ + }) +#define DOT_PRODUCT10_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ + ({ \ + DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \ + DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89), ((b).s89), c); \ + }) +#define DOT_PRODUCT11_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ + ({ \ + DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \ + DOT_PRODUCT3_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89A), ((b).s89A), c); \ + }) +#define DOT_PRODUCT12_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ + ({ \ + DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \ + DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89AB), ((b).s89AB), c); \ + }) +#define DOT_PRODUCT13_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ + ({ \ + DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \ + DOT_PRODUCT5_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89ABC), ((b).s89ABC), c); \ + }) +#define DOT_PRODUCT14_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ + ({ \ + DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \ + DOT_PRODUCT6_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89ABCD), ((b).s89ABCD), c); \ + }) +#define DOT_PRODUCT15_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ + ({ \ + DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \ + DOT_PRODUCT7_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89ABCDE), ((b).s89ABCDE), c); \ }) -#define DOT_PRODUCT16_INTEGER8(DST_DATA_TYPE, a, b, c) \ - ({ \ - DOT_PRODUCT8_INTEGER8(DST_DATA_TYPE, (a.lo), (b.lo), c); \ - DOT_PRODUCT8_INTEGER8(DST_DATA_TYPE, (a.hi), (b.hi), c); \ +#define DOT_PRODUCT16_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ + ({ \ + DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).lo), ((b).lo), c); \ + DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).hi), ((b).hi), c); \ }) +/** Dot product integet 8bit function + * + * @note Performs: c += dot(a, b) + * + * @param[in] A_DATA_TYPE A (lhs) data type + * @param[in] B_DATA_TYPE B (rhs) data type + * @param[in] C_DATA_TYPE C (accumulator) data type + * @param[in] K0 Number of accumulations + * @param[in] a OpenCL vector a + * @param[in] c Scalar variable c + */ +#define REDUCE_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, K0, a, c) REDUCE_INTEGER8_STR(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, K0, a, c) +#define REDUCE_INTEGER8_STR(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, K0, a, c) DOT_PRODUCT_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, K0, a, (TILE_VECTOR_TYPE##K0(B_DATA_TYPE))1, c) + /** Load a vector from global memory (tensor) * * @param[in] DATA_TYPE Data type @@ -296,7 +400,7 @@ #define V_LOAD_STR(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, Y, STRIDE_Y) V_LOAD_##TENSOR_TYPE(DATA_TYPE, WIDTH, TENSOR, X, Y, STRIDE_Y) #define V_LOAD_BUFFER(DATA_TYPE, WIDTH, TENSOR, X, Y, STRIDE_Y) \ VLOAD(WIDTH) \ - (0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (X) * sizeof(DATA_TYPE) + (Y)*STRIDE_Y)) + (0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (X) * sizeof(DATA_TYPE) + (Y) * (STRIDE_Y))) #define V_LOAD_IMAGE(DATA_TYPE, WIDTH, TENSOR, X, Y, STRIDE_Y) READ_IMAGE2D(DATA_TYPE, CONVERT_VECTOR_SIZE_TO_PIXEL_UNIT(WIDTH), TENSOR##_img, (X) / 4, (Y)) /** Load a tile from global memory (tensor) @@ -379,6 +483,53 @@ }) \ }) +/** Load a tile from global memory (tensor) when the tensor is stored using a NHWC layout with dilation for the X and Y increments + * + * @param[in] DATA_TYPE Data type + * @param[in] TILE_HEIGHT Number of elements to load from Y (height) dimension + * @param[in] TILE_WIDTH Number of elements to load from X (width) dimension + * @param[in] TILE_CHANNELS Number of elements to load from C (channel) dimension + * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). Currently BUFFER only is supported + * In case of cl_image, only TILE_CHANNELS multiples of 4 are supported (4, 8, 16) + * @param[in] TENSOR Tensor basename + * @param[in] B Starting batch index + * @param[in] Y Starting Y index + * @param[in] X Starting X index + * @param[in] C Starting C index + * @param[in] TENSOR_HEIGHT Number of elements to load from Y (height) dimension + * @param[in] TENSOR_WIDTH Number of elements to load from X (width) dimension + * @param[in] DILATION_X Dilation for the X increment + * @param[in] DILATION_Y Dilation for the Y increment + * @param[in] BOUNDARY_CHECK Boundary check flag. If true, it checks for any out-of-bound reads + * @param[out] dst Output tile + */ +#define T_LOAD_NHWC_WITH_DILATION(DATA_TYPE, TILE_HEIGHT, TILE_WIDTH, TILE_CHANNELS, TENSOR_TYPE, TENSOR, B, Y, X, C, TENSOR_WIDTH, TENSOR_HEIGHT, DILATION_X, DILATION_Y, BOUNDARY_CHECK, dst) \ + ({ \ + LOOP_UNROLLING(int, _yk, 0, 1, TILE_HEIGHT, \ + { \ + LOOP_UNROLLING(int, _xk, 0, 1, TILE_WIDTH, \ + { \ + int _src_y = (X) + _xk * (DILATION_X); \ + int _src_z = ((Y) + _yk * (DILATION_Y)); \ + int _src_w = (B); \ + bool _src_valid_y = (((X) + _xk * (DILATION_X)) >= 0) && (((X) + _xk * (DILATION_X)) < (int)(TENSOR_WIDTH)) && (((Y) + _yk * (DILATION_Y)) >= 0) && (((Y) + _yk * (DILATION_Y)) < (int)(TENSOR_HEIGHT)); \ + if(!(BOUNDARY_CHECK)) \ + { \ + dst[_xk + _yk * (TILE_WIDTH)].v = VLOAD(TILE_CHANNELS) \ + (0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (C) * sizeof(DATA_TYPE) + (_src_y) * (TENSOR##_stride_y) + (_src_z) * (TENSOR##_stride_z) + (_src_w) * (TENSOR##_stride_w))); \ + } \ + else \ + { \ + if(_src_valid_y) \ + { \ + dst[_xk + _yk * (TILE_WIDTH)].v = VLOAD(TILE_CHANNELS) \ + (0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (C) * sizeof(DATA_TYPE) + (_src_y) * (TENSOR##_stride_y) + (_src_z) * (TENSOR##_stride_z) + (_src_w) * (TENSOR##_stride_w))); \ + } \ + } \ + }) \ + }) \ + }) + /** Load a tile from global memory (tensor) when the tensor is stored using a NHWC layout using indirect X and Y coordinates * * @param[in] DATA_TYPE Data type @@ -479,40 +630,160 @@ dst[_m0].s[_n0] += ((ACC_DATA_TYPE)rhs[_n0].s[_k0] * (ACC_DATA_TYPE)SRC_OFFSET); \ }) \ }) \ - }); \ + }) \ + }) + +/** 8-bit quantization with fixed-point scale + * + * @param[in] SRC_DATA_TYPE SRC data type + * @param[in] DST_DATA_TYPE DST data type + * @param[in] QUANTIZATION_TYPE Quantization type (PER_TENSOR or PER_CHANNEL) + * @param[in] M0 Number of src/dst rows + * @param[in] N0 Number of src/dst columns + * @param[in] DST_OFFSET Quantization offset used for both the per-tensor and per-channel quantization + * @param[in] DST_SHIFT Quantization shift for the per-tensor quantization + * @param[in] DST_MULTIPLIER Quantization multiplier for the per-tensor quantization + * @param[in] src Input tile + * @param[in] dst_multipliers Output multipliers tile for the per-channel quantization + * @param[in] dst_shifts Output shift tile for the per-channel quantization + * @param[out] dst Output tile + */ +#define T_QUANTIZE8(SRC_DATA_TYPE, DST_DATA_TYPE, QUANTIZATION_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst_multipliers, dst_shifts, dst) T_QUANTIZE8_STR(SRC_DATA_TYPE, DST_DATA_TYPE, QUANTIZATION_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst_multipliers, dst_shifts, dst) +#define T_QUANTIZE8_STR(SRC_DATA_TYPE, DST_DATA_TYPE, QUANTIZATION_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst_multipliers, dst_shifts, dst) T_QUANTIZE8_##QUANTIZATION_TYPE(SRC_DATA_TYPE, DST_DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst_multipliers, dst_shifts, dst) + +/** 8-bit per-tensor quantization with fixed-point scale + * + * @param[in] SRC_DATA_TYPE SRC data type + * @param[in] DST_DATA_TYPE DST data type + * @param[in] M0 Number of src/dst rows + * @param[in] N0 Number of src/dst columns + * @param[in] DST_OFFSET Quantization offset + * @param[in] DST_SHIFT Quantization shift for the per-tensor quantization + * @param[in] DST_MULTIPLIER Quantization multiplier for the per-tensor quantization + * @param[in] src Input tile + * @param[in] dst_multipliers (unused) + * @param[in] dst_shifts (unused) + * @param[out] dst Output tile + */ +#define T_QUANTIZE8_PER_TENSOR(SRC_DATA_TYPE, DST_DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst_multipliers, dst_shifts, dst) \ + ({ \ + LOOP_UNROLLING(int, _m0, 0, 1, M0, \ + { \ + LOOP_UNROLLING(int, _n0, 0, 1, N0, \ + { \ + SRC_DATA_TYPE _tmp = 0; \ + SRC_DATA_TYPE _src = src[_m0].s[_n0]; \ + _src *= select((SRC_DATA_TYPE)1, ((SRC_DATA_TYPE)1 << (SRC_DATA_TYPE)(-DST_SHIFT)), ((SRC_DATA_TYPE)DST_SHIFT < (SRC_DATA_TYPE)0)); \ + SRC_DATA_TYPE overflow = _src == DST_MULTIPLIER && _src == INT_MIN; \ + long a_64 = (long)(_src); \ + long b_64 = (long)(DST_MULTIPLIER); \ + long ab_64 = a_64 * b_64; \ + long mask1 = 1 << 30; \ + long mask2 = 1 - (1 << 30); \ + long is_positive_or_zero = ab_64 >= 0; \ + long nudge = select(mask2, mask1, is_positive_or_zero); \ + SRC_DATA_TYPE ab_x2_high32 = CONVERT((ab_64 + nudge) / (long)(1ll << 31), SRC_DATA_TYPE); \ + _tmp = select(ab_x2_high32, (SRC_DATA_TYPE)INT_MAX, overflow); \ + if(DST_SHIFT >= 0) \ + { \ + long mask = ((((int)1) << DST_SHIFT) - (int)1); \ + long threshold = _tmp < (int)0 ? (mask >> 1) + (long)1 : (mask >> 1) + 0; \ + _tmp = (_tmp & mask) > threshold ? (_tmp >> DST_SHIFT) + (int)1 : (_tmp >> DST_SHIFT); \ + } \ + _tmp += DST_OFFSET; \ + dst[_m0].s[_n0] = CONVERT_SAT(_tmp, DST_DATA_TYPE); \ + }) \ + }) \ + }) + +/** 8-bit per-channel quantization with fixed-point scale + * + * @param[in] SRC_DATA_TYPE SRC data type + * @param[in] DST_DATA_TYPE DST data type + * @param[in] M0 Number of src/dst rows + * @param[in] N0 Number of src/dst columns + * @param[in] DST_OFFSET Quantization offset + * @param[in] DST_SHIFT (unused) + * @param[in] DST_MULTIPLIER (unused) + * @param[in] src Input tile + * @param[in] dst_multipliers Output multipliers tile for the per-channel quantization + * @param[in] dst_shifts Output shift tile for the per-channel quantization + * @param[out] dst Output tile + */ +#define T_QUANTIZE8_PER_CHANNEL(SRC_DATA_TYPE, DST_DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst_multipliers, dst_shifts, dst) \ + ({ \ + LOOP_UNROLLING(int, _m0, 0, 1, M0, \ + { \ + LOOP_UNROLLING(int, _n0, 0, 1, N0, \ + { \ + SRC_DATA_TYPE _tmp = 0; \ + SRC_DATA_TYPE _src = src[_m0].s[_n0]; \ + SRC_DATA_TYPE _dst_multiplier = dst_multipliers[0].s[_n0]; \ + SRC_DATA_TYPE _dst_shift = dst_shifts[0].s[_n0]; \ + _src *= select((SRC_DATA_TYPE)1, ((SRC_DATA_TYPE)1 << (SRC_DATA_TYPE)(-_dst_shift)), ((SRC_DATA_TYPE)_dst_shift < (SRC_DATA_TYPE)0)); \ + SRC_DATA_TYPE overflow = _src == _dst_multiplier && _src == INT_MIN; \ + long a_64 = (long)(_src); \ + long b_64 = (long)(_dst_multiplier); \ + long ab_64 = a_64 * b_64; \ + long mask1 = 1 << 30; \ + long mask2 = 1 - (1 << 30); \ + long is_positive_or_zero = ab_64 >= 0; \ + long nudge = select(mask2, mask1, is_positive_or_zero); \ + SRC_DATA_TYPE ab_x2_high32 = CONVERT((ab_64 + nudge) / (long)(1ll << 31), SRC_DATA_TYPE); \ + _tmp = select(ab_x2_high32, (SRC_DATA_TYPE)INT_MAX, overflow); \ + if(_dst_shift >= 0) \ + { \ + long mask = ((((int)1) << _dst_shift) - (int)1); \ + long threshold = _tmp < (int)0 ? (mask >> 1) + (long)1 : (mask >> 1) + 0; \ + _tmp = (_tmp & mask) > threshold ? (_tmp >> _dst_shift) + (int)1 : (_tmp >> _dst_shift); \ + } \ + _tmp += DST_OFFSET; \ + dst[_m0].s[_n0] = CONVERT_SAT(_tmp, DST_DATA_TYPE); \ + }) \ + }) \ }) -/** Quantized the tile (ASYMMETRIC) with fixed-point scale +/** Quantized the 8-bit tile with fixed-point scale for asymmetric * * @param[in] SRC_DATA_TYPE SRC data type * @param[in] DST_DATA_TYPE DST data type * @param[in] M0 Number of src/dst rows * @param[in] N0 Number of src/dst columns - * @param[in] DST_OFFSET Quantization offset - * @param[in] DST_SHIFT Quantization shift - * @param[in] DST_MULTIPLIER Quantization multiplier + * @param[in] DST_OFFSET Quantization offset used for both the per-tensor and per-channel quantization + * @param[in] DST_SHIFT Quantization shift for the per-tensor quantization + * @param[in] DST_MULTIPLIER Quantization multiplier for the per-tensor quantization * @param[in] src Input tile * @param[out] dst Output tile */ -#define T_QUANTIZE8_ASYMMETRIC(SRC_DATA_TYPE, DST_DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst) \ - ({ \ - LOOP_UNROLLING(int, _m0, 0, 1, M0, \ - { \ - LOOP_UNROLLING(int, _n0, 0, 1, N0, \ - { \ - SRC_DATA_TYPE _tmp = 0; \ - if(DST_SHIFT < 0) \ - { \ - _tmp = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(src[_m0].s[_n0], DST_MULTIPLIER, DST_SHIFT, 1); \ - } \ - else \ - { \ - _tmp = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(src[_m0].s[_n0], DST_MULTIPLIER, DST_SHIFT, 1); \ - } \ - _tmp += DST_OFFSET; \ - dst[_m0].s[_n0] = CONVERT_SAT(_tmp, DST_DATA_TYPE); \ - }) \ - }) \ +#define T_QUANTIZE8_ASYMMETRIC(SRC_DATA_TYPE, DST_DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst) \ + ({ \ + LOOP_UNROLLING(int, _m0, 0, 1, M0, \ + { \ + LOOP_UNROLLING(int, _n0, 0, 1, N0, \ + { \ + SRC_DATA_TYPE _tmp = 0; \ + SRC_DATA_TYPE _src = src[_m0].s[_n0]; \ + _src *= select((SRC_DATA_TYPE)1, ((SRC_DATA_TYPE)1 << (SRC_DATA_TYPE)(-DST_SHIFT)), ((SRC_DATA_TYPE)DST_SHIFT < (SRC_DATA_TYPE)0)); \ + SRC_DATA_TYPE overflow = _src == DST_MULTIPLIER && _src == INT_MIN; \ + long a_64 = (long)(_src); \ + long b_64 = (long)(DST_MULTIPLIER); \ + long ab_64 = a_64 * b_64; \ + long mask1 = 1 << 30; \ + long mask2 = 1 - (1 << 30); \ + long is_positive_or_zero = ab_64 >= 0; \ + long nudge = select(mask2, mask1, is_positive_or_zero); \ + SRC_DATA_TYPE ab_x2_high32 = CONVERT((ab_64 + nudge) / (long)(1ll << 31), SRC_DATA_TYPE); \ + _tmp = select(ab_x2_high32, (SRC_DATA_TYPE)INT_MAX, overflow); \ + if(DST_SHIFT >= 0) \ + { \ + long mask = ((((int)1) << DST_SHIFT) - (int)1); \ + long threshold = _tmp < (int)0 ? (mask >> 1) + (long)1 : (mask >> 1) + 0; \ + _tmp = (_tmp & mask) > threshold ? (_tmp >> DST_SHIFT) + (int)1 : (_tmp >> DST_SHIFT); \ + } \ + _tmp += DST_OFFSET; \ + dst[_m0].s[_n0] = CONVERT_SAT(_tmp, DST_DATA_TYPE); \ + }) \ + }) \ }) /** Conditional rowset (memset by row) @@ -537,7 +808,7 @@ }) \ }) -/** Element-wise activation +/** Element-wise activation for floating point types * * @note Performs: activation(LHS) = DST * @@ -558,6 +829,42 @@ }) \ }) +// RELU Activation +#define relu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (max((DATA_TYPE)ZERO_VALUE, x)) +// Bounded RELU Activation +#define brelu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (min((DATA_TYPE)A_VAL, max((DATA_TYPE)ZERO_VALUE, x))) +// Lower Upper Bounded RELU Activation +#define lu_brelu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (min(max(x, (DATA_TYPE)B_VAL), (DATA_TYPE)A_VAL)) +// Hard Swish Activation +#define hard_swish_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (x * ((min(max((DATA_TYPE)(x + (DATA_TYPE)3.f), (DATA_TYPE)0.f), (DATA_TYPE)6.f)) * (DATA_TYPE)0.166666667f)) +// Identity Activation +#define identity_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (x) + +#define ACT_OP_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) op##_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) +#define ACTIVATION_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) ACT_OP_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) + +/** Element-wise activation for quantized types + * + * @note Performs: activation(LHS) = DST + * + * @param[in] DATA_TYPE SRC/DST data type + * @param[in] M0 Number of SRC/DST rows + * @param[in] N0 Number of SRC/DST columns + * @param[in] ACTIVATION_TYPE Activation type + * @param[in] ZERO_VALUE The zero value to consider in the computation + * @param[in] A_VAL A value used for the activation (e.g. tanh_op, brelu,..) + * @param[in] B_VAL B value used for the activation (e.g. tanh_op, brelu,..) + * @param[out] src SRC tile + * @param[out] dst DST tile + */ +#define T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_VALUE, A_VAL, B_VAL, src, dst) \ + ({ \ + LOOP_UNROLLING(int, _m0, 0, 1, M0, \ + { \ + dst[_m0].v = ACTIVATION_QUANTIZED(ACTIVATION_TYPE, DATA_TYPE, N0, ZERO_VALUE, A_VAL, B_VAL, src[_m0].v); \ + }) \ + }) + /** Element-wise addition with a constant value * * @note Performs: LHS + constant = DST @@ -617,13 +924,13 @@ * @param[in, out] dst DST tile */ #define T_MMUL(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, LHS_LAYOUT, RHS_LAYOUT, lhs, rhs, dst) T_MMUL_##LHS_LAYOUT##_##RHS_LAYOUT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) -#define T_MMUL_NT_T(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_##LHS_DATA_TYPE##_##RHS_DATA_TYPE##_##DST_DATA_TYPE(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) -#define T_MMUL_NT_T_float_float_float(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_FLOAT(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) -#define T_MMUL_NT_T_half_half_half(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_FLOAT(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) -#define T_MMUL_NT_T_char_char_int(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_INTEGER8(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) -#define T_MMUL_NT_T_uchar_uchar_uint(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_INTEGER8(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) -#define T_MMUL_NT_T_uchar_uchar_int(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_INTEGER8(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) -#define T_MMUL_NT_T_FLOAT(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) \ +#define T_MMUL_NT_T(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_##LHS_DATA_TYPE##_##RHS_DATA_TYPE##_##DST_DATA_TYPE(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_T_float_float_float(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_FLOAT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_T_half_half_half(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_FLOAT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_T_char_char_int(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_INTEGER8(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_T_uchar_uchar_uint(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_INTEGER8(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_T_uchar_uchar_int(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_INTEGER8(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_T_FLOAT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) \ { \ LOOP_UNROLLING(int, _m, 0, 1, M0, \ { \ @@ -636,15 +943,16 @@ }) \ }) \ } -#define T_MMUL_NT_T_INTEGER8(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) \ - ({ \ - LOOP_UNROLLING(int, _m, 0, 1, M0, \ - { \ - LOOP_UNROLLING(int, _n, 0, 1, N0, \ - { \ - DOT_PRODUCT_INTEGER8(DST_DATA_TYPE, K0, (lhs[_m].v), (rhs[_n].v), dst[_m].s[_n]); \ - }) \ - }) \ + +#define T_MMUL_NT_T_INTEGER8(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) \ + ({ \ + LOOP_UNROLLING(int, _m, 0, 1, M0, \ + { \ + LOOP_UNROLLING(int, _n, 0, 1, N0, \ + { \ + DOT_PRODUCT_INTEGER8(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, K0, (lhs[_m].v), (rhs[_n].v), dst[_m].s[_n]); \ + }) \ + }) \ }) /** 8-bit quantization with fixed-point scale @@ -757,4 +1065,4 @@ }) \ }) // clang-format on -// *INDENT-ON*
\ No newline at end of file +// *INDENT-ON* diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp deleted file mode 100644 index dda70d2231..0000000000 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp +++ /dev/null @@ -1,432 +0,0 @@ -/* - * Copyright (c) 2018-2021 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. - */ -#include "src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h" - -#include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLKernelLibrary.h" -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "arm_compute/core/utils/quantization/AsymmHelpers.h" -#include "src/core/AccessWindowStatic.h" -#include "src/core/CL/CLValidate.h" -#include "src/core/CL/ICLKernel.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" -#include "support/StringSupport.h" - -namespace arm_compute -{ -using namespace arm_compute::misc::shape_calculator; - -namespace -{ -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D dilation, - const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) -{ - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.enabled()) && (input->data_type() == DataType::QASYMM8 || input->data_type() == DataType::QASYMM8_SIGNED) - && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU) - && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU) - && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU) - && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC), - "For QASYMM8 only logistic, relu, lower bounded relu and lower-upper bounded relu are supported"); - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != 3 || weights->dimension(1) != 3); - ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1 || conv_info.stride().first > 3); - - ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); - - const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type()); - - if(biases != nullptr) - { - if(is_qasymm) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); - } - ARM_COMPUTE_RETURN_ERROR_ON((biases->dimension(0) != weights->dimension(2)) && (weights->dimension(2) != 1 || biases->dimension(0) != weights->dimension(3))); - ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - } - - if(is_qasymm) - { - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output_multipliers, output_shifts); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_multipliers, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_shifts, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1); - ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1); - - if(is_data_type_quantized_per_channel(weights->data_type())) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL); - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != output_multipliers->dimension(0)); - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != output_shifts->dimension(0)); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); - ARM_COMPUTE_RETURN_ERROR_ON(1 != output_multipliers->dimension(0)); - ARM_COMPUTE_RETURN_ERROR_ON(1 != output_shifts->dimension(0)); - } - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); - } - - if(output->total_size() != 0) - { - const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation }; - const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, info); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); - } - - return Status{}; -} - -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, std::string &kernel_name, const Size2D dilation) -{ - // Output auto inizialitation if not yet initialized - const ConvolutionInfo info - { - conv_info, depth_multiplier, ActivationLayerInfo(), dilation - }; - const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, info); - auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_quantization_info(output->quantization_info())); - - const unsigned int conv_stride_x = conv_info.stride().first; - const unsigned int conv_stride_y = conv_info.stride().second; - const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type()); - - // Configure kernel window - unsigned int num_elems_read_per_iteration_x = 0; - unsigned int num_elems_read_per_iteration_y = 0; - unsigned int num_elems_written_per_iteration_x = 0; - unsigned int num_elems_written_per_iteration_y = 0; - - if(input->data_type() == DataType::F16) - { - kernel_name = "depthwise_convolution_3x3_f16"; - num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type()); - num_elems_written_per_iteration_y = 1; - num_elems_read_per_iteration_y = 3; - switch(conv_stride_x) - { - case 1: - num_elems_read_per_iteration_x = 8; - break; - case 2: - num_elems_read_per_iteration_x = 9; - break; - case 3: - num_elems_read_per_iteration_x = 16; - break; - default: - num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x; - break; - } - if(conv_stride_x == 1 && conv_stride_y == 1) - { - kernel_name = "depthwise_convolution_3x3_stridex1_stridey1_f16"; - num_elems_read_per_iteration_x = 8; - num_elems_written_per_iteration_x = 4; - num_elems_read_per_iteration_y = 6; - num_elems_written_per_iteration_y = 4; - } - else if(conv_stride_x == 2 && conv_stride_y == 2) - { - kernel_name = "depthwise_convolution_3x3_stridex2_stridey2_f16"; - num_elems_read_per_iteration_x = 10; - num_elems_written_per_iteration_x = 4; - num_elems_read_per_iteration_y = 5; - num_elems_written_per_iteration_y = 2; - } - } - else if(input->data_type() == DataType::F32) - { - if(conv_stride_x == 1 && conv_stride_y == 1) - { - kernel_name = "depthwise_convolution_3x3_stridex1_stridey1_f32"; - num_elems_read_per_iteration_x = 4; - num_elems_read_per_iteration_y = 6; - num_elems_written_per_iteration_x = 2; - num_elems_written_per_iteration_y = 4; - } - else if(conv_stride_x == 2 && conv_stride_y == 2) - { - kernel_name = "depthwise_convolution_3x3_stridex2_stridey2_f32"; - num_elems_read_per_iteration_x = 6; - num_elems_read_per_iteration_y = 5; - num_elems_written_per_iteration_x = 2; - num_elems_written_per_iteration_y = 2; - } - else - { - kernel_name = "depthwise_convolution_3x3"; - num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type()); - num_elems_written_per_iteration_y = 1; - num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x; - num_elems_read_per_iteration_y = 3; - } - } - else - { - const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()) && !is_data_type_quantized_per_channel(weights->data_type()); - - kernel_name = is_qasymm ? "dwc_3x3_native_quantized8" : "depthwise_convolution_3x3"; - kernel_name += (is_qasymm && is_dot8_supported ? "_dot8" : ""); - kernel_name += (is_qasymm ? "_nchw" : ""); - - num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type()); - num_elems_written_per_iteration_y = (is_qasymm && conv_stride_y == 1 && dilation.y() == 1) ? 2 : 1; - num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x + (conv_stride_x > 1 ? 1 : 0); - num_elems_read_per_iteration_y = num_elems_written_per_iteration_y + 2; - } - // The OpenCL routine convolution1x3 does loadn(addr), loadn(addr + dilation_x) and loadn(addr + 2 * dilation_x) on the input. - // Each of the three convolution1x3 gets called by passing addr, (addr + dilation_y) and (addr + 2 * dilation_y) - // Hence we must add 2 * dilation.x/y() to the number of elements read in those axes per thread - num_elems_read_per_iteration_x += 2 * dilation.x(); - num_elems_read_per_iteration_y += 2 * dilation.y(); - - // Create window and update padding - Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y)); - - AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.pad_top(), - num_elems_read_per_iteration_x, num_elems_read_per_iteration_y, - conv_stride_x, conv_stride_y); - AccessWindowStatic weights_access(weights, 0, 0, 3, 3); - AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y); - - bool window_changed = update_window_and_padding(win, input_access, weights_access, output_access); - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_pair(err, win); -} -} // namespace - -CLDepthwiseConvolutionLayer3x3NCHWKernel::CLDepthwiseConvolutionLayer3x3NCHWKernel() - : _border_size(0), _input(), _output(), _weights(), _biases(), _conv_stride_y(1), _output_multipliers(), _output_shifts(), _is_quantized(false), _conv_stride_x(0), _conv_pad_top(0), _conv_pad_left(0) -{ -} - -BorderSize CLDepthwiseConvolutionLayer3x3NCHWKernel::border_size() const -{ - return _border_size; -} - -void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation, - const ICLTensor *output_multipliers, const ICLTensor *output_shifts) -{ - configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation, output_multipliers, output_shifts); -} - -void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation, - const ICLTensor *output_multipliers, const ICLTensor *output_shifts) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), - conv_info, depth_multiplier, act_info, dilation, - (output_multipliers != nullptr) ? output_multipliers->info() : nullptr, - (output_shifts != nullptr) ? output_shifts->info() : nullptr)); - - _input = input; - _output = output; - _weights = weights; - _biases = biases; - _conv_stride_x = conv_info.stride().first; - _conv_stride_y = conv_info.stride().second; - _conv_pad_left = conv_info.pad_left(); - _conv_pad_top = conv_info.pad_top(); - _output_multipliers = output_multipliers; - _output_shifts = output_shifts; - _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); - - // Configure kernel window - std::string kernel_name; - - auto win_config = validate_and_configure_window(input->info(), weights->info(), output->info(), conv_info, depth_multiplier, kernel_name, dilation); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - ICLKernel::configure_internal(win_config.second); - - _border_size = BorderSize(input->info()->padding()); - - // Set build options - CLBuildOptions build_opts; - build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation()))); - build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(_output->info()->tensor_shape().z())); - build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier)); - build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(_conv_stride_x)); - build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x())); - build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y())); - build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); - - if(_is_quantized) - { - const UniformQuantizationInfo iq_info = _input->info()->quantization_info().uniform(); - const UniformQuantizationInfo wq_info = _weights->info()->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = _output->info()->quantization_info().uniform(); - - const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->info()->data_type()); - const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()) && !is_quantized_per_channel; - build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(_conv_stride_y)); - build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-iq_info.offset)); - build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wq_info.offset)); - build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oq_info.offset)); - build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * iq_info.offset * wq_info.offset)); - build_opts.add_option_if(is_quantized_per_channel, "-DPER_CHANNEL_QUANTIZATION"); - build_opts.add_option_if(is_dot8_supported, "-DIS_DOT8"); - - // Compute non-per-channel multiplier and shift anyway to make OpenCL kernel simpler - float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; - int output_multiplier = 0; - int output_shift = 0; - quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); - build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); - build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift)); - - if(act_info.enabled()) - { - int a_val{}; - int b_val{}; - std::tie(b_val, a_val) = get_quantized_activation_min_max(act_info, input->info()->data_type(), oq_info); - - const int o1 = oq_info.offset; - - build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val)); - build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val)); - build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1)); - - const float s1 = iq_info.scale; - build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1)); - build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1)); - } - - build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); - build_opts.add_option("-DWEIGHTS_TYPE=" + get_cl_type_from_data_type(weights->info()->data_type())); - build_opts.add_option("-DWEIGHTS_PROMOTED_TYPE=" + get_cl_promoted_type_from_data_type(weights->info()->data_type())); - } - else - { - build_opts.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a())); - build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b())); - build_opts.add_option_if(act_info.enabled(), "-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); - build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(win_config.second.x().step())); - } - - build_opts.add_option_if(input->info()->data_type() == DataType::F16, "-DIS_F16"); - build_opts.add_option_if(input->info()->data_type() == DataType::F32, "-DIS_F32"); - - _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); - - // Set config_id for enabling LWS tuning - _config_id = kernel_name; - _config_id += "_"; - _config_id += lower_string(string_from_data_type(input->info()->data_type())); - _config_id += "_"; - _config_id += support::cpp11::to_string(input->info()->dimension(0)); - _config_id += "_"; - _config_id += support::cpp11::to_string(input->info()->dimension(1)); - _config_id += "_"; - _config_id += support::cpp11::to_string(input->info()->dimension(2)); - _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(0)); - _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(1)); -} - -Status CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, - const Size2D &dilation, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) -{ - std::string kernel_name; - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation, output_multipliers, output_shifts)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), - conv_info, depth_multiplier, kernel_name, dilation) - .first); - - return Status{}; -} - -void CLDepthwiseConvolutionLayer3x3NCHWKernel::run(const Window &window, cl::CommandQueue &queue) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); - - Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); - - // Create input window and adjust - Window collapsed_in = collapsed; - collapsed_in.adjust(Window::DimX, -_conv_pad_left, true); - collapsed_in.adjust(Window::DimY, -_conv_pad_top, true); - collapsed_in.set_dimension_step(Window::DimX, collapsed_in.x().step() * _conv_stride_x); - collapsed_in.set_dimension_step(Window::DimY, collapsed_in.y().step() * _conv_stride_y); - - Window slice_in = collapsed_in.first_slice_window_3D(); - Window slice_out = collapsed.first_slice_window_3D(); - Window slice_weights = window.first_slice_window_3D(); - slice_weights.set_dimension_step(Window::DimX, 0); - slice_weights.set_dimension_step(Window::DimY, 0); - - unsigned int idx = 3 * num_arguments_per_3D_tensor(); - - // Set output multipliers in case of quantized data type - if(_is_quantized) - { - Window slice; - slice.use_tensor_dimensions(_output_multipliers->info()->tensor_shape()); - add_1D_tensor_argument(idx, _output_multipliers, slice); - add_1D_tensor_argument(idx, _output_shifts, slice); - } - - // Set biases - if(_biases != nullptr) - { - Window slice_biases; - slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape()); - add_1D_tensor_argument(idx, _biases, slice_biases); - } - - do - { - idx = 0; - add_3D_tensor_argument(idx, _input, slice_in); - add_3D_tensor_argument(idx, _output, slice_out); - add_3D_tensor_argument(idx, _weights, slice_weights); - - enqueue(queue, *this, slice_out, lws_hint()); - } - while(collapsed.slide_window_slice_3D(slice_out) && collapsed_in.slide_window_slice_3D(slice_in)); -} -} // namespace arm_compute diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h deleted file mode 100644 index c4e475f6f2..0000000000 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h +++ /dev/null @@ -1,131 +0,0 @@ -/* - * Copyright (c) 2018-2021 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_CLDEPTHWISECONVOLUTIONNCHWKERNEL3x3_H -#define ARM_COMPUTE_CLDEPTHWISECONVOLUTIONNCHWKERNEL3x3_H - -#include "src/core/CL/ICLKernel.h" - -namespace arm_compute -{ -class ICLTensor; - -/** Interface for the kernel to run a 3x3 depthwise convolution on a tensor when the data layout is NCHW. - */ -class CLDepthwiseConvolutionLayer3x3NCHWKernel : public ICLKernel -{ -public: - /** Default constructor */ - CLDepthwiseConvolutionLayer3x3NCHWKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLDepthwiseConvolutionLayer3x3NCHWKernel(const CLDepthwiseConvolutionLayer3x3NCHWKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLDepthwiseConvolutionLayer3x3NCHWKernel &operator=(const CLDepthwiseConvolutionLayer3x3NCHWKernel &) = delete; - /** Default Move Constructor. */ - CLDepthwiseConvolutionLayer3x3NCHWKernel(CLDepthwiseConvolutionLayer3x3NCHWKernel &&) = default; - /** Default move assignment operator */ - CLDepthwiseConvolutionLayer3x3NCHWKernel &operator=(CLDepthwiseConvolutionLayer3x3NCHWKernel &&) = default; - /** Initialize the function's source, destination, conv and border_size. - * - * @param[in] input Source tensor. DataType supported: QASYMM8/QASYMM8_SIGNED/F16/F32. - * @param[in] weights Weights tensor. A 3D tensor with dimensions [3, 3, IFM]. - * Data type supported: Same as @p input or QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL when @p input is QASYMM8/QASYMM8_SIGNED. - * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input, S32 when input is QASYMM8/QASYMM8_SIGNED. - * @param[out] output Destination tensor. Data type supported: Same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU for QASYMM8 supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - * @param[in] output_multipliers (Optional) Output multipliers tensor for quantized computations. In case of per-channel quantization, - * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 - * @param[in] output_shifts (Optional) Output shifts tensor for quantized computations. In case of per-channel quantization, - * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 - */ - void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U), - const ICLTensor *output_multipliers = nullptr, const ICLTensor *output_shifts = nullptr); - /** Initialize the function's source, destination, conv and border_size. - * - * @param[in] compile_context The compile context to be used. - * @param[in] input Source tensor. DataType supported: QASYMM8/QASYMM8_SIGNED/F16/F32. - * @param[in] weights Weights tensor. A 3D tensor with dimensions [3, 3, IFM]. - * Data type supported: Same as @p input or QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL when @p input is QASYMM8/QASYMM8_SIGNED. - * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input, S32 when input is QASYMM8/QASYMM8_SIGNED. - * @param[out] output Destination tensor. Data type supported: Same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU for QASYMM8 supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - * @param[in] output_multipliers (Optional) Output multipliers tensor for quantized computations. In case of per-channel quantization, - * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 - * @param[in] output_shifts (Optional) Output shifts tensor for quantized computations. In case of per-channel quantization, - * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 - */ - void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U), - const ICLTensor *output_multipliers = nullptr, const ICLTensor *output_shifts = nullptr); - /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayer3x3NCHWKernel - * - * @param[in] input Source tensor info. DataType supported: QASYMM8/QASYMM8_SIGNED/F16/F32. - * @param[in] weights Weights tensor info. A 3D tensor with dimensions [3, 3, IFM]. - * Data type supported: Same as @p input or QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL when @p input is QASYMM8/QASYMM8_SIGNED. - * @param[in] biases Biases tensor info. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input, S32 when input is QASYMM8/QASYMM8_SIGNED. - * @param[in] output Destination tensor. Data type supported: Same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU are supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - * @param[in] output_multipliers (Optional) Output multipliers tensor info for quantized computations. In case of per-channel quantization, - * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 - * @param[in] output_shifts (Optional) Output shifts tensor for quantized computations. In case of per-channel quantization, - * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, ActivationLayerInfo act_info = ActivationLayerInfo(), - const Size2D &dilation = Size2D(1U, 1U), const ITensorInfo *output_multipliers = nullptr, const ITensorInfo *output_shifts = nullptr); - - void run(const Window &window, cl::CommandQueue &queue) override; - BorderSize border_size() const override; - -private: - BorderSize _border_size; - const ICLTensor *_input; - ICLTensor *_output; - const ICLTensor *_weights; - const ICLTensor *_biases; - unsigned int _conv_stride_y; - const ICLTensor *_output_multipliers; - const ICLTensor *_output_shifts; - bool _is_quantized; - - unsigned int _conv_stride_x; - unsigned int _conv_pad_top; - unsigned int _conv_pad_left; -}; -} // namespace arm_compute -#endif /*ARM_COMPUTE_CLDEPTHWISECONVOLUTIONNCHWKERNEL3x3_H */ diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp deleted file mode 100644 index 91a2f5745a..0000000000 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp +++ /dev/null @@ -1,238 +0,0 @@ -/* - * Copyright (c) 2018-2021 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. - */ -#include "src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h" - -#include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLKernelLibrary.h" -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "src/core/AccessWindowStatic.h" -#include "src/core/CL/CLValidate.h" -#include "src/core/CL/ICLKernel.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" -#include "support/StringSupport.h" - -namespace arm_compute -{ -namespace -{ -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation) -{ - ARM_COMPUTE_UNUSED(act_info); - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier > 1); - - ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1); - ARM_COMPUTE_RETURN_ERROR_ON(std::max(conv_info.pad_top(), conv_info.pad_bottom()) > 4); - - ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); - - const size_t weights_width = 3; - const size_t weights_height = 3; - - const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation }; - - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape( - *input, TensorInfo(TensorShape(weights_width, weights_height), 1, weights->data_type()).set_data_layout(DataLayout::NCHW), info); - - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); - ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(1) != weights_width) || (weights->dimension(2) != weights_height)); - - if(biases != nullptr) - { - ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != output_shape[0]); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); - - ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - } - - if(output->total_size() != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); - } - - return Status{}; -} - -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) -{ - ARM_COMPUTE_UNUSED(weights, bias); - ARM_COMPUTE_UNUSED(depth_multiplier); - - const bool is_stride_1_dilation_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1) && dilation.x() == 1 && dilation.y() == 1); - unsigned int num_rows_processed_per_iteration = is_stride_1_dilation_1 ? 2 : 1; - - Window win{}; - Status err{}; - - unsigned int num_elems_accessed_per_iteration = adjust_vec_size(4 / input->element_size(), input->dimension(0)); - win = calculate_max_window(*output, Steps(num_elems_accessed_per_iteration, num_rows_processed_per_iteration)); - - return std::make_pair(err, win); -} -} // namespace - -CLDepthwiseConvolutionLayer3x3NHWCKernel::CLDepthwiseConvolutionLayer3x3NHWCKernel() - : _input(), _output(), _weights(), _biases(), _num_planes_processed_per_iteration(1) -{ -} - -void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) -{ - configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); -} - -void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), - conv_info, depth_multiplier, act_info, dilation)); - - auto padding_info = get_padding_info({ input, weights, biases, output }); - - auto win_config = validate_and_configure_window(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), - conv_info, depth_multiplier, dilation); - - const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1)); - const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1); - - _input = input; - _output = output; - _weights = weights; - _biases = biases; - _num_planes_processed_per_iteration = is_stride_1_dilation_1 ? 2 : 1; - - unsigned int num_elems_accessed_per_iteration = adjust_vec_size(4 / input->info()->element_size(), input->info()->dimension(0)); - unsigned int num_rows_processed_per_iteration = is_stride_1_dilation_1 ? 2 : 1; - - CLBuildOptions build_opts; - build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(_input->info()->data_type())); - build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation()))); - build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_accessed_per_iteration)); - build_opts.add_option("-DSRC_DIM_1=" + support::cpp11::to_string(_input->info()->dimension(1))); - build_opts.add_option("-DSRC_DIM_2=" + support::cpp11::to_string(_input->info()->dimension(2))); - build_opts.add_option("-DCONV_PAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())); - build_opts.add_option("-DCONV_PAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())); - build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(input->info()->dimension(0) % num_elems_accessed_per_iteration)); - build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); - build_opts.add_option_if(_input->info()->tensor_shape().total_size_upper(3) > 1, - "-DDST_DEPTH=" + support::cpp11::to_string(static_cast<int>(std::ceil(_output->info()->dimension(2) / static_cast<float>(_num_planes_processed_per_iteration))))); - build_opts.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a())); - build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b())); - - if(is_stride_1_dilation_1) - { - build_opts.add_option("-DNUM_ROWS_PROCESSED=" + support::cpp11::to_string(num_rows_processed_per_iteration)); - build_opts.add_option("-DNUM_PLANES_PROCESSED=" + support::cpp11::to_string(_num_planes_processed_per_iteration)); - build_opts.add_option("-DDST_DIM_1=" + support::cpp11::to_string(_output->info()->dimension(1))); - build_opts.add_option("-DDST_DIM_2=" + support::cpp11::to_string(_output->info()->dimension(2))); - build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string((input->info()->dimension(1) + conv_info.pad_left() + conv_info.pad_right()) % num_rows_processed_per_iteration)); - } - else - { - build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(conv_info.stride().first)); - build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second)); - build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x())); - build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y())); - } - - // Create kernel - std::string kernel_name; - kernel_name = std::string("depthwise_convolution_3x3_nhwc"); - kernel_name += (is_stride_1_dilation_1 ? "_stride1" : ""); - - ICLKernel::configure_internal(win_config.second); - _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); - - ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); - - // Set config_id for enabling LWS tuning - _config_id = kernel_name; - _config_id += "_"; - _config_id += support::cpp11::to_string(input->info()->dimension(0)); - _config_id += "_"; - _config_id += support::cpp11::to_string(input->info()->dimension(1)); - _config_id += "_"; - _config_id += support::cpp11::to_string(input->info()->dimension(2)); - _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(0)); - _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(1)); - _config_id += "_"; - _config_id += string_from_data_type(input->info()->data_type()); -} - -Status CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), - biases != nullptr ? biases->clone().get() : nullptr, - output->clone().get(), conv_info, depth_multiplier, dilation) - .first); - return Status{}; -} - -void CLDepthwiseConvolutionLayer3x3NHWCKernel::run(const Window &window, cl::CommandQueue &queue) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); - - const size_t total_batches = _input->info()->tensor_shape().total_size_upper(3); - - Window win = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); - win.set(Window::DimZ, Window::Dimension(0, std::ceil(_output->info()->dimension(2) / static_cast<float>(_num_planes_processed_per_iteration)) * total_batches, 1)); - - unsigned int idx = 2 * num_arguments_per_4D_tensor() + num_arguments_per_3D_tensor(); - - if(_biases != nullptr) - { - Window win_biases; - win_biases.use_tensor_dimensions(_biases->info()->tensor_shape()); - win_biases.set_dimension_step(Window::DimX, window.x().step()); - add_1D_tensor_argument(idx, _biases, win_biases); - } - - Window slice = win.first_slice_window_4D(); - do - { - unsigned int idx = 0; - add_4D_tensor_argument(idx, _input, slice); - add_4D_tensor_argument(idx, _output, slice); - add_3D_tensor_argument(idx, _weights, slice); - - enqueue(queue, *this, slice, lws_hint()); - } - while(win.slide_window_slice_4D(slice)); -} -} // namespace arm_compute diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h deleted file mode 100644 index ee47d98807..0000000000 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h +++ /dev/null @@ -1,110 +0,0 @@ -/* - * Copyright (c) 2018-2021 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_CLDEPTHWISECONVOLUTIONNHWCKERNEL3x3_H -#define ARM_COMPUTE_CLDEPTHWISECONVOLUTIONNHWCKERNEL3x3_H - -#include "src/core/CL/ICLKernel.h" - -namespace arm_compute -{ -class ICLTensor; - -/** Interface for the kernel to run a 3x3 depthwise convolution on a tensor when the data layout is NHWC. - */ -class CLDepthwiseConvolutionLayer3x3NHWCKernel : public ICLKernel -{ -public: - /** Default constructor */ - CLDepthwiseConvolutionLayer3x3NHWCKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLDepthwiseConvolutionLayer3x3NHWCKernel(const CLDepthwiseConvolutionLayer3x3NHWCKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLDepthwiseConvolutionLayer3x3NHWCKernel &operator=(const CLDepthwiseConvolutionLayer3x3NHWCKernel &) = delete; - /** Default Move Constructor. */ - CLDepthwiseConvolutionLayer3x3NHWCKernel(CLDepthwiseConvolutionLayer3x3NHWCKernel &&) = default; - /** Default move assignment operator */ - CLDepthwiseConvolutionLayer3x3NHWCKernel &operator=(CLDepthwiseConvolutionLayer3x3NHWCKernel &&) = default; - /** Default move assignment operator. */ - /** Initialize the function's source, destination, conv and border_size. - * - * @param[in] input Source tensor. DataType supported: F16/F32. - * @param[in] weights Weights tensor. A 3D tensor with dimensions [IFM, 3, 3]. - * Data type supported: Same as @p input. - * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input. - * @param[out] output Destination tensor. Data type supported: Same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU are supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - */ - void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); - /** Initialize the function's source, destination, conv and border_size. - * - * @param[in] compile_context The compile context to be used. - * @param[in] input Source tensor. DataType supported: F16/F32. - * @param[in] weights Weights tensor. A 3D tensor with dimensions [IFM, 3, 3]. - * Data type supported: Same as @p input. - * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input. - * @param[out] output Destination tensor. Data type supported: Same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU are supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - */ - void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); - /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayer3x3NHWCKernel - * - * @param[in] input Source tensor info. DataType supported: F16/F32. - * @param[in] weights Weights tensor info. A 3D tensor with dimensions [IFM, 3, 3]. - * Data type supported: Same as @p input. - * @param[in] biases Biases tensor info. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input. - * @param[in] output Destination tensor info. Data type supported: Same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU are supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); - - // Inherited methods overridden: - void run(const Window &window, cl::CommandQueue &queue) override; - -private: - const ICLTensor *_input; - ICLTensor *_output; - const ICLTensor *_weights; - const ICLTensor *_biases; - - unsigned int _num_planes_processed_per_iteration; -}; -} // namespace arm_compute -#endif /*ARM_COMPUTE_CLDEPTHWISECONVOLUTIONNHWCKERNEL3x3_H */ diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp index eb1cf146af..4bde303f1e 100644 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp @@ -31,8 +31,10 @@ #include "arm_compute/core/Utils.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/utils/quantization/AsymmHelpers.h" +#include "src/core/CL/CLUtils.h" #include "src/core/CL/CLValidate.h" #include "src/core/CL/ICLKernel.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "support/StringSupport.h" @@ -41,9 +43,8 @@ namespace arm_compute { namespace { -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const DWCWeightsKernelInfo &dwc_weights_info, - const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation, - const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const DWCComputeKernelInfo &dwc_info, + const ConvolutionInfo &conv_info, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) { ARM_COMPUTE_UNUSED(dwc_info); bool in_place = false; @@ -56,13 +57,18 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(input, DataLayout::NHWC); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier > 1 && dwc_weights_info.n0 != 1); - ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1); - ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().second < 1); - ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.depth_multiplier > 1 && dwc_info.n0 != 1); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride().first > 1 && dwc_info.m0 != 1); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.dilation.x() > 1 && dwc_info.m0 != 1); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((dwc_info.export_weights_to_cl_image == true) && (export_weights_to_cl_image(weights) == false), "Export to cl_image not supported!"); + ARM_COMPUTE_RETURN_ERROR_ON((dwc_info.export_weights_to_cl_image == true) && (conv_info.depth_multiplier > 1)); + ARM_COMPUTE_RETURN_ERROR_ON((dwc_info.export_weights_to_cl_image == true) && ((dwc_info.n0 % 4) != 0)); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride().first < 1); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride().second < 1); + ARM_COMPUTE_RETURN_ERROR_ON((conv_info.dilation.x() < 1) || (conv_info.dilation.y() < 1)); const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL); ARM_COMPUTE_UNUSED(idx_c); - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_c) != (input->dimension(idx_c) * depth_multiplier)); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_c) != (input->dimension(idx_c) * conv_info.depth_multiplier)); // In place restrictions if(in_place) @@ -70,14 +76,14 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const int weights_width_idx = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::WIDTH); const int weights_height_idx = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::HEIGHT); ARM_COMPUTE_RETURN_ERROR_ON(weights->tensor_shape()[weights_width_idx] != 1U || weights->tensor_shape()[weights_height_idx] != 1U); - ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier != 1U); - ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride() != std::make_pair(1U, 1U)); - ARM_COMPUTE_RETURN_ERROR_ON(dilation != Size2D(1U, 1U)); - ARM_COMPUTE_RETURN_ERROR_ON(conv_info.has_padding()); // Note that in princple padding can be supported with in_place but we choose not to support it + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.depth_multiplier != 1U); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride() != std::make_pair(1U, 1U)); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.dilation != Size2D(1U, 1U)); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.has_padding()); // Note that in princple padding can be supported with in_place but we choose not to support it } - const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation }; - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, info); + const ConvolutionInfo info{ conv_info.pad_stride_info, conv_info.depth_multiplier, ActivationLayerInfo(), conv_info.dilation }; + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info); const bool is_quantized = is_data_type_quantized(input->data_type()); @@ -152,21 +158,21 @@ CLDepthwiseConvolutionLayerNativeKernel::CLDepthwiseConvolutionLayerNativeKernel _depth_multiplier(1), _output_multipliers(nullptr), _output_shifts(nullptr), + _export_to_cl_image(false), _is_quantized(false) { _type = CLKernelType::DEPTHWISE; } void CLDepthwiseConvolutionLayerNativeKernel::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const DWCWeightsKernelInfo &dwc_weights_info, const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation, + const DWCComputeKernelInfo &dwc_info, const ConvolutionInfo &conv_info, const ICLTensor *output_multipliers, const ICLTensor *output_shifts) { - configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation, output_multipliers, output_shifts); + configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, dwc_info, conv_info, output_multipliers, output_shifts); } void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const DWCWeightsKernelInfo &dwc_weights_info, - const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation, + const DWCComputeKernelInfo &dwc_info, const ConvolutionInfo &conv_info, const ICLTensor *output_multipliers, const ICLTensor *output_shifts) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); @@ -176,93 +182,113 @@ void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext & output = input; } ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), - dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation, - (output_multipliers != nullptr) ? output_multipliers->info() : nullptr, (output_shifts != nullptr) ? output_shifts->info() : nullptr)); + dwc_info, conv_info, (output_multipliers != nullptr) ? output_multipliers->info() : nullptr, (output_shifts != nullptr) ? output_shifts->info() : nullptr)); auto padding_info = get_padding_info({ input, output }); - const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation }; - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*(input->info()), *(weights->info()), info); + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*(input->info()), *(weights->info()), conv_info); auto_init_if_empty(*(output->info()), input->info()->clone()->set_tensor_shape(output_shape).set_quantization_info(output->info()->quantization_info())); _input = input; _output = output; _weights = weights; _biases = biases; - _depth_multiplier = depth_multiplier; + _depth_multiplier = conv_info.depth_multiplier; _output_multipliers = output_multipliers; _output_shifts = output_shifts; + _export_to_cl_image = dwc_info.export_weights_to_cl_image; _is_quantized = is_data_type_quantized(input->info()->data_type()); - const unsigned int n0 = adjust_vec_size(dwc_weights_info.n0, input->info()->dimension(0)); + const unsigned int n0 = adjust_vec_size(dwc_info.n0, input->info()->dimension(0)); + const unsigned int m0 = std::min(dwc_info.m0, (unsigned int)output->info()->dimension(1)); + std::string kernel_name = ""; CLBuildOptions build_opts; - build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); - build_opts.add_option_if(_input->info()->tensor_shape().total_size_upper(3) > 1, "-DDST_DEPTH=" + support::cpp11::to_string(static_cast<int>(_output->info()->dimension(2)))); - build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(_input->info()->data_type())); - build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(dwc_info.activation_info.activation()))); - build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier)); + + // Update the padding for the weights tensor if we can export to cl_image + if(_export_to_cl_image) + { + arm_compute::opencl::kernels::gemm::update_padding_for_cl_image(weights->info()); + } + + build_opts.add_option("-cl-fast-relaxed-math"); + build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(conv_info.act_info.activation()))); + build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(conv_info.depth_multiplier)); + build_opts.add_option("-DSRC_TENSOR_TYPE=BUFFER"); + build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(_input->info()->dimension(1))); + build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(_input->info()->dimension(2))); + // Note: SRC_DATA_TYPE must have the same data type of WEI_DATA_TYPE. In quantized, we could + // have a case where the data types for the activation and weights are different. However, since the implementation + // only works when both have same data type, we have to change the offset to take into account this aspect + build_opts.add_option("-DSRC_DATA_TYPE=" + get_cl_type_from_data_type(_input->info()->data_type())); + build_opts.add_option("-DDST_TENSOR_TYPE=BUFFER"); + build_opts.add_option("-DDST_WIDTH=" + support::cpp11::to_string(_output->info()->dimension(1))); + build_opts.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(_output->info()->dimension(2))); + build_opts.add_option("-DDST_DATA_TYPE=" + get_cl_type_from_data_type(_output->info()->data_type())); + build_opts.add_option_if_else(_export_to_cl_image, "-DWEI_TENSOR_TYPE=IMAGE", "-DWEI_TENSOR_TYPE=BUFFER"); + build_opts.add_option("-DWEI_WIDTH=" + support::cpp11::to_string(_weights->info()->dimension(1))); + build_opts.add_option("-DWEI_HEIGHT=" + support::cpp11::to_string(_weights->info()->dimension(2))); + build_opts.add_option("-DWEI_DATA_TYPE=" + get_cl_type_from_data_type(_weights->info()->data_type())); + build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_stride_info.pad_top())); + build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_stride_info.pad_left())); + build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_info.pad_stride_info.stride().first)); + build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_info.pad_stride_info.stride().second)); + build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(conv_info.dilation.x())); + build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(conv_info.dilation.y())); build_opts.add_option("-DN0=" + support::cpp11::to_string(n0)); - build_opts.add_option("-DSRC_DIM1=" + support::cpp11::to_string(_input->info()->dimension(1))); - build_opts.add_option("-DSRC_DIM2=" + support::cpp11::to_string(_input->info()->dimension(2))); - build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(weights->info()->dimension(1))); - build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(weights->info()->dimension(2))); - build_opts.add_option("-DCONV_PAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())); - build_opts.add_option("-DCONV_PAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())); - build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(conv_info.stride().first)); - build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second)); - build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x())); - build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y())); - build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(_input->info()->dimension(0) % n0)); - - std::string kernel_name = (_is_quantized) ? "dwc_MxN_native_quantized8_nhwc" : "dwc_MxN_native_fp_nhwc"; + build_opts.add_option("-DM0=" + support::cpp11::to_string(m0)); + build_opts.add_option("-DM0_A=" + support::cpp11::to_string(weights->info()->dimension(1) + m0 - 1)); + build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(_input->info()->dimension(0) % n0)); + build_opts.add_option_if(_input->info()->num_dimensions() > 3, "-DBATCHED_EXECUTION"); + if(biases != nullptr) + { + build_opts.add_option(std::string("-DHAS_BIAS")); + build_opts.add_option(std::string("-DBIA_DATA_TYPE=" + get_cl_type_from_data_type(biases->info()->data_type()))); + } if(_is_quantized) { - const UniformQuantizationInfo iq_info = _input->info()->quantization_info().uniform(); - const UniformQuantizationInfo wq_info = _weights->info()->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = _output->info()->quantization_info().uniform(); + kernel_name = "dwc_native_quantized_nhwc"; + const UniformQuantizationInfo iqinfo = input->info()->quantization_info().uniform(); + const UniformQuantizationInfo wqinfo = weights->info()->quantization_info().uniform(); + const UniformQuantizationInfo oqinfo = output->info()->quantization_info().uniform(); - build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-iq_info.offset)); - build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wq_info.offset)); - build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oq_info.offset)); - build_opts.add_option_if(is_data_type_quantized_per_channel(weights->info()->data_type()), "-DPER_CHANNEL_QUANTIZATION"); + PixelValue zero_value = PixelValue(0, input->info()->data_type(), input->info()->quantization_info()); + int zero_value_s32; + zero_value.get(zero_value_s32); - // Compute non-per-channel multiplier and shift anyway to make OpenCL kernel simpler - float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; + float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale; int output_multiplier = 0; int output_shift = 0; quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); - build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); - build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift)); - - if(dwc_info.activation_info.enabled()) - { - int a_val{}; - int b_val{}; - std::tie(b_val, a_val) = get_quantized_activation_min_max(dwc_info.activation_info, input->info()->data_type(), oq_info); - - const int o1 = oq_info.offset; - - build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val)); - build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val)); - build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1)); - - const float s1 = iq_info.scale; - build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1)); - build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1)); - } - - build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); - build_opts.add_option("-DWEIGHTS_TYPE=" + get_cl_type_from_data_type(weights->info()->data_type())); + build_opts.add_option("-DDST_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); + build_opts.add_option("-DDST_SHIFT=" + support::cpp11::to_string(output_shift)); + build_opts.add_option("-DSRC_OFFSET=" + support::cpp11::to_string(-iqinfo.offset)); + build_opts.add_option("-DWEI_OFFSET=" + support::cpp11::to_string(-wqinfo.offset)); + build_opts.add_option("-DDST_OFFSET=" + support::cpp11::to_string(oqinfo.offset)); + build_opts.add_option("-DZERO_VALUE=" + support::cpp11::to_string(zero_value_s32)); + build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(DataType::S32)); + build_opts.add_option("-DDST_MULTIPLIERS_DATA_TYPE=" + get_cl_type_from_data_type(_output_multipliers->info()->data_type())); + build_opts.add_option("-DDST_SHIFTS_DATA_TYPE=" + get_cl_type_from_data_type(_output_shifts->info()->data_type())); + build_opts.add_option_if_else(weights->info()->data_type() == DataType::QSYMM8_PER_CHANNEL, "-DQUANTIZATION_TYPE=PER_CHANNEL", "-DQUANTIZATION_TYPE=PER_TENSOR"); + // Note: We expect the input and output tensors to always adopt a per-tensor quantization approach + int a_val{}; + int b_val{}; + std::tie(b_val, a_val) = get_quantized_activation_min_max(conv_info.act_info, input->info()->data_type(), oqinfo); + + build_opts.add_option_if(conv_info.act_info.enabled(), "-DA_VAL=" + support::cpp11::to_string(a_val)); + build_opts.add_option_if(conv_info.act_info.enabled(), "-DB_VAL=" + support::cpp11::to_string(b_val)); } else { - build_opts.add_option_if(dwc_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(dwc_info.activation_info.a())); - build_opts.add_option_if(dwc_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(dwc_info.activation_info.b())); + kernel_name = "dwc_native_fp_nhwc"; + build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.add_option("-DZERO_VALUE=" + support::cpp11::to_string(0)); + build_opts.add_option_if(conv_info.act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(conv_info.act_info.a())); + build_opts.add_option_if(conv_info.act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(conv_info.act_info.b())); } - Window win = calculate_max_window(*(output->info()), Steps(n0)); + Window win = calculate_max_window(*(output->info()), Steps(n0, m0)); ICLKernel::configure_internal(win); _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); @@ -288,10 +314,9 @@ void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext & } Status CLDepthwiseConvolutionLayerNativeKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, - const DWCWeightsKernelInfo &dwc_weights_info, const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, const Size2D &dilation, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) + const DWCComputeKernelInfo &dwc_info, const ConvolutionInfo &conv_info, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation, output_multipliers, output_shifts)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, dwc_info, conv_info, output_multipliers, output_shifts)); return Status{}; } @@ -302,37 +327,46 @@ void CLDepthwiseConvolutionLayerNativeKernel::run(const Window &window, cl::Comm // Collapse window Window window_collapsed = window.collapse(ICLKernel::window(), Window::DimZ); - Window slice_in = window.first_slice_window_4D(); - Window slice_out = window_collapsed.first_slice_window_4D(); + + Window slice = window_collapsed.first_slice_window_4D(); if(_depth_multiplier != 1) { - ARM_COMPUTE_ERROR_ON(slice_out.x().step() != 1); - slice_out.set(Window::DimX, Window::Dimension(0, _input->info()->tensor_shape()[0], 1)); + // If the depth multiplier > 1, we need to use the input channels rather than the output channels + ARM_COMPUTE_ERROR_ON(slice.x().step() != 1); + slice.set(Window::DimX, Window::Dimension(0, _input->info()->tensor_shape()[0], 1)); } - unsigned int idx = 2 * num_arguments_per_4D_tensor() + num_arguments_per_3D_tensor(); + cl::Image2D weights_cl_image; - // Set output multipliers in case of quantized data type - if(_is_quantized) + if(_export_to_cl_image) { - add_1D_tensor_argument(idx, _output_multipliers, slice_in); - add_1D_tensor_argument(idx, _output_shifts, slice_in); + const size_t image_w = _weights->info()->dimension(0) / 4; + const size_t image_h = _weights->info()->dimension(1) * _weights->info()->dimension(2) * _weights->info()->dimension(3); + const TensorShape shape2d(image_w, image_h); + const size_t image_row_pitch = _weights->info()->strides_in_bytes()[1]; + + // Export cl_buffer to cl_image + weights_cl_image = create_image2d_from_buffer(CLKernelLibrary::get().context(), _weights->cl_buffer(), shape2d, _weights->info()->data_type(), image_row_pitch); } - if(_biases != nullptr) + unsigned int idx = 0; + add_4D_tensor_argument(idx, _input, slice); + add_4D_tensor_argument(idx, _output, slice); + if(_export_to_cl_image) { - add_1D_tensor_argument(idx, _biases, slice_in); + _kernel.setArg(idx++, weights_cl_image); } - - do + add_4D_tensor_argument(idx, _weights, slice); + if(_is_quantized) + { + add_1D_tensor_argument(idx, _output_multipliers, slice); + add_1D_tensor_argument(idx, _output_shifts, slice); + } + if(_biases != nullptr) { - idx = 0; - add_4D_tensor_argument(idx, _input, slice_in); - add_4D_tensor_argument(idx, _output, slice_out); - add_3D_tensor_argument(idx, _weights, slice_out); - enqueue(queue, *this, slice_out, lws_hint()); + add_1D_tensor_argument(idx, _biases, slice); } - while(window_collapsed.slide_window_slice_4D(slice_out) && window.slide_window_slice_4D(slice_in)); + enqueue(queue, *this, slice, lws_hint()); } } // namespace arm_compute diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h index 068131f434..eeed115832 100644 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h @@ -47,23 +47,21 @@ public: CLDepthwiseConvolutionLayerNativeKernel(CLDepthwiseConvolutionLayerNativeKernel &&) = default; /** Allow instances of this class to be moved */ CLDepthwiseConvolutionLayerNativeKernel &operator=(CLDepthwiseConvolutionLayerNativeKernel &&) = default; + /** Initialize the function's source, destination and parameters * - * @param[in] compile_context The compile context to be used. - * @param[in,out] input Source tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/FP32/FP16. Data layout supported: NHWC - * @param[in] weights Weights tensor. A 3D tensor with dimensions [IFM, N, M]. - * Data type supported: Same as @p input or QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL when @p input is QASYMM8. - * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input, S32 when input is QASYMM8/QASYMM8_SIGNED. - * @param[out] output Destination tensor. Pass in nullptr or @p input for in-place operation. Data type supported: Same as @p input. - * @param[in] dwc_weights_info Depthwise convolution layer weights info to retrieve the number of output elements processed by each thread - * @param[in] dwc_info Depthwise convolution layer info - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - * @param[in] output_multipliers (Optional) Output multipliers tensor for quantized computations. In case of per-channel quantization, - * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 - * @param[in] output_shifts (Optional) Output shifts tensor for quantized computations. In case of per-channel quantization, + * @param[in] compile_context The compile context to be used. + * @param[in] input Source tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/FP32/FP16. Data layout supported: NHWC + * @param[in] weights Weights tensor. A 3D tensor with dimensions [IFM, N, M]. + * Data type supported: Same as @p input or QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL when @p input is QASYMM8. + * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. + * Data type supported: Same as @p input, S32 when input is QASYMM8/QASYMM8_SIGNED. + * @param[out] output Destination tensor. Pass in nullptr or @p input for in-place operation. Data type supported: Same as @p input. + * @param[in] dwc_info Depthwise convolution layer info + * @param[in] conv_info Convolution info (padding, stride, dilation, ...) + * @param[in] output_multipliers (Optional) Output multipliers tensor for quantized computations. In case of per-channel quantization, + * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 + * @param[in] output_shifts (Optional) Output shifts tensor for quantized computations. In case of per-channel quantization, * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 * * @note: In-place is only supported when @@ -75,17 +73,15 @@ public: * * no padding * * no change of data layout after configure */ - void configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const DWCWeightsKernelInfo &dwc_weights_info, - const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1, const Size2D &dilation = Size2D(1U, 1U), - const ICLTensor *output_multipliers = nullptr, const ICLTensor *output_shifts = nullptr); + void configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const DWCComputeKernelInfo &dwc_info, + const ConvolutionInfo &conv_info, const ICLTensor *output_multipliers = nullptr, const ICLTensor *output_shifts = nullptr); - /** Initialize the function's source, destination and parameters + /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayerNativeKernel * * Similar to @ref CLDepthwiseConvolutionLayerNativeKernel::configure() */ - void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const DWCWeightsKernelInfo &dwc_weights_info, - const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1, const Size2D &dilation = Size2D(1U, 1U), - const ICLTensor *output_multipliers = nullptr, const ICLTensor *output_shifts = nullptr); + void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const DWCComputeKernelInfo &dwc_info, + const ConvolutionInfo &conv_info, const ICLTensor *output_multipliers = nullptr, const ICLTensor *output_shifts = nullptr); /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayerNativeKernel * @@ -93,9 +89,8 @@ public: * * @return a status */ - static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const DWCWeightsKernelInfo &dwc_weights_info, - const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1, const Size2D &dilation = Size2D(1U, 1U), - const ITensorInfo *output_multipliers = nullptr, const ITensorInfo *output_shifts = nullptr); + static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const DWCComputeKernelInfo &dwc_info, + const ConvolutionInfo &conv_info, const ITensorInfo *output_multipliers = nullptr, const ITensorInfo *output_shifts = nullptr); // Inherited methods overridden: void run(const Window &window, cl::CommandQueue &queue) override; @@ -108,6 +103,7 @@ private: unsigned int _depth_multiplier{ 0 }; const ICLTensor *_output_multipliers{}; const ICLTensor *_output_shifts{}; + bool _export_to_cl_image { true }; bool _is_quantized{ false }; }; } // namespace arm_compute diff --git a/src/core/gpu/cl/ClKernelLibrary.cpp b/src/core/gpu/cl/ClKernelLibrary.cpp index 9d516e54a7..73da93c1f5 100644 --- a/src/core/gpu/cl/ClKernelLibrary.cpp +++ b/src/core/gpu/cl/ClKernelLibrary.cpp @@ -223,23 +223,13 @@ const std::map<std::string, std::string> ClKernelLibrary::_kernel_program_map = { "crop_tensor", "crop_tensor.cl" }, { "deconvolution_reshape", "deconvolution_layer.cl" }, { "deconvolution_upsample", "deconvolution_layer.cl" }, - { "depthwise_convolution_3x3", "depthwise_convolution.cl" }, - { "depthwise_convolution_3x3_f16", "depthwise_convolution.cl" }, - { "depthwise_convolution_3x3_nhwc", "depthwise_convolution.cl" }, - { "depthwise_convolution_3x3_nhwc_stride1", "depthwise_convolution.cl" }, - { "dwc_MxN_native_fp_nhwc", "depthwise_convolution.cl" }, - { "dwc_MxN_native_quantized8_nhwc", "depthwise_convolution_quantized.cl" }, - { "dwc_3x3_native_quantized8_nchw", "depthwise_convolution_quantized.cl" }, - { "dwc_3x3_native_quantized8_dot8_nchw", "depthwise_convolution_quantized.cl" }, { "depth_to_space_nchw", "depth_to_space.cl" }, { "depth_to_space_nhwc", "depth_to_space.cl" }, - { "depthwise_convolution_3x3_stridex1_stridey1_f16", "depthwise_convolution.cl" }, - { "depthwise_convolution_3x3_stridex2_stridey2_f16", "depthwise_convolution.cl" }, - { "depthwise_convolution_3x3_stridex1_stridey1_f32", "depthwise_convolution.cl" }, - { "depthwise_convolution_3x3_stridex2_stridey2_f32", "depthwise_convolution.cl" }, { "dequantization_layer", "dequantization_layer.cl" }, { "dequantization_layer_per_channel_nhwc", "dequantization_layer.cl" }, { "dequantization_layer_per_channel_nchw", "dequantization_layer.cl" }, + { "dwc_native_fp_nhwc", "dwc_native_fp_nhwc.cl" }, + { "dwc_native_quantized_nhwc", "dwc_native_quantized_nhwc.cl" }, { "direct_convolution_nhwc", "direct_convolution.cl" }, { "direct_convolution1x1", "direct_convolution1x1.cl" }, { "direct_convolution1x1_f32_bifrost", "direct_convolution1x1.cl" }, @@ -575,14 +565,6 @@ const std::map<std::string, std::string> ClKernelLibrary::_program_source_map = #include "./cl_kernels/depth_to_space.clembed" }, { - "depthwise_convolution.cl", -#include "./cl_kernels/depthwise_convolution.clembed" - }, - { - "depthwise_convolution_quantized.cl", -#include "./cl_kernels/depthwise_convolution_quantized.clembed" - }, - { "dequantization_layer.cl", #include "./cl_kernels/dequantization_layer.clembed" }, @@ -607,6 +589,14 @@ const std::map<std::string, std::string> ClKernelLibrary::_program_source_map = #include "./cl_kernels/direct_convolution.clembed" }, { + "dwc_native_fp_nhwc.cl", +#include "./cl_kernels/dwc_native_fp_nhwc.clembed" + }, + { + "dwc_native_quantized_nhwc.cl", +#include "./cl_kernels/dwc_native_quantized_nhwc.clembed" + }, + { "elementwise_operation.cl", #include "./cl_kernels/elementwise_operation.clembed" }, diff --git a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp index a826f85c5c..84798fa672 100644 --- a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp @@ -23,16 +23,15 @@ */ #include "arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h" +#include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/PixelValue.h" +#include "arm_compute/core/Utils.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/utils/quantization/AsymmHelpers.h" #include "arm_compute/runtime/CL/CLScheduler.h" -#include "src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h" -#include "src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h" #include "src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h" -#include "src/core/CL/kernels/CLFillBorderKernel.h" namespace arm_compute { @@ -41,70 +40,97 @@ using namespace arm_compute::misc::shape_calculator; namespace { -Status validate_arguments_3x3(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) +bool export_weights_to_cl_image_heuristic(const ITensorInfo *weights, unsigned int depth_multiplier, GPUTarget gpu_target) { - // This function should be removed and incorporated inside CLDepthwiseConvolutionLayerInternal3x3 once CLDepthwiseConvolutionLayer3x3 is properly removed - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN); + if(!export_weights_to_cl_image(weights)) + { + return false; + } - const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); - const bool is_nhwc = input->data_layout() == DataLayout::NHWC; - const bool needs_permute = is_nhwc && (depth_multiplier > 1); + const size_t idx_w = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::WIDTH); + const size_t idx_h = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::HEIGHT); + const size_t kernel_w = weights->tensor_shape()[idx_w]; + const size_t kernel_h = weights->tensor_shape()[idx_h]; - ARM_COMPUTE_RETURN_ERROR_ON(is_quantized && is_nhwc && !needs_permute); + if((kernel_w == 1) && (kernel_h == 1)) + { + return false; + } - TensorInfo output_multipliers_shifts_info(TensorInfo(TensorShape(1U), 1, DataType::S32)); - if(is_quantized) + if(depth_multiplier > 1) { - if(is_data_type_quantized_per_channel(weights->data_type())) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL); + return false; + } - const size_t idx_c = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::CHANNEL); - output_multipliers_shifts_info.set_tensor_shape(TensorShape(weights->dimension(idx_c))); + if(gpu_target == GPUTarget::G71 || get_arch_from_target(gpu_target) == GPUTarget::MIDGARD) + { + return false; + } + + return true; +} + +void initialize_dwc_native_compute_info(DWCComputeKernelInfo &dwc_compute_info, const ITensorInfo *weights, const PadStrideInfo &conv_info, const Size2D &dilation, unsigned int depth_multiplier, + GPUTarget gpu_target) +{ + if(!is_data_type_float(weights->data_type())) + { + dwc_compute_info.export_weights_to_cl_image = false; + dwc_compute_info.n0 = (depth_multiplier == 1) ? 4 : 1; + if(conv_info.stride().first == 1 && dilation.x() == 1 && depth_multiplier == 1) + { + dwc_compute_info.m0 = 2; } else { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + dwc_compute_info.m0 = 1; } - } - if(needs_permute) - { - TensorShape permuted_input_shape = input->tensor_shape(); - TensorShape permuted_weights_shape = weights->tensor_shape(); - const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation }; - TensorShape permuted_output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, info); + return; + } - permute(permuted_input_shape, PermutationVector(1U, 2U, 0U)); - permute(permuted_weights_shape, PermutationVector(1U, 2U, 0U)); - permute(permuted_output_shape, PermutationVector(1U, 2U, 0U)); + // Floating point path - const TensorInfo permuted_input = input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NCHW); - const TensorInfo permuted_weights = weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NCHW); - const TensorInfo permuted_output = output->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_output_shape).set_data_layout(DataLayout::NCHW); + // First check if we can export to cl_image. + dwc_compute_info.export_weights_to_cl_image = export_weights_to_cl_image_heuristic(weights, depth_multiplier, gpu_target); - ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output, - conv_info, depth_multiplier, act_info, - dilation, &output_multipliers_shifts_info, &output_multipliers_shifts_info)); + // Set n0 + if(depth_multiplier == 1) + { + if(dwc_compute_info.export_weights_to_cl_image == false && weights->data_type() == DataType::F16) + { + dwc_compute_info.n0 = 8; + } + else + { + dwc_compute_info.n0 = 4; + } } - else if(is_nhwc) + else { - ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, - dilation)); + dwc_compute_info.n0 = 1; + } + + dwc_compute_info.n0 = adjust_vec_size(dwc_compute_info.n0, weights->dimension(0)); + + // Set m0 only if stride_x == 1 and dilation_x == 1 + if(conv_info.stride().first == 1 && dilation.x() == 1) + { + const size_t idx_w = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::WIDTH); + const size_t kernel_w = weights->tensor_shape()[idx_w]; + + dwc_compute_info.m0 = (kernel_w >= 9) || (kernel_w == 1) ? 1 : 2; } else { - ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, - dilation, &output_multipliers_shifts_info, &output_multipliers_shifts_info)); + dwc_compute_info.m0 = 1; } - return Status{}; + return; } + } // namespace -CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::CLDepthwiseConvolutionLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager) +CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) : _memory_group(std::move(memory_manager)), _dwc_native_kernel(std::make_unique<CLDepthwiseConvolutionLayerNativeKernel>()), _permute_input_to_nhwc(), @@ -126,15 +152,15 @@ CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::CLDepthwiseConv CLDepthwiseConvolutionLayer::~CLDepthwiseConvolutionLayer() = default; -void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation) +void CLDepthwiseConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, + unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) { configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); } -void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, - ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation) +void CLDepthwiseConvolutionLayer::configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, + ICLTensor *output, const PadStrideInfo &conv_info, + unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); ARM_COMPUTE_ERROR_THROW_ON(CLDepthwiseConvolutionLayer::validate(input->info(), @@ -153,6 +179,8 @@ void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::configure( _output = output; _needs_permute = input->info()->data_layout() == DataLayout::NCHW; + const GPUTarget gpu_target = CLScheduler::get().target(); + ICLTensor *input_to_use = input; const ICLTensor *weights_to_use = weights; ICLTensor *output_to_use = output; @@ -191,13 +219,13 @@ void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::configure( output_shifts_to_use = &_output_shifts; } - DWCWeightsKernelInfo dwc_weights_info; - dwc_weights_info.n0 = (depth_multiplier == 1) ? 8 : 1; - DWCKernelInfo dwc_info; - dwc_info.activation_info = act_info; + DWCComputeKernelInfo dwc_native_compute_info; + initialize_dwc_native_compute_info(dwc_native_compute_info, weights_to_use->info(), conv_info, dilation, depth_multiplier, gpu_target); + + const ConvolutionInfo conv_kernel_info{ conv_info, depth_multiplier, act_info, dilation }; + _dwc_native_kernel->configure(compile_context, input_to_use, weights_to_use, biases, output_to_use, - dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation, - output_multipliers_to_use, output_shifts_to_use); + dwc_native_compute_info, conv_kernel_info, output_multipliers_to_use, output_shifts_to_use); if(_needs_permute) { @@ -216,9 +244,9 @@ void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::configure( } } -Status CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, - const PadStrideInfo &conv_info, - unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation) +Status CLDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, + const PadStrideInfo &conv_info, + unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) { const bool in_place = input == output || output == nullptr; if(in_place) @@ -232,10 +260,9 @@ Status CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::validate ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) + (weights->dimension(idx_w) - 1) * (dilation.x() - 1) > input->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right()); ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_h) + (weights->dimension(idx_h) - 1) * (dilation.y() - 1) > input->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom()); - DWCWeightsKernelInfo dwc_weights_info; - dwc_weights_info.n0 = (depth_multiplier == 1) ? 8 : 1; - DWCKernelInfo dwc_info; - dwc_info.activation_info = act_info; + const GPUTarget gpu_target = CLScheduler::get().target(); + + const ConvolutionInfo conv_kernel_info{ conv_info, depth_multiplier, act_info, dilation }; const bool needs_permute = input->data_layout() == DataLayout::NCHW; @@ -275,20 +302,25 @@ Status CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::validate ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(input, &permuted_input, PermutationVector(2U, 0U, 1U))); ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(weights, &permuted_weights, PermutationVector(2U, 0U, 1U))); - ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerNativeKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output, dwc_weights_info, - dwc_info, conv_info, depth_multiplier, dilation, - &output_multipliers_shifts_info, &output_multipliers_shifts_info)); + + DWCComputeKernelInfo dwc_native_compute_info; + initialize_dwc_native_compute_info(dwc_native_compute_info, &permuted_weights, conv_info, dilation, depth_multiplier, gpu_target); + + ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerNativeKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output, + dwc_native_compute_info, conv_kernel_info, &output_multipliers_shifts_info, &output_multipliers_shifts_info)); ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(&permuted_output, output, PermutationVector(1U, 2U, 0U))); } else { - ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerNativeKernel::validate(input, weights, biases, output, dwc_weights_info, dwc_info, conv_info, depth_multiplier, - dilation, &output_multipliers_shifts_info, &output_multipliers_shifts_info)); + DWCComputeKernelInfo dwc_native_compute_info; + initialize_dwc_native_compute_info(dwc_native_compute_info, weights, conv_info, dilation, depth_multiplier, gpu_target); + ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerNativeKernel::validate(input, weights, biases, output, dwc_native_compute_info, conv_kernel_info, &output_multipliers_shifts_info, + &output_multipliers_shifts_info)); } return Status{}; } -void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::run() +void CLDepthwiseConvolutionLayer::run() { prepare(); @@ -305,7 +337,7 @@ void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::run() } } -void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::prepare() +void CLDepthwiseConvolutionLayer::prepare() { if(!_is_prepared) { @@ -333,308 +365,4 @@ void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::prepare() _is_prepared = true; } } - -CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerInternal3x3::CLDepthwiseConvolutionLayerInternal3x3(std::shared_ptr<IMemoryManager> memory_manager) - : _memory_group(std::move(memory_manager)), - _kernel_nchw(nullptr), - _kernel_nhwc(nullptr), - _border_handler(std::make_unique<CLFillBorderKernel>()), - _permute_input_to_nchw(), - _permute_weights_to_nchw(), - _permute_output_to_nhwc(), - _permuted_input(), - _permuted_weights(), - _permuted_output(), - _output_multipliers(), - _output_shifts(), - _original_weights(nullptr), - _input(nullptr), - _output(nullptr), - _needs_permute(false), - _is_prepared(false), - _is_quantized(false), - _is_nhwc(false) -{ -} - -void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerInternal3x3::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) -{ - configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); -} - -void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerInternal3x3::configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, - ICLTensor *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) -{ - // Perform validation step - ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_THROW_ON(CLDepthwiseConvolutionLayerInternal3x3::validate(input->info(), - weights->info(), - biases != nullptr ? biases->info() : nullptr, - output->info(), - conv_info, - depth_multiplier, - act_info, - dilation)); - - _is_nhwc = input->info()->data_layout() == DataLayout::NHWC; - _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); - _needs_permute = _is_nhwc && (depth_multiplier > 1); - - _is_prepared = false; - _original_weights = weights; - _input = input; - _output = output; - - ICLTensor *input_to_use = input; - const ICLTensor *weights_to_use = weights; - ICLTensor *output_to_use = output; - - const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->info()->data_type()); - - if(_needs_permute) - { - _memory_group.manage(&_permuted_input); - _memory_group.manage(&_permuted_output); - - // Configure the function to transform the input tensor from NHWC -> NCHW - _permute_input_to_nchw.configure(compile_context, input, &_permuted_input, PermutationVector(1U, 2U, 0U)); - _permuted_input.info()->set_data_layout(DataLayout::NCHW); - - // Configure the function to transform the weights tensor from HWI -> IHW - _permute_weights_to_nchw.configure(compile_context, weights, &_permuted_weights, PermutationVector(1U, 2U, 0U)); - _permuted_weights.info()->set_data_layout(DataLayout::NCHW); - _permuted_output.info()->set_quantization_info(output->info()->quantization_info()); - - input_to_use = &_permuted_input; - weights_to_use = &_permuted_weights; - output_to_use = &_permuted_output; - - _kernel_nchw = std::make_unique<CLDepthwiseConvolutionLayer3x3NCHWKernel>(); - } - else if(_is_nhwc) - { - _kernel_nhwc = std::make_unique<CLDepthwiseConvolutionLayer3x3NHWCKernel>(); - } - else - { - _kernel_nchw = std::make_unique<CLDepthwiseConvolutionLayer3x3NCHWKernel>(); - } - - CLTensor *output_multipliers_to_use = nullptr; - CLTensor *output_shifts_to_use = nullptr; - if(_is_quantized) - { - const size_t idx_c = get_data_layout_dimension_index(weights->info()->data_layout(), DataLayoutDimension::CHANNEL); - const size_t num_filters = (is_quantized_per_channel) ? weights->info()->dimension(idx_c) : 1; - - _output_multipliers.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32)); - _output_shifts.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32)); - - output_multipliers_to_use = &_output_multipliers; - output_shifts_to_use = &_output_shifts; - } - - // Configure kernel - if(_is_nhwc && !_needs_permute) - { - _kernel_nhwc->configure(compile_context, input_to_use, weights_to_use, biases, output_to_use, conv_info, depth_multiplier, - act_info, dilation); - } - else - { - _kernel_nchw->configure(compile_context, input_to_use, weights_to_use, biases, output_to_use, conv_info, depth_multiplier, - act_info, dilation, output_multipliers_to_use, output_shifts_to_use); - } - - if(_is_quantized) - { - _output_multipliers.allocator()->allocate(); - _output_shifts.allocator()->allocate(); - } - - // Permute output if needed - if(_needs_permute) - { - // Configure the function to transform the convoluted output to ACL's native ordering format NCHW - _permuted_output.info()->set_data_layout(DataLayout::NCHW); - _permute_output_to_nhwc.configure(compile_context, &_permuted_output, output, PermutationVector(2U, 0U, 1U)); - - // Allocate tensors - _permuted_input.allocator()->allocate(); - _permuted_output.allocator()->allocate(); - } - // Configure border handler - PixelValue &&zero_value(0.f); - if(is_data_type_quantized_asymmetric(input->info()->data_type())) - { - zero_value = PixelValue(static_cast<uint8_t>(input->info()->quantization_info().uniform().offset)); - } - if(!_is_nhwc || _needs_permute) - { - _border_handler->configure(compile_context, input_to_use, _kernel_nchw->border_size(), BorderMode::CONSTANT, zero_value); - } -} - -Status CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerInternal3x3::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) -{ - return validate_arguments_3x3(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); -} - -void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerInternal3x3::run() -{ - prepare(); - - MemoryGroupResourceScope scope_mg(_memory_group); - - if(_needs_permute) - { - _permute_input_to_nchw.run(); - } - CLScheduler::get().enqueue(*_border_handler); - if(_is_nhwc && !_needs_permute) - { - CLScheduler::get().enqueue(*_kernel_nhwc); - } - else - { - CLScheduler::get().enqueue(*_kernel_nchw); - } - - if(_needs_permute) - { - _permute_output_to_nhwc.run(); - } -} - -void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerInternal3x3::prepare() -{ - if(!_is_prepared) - { - if(_is_quantized) - { - _output_multipliers.map(); - _output_shifts.map(); - quantization::compute_quantized_multipliers_and_shifts(_input->info(), - _original_weights->info(), - _output->info(), - reinterpret_cast<int32_t *>(_output_multipliers.ptr_to_element(Coordinates(0))), - reinterpret_cast<int32_t *>(_output_shifts.ptr_to_element(Coordinates(0)))); - _output_multipliers.unmap(); - _output_shifts.unmap(); - } - - if(_needs_permute) - { - ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); - - _permuted_weights.allocator()->allocate(); - _permute_weights_to_nchw.run(); - _original_weights->mark_as_unused(); - } - - _is_prepared = true; - } -} - -CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) - : _memory_manager(std::move(memory_manager)), _depth_conv_func(DepthwiseConvolutionFunction::GENERIC), _func_3x3(), _func_generic() -{ -} - -void CLDepthwiseConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - ActivationLayerInfo act_info, const Size2D &dilation) -{ - configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); -} - -void CLDepthwiseConvolutionLayer::configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const PadStrideInfo &conv_info, - unsigned int depth_multiplier, - ActivationLayerInfo act_info, const Size2D &dilation) -{ - if(output == nullptr) - { - // In-place - output = input; - } - _depth_conv_func = get_depthwiseconvolution_function(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info, depth_multiplier, act_info, - dilation); - switch(_depth_conv_func) - { - case DepthwiseConvolutionFunction::OPTIMIZED: - _func_3x3.set_memory_group(_memory_manager); - _func_3x3.configure(compile_context, input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); - break; - case DepthwiseConvolutionFunction::GENERIC: - { - _func_generic.set_memory_group(_memory_manager); - _func_generic.configure(compile_context, input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); - } - break; - default: - ARM_COMPUTE_ERROR("Unsupported DepthwiseConvolutionFunction"); - } -} - -Status CLDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) -{ - DepthwiseConvolutionFunction depth_conv_func = get_depthwiseconvolution_function(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); - switch(depth_conv_func) - { - case DepthwiseConvolutionFunction::OPTIMIZED: - return CLDepthwiseConvolutionLayerInternal3x3::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); - case DepthwiseConvolutionFunction::GENERIC: - return CLDepthwiseConvolutionLayerGeneric::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); - default: - ARM_COMPUTE_ERROR("Unsupported DepthwiseConvolutionFunction"); - } -} - -DepthwiseConvolutionFunction CLDepthwiseConvolutionLayer::get_depthwiseconvolution_function(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, - const PadStrideInfo &conv_info, - unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) -{ - if(bool(CLDepthwiseConvolutionLayerInternal3x3::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation))) - { - return DepthwiseConvolutionFunction::OPTIMIZED; - } - else - { - return DepthwiseConvolutionFunction::GENERIC; - } -} - -void CLDepthwiseConvolutionLayer::run() -{ - switch(_depth_conv_func) - { - case DepthwiseConvolutionFunction::OPTIMIZED: - _func_3x3.run(); - break; - case DepthwiseConvolutionFunction::GENERIC: - _func_generic.run(); - break; - default: - ARM_COMPUTE_ERROR("DepthwiseConvolutionFunction not properly configured"); - } -} - -void CLDepthwiseConvolutionLayer::prepare() -{ - switch(_depth_conv_func) - { - case DepthwiseConvolutionFunction::OPTIMIZED: - _func_3x3.prepare(); - break; - case DepthwiseConvolutionFunction::GENERIC: - _func_generic.prepare(); - break; - default: - ARM_COMPUTE_ERROR("DepthwiseConvolutionFunction not properly configured"); - } -} } // namespace arm_compute diff --git a/tests/datasets/DepthwiseConvolutionLayerDataset.h b/tests/datasets/DepthwiseConvolutionLayerDataset.h index 53b5248374..3b17910eac 100644 --- a/tests/datasets/DepthwiseConvolutionLayerDataset.h +++ b/tests/datasets/DepthwiseConvolutionLayerDataset.h @@ -121,7 +121,7 @@ public: SmallDepthwiseConvolutionLayerDataset() { add_config(TensorShape(7U, 7U, 1U), Size2D(1U, 1U), PadStrideInfo(1, 1, 0, 0)); - add_config(TensorShape(23U, 27U, 5U), Size2D(3U, 5U), PadStrideInfo(2, 1, 0, 0)); + add_config(TensorShape(3U, 3U, 2U), Size2D(2U, 2U), PadStrideInfo(1, 1, 0, 0)); add_config(TensorShape(33U, 27U, 7U), Size2D(7U, 3U), PadStrideInfo(3, 2, 1, 0)); // Asymmetric padding add_config(TensorShape(33U, 27U, 7U), Size2D(5U, 7U), PadStrideInfo(3, 2, 1, 1, 2, 0, DimensionRoundingType::FLOOR)); diff --git a/tests/validation/CL/DepthwiseConvolutionLayer.cpp b/tests/validation/CL/DepthwiseConvolutionLayer.cpp index 22922f41a2..79a2678b44 100644 --- a/tests/validation/CL/DepthwiseConvolutionLayer.cpp +++ b/tests/validation/CL/DepthwiseConvolutionLayer.cpp @@ -48,14 +48,13 @@ constexpr RelativeTolerance<float> tolerance_f32(0.01f); /**< constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(0); /**< Tolerance value for comparing reference's output against implementation's output for DataType::QASYMM8 */ constexpr float tolerance_num = 0.05f; /**< Tolerance number */ -const auto depth_multipliers = framework::dataset::make("DepthMultiplier", { 1, 2, 5 }); +const auto depth_multipliers = framework::dataset::make("DepthMultiplier", { 1, 5 }); const auto large_depth_multipliers = framework::dataset::make("DepthMultiplier", { 1, 2, 5, 8 }); //Activation Functions const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", { ActivationLayerInfo(), - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f, 0.f) }); } // namespace @@ -481,8 +480,8 @@ FIXTURE_DATA_TEST_CASE_NEW(RunSmall, CLDepthwiseConvolutionLayerQuantizedFixture combine(combine(combine(combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset(), depth_multipliers), framework::dataset::make("DataType", DataType::QASYMM8)), - framework::dataset::make("SrcQuantizationInfo", { QuantizationInfo(0.3f, 10), QuantizationInfo(2.2f, 10) })), - framework::dataset::make("DstQuantizationInfo", { QuantizationInfo(0.5f, 4) })), + framework::dataset::make("SrcQuantizationInfo", { QuantizationInfo(0.5f, 128), QuantizationInfo(2.2f, 10) })), + framework::dataset::make("DstQuantizationInfo", { QuantizationInfo(1.f, 128) })), framework::dataset::make("DataLayout", { DataLayout::NHWC })), // NCHW is tested with int8 ActivationFunctionsDataset)) { diff --git a/tests/validation/CL/DepthwiseConvolutionLayerNative.cpp b/tests/validation/CL/DepthwiseConvolutionLayerNative.cpp index f640ee2b18..f565255719 100644 --- a/tests/validation/CL/DepthwiseConvolutionLayerNative.cpp +++ b/tests/validation/CL/DepthwiseConvolutionLayerNative.cpp @@ -62,7 +62,7 @@ RelativeTolerance<half_float::half> rel_tolerance_f16(half_float::half(0.01f)); constexpr float abs_tolerance_f16(0.03f); /** Width values to test - Precommit */ -const auto width_values_precommit = framework::dataset::make("width", { 1U, 17U, 32U } ); +const auto width_values_precommit = framework::dataset::make("width", { 1U, 33U } ); /** Width values to test - Nightly */ const auto width_values_nightly = framework::dataset::make("width", { 53U, 47U } ); @@ -79,6 +79,12 @@ const auto channel_values_precommit = framework::dataset::make("channels", { 15U /** Channel values to test - Nightly */ const auto channel_values_nightly = framework::dataset::make("channels", { 33U, 19U }); +/** Channel values to test with cl_image support - Precommit */ +const auto channel_values_export_to_cl_image_precommit = framework::dataset::make("channels", { 16U }); + +/** Channel values to test with cl_image support - Nightly */ +const auto channel_values_export_to_cl_image_nightly = framework::dataset::make("channels", { 32U }); + /** Batch values to test - Precommit */ const auto batch_values_precommit = framework::dataset::make("batch", { 1U, 2U }); @@ -115,11 +121,17 @@ const auto n0_values_precommit = framework::dataset::make("N0", {2, 4}); /** N0 values to test - Nightly */ const auto n0_values_nightly = framework::dataset::make("N0", {3, 8}); +/** N0 values to test with cl_image support - Precommit */ +const auto n0_values_export_to_cl_image_precommit = framework::dataset::make("N0", {4}); + +/** N0 values to test with cl_image support - Nightly */ +const auto n0_values_export_to_cl_image_nightly = framework::dataset::make("N0", {8}); + /** Activation values to test */ const auto act_values = framework::dataset::make("Activation", { ActivationLayerInfo(), - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 8.f, 2.f), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.0f, 0.5f), }); } // namespace @@ -129,7 +141,7 @@ TEST_SUITE(DepthwiseConvolutionLayerNative) TEST_SUITE(Float) TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE_NEW(RunSmall, CLDepthwiseConvolutionLayerNativeFixture<float>, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( width_values_precommit, height_values_precommit), channel_values_precommit), @@ -142,14 +154,15 @@ FIXTURE_DATA_TEST_CASE_NEW(RunSmall, CLDepthwiseConvolutionLayerNativeFixture<fl framework::dataset::make("DataType", DataType::F32)), data_layout_values), act_values), - n0_values_precommit)) + n0_values_precommit), + framework::dataset::make("ExportToCLImage", false))) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE_NEW(RunLarge, CLDepthwiseConvolutionLayerNativeFixture<float>, framework::DatasetMode::NIGHTLY, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( width_values_nightly, height_values_nightly), channel_values_nightly), @@ -162,16 +175,79 @@ FIXTURE_DATA_TEST_CASE_NEW(RunLarge, CLDepthwiseConvolutionLayerNativeFixture<fl framework::dataset::make("DataType", DataType::F32)), data_layout_values), act_values), - n0_values_nightly)) + n0_values_nightly), + framework::dataset::make("ExportToCLImage", false))) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } + +TEST_SUITE(ExportWeightsToCLImage) +FIXTURE_DATA_TEST_CASE_NEW(RunSmall, CLDepthwiseConvolutionLayerNativeFixture<float>, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + width_values_precommit, + height_values_precommit), + channel_values_export_to_cl_image_precommit), + batch_values_precommit), + kernel_sz_values_precommit), + framework::dataset::make("depth_multiplier", 1)), + dilation_values), + stride_values), + padding_valid_values), + framework::dataset::make("DataType", DataType::F32)), + data_layout_values), + act_values), + n0_values_export_to_cl_image_precommit), + framework::dataset::make("ExportToCLImage", true))) +{ + // Validate output + if(_validate_output) + { + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); + } + else + { + ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); + framework::ARM_COMPUTE_PRINT_INFO(); + } +} + +FIXTURE_DATA_TEST_CASE_NEW(RunLarge, CLDepthwiseConvolutionLayerNativeFixture<float>, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + width_values_nightly, + height_values_nightly), + channel_values_export_to_cl_image_nightly), + batch_values_nightly), + kernel_sz_values_nightly), + framework::dataset::make("depth_multiplier", 1)), + dilation_values), + stride_values), + padding_valid_values), + framework::dataset::make("DataType", DataType::F32)), + data_layout_values), + act_values), + n0_values_export_to_cl_image_nightly), + framework::dataset::make("ExportToCLImage", true))) +{ + // Validate output + if(_validate_output) + { + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); + } + else + { + ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); + framework::ARM_COMPUTE_PRINT_INFO(); + } +} +TEST_SUITE_END() // ExportWeightsToCLImage TEST_SUITE_END() // FP32 TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE_NEW(RunSmall, CLDepthwiseConvolutionLayerNativeFixture<half>, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( width_values_precommit, height_values_precommit), channel_values_precommit), @@ -184,14 +260,15 @@ FIXTURE_DATA_TEST_CASE_NEW(RunSmall, CLDepthwiseConvolutionLayerNativeFixture<ha framework::dataset::make("DataType", DataType::F16)), data_layout_values), act_values), - n0_values_precommit)) + n0_values_precommit), + framework::dataset::make("ExportToCLImage", false))) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16); } FIXTURE_DATA_TEST_CASE_NEW(RunLarge, CLDepthwiseConvolutionLayerNativeFixture<half>, framework::DatasetMode::NIGHTLY, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( width_values_nightly, height_values_nightly), channel_values_nightly), @@ -204,18 +281,80 @@ FIXTURE_DATA_TEST_CASE_NEW(RunLarge, CLDepthwiseConvolutionLayerNativeFixture<ha framework::dataset::make("DataType", DataType::F16)), data_layout_values), act_values), - n0_values_nightly)) + n0_values_nightly), + framework::dataset::make("ExportToCLImage", false))) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); } +TEST_SUITE(ExportWeightsToCLImage) +FIXTURE_DATA_TEST_CASE_NEW(RunSmall, CLDepthwiseConvolutionLayerNativeFixture<half>, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + width_values_precommit, + height_values_precommit), + channel_values_export_to_cl_image_precommit), + batch_values_precommit), + kernel_sz_values_precommit), + framework::dataset::make("depth_multiplier", 1)), + dilation_values), + stride_values), + padding_valid_values), + framework::dataset::make("DataType", DataType::F16)), + data_layout_values), + act_values), + n0_values_export_to_cl_image_precommit), + framework::dataset::make("ExportToCLImage", true))) +{ + // Validate output + if(_validate_output) + { + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); + } + else + { + ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); + framework::ARM_COMPUTE_PRINT_INFO(); + } +} + +FIXTURE_DATA_TEST_CASE_NEW(RunLarge, CLDepthwiseConvolutionLayerNativeFixture<half>, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + width_values_nightly, + height_values_nightly), + channel_values_export_to_cl_image_nightly), + batch_values_nightly), + kernel_sz_values_nightly), + framework::dataset::make("depth_multiplier", 1)), + dilation_values), + stride_values), + padding_valid_values), + framework::dataset::make("DataType", DataType::F16)), + data_layout_values), + act_values), + n0_values_export_to_cl_image_nightly), + framework::dataset::make("ExportToCLImage", true))) +{ + // Validate output + if(_validate_output) + { + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); + } + else + { + ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); + framework::ARM_COMPUTE_PRINT_INFO(); + } +} +TEST_SUITE_END() // ExportWeightsToCLImage TEST_SUITE_END() // FP16 TEST_SUITE_END() // Float TEST_SUITE(DepthMultiplier) TEST_SUITE(Float) TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE_NEW(RunSmall, CLDepthwiseConvolutionLayerNativeFixture<float>, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( width_values_precommit, height_values_precommit), channel_values_precommit), @@ -228,14 +367,15 @@ FIXTURE_DATA_TEST_CASE_NEW(RunSmall, CLDepthwiseConvolutionLayerNativeFixture<fl framework::dataset::make("DataType", DataType::F32)), data_layout_values), act_values), - framework::dataset::make("N0", 1))) + framework::dataset::make("N0", 1)), + framework::dataset::make("ExportToCLImage", false))) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE_NEW(RunLarge, CLDepthwiseConvolutionLayerNativeFixture<float>, framework::DatasetMode::NIGHTLY, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( width_values_nightly, height_values_nightly), channel_values_nightly), @@ -248,7 +388,8 @@ FIXTURE_DATA_TEST_CASE_NEW(RunLarge, CLDepthwiseConvolutionLayerNativeFixture<fl framework::dataset::make("DataType", DataType::F32)), data_layout_values), act_values), - framework::dataset::make("N0", 1))) + framework::dataset::make("N0", 1)), + framework::dataset::make("ExportToCLImage", false))) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); @@ -257,7 +398,7 @@ TEST_SUITE_END() // FP32 TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE_NEW(RunSmall, CLDepthwiseConvolutionLayerNativeFixture<half>, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( width_values_precommit, height_values_precommit), channel_values_precommit), @@ -270,14 +411,15 @@ FIXTURE_DATA_TEST_CASE_NEW(RunSmall, CLDepthwiseConvolutionLayerNativeFixture<ha framework::dataset::make("DataType", DataType::F16)), data_layout_values), act_values), - framework::dataset::make("N0", 1))) + framework::dataset::make("N0", 1)), + framework::dataset::make("ExportToCLImage", false))) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16); } FIXTURE_DATA_TEST_CASE_NEW(RunLarge, CLDepthwiseConvolutionLayerNativeFixture<half>, framework::DatasetMode::NIGHTLY, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( width_values_nightly, height_values_nightly), channel_values_nightly), @@ -290,7 +432,8 @@ FIXTURE_DATA_TEST_CASE_NEW(RunLarge, CLDepthwiseConvolutionLayerNativeFixture<ha framework::dataset::make("DataType", DataType::F16)), data_layout_values), act_values), - framework::dataset::make("N0", 1))) + framework::dataset::make("N0", 1)), + framework::dataset::make("ExportToCLImage", false))) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); diff --git a/tests/validation/fixtures/DepthwiseConvolutionLayerFixture.h b/tests/validation/fixtures/DepthwiseConvolutionLayerFixture.h index c255cc5c13..0e02ae28ca 100644 --- a/tests/validation/fixtures/DepthwiseConvolutionLayerFixture.h +++ b/tests/validation/fixtures/DepthwiseConvolutionLayerFixture.h @@ -198,7 +198,7 @@ protected: { case DataType::QASYMM8: { - std::uniform_int_distribution<uint8_t> distribution(0, 10); + std::uniform_int_distribution<uint8_t> distribution(0, 15); library->fill(tensor, distribution, i); break; } @@ -292,7 +292,7 @@ public: if(padding_valid) { - _conv_info = PadStrideInfo(); + _conv_info = PadStrideInfo(stride.width, stride.height); } else { @@ -333,6 +333,7 @@ public: void allocate_and_run_target() { add_padding_x({ &_src, &_weights, &_biases, &_target }, _data_layout); + add_padding_y({ &_src, &_target }, _data_layout); // Allocate tensors _src.allocator()->allocate(); @@ -416,15 +417,16 @@ class DepthwiseConvolutionLayerNativeConfigurableValidationFixture : public Dept public: template <typename...> void setup(size_t width, size_t height, size_t channel, size_t batch, Size2D kernel_size, size_t depth_multiplier, Size2D dilation, Size2D stride, bool padding_valid, DataType data_type, - DataLayout data_layout, const ActivationLayerInfo &act_info, unsigned int n0) + DataLayout data_layout, const ActivationLayerInfo &act_info, unsigned int n0, bool export_to_cl_image) { - _dilation = dilation; - _depth_multiplier = depth_multiplier; - _data_type = data_type; - _data_layout = data_layout; - _act_info = act_info; - _n0 = n0; - _in_place = in_place; + _dilation = dilation; + _depth_multiplier = depth_multiplier; + _data_type = data_type; + _data_layout = data_layout; + _act_info = act_info; + _n0 = n0; + _export_to_cl_image = export_to_cl_image; + _in_place = in_place; _input_shape = TensorShape(width, height, channel, batch); _weights_shape = TensorShape(kernel_size.width, kernel_size.height, channel * _depth_multiplier); @@ -432,11 +434,11 @@ public: if(padding_valid) { - _conv_info = PadStrideInfo(); + _conv_info = calculate_same_pad(_input_shape, _weights_shape, PadStrideInfo(stride.width, stride.height), DataLayout::NCHW, _dilation); } else { - _conv_info = calculate_same_pad(_input_shape, _weights_shape, PadStrideInfo(stride.width, stride.height), DataLayout::NCHW, _dilation); + _conv_info = PadStrideInfo(stride.width, stride.height); } } @@ -462,14 +464,26 @@ public: target_to_use = &_target; } - DWCWeightsKernelInfo dwc_weights_info; - dwc_weights_info.n0 = _n0; + DWCComputeKernelInfo dwc_info; + dwc_info.n0 = _n0; + dwc_info.m0 = _conv_info.stride().first == 1 && _dilation.x() == 1 ? 8 : 1; + dwc_info.export_weights_to_cl_image = _export_to_cl_image; - DWCKernelInfo dwc_info; - dwc_info.activation_info = _act_info; +#if defined(ARM_COMPUTE_OPENCL_ENABLED) + if(_export_to_cl_image) + { + _validate_output |= image2d_from_buffer_supported(CLKernelLibrary::get().get_device()); + _validate_output |= (get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()) != 0); + } +#endif // ARM_COMPUTE_OPENCL_ENABLED + + const ConvolutionInfo conv_kernel_info + { + _conv_info, _depth_multiplier, _act_info, _dilation + }; // Create Depthwise Convolution configure function - _dwc.configure(&_src, &_weights, &_biases, target_to_use, dwc_weights_info, dwc_info, _conv_info, _depth_multiplier, _dilation); + _dwc.configure(&_src, &_weights, &_biases, target_to_use, dwc_info, conv_kernel_info); ARM_COMPUTE_ASSERT(_src.info()->is_resizable()); ARM_COMPUTE_ASSERT(_weights.info()->is_resizable()); @@ -479,7 +493,8 @@ public: void allocate_and_run_target() { - add_padding_x({ &_src, &_weights, &_biases, &_target }, _data_layout); + add_padding_x({ &_src, &_biases, &_target }, _data_layout); + add_padding_x({ &_weights }, _data_layout, _export_to_cl_image); // Don't add left padding if cl image will be used // Allocate tensors _src.allocator()->allocate(); @@ -508,7 +523,10 @@ public: } // Compute function - _dwc.run(); + if(_validate_output) + { + _dwc.run(); + } // Reinstating original data layout for the test suite to properly check the values if(!_in_place) @@ -529,7 +547,10 @@ public: const ConvolutionInfo info{ _conv_info, _depth_multiplier, _act_info, _dilation }; const TensorShape dst_shape = compute_depthwise_convolution_shape(TensorInfo(_input_shape, 1, _data_type), TensorInfo(_weights_shape, 1, _data_type), info); - _reference = reference::activation_layer(reference::depthwise_convolution(src, weights, biases, dst_shape, _conv_info, _depth_multiplier, _dilation), _act_info); + if(_validate_output) + { + _reference = reference::activation_layer(reference::depthwise_convolution(src, weights, biases, dst_shape, _conv_info, _depth_multiplier, _dilation), _act_info); + } } protected: @@ -573,6 +594,8 @@ protected: Size2D _dilation{}; unsigned int _depth_multiplier{}; unsigned int _n0{}; + bool _export_to_cl_image{}; + bool _validate_output{ true }; bool _in_place{ false }; }; |