diff options
6 files changed, 80 insertions, 118 deletions
diff --git a/arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.h b/arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.h index 5a5d3938b7..7256095c03 100644 --- a/arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.h +++ b/arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.h @@ -58,34 +58,30 @@ public: CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel &operator=(CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel &&) = default; /** Initialise the kernel's input and output. * - * @param[in] input Input tensor. Data type supported: S32 - * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required. - * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. - * @param[out] output Output tensor. Data type supported: Data type supported: QASYMM8 - * @param[in] multiplier Float multiplier to be multiplied to each element of the input matrix - * @param[in] offset Offset to be applied to result before converting it back to QASYMM8 - * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 - * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8, - * Along with @p min, this value can be used to implement "rectified linear unit" activation functions - * @param[in] output_3d_depth (Optional) Depth of output in 3D (Defaults to 1) + * @param[in] input Input tensor. Data type supported: S32 + * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required. + * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. + * @param[out] output Output tensor. Data type supported: Data type supported: QASYMM8 + * @param[in] multiplier Float multiplier to be multiplied to each element of the input matrix + * @param[in] offset Offset to be applied to result before converting it back to QASYMM8 + * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 + * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8, + * Along with @p min, this value can be used to implement "rectified linear unit" activation functions */ - void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, float multiplier, int offset, - int min = 0, int max = 0, unsigned int output_3d_depth = 1); + void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, float multiplier, int offset, int min = 0, int max = 0); /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel * - * @param[in] input Input tensor. Data type supported: S32 - * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required. - * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. - * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8 - * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 - * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8, - * Along with @p min, this value can be used to implement "rectified linear unit" activation functions - * @param[in] output_3d_depth (Optional) Depth of output in 3D (Defaults to 1) + * @param[in] input Input tensor. Data type supported: S32 + * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required. + * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. + * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8 + * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 + * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8, + * Along with @p min, this value can be used to implement "rectified linear unit" activation functions * * @return a status */ - static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, - int min = 0, int max = 0, unsigned int output_3d_depth = 1); + static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0); // Inherited methods overridden: void run(const Window &window, cl::CommandQueue &queue) override; @@ -94,7 +90,6 @@ private: const ICLTensor *_input; const ICLTensor *_bias; ICLTensor *_output; - bool _reinterpret_as_3d; }; } // namespace arm_compute #endif /* __ARM_COMPUTE_CLGEMMLOWPQUANTIZEDOWNINT32TOUINT8SCALEBYFLOATKERNEL_H__ */ diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h index 3330b40d8a..cfd1f08519 100644 --- a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h +++ b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h @@ -163,32 +163,30 @@ class CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat : public ICLSimpleFunction public: /** Initialise the kernel's inputs, output * - * @param[in] input Input tensor. Data type supported: S32 - * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required. - * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. - * @param[out] output Output tensor. Data type supported: Data type supported: QASYMM8 - * @param[in] multiplier Float multiplier to be multiplied to each element of the input matrix - * @param[in] offset Offset to be applied to result before converting it back to QASYMM8 - * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 - * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8, - * Along with @p min, this value can be used to implement "rectified linear unit" activation functions - * @param[in] output_3d_depth (Optional) Depth of output in 3D (Defaults to 1) + * @param[in] input Input tensor. Data type supported: S32 + * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required. + * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. + * @param[out] output Output tensor. Data type supported: Data type supported: QASYMM8 + * @param[in] multiplier Float multiplier to be multiplied to each element of the input matrix + * @param[in] offset Offset to be applied to result before converting it back to QASYMM8 + * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 + * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8, + * Along with @p min, this value can be used to implement "rectified linear unit" activation functions */ - void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, float multiplier, int offset, int min = 0, int max = 0, unsigned int output_3d_depth = 1); + void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, float multiplier, int offset, int min = 0, int max = 0); /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint * - * @param[in] input Input tensor. It is the output of @ref CLGEMMLowpMatrixMultiplyCore function. Data type supported: S32 - * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required. - * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. - * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8 - * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 - * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8, - * Along with @p min, this value can be used to implement "rectified linear unit" activation functions - * @param[in] output_3d_depth (Optional) Depth of output in 3D (Defaults to 1) + * @param[in] input Input tensor. It is the output of @ref CLGEMMLowpMatrixMultiplyCore function. Data type supported: S32 + * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required. + * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. + * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8 + * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 + * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8, + * Along with @p min, this value can be used to implement "rectified linear unit" activation functions * * @return a status */ - static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0, unsigned int output_3d_depth = 1); + static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0); }; } // namespace arm_compute #endif /*__ARM_COMPUTE_CLGEMMLOWPOUTPUTSTAGE_H__ */
\ No newline at end of file diff --git a/src/core/CL/cl_kernels/gemmlowp.cl b/src/core/CL/cl_kernels/gemmlowp.cl index 35e0d9dba5..f2467b721a 100644 --- a/src/core/CL/cl_kernels/gemmlowp.cl +++ b/src/core/CL/cl_kernels/gemmlowp.cl @@ -710,7 +710,7 @@ __kernel void gemmlowp_mm_interleaved_transposed_bifrost_dot8(IMAGE_DECLARATION( { // Load values from matrix A (interleaved) and matrix B (transposed) uchar16 a0 = vload16(0, src_addr_a + (i_left_over % 4) + ((i_left_over / 4) * 16)); - uchar4 b0 = vload4(0, src_addr_b); + uchar4 b0 = vload4(0, src_addr_b); c00 += a0.s0 * b0.s0; c01 += a0.s0 * b0.s1; @@ -3225,40 +3225,38 @@ __kernel void gemmlowp_output_stage_quantize_down_float(TENSOR3D_DECLARATION(src #endif // defined(DST_HEIGHT) { // Compute source and destination addresses - Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); -#if defined(DST_HEIGHT) - Tensor4D dst = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(dst, 1); - dst.ptr += get_global_id(0) * dst_step_x + (get_global_id(1) % DST_HEIGHT) * dst_step_y + (get_global_id(1) / DST_HEIGHT) * dst_step_z + get_global_id(2) * dst_step_w; -#else // defined(DST_HEIGHT) - Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); -#endif // defined(DST_HEIGHT) + int x = get_global_id(0) * 4; + int y = get_global_id(1); + int z = get_global_id(2); -#if defined(ADD_BIAS) - Vector biases = CONVERT_TO_VECTOR_STRUCT(biases); -#endif // defined(ADD_BIAS) + __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(int) + y * src_stride_y + z * src_stride_z; - int16 input_values = vload16(0, (__global int *)src.ptr); + __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x + y * dst_stride_y + z * dst_stride_z; + + int4 input_values = vload4(0, (__global int *)src_addr); #if defined(ADD_BIAS) // Add bias - const int16 biases_values = vload16(0, (__global int *)biases.ptr); - input_values += (int16)biases_values; + __global uchar *bias_addr = biases_ptr + biases_offset_first_element_in_bytes + x * sizeof(int); + + int4 biases_values = vload4(0, (__global int *)bias_addr); + input_values += (int4)biases_values; #endif // defined(ADD_BIAS) // Convert to float - float16 input_values_f = convert_float16(input_values); + float16 input_values_f = convert_float4(input_values); input_values_f = round(input_values_f * (float)REAL_MULTIPLIER + (float)OUTPUT_OFFSET); - uchar16 res = convert_uchar16_sat(input_values_f); + uchar4 res = convert_uchar4_sat(input_values_f); #if defined(MIN_BOUND) - res = max(res, (uchar16)MIN_BOUND); + res = max(res, (uchar4)MIN_BOUND); #endif // defined(MIN_BOUND) #if defined(MAX_BOUND) - res = min(res, (uchar16)MAX_BOUND); + res = min(res, (uchar4)MAX_BOUND); #endif // defined(MAX_BOUND) // Store the result - vstore16(res, 0, dst.ptr); + vstore4(res, 0, dst_addr); } #endif // defined(REAL_MULTIPLIER) && defined(OUTPUT_OFFSET) diff --git a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp index 38e0474dde..b7eff0f8ec 100644 --- a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp +++ b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp @@ -69,6 +69,9 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen { constexpr unsigned int num_elems_processed_per_iteration = 4; + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output, input->clone()->set_data_type(DataType::QASYMM8)); + // Configure kernel window Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); @@ -124,10 +127,6 @@ void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const { // Perform validate step ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8)); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(), min, max)); diff --git a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp index f0096bd3ad..b7730d5060 100644 --- a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp +++ b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp @@ -42,7 +42,7 @@ namespace arm_compute namespace { Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, - int min, int max, unsigned int output_3d_depth) + int min, int max) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); ARM_COMPUTE_RETURN_ERROR_ON(max > 255); @@ -58,10 +58,8 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, con if(output->total_size() != 0) { - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input, output_3d_depth, true); - const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(output_shape); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); } return Status{}; @@ -69,7 +67,10 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, con std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output) { - constexpr unsigned int num_elems_processed_per_iteration = 16; + constexpr unsigned int num_elems_processed_per_iteration = 4; + + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output, input->clone()->set_data_type(DataType::QASYMM8)); // Configure kernel window Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); @@ -103,15 +104,14 @@ class Coordinates; } // namespace arm_compute CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel() - : _input(nullptr), _bias(nullptr), _output(nullptr), _reinterpret_as_3d(false) + : _input(nullptr), _bias(nullptr), _output(nullptr) { } -Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, - int min, int max, unsigned int output_3d_depth) +Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max, output_3d_depth)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (bias != nullptr) ? bias->clone().get() : nullptr, output->clone().get()) @@ -122,22 +122,15 @@ Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::validate(const ITen void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, float multiplier, int offset, - int min, int max, unsigned int output_3d_depth) + int min, int max) { // Perform validate step ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(), min, max)); - // Output auto inizialitation if not yet initialized - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input->info(), output_3d_depth, true); - auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8).set_tensor_shape(output_shape)); - - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(), - min, max, output_3d_depth)); - - _input = input; - _bias = bias; - _output = output; - _reinterpret_as_3d = output_3d_depth > 1; + _input = input; + _bias = bias; + _output = output; // Set the arguments to pass at compile time CLBuildOptions build_opts; @@ -146,7 +139,6 @@ void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::configure(const ICLTe build_opts.add_option_if((min != 0) && (min != max), "-DMIN_BOUND=" + support::cpp11::to_string(min)); build_opts.add_option_if((max != 255) && (min != max), "-DMAX_BOUND=" + support::cpp11::to_string(max)); build_opts.add_option_if(bias != nullptr, "-DADD_BIAS"); - build_opts.add_option_if(_reinterpret_as_3d, "-DDST_HEIGHT=" + support::cpp11::to_string(input->info()->tensor_shape().y() / output_3d_depth)); // Create kernel _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemmlowp_output_stage_quantize_down_float", build_opts.options())); @@ -176,32 +168,12 @@ void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::run(const Window &win add_1D_tensor_argument(idx1, _bias, biases_slice); } - if(_reinterpret_as_3d) - { - // Create output window - Window window_out; - window_out.use_tensor_dimensions(_output->info()->tensor_shape()); - Window collapsed_out = window_out.collapse_if_possible(window_out, 3); - Window slice_out = collapsed.first_slice_window_4D(); - - do - { - unsigned int idx = 0; - add_3D_tensor_argument(idx, _input, slice); - add_4D_tensor_argument(idx1, _output, slice_out); - enqueue(queue, *this, slice); - } - while(collapsed.slide_window_slice_3D(slice) && collapsed_out.slide_window_slice_4D(slice_out)); - } - else + do { - do - { - unsigned int idx = 0; - add_3D_tensor_argument(idx, _input, slice); - add_3D_tensor_argument(idx1, _output, slice); - enqueue(queue, *this, slice); - } - while(collapsed.slide_window_slice_3D(slice)); + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input, slice); + add_3D_tensor_argument(idx1, _output, slice); + enqueue(queue, *this, slice); } + while(collapsed.slide_window_slice_3D(slice)); } diff --git a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp index f1c24626dc..f1282cbde9 100644 --- a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp +++ b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp @@ -60,16 +60,16 @@ Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITens void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, float multiplier, int offset, - int min, int max, unsigned int output_3d_depth) + int min, int max) { auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel>(); - k->configure(input, bias, output, multiplier, offset, min, max, output_3d_depth); + k->configure(input, bias, output, multiplier, offset, min, max); _kernel = std::move(k); } Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, - int min, int max, unsigned int output_3d_depth) + int min, int max) { - return CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::validate(input, bias, output, min, max, output_3d_depth); + return CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::validate(input, bias, output, min, max); } } // namespace arm_compute
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