diff options
Diffstat (limited to 'src')
15 files changed, 263 insertions, 120 deletions
diff --git a/src/core/CL/cl_kernels/convolution_layer.cl b/src/core/CL/cl_kernels/convolution_layer.cl index ce0849bf7a..77b9b64945 100644 --- a/src/core/CL/cl_kernels/convolution_layer.cl +++ b/src/core/CL/cl_kernels/convolution_layer.cl @@ -97,13 +97,14 @@ __kernel void reshape_to_columns( } } -#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) +#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(PAD_VALUE) /** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM. * * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float + * @note The value to use for the paddings must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0 * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32 + * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/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) @@ -149,14 +150,10 @@ __kernel void im2col_generic( { #if PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y)); -#else // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 +#else // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 if(x < 0 || x >= SRC_WIDTH || y < 0 || y >= SRC_HEIGHT) { -#if defined(OFFSET) - *output_ptr = OFFSET; -#else /* OFFSET */ - *output_ptr = 0; -#endif /* OFFSET */ + *output_ptr = PAD_VALUE; } else { @@ -183,7 +180,7 @@ __kernel void im2col_generic( * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32 + * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/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) @@ -252,7 +249,7 @@ __kernel void im2col_kernel3x3_padx0_pady0( * * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32 + * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/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) @@ -291,7 +288,7 @@ __kernel void col2im( * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float * @note In case biases will be added in late stage, -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/F16/F32 + * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/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) diff --git a/src/core/CL/cl_kernels/gemmlowp.cl b/src/core/CL/cl_kernels/gemmlowp.cl index a8e8e600fe..a92881320e 100644 --- a/src/core/CL/cl_kernels/gemmlowp.cl +++ b/src/core/CL/cl_kernels/gemmlowp.cl @@ -380,6 +380,7 @@ __kernel void gemmlowp_matrix_b_reduction(TENSOR3D_DECLARATION(src), * @attention The k_offset = a_offset * b_offset * k (where k is the number of matrix A columns) needs to be passed at compile time using -DK_OFFSET (i.e. -DK_OFFSET=1200) * @note In case the offset contribution due to a_offset is required, a_offset needs to be passed at compile time using -DA_OFFSET (i.e. -DA_OFFSET=1) * @note In case the offset contribution due to b_offset is required, b_offset needs to be passed at compile time using -DB_OFFSET (i.e. -DB_OFFSET=6) + * @note In case sum_col has batches, -DSUM_COL_HAS_BATCHES must be passed at compile time. Usually if gemmlowp is used to accelerate convolution layer, sum_col will not have batches * * The final result is: * @@ -429,7 +430,12 @@ __kernel void gemmlowp_offset_contribution(TENSOR3D_DECLARATION(mm_result) Image sum_col = CONVERT_TO_IMAGE_STRUCT(sum_col); // Compute the offset contribution due to A_OFFSET +#if defined(SUM_COL_HAS_BATCHES) + a_offset_s32 = vload16(0, (__global int *)(sum_col.ptr + get_global_id(2) * sum_col_stride_y)); +#else // defined(MATRIX_B_HAS_BATCHES) a_offset_s32 = vload16(0, (__global int *)(sum_col.ptr)); +#endif // defined(MATRIX_B_HAS_BATCHES) + a_offset_s32 *= (int16)A_OFFSET; #endif // defined(A_OFFSET) @@ -615,4 +621,4 @@ __kernel void gemmlowp_output_stage_quantize_down_fixedpoint(TENSOR3D_DECLARATIO // Store the result vstore16(res, 0, dst.ptr); } -#endif // defined(RESULT_OFFSET_AFTER_SHIFT) && defined(RESULT_FIXEDPOINT_MULTIPLIER) && defined(RESULT_SHIFT)
\ No newline at end of file +#endif // defined(RESULT_OFFSET_AFTER_SHIFT) && defined(RESULT_FIXEDPOINT_MULTIPLIER) && defined(RESULT_SHIFT) diff --git a/src/core/CL/kernels/CLCol2ImKernel.cpp b/src/core/CL/kernels/CLCol2ImKernel.cpp index f2886c569a..499e1e8fe0 100644 --- a/src/core/CL/kernels/CLCol2ImKernel.cpp +++ b/src/core/CL/kernels/CLCol2ImKernel.cpp @@ -43,7 +43,7 @@ CLCol2ImKernel::CLCol2ImKernel() void CLCol2ImKernel::configure(const ICLTensor *input, ICLTensor *output, std::pair<unsigned int, unsigned int> convolved_dims) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::QASYMM8, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_NULLPTR(output); TensorShape output_shape = input->info()->tensor_shape(); @@ -52,7 +52,7 @@ void CLCol2ImKernel::configure(const ICLTensor *input, ICLTensor *output, std::p output_shape.set(2, input->info()->tensor_shape()[0]); // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position(), input->info()->quantization_info()); + auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); @@ -62,15 +62,15 @@ void CLCol2ImKernel::configure(const ICLTensor *input, ICLTensor *output, std::p _output = output; _convolved_dims = convolved_dims; + const DataType data_type = input->info()->data_type(); + // Create kernel - std::set<std::string> build_opts = { ("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())) }; - build_opts.emplace("-DWIDTH_OUTPUT=" + support::cpp11::to_string(_convolved_dims.first)); - if(is_data_type_fixed_point(input->info()->data_type())) - { - build_opts.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); - } + CLBuildOptions build_opts; + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); + build_opts.add_option("-DWIDTH_OUTPUT=" + support::cpp11::to_string(_convolved_dims.first)); + build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); - _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("col2im", build_opts)); + _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("col2im", build_opts.options())); // Configure the local work size for Bifrost with a value obtained // via exhaustive autotuning over 30 representative tensor shapes. diff --git a/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp b/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp index d49aed3171..2877a74be8 100644 --- a/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp +++ b/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp @@ -63,6 +63,7 @@ void CLGEMMLowpOffsetContributionKernel::configure(ICLTensor *mm_result, const I ARM_COMPUTE_ERROR_ON(vector_sum_col->info()->dimension(0) != mm_result->info()->dimension(0)); build_opts.add_option("-DA_OFFSET=" + support::cpp11::to_string(a_offset)); + build_opts.add_option_if(vector_sum_col->info()->tensor_shape().num_dimensions() > 1, "-DSUM_COL_HAS_BATCHES"); } // If b_offset == 0, vector_sum_row can be a nullptr diff --git a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp index 37a430e8b0..62288cb771 100644 --- a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp +++ b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp @@ -41,8 +41,6 @@ namespace Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON(max > 255); ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max); @@ -53,6 +51,13 @@ Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, cons ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); } + + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + } + return Error{}; } @@ -64,11 +69,17 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); bool window_changed = update_window_and_padding(win, - input_access, - output_result_access); + input_access); + + if(output->total_size() != 0) + { + AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); + window_changed = window_changed || update_window_and_padding(win, output_result_access); + + output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + } if(bias != nullptr) { @@ -76,8 +87,6 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens window_changed = window_changed || update_window_and_padding(win, bias_access); } - output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); - Error err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Error{}; return std::make_pair(err, win); } @@ -93,6 +102,7 @@ CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::CLGEMMLowpQuantizeDow Error CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::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)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (bias != nullptr) ? bias->clone().get() : nullptr, diff --git a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp index 343c31c73d..5d4b25c142 100644 --- a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp +++ b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp @@ -41,8 +41,6 @@ namespace Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON(max > 255); ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max); @@ -53,6 +51,13 @@ Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, cons ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); } + + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + } + return Error{}; } @@ -64,11 +69,17 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); bool window_changed = update_window_and_padding(win, - input_access, - output_result_access); + input_access); + + if(output->total_size() != 0) + { + AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); + window_changed = window_changed || update_window_and_padding(win, output_result_access); + + output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + } if(bias != nullptr) { @@ -76,8 +87,6 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens window_changed = window_changed || update_window_and_padding(win, bias_access); } - output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); - Error err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Error{}; return std::make_pair(err, win); } @@ -92,6 +101,7 @@ CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::CLGEMMLowpQuantizeDownInt32ToUint } Error CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::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)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (bias != nullptr) ? bias->clone().get() : nullptr, @@ -163,4 +173,4 @@ void CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run(const Window &window, cl enqueue(queue, *this, slice); } while(collapsed.slide_window_slice_3D(slice)); -}
\ No newline at end of file +} diff --git a/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp b/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp index 6f410d3b14..bcf04b0982 100644 --- a/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp +++ b/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp @@ -126,7 +126,7 @@ void CLGEMMLowpMatrixBReductionKernel::configure(const ICLTensor *mtx_b, ICLTens // Configure kernel window Window win = calculate_max_window(*vector_sum_col->info(), Steps(num_elems_processed_per_iteration)); - AccessWindowStatic input_access(_input->info(), 0, 0, ceil_to_multiple(_input->info()->dimension(0), 16), _input->info()->dimension(1)); + AccessWindowStatic input_access(_input->info(), 0, 0, ceil_to_multiple(_input->info()->dimension(0), num_elems_processed_per_iteration), _input->info()->dimension(1)); AccessWindowHorizontal output_access(_output->info(), 0, num_elems_processed_per_iteration); update_window_and_padding(win, diff --git a/src/core/CL/kernels/CLIm2ColKernel.cpp b/src/core/CL/kernels/CLIm2ColKernel.cpp index f7cf9a3cb4..6514d6cf91 100644 --- a/src/core/CL/kernels/CLIm2ColKernel.cpp +++ b/src/core/CL/kernels/CLIm2ColKernel.cpp @@ -61,7 +61,6 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const build_opts.add_option(("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type))); build_opts.add_option_if(has_bias, "-DHAS_BIAS"); build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); - build_opts.add_option_if(is_data_type_quantized_asymmetric(data_type), "-DOFFSET=" + support::cpp11::to_string(input->info()->quantization_info().offset)); int stride_x = 0; int stride_y = 0; @@ -95,6 +94,7 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const build_opts.add_option("-DPAD_BOTTOM=" + support::cpp11::to_string(conv_info.pad_bottom())); build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0))); build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1))); + build_opts.add_option_if_else(is_data_type_quantized(data_type), "-DPAD_VALUE=" + support::cpp11::to_string(input->info()->quantization_info().offset), "-DPAD_VALUE=0"); if(kernel_dims.width == 3 && kernel_dims.height == 3 && !conv_info.has_padding()) { diff --git a/src/core/CL/kernels/CLWeightsReshapeKernel.cpp b/src/core/CL/kernels/CLWeightsReshapeKernel.cpp index be633b2418..3a9a32e58f 100644 --- a/src/core/CL/kernels/CLWeightsReshapeKernel.cpp +++ b/src/core/CL/kernels/CLWeightsReshapeKernel.cpp @@ -41,12 +41,12 @@ CLWeightsReshapeKernel::CLWeightsReshapeKernel() void CLWeightsReshapeKernel::configure(const ICLTensor *input, const ICLTensor *biases, ICLTensor *output) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::QASYMM8, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_NULLPTR(output); - const DataType dt = input->info()->data_type(); - const int fixed_point_position = input->info()->fixed_point_position(); + const DataType data_type = input->info()->data_type(); + // Calculate output shape TensorShape output_shape{ input->info()->tensor_shape() }; output_shape.collapse(3); const size_t tmp_dim = output_shape[0]; @@ -54,7 +54,7 @@ void CLWeightsReshapeKernel::configure(const ICLTensor *input, const ICLTensor * output_shape.set(1, tmp_dim + (biases != nullptr ? 1 : 0)); // Output tensor auto inizialitation if not yet initialized - auto_init_if_empty(*output->info(), output_shape, 1, dt, fixed_point_position, input->info()->quantization_info()); + auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); @@ -62,6 +62,7 @@ void CLWeightsReshapeKernel::configure(const ICLTensor *input, const ICLTensor * if(biases != nullptr) { + ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(data_type)); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases); ARM_COMPUTE_ERROR_ON((input->info()->num_dimensions() == 4) && (biases->info()->num_dimensions() != 1)); @@ -75,16 +76,13 @@ void CLWeightsReshapeKernel::configure(const ICLTensor *input, const ICLTensor * _input = input; // Create build options - std::set<std::string> build_opts; - build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()))); - build_opts.emplace(((biases != nullptr) ? "-DHAS_BIAS" : "")); - if(is_data_type_fixed_point(input->info()->data_type())) - { - build_opts.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); - } + CLBuildOptions build_opts; + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); + build_opts.add_option_if(biases != nullptr, "-DHAS_BIAS"); + build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); // Create kernel - _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("reshape_to_columns", build_opts)); + _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("reshape_to_columns", build_opts.options())); // Set static arguments unsigned int idx = num_arguments_per_3D_tensor() + num_arguments_per_2D_tensor(); diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp index 102d08c7ba..c6f7ca4fb2 100644 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp +++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp @@ -44,8 +44,6 @@ namespace Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON(max > 255); ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max); @@ -56,6 +54,13 @@ Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, cons ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); } + + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + } + return Error{}; } @@ -67,11 +72,17 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); bool window_changed = update_window_and_padding(win, - input_access, - output_result_access); + input_access); + + if(output->total_size() != 0) + { + AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); + window_changed = window_changed || update_window_and_padding(win, output_result_access); + + output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + } if(bias != nullptr) { @@ -79,8 +90,6 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens window_changed = window_changed || update_window_and_padding(win, bias_access); } - output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); - Error err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Error{}; return std::make_pair(err, win); } @@ -255,6 +264,7 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const Error NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::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)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (bias != nullptr) ? bias->clone().get() : nullptr, diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp index edd6a9fd80..68b81d8a79 100644 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp +++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp @@ -43,8 +43,6 @@ namespace Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON(max > 255); ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max); @@ -55,6 +53,13 @@ Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, cons ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); } + + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + } + return Error{}; } @@ -66,11 +71,17 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); bool window_changed = update_window_and_padding(win, - input_access, - output_result_access); + input_access); + + if(output->total_size() != 0) + { + AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); + window_changed = window_changed || update_window_and_padding(win, output_result_access); + + output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + } if(bias != nullptr) { @@ -78,8 +89,6 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens window_changed = window_changed || update_window_and_padding(win, bias_access); } - output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); - Error err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Error{}; return std::make_pair(err, win); } @@ -262,6 +271,7 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::configure(const ITensor *inp Error NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::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)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (bias != nullptr) ? bias->clone().get() : nullptr, diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp index 8d45416b30..66548d19b2 100644 --- a/src/runtime/CL/functions/CLConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp @@ -27,6 +27,7 @@ #include "arm_compute/core/Size2D.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" #include "arm_compute/runtime/CL/CLScheduler.h" #include <cmath> @@ -42,19 +43,22 @@ CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights(std::shared_p void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose1xW) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output); ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output); ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); if(biases != nullptr) { + ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(weights->info()->data_type())); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3)); ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); } - const bool _has_bias = (biases != nullptr); + const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type()); + const unsigned bias_element = (append_biases) ? 1 : 0; + const ICLTensor *biases_to_use = (append_biases) ? biases : nullptr; _transpose1xW = transpose1xW; @@ -62,7 +66,7 @@ void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const { // Create tensor to store the reshaped weights const unsigned int mat_weights_cols = weights->info()->dimension(3); - const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0); + const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element; TensorShape shape_wr(mat_weights_cols, mat_weights_rows); const DataType dt = weights->info()->data_type(); const int fixed_point_position = weights->info()->fixed_point_position(); @@ -70,13 +74,13 @@ void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const _weights_reshaped.allocator()->init(info_wr); _memory_group.manage(&_weights_reshaped); - _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped); + _weights_reshape_kernel.configure(weights, biases_to_use, &_weights_reshaped); _weights_transposed_kernel.configure(&_weights_reshaped, output); _weights_reshaped.allocator()->allocate(); } else { - _weights_reshape_kernel.configure(weights, biases, output); + _weights_reshape_kernel.configure(weights, biases_to_use, output); } } @@ -95,36 +99,73 @@ void CLConvolutionLayerReshapeWeights::run() } CLConvolutionLayer::CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) - : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(), - _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false) + : _memory_group(memory_manager), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _output_col2im_kernel(), + _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false), + _are_weights_reshaped(false), _is_quantized(false) { } +void CLConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed) +{ + if(_is_quantized) + { + // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() + // Extract and negate input and weights offset + const QuantizationInfo input_quantization_info = input->info()->quantization_info(); + const QuantizationInfo weights_quantization_info = weights->info()->quantization_info(); + + input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset)); + weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); + + _mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)); + + // Revert back QuantizatioInfo as input and weights could be used in other convolution layers + input->info()->set_quantization_info(input_quantization_info); + weights->info()->set_quantization_info(weights_quantization_info); + } + else + { + _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed); + } +} + void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights); ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2)); ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); + ARM_COMPUTE_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->info()->data_type())); + + _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); if(biases != nullptr) { - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); + if(_is_quantized) + { + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); + } + else + { + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); + } ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases); ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3)); ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); } - const DataType dt = input->info()->data_type(); - const int fixed_point_position = input->info()->fixed_point_position(); + const DataType dt = input->info()->data_type(); // Set the GPU target for matrix multiply _mm_kernel.set_target(CLScheduler::get().target()); - _has_bias = (biases != nullptr); + _append_bias = (biases != nullptr) && (!_is_quantized); _are_weights_reshaped = weights_info.are_reshaped(); + const unsigned bias_element = (_append_bias) ? 1 : 0; + const ICLTensor *biases_to_use = (_append_bias) ? biases : nullptr; + // Get parameters from conv_info unsigned int stride_x = 0; unsigned int stride_y = 0; @@ -141,36 +182,36 @@ void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weig // Check if its a "fully connected" convolution _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1)); + const bool run_interleaved = (!_is_fully_connected_convolution && !_is_quantized); unsigned int mat_weights_cols = weights->info()->dimension(3); - unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0); + unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element; // Reshape weights if needed if(_are_weights_reshaped) { mat_weights_cols = weights_info.num_kernels(); const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4; - mat_weights_rows = (_has_bias ? 1 + quarter_reshaped_cols : quarter_reshaped_cols); + mat_weights_rows = quarter_reshaped_cols + bias_element; } else { - if(_is_fully_connected_convolution) + if(_is_fully_connected_convolution || _is_quantized) { // Create tensor to store the reshaped weights TensorShape shape_wr(mat_weights_cols, mat_weights_rows); - TensorInfo info_wr(shape_wr, 1, dt, fixed_point_position); - _weights_reshaped.allocator()->init(info_wr); - _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */); + _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wr)); + _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false /* 1xW transpose */); } else { // Create tensor to store transposed weights const float transpose_width = 16.0f / input->info()->element_size(); TensorShape shape_wt(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width))); - TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position); - _weights_reshaped.allocator()->init(info_wt); - _reshape_weights.configure(weights, biases, &_weights_reshaped, true /* 1xW transpose */); + _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wt)); + _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, true /* 1xW transpose */); } + _weights_reshaped.info()->set_quantization_info(weights->info()->quantization_info()); weights = &_weights_reshaped; } @@ -181,16 +222,16 @@ void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weig shape_im2col.set(0, mat_input_cols); shape_im2col.set(1, mat_input_rows); shape_im2col.set(2, 1); - _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position)); + _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col)); _memory_group.manage(&_input_im2col_reshaped); // Create tensor (interleave) to prepare input tensor for GEMM - if(!_is_fully_connected_convolution) + if(run_interleaved) { TensorShape shape_interleaved = shape_im2col; shape_interleaved.set(0, shape_interleaved.x() * 4); shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f)); - _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position)); + _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved)); _memory_group.manage(&_input_interleaved_reshaped); } @@ -198,30 +239,51 @@ void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weig TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape(); shape_gemm.set(0, mat_weights_cols); shape_gemm.set(1, mat_input_rows); - _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, dt, fixed_point_position)); + const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt; + // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input. + TensorInfo info_gemm(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_gemm).set_data_type(gemm_data_type).set_quantization_info( + output->info()->quantization_info())); + _gemm_output.allocator()->init(info_gemm); _memory_group.manage(&_gemm_output); // Configure kernels - _input_im2col_kernel.set_target(CLScheduler::get().target()); - _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias); + _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias); // Configure matrix multiply - if(_is_fully_connected_convolution) + if(run_interleaved) { - // The matrix A and Matrix B have not been reshaped - _mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f, false); + _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped); + configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output); + _input_interleaved_reshaped.allocator()->allocate(); } else { - _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped); - _mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f); - _input_interleaved_reshaped.allocator()->allocate(); + configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, false); } _input_im2col_reshaped.allocator()->allocate(); + + // Configure output stage for quantized case + if(_is_quantized) + { + float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output->info()->quantization_info().scale; + int output_multiplier, output_shift; + quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output->info()->quantization_info().offset); + _gemm_output.allocator()->allocate(); + } + + // Configure Col2Im _output_col2im_kernel.set_target(CLScheduler::get().target()); - _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h)); - _gemm_output.allocator()->allocate(); + _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(conv_w, conv_h)); + if(_is_quantized) + { + _tmp_output.allocator()->allocate(); + } + else + { + _gemm_output.allocator()->allocate(); + } ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one"); @@ -243,15 +305,30 @@ void CLConvolutionLayer::run() _memory_group.acquire(); - // Run input reshaping + // Run im2col CLScheduler::get().enqueue(_input_im2col_kernel); - if(!_is_fully_connected_convolution) + + if(!_is_fully_connected_convolution && !_is_quantized) { + // Run interleave4x4 CLScheduler::get().enqueue(_input_interleave_kernel); } // Runs matrix multiply on reshaped matrices - CLScheduler::get().enqueue(_mm_kernel); + if(_is_quantized) + { + _mm_gemmlowp.run(); + } + else + { + CLScheduler::get().enqueue(_mm_kernel); + } + + // Run output stage for quantized case + if(_is_quantized) + { + _gemmlowp_output_stage.run(); + } // Reshape output matrix CLScheduler::get().enqueue(_output_col2im_kernel, false); diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp index 6cc2f4bdb7..7fd81cdb94 100644 --- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp +++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp @@ -50,13 +50,20 @@ void CLFullyConnectedLayer::configure_mm(const ICLTensor *input, const ICLTensor { if(_is_quantized) { + // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() // Extract and negate input and weights offset - QuantizationInfo input_quantization_info = input->info()->quantization_info(); - QuantizationInfo weights_quantization_info = weights->info()->quantization_info(); + const QuantizationInfo input_quantization_info = input->info()->quantization_info(); + const QuantizationInfo weights_quantization_info = weights->info()->quantization_info(); + input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset)); weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); + // Configure gemmlowp function _mm_gemmlowp.configure(input, weights, output); + + // Revert back QuantizatioInfo as input and weights could be used in other fully connected layers + input->info()->set_quantization_info(input_quantization_info); + weights->info()->set_quantization_info(weights_quantization_info); } else { diff --git a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp index 5d2d13e243..5c6f5b4ed0 100644 --- a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp +++ b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp @@ -35,11 +35,11 @@ using namespace arm_compute; CLGEMMLowpMatrixMultiplyCore::CLGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager) : _memory_group(std::move(memory_manager)), _mm_kernel(), _mtx_a_reshape_kernel(), _mtx_b_reshape_kernel(), _mtx_a_reduction_kernel(), _mtx_b_reduction_kernel(), _offset_contribution_kernel(), - _vector_sum_col(), _vector_sum_row(), _tmp_a(), _tmp_b(), _a_offset(0), _b_offset(0), _is_interleaved_transposed(true) + _vector_sum_col(), _vector_sum_row(), _tmp_a(), _tmp_b(), _a_offset(0), _b_offset(0), _is_interleaved_transposed(true), _is_first_run(true), _reshape_b_only_on_first_run(false) { } -void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor *b, ICLTensor *output) +void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor *b, ICLTensor *output, const GEMMInfo &gemm_info) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); @@ -47,9 +47,12 @@ void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor ARM_COMPUTE_ERROR_ON_MSG((a)->info()->dimension(0) != (b)->info()->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B"); ARM_COMPUTE_ERROR_ON_MSG((a)->info()->dimension(1) != (output)->info()->dimension(1), "The output matrix must have the same number of rows as the matrix A"); ARM_COMPUTE_ERROR_ON_MSG((b)->info()->dimension(0) != (output)->info()->dimension(0), "The output matrix must have the same number of columns as the matrix B"); + ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported"); + ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported"); - _a_offset = a->info()->quantization_info().offset; - _b_offset = b->info()->quantization_info().offset; + _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); + _a_offset = a->info()->quantization_info().offset; + _b_offset = b->info()->quantization_info().offset; // If the input tensor has less than 16 rows, we run a special version of GEMMLowp without reshaping the input tensors _is_interleaved_transposed = a->info()->dimension(1) > 16; @@ -93,7 +96,8 @@ void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor if(_a_offset != 0) { TensorShape shape_vector_sum_col = b->info()->tensor_shape(); - if(b->info()->num_dimensions() > 1) + + if(shape_vector_sum_col.num_dimensions() > 1) { shape_vector_sum_col.remove_dimension(1); } @@ -152,8 +156,21 @@ void CLGEMMLowpMatrixMultiplyCore::run() // Run reshape matrix A CLScheduler::get().enqueue(_mtx_a_reshape_kernel, false); - // Run reshape matrix B - CLScheduler::get().enqueue(_mtx_b_reshape_kernel, false); + if(_is_first_run || !_reshape_b_only_on_first_run) + { + // Run reshape matrix B + CLScheduler::get().enqueue(_mtx_b_reshape_kernel, false); + } + } + + // Note: if _reshape_b_only_on_first_run = true, the reduction kernel can be executed only once + if(_is_first_run || !_reshape_b_only_on_first_run) + { + // Run matrix B reduction kernel only if _a_offset is not equal to 0 + if(_a_offset != 0) + { + CLScheduler::get().enqueue(_mtx_b_reduction_kernel, false); + } } // Run matrix multiply @@ -165,14 +182,10 @@ void CLGEMMLowpMatrixMultiplyCore::run() CLScheduler::get().enqueue(_mtx_a_reduction_kernel, false); } - // Run matrix B reduction kernel only if _a_offset is not equal to 0 - if(_a_offset != 0) - { - CLScheduler::get().enqueue(_mtx_b_reduction_kernel, false); - } - // Run offset contribution kernel CLScheduler::get().enqueue(_offset_contribution_kernel, true); _memory_group.release(); + + _is_first_run = false; } diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp index 2c6515c1df..a18f48d9a7 100644 --- a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp +++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp @@ -52,10 +52,11 @@ NEGEMMLowpMatrixMultiplyCore::NEGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemo { } -void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, ITensor *output) +void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, ITensor *output, const GEMMInfo &gemm_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); - ARM_COMPUTE_ERROR_THROW_ON(NEGEMMLowpMatrixMultiplyCore::validate(a->info(), b->info(), output->info())); + ARM_COMPUTE_UNUSED(gemm_info); + ARM_COMPUTE_ERROR_THROW_ON(NEGEMMLowpMatrixMultiplyCore::validate(a->info(), b->info(), output->info(), gemm_info)); _a_offset = a->info()->quantization_info().offset; _b_offset = b->info()->quantization_info().offset; @@ -198,7 +199,7 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, } } -Error NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output) +Error NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output, const GEMMInfo &gemm_info) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); @@ -209,6 +210,9 @@ Error NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensor "The output matrix must have the same number of rows as the matrix A"); ARM_COMPUTE_RETURN_ERROR_ON_MSG((b)->dimension(0) != (output)->dimension(0), "The output matrix must have the same number of columns as the matrix B"); + ARM_COMPUTE_UNUSED(gemm_info); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported"); int32_t a_offset = a->quantization_info().offset; int32_t b_offset = b->quantization_info().offset; |