| // Copyright (c) Facebook, Inc. and its affiliates. |
| // All rights reserved. |
| // |
| // Copyright 2019 Google LLC |
| // |
| // This source code is licensed under the BSD-style license found in the |
| // LICENSE file in the root directory of this source tree. |
| |
| #include <assert.h> |
| #include <stdbool.h> |
| #include <stddef.h> |
| #include <stdint.h> |
| #include <string.h> |
| #include <math.h> |
| |
| #include <xnnpack.h> |
| #include <xnnpack/allocator.h> |
| #include <xnnpack/indirection.h> |
| #include <xnnpack/log.h> |
| #include <xnnpack/math.h> |
| #include <xnnpack/operator.h> |
| #include <xnnpack/pack.h> |
| #include <xnnpack/params-init.h> |
| #include <xnnpack/params.h> |
| |
| |
| static inline size_t compute_output_dimension( |
| size_t input_dimension, |
| size_t output_padding_dimension, |
| size_t adjustment_dimension, |
| size_t kernel_dimension, |
| size_t dilation_dimension, |
| size_t stride_dimension) |
| { |
| const size_t effective_kernel_dimension = (kernel_dimension - 1) * dilation_dimension + 1; |
| return doz( |
| stride_dimension * (input_dimension - 1) + adjustment_dimension + effective_kernel_dimension, |
| output_padding_dimension); |
| } |
| |
| static enum xnn_status create_deconvolution2d_nhwc( |
| uint32_t output_padding_top, |
| uint32_t output_padding_right, |
| uint32_t output_padding_bottom, |
| uint32_t output_padding_left, |
| uint32_t kernel_height, |
| uint32_t kernel_width, |
| uint32_t stride_height, |
| uint32_t stride_width, |
| uint32_t dilation_height, |
| uint32_t dilation_width, |
| uint32_t groups, |
| size_t group_input_channels, |
| size_t group_output_channels, |
| size_t input_pixel_stride, |
| size_t output_pixel_stride, |
| const void* kernel, |
| const void* bias, |
| uint32_t flags, |
| uint32_t log2_input_element_size, |
| uint32_t log2_filter_element_size, |
| uint32_t bias_element_size, |
| xnn_pack_conv_goki_w_function pack_conv_goki_w, |
| xnn_pack_deconv_goki_w_function pack_deconv_goki_w, |
| const void* packing_params, |
| int input_padding_byte, |
| int packed_weights_padding_byte, |
| const void* params, |
| size_t params_size, |
| const struct gemm_parameters* gemm_parameters, |
| const struct gemm_fused_ukernels* gemm_ukernels, |
| enum xnn_operator_type operator_type, |
| xnn_operator_t* deconvolution_op_out) |
| { |
| xnn_operator_t deconvolution_op = NULL; |
| enum xnn_status status = xnn_status_uninitialized; |
| |
| if ((xnn_params.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) { |
| xnn_log_error("failed to create %s operator: XNNPACK is not initialized", |
| xnn_operator_type_to_string(operator_type)); |
| goto error; |
| } |
| |
| status = xnn_status_invalid_parameter; |
| |
| if (kernel_width == 0 || kernel_height == 0) { |
| xnn_log_error( |
| "failed to create %s operator with %" PRIu32 "x%" PRIu32 " kernel: kernel dimensions must be non-zero", |
| xnn_operator_type_to_string(operator_type), kernel_width, kernel_height); |
| goto error; |
| } |
| |
| if (stride_width == 0 || stride_height == 0) { |
| xnn_log_error( |
| "failed to create %s operator with %" PRIu32 "x%" PRIu32 " stride: stride dimensions must be non-zero", |
| xnn_operator_type_to_string(operator_type), stride_width, stride_height); |
| goto error; |
| } |
| |
| if (dilation_width == 0 || dilation_height == 0) { |
| xnn_log_error( |
| "failed to create %s operator with %" PRIu32 "x%" PRIu32 " dilation: dilation dimensions must be non-zero", |
| xnn_operator_type_to_string(operator_type), dilation_width, dilation_height); |
| goto error; |
| } |
| |
| if (groups == 0) { |
| xnn_log_error( |
| "failed to create %s operator with %" PRIu32 " groups: number of groups must be non-zero", |
| xnn_operator_type_to_string(operator_type), groups); |
| goto error; |
| } |
| |
| if (group_input_channels == 0) { |
| xnn_log_error( |
| "failed to create %s operator with %zu input channels per group: number of channels must be non-zero", |
| xnn_operator_type_to_string(operator_type), group_input_channels); |
| goto error; |
| } |
| |
| if (group_output_channels == 0) { |
| xnn_log_error( |
| "failed to create %s operator with %zu output channels per group: number of channels must be non-zero", |
| xnn_operator_type_to_string(operator_type), group_output_channels); |
| goto error; |
| } |
| |
| const size_t input_channels = groups * group_input_channels; |
| if (input_pixel_stride < input_channels) { |
| xnn_log_error( |
| "failed to create %s operator with input pixel stride of %zu: " |
| "stride must be at least as large as the number of output channels (%" PRIu32 "x%zu)", |
| xnn_operator_type_to_string(operator_type), |
| input_pixel_stride, groups, group_input_channels); |
| goto error; |
| } |
| |
| const size_t output_channels = groups * group_output_channels; |
| if (output_pixel_stride < output_channels) { |
| xnn_log_error( |
| "failed to create %s operator with output pixel stride of %zu: " |
| "stride must be at least as large as the number of output channels (%" PRIu32 "x%zu)", |
| xnn_operator_type_to_string(operator_type), |
| output_pixel_stride, groups, group_output_channels); |
| goto error; |
| } |
| |
| const bool any_padding = (output_padding_left | output_padding_top | output_padding_right | output_padding_bottom) != 0; |
| if (any_padding && (flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) != 0) { |
| xnn_log_error( |
| "failed to create %s operator with %" PRIu32 "+%" PRIu32 "x%" PRIu32 "+%" PRIu32" padding: " |
| "TensorFlow SAME padding can't be combined with explicit padding specification", |
| xnn_operator_type_to_string(operator_type), |
| output_padding_top, output_padding_left, output_padding_bottom, output_padding_right); |
| goto error; |
| } |
| |
| status = xnn_status_out_of_memory; |
| |
| deconvolution_op = xnn_allocate_zero_simd_memory(sizeof(struct xnn_operator)); |
| if (deconvolution_op == NULL) { |
| xnn_log_error( |
| "failed to allocate %zu bytes for %s operator descriptor", |
| sizeof(struct xnn_operator), xnn_operator_type_to_string(operator_type)); |
| goto error; |
| } |
| |
| const uint32_t mr = gemm_parameters->mr; |
| const uint32_t nr = gemm_parameters->nr; |
| const uint32_t kr = UINT32_C(1) << gemm_parameters->log2_kr; |
| const uint32_t sr = UINT32_C(1) << gemm_parameters->log2_sr; |
| |
| const uint32_t n_stride = round_up(group_output_channels, nr); |
| const uint32_t k_stride = round_up_po2(group_input_channels, kr); |
| const uint32_t kernel_size = kernel_height * kernel_width; |
| enum xnn_ukernel_type ukernel_type = xnn_ukernel_type_igemm; |
| size_t packed_group_weights_size = (sizeof(float) * kernel_size * k_stride + sizeof(float)) * n_stride; |
| if (max(stride_height, stride_width) > 1 && max(dilation_height, dilation_width) == 1 && stride_width <= kernel_width && stride_height <= kernel_height) { |
| ukernel_type = xnn_ukernel_type_subconv2d; |
| const size_t subkernels = stride_height * stride_width; |
| packed_group_weights_size = n_stride * |
| (sizeof(float) * kernel_size * k_stride + sizeof(float) * subkernels); |
| |
| const size_t subconvolution_buffer_size = sizeof(struct subconvolution_params) * subkernels; |
| deconvolution_op->subconvolution_buffer = xnn_allocate_zero_memory(subconvolution_buffer_size); |
| if (deconvolution_op->subconvolution_buffer == NULL) { |
| xnn_log_error( |
| "failed to allocate %zu bytes for %s operator subconvolution buffer", |
| subconvolution_buffer_size, xnn_operator_type_to_string(operator_type)); |
| goto error; |
| } |
| |
| struct subconvolution_params* subconvolution_params = deconvolution_op->subconvolution_buffer; |
| for (size_t offset_y = 0; offset_y < stride_height; offset_y++) { |
| for (size_t offset_x = 0; offset_x < stride_width; offset_x++) { |
| const size_t subkernel_height = divide_round_up(kernel_height - offset_y, stride_height); |
| const size_t subkernel_width = divide_round_up(kernel_width - offset_x, stride_width); |
| const size_t subkernel_size = subkernel_height * subkernel_width; |
| |
| subconvolution_params->indirection_x_stride = sizeof(void*) * subkernel_size; |
| subconvolution_params->w_stride = sizeof(float) + k_stride * subkernel_size * sizeof(float); |
| subconvolution_params++; |
| } |
| } |
| } |
| deconvolution_op->packed_weights = xnn_allocate_simd_memory(packed_group_weights_size * groups); |
| if (deconvolution_op->packed_weights == NULL) { |
| xnn_log_error( |
| "failed to allocate %zu bytes for %s operator packed weights", |
| packed_group_weights_size * groups, xnn_operator_type_to_string(operator_type)); |
| goto error; |
| } |
| memset(deconvolution_op->packed_weights, packed_weights_padding_byte, packed_group_weights_size * groups); |
| |
| switch (ukernel_type) { |
| case xnn_ukernel_type_igemm: |
| pack_conv_goki_w( |
| groups, group_output_channels, kernel_size, group_input_channels, |
| nr, kr, sr, |
| kernel, bias, deconvolution_op->packed_weights, |
| packing_params); |
| break; |
| case xnn_ukernel_type_subconv2d: |
| pack_deconv_goki_w( |
| groups, group_output_channels, kernel_height, kernel_width, group_input_channels, |
| stride_height, stride_width, |
| nr, kr, sr, |
| kernel, bias, deconvolution_op->packed_weights, deconvolution_op->subconvolution_buffer, |
| packing_params); |
| break; |
| default: |
| XNN_UNREACHABLE; |
| } |
| |
| const size_t zero_size = (k_stride << log2_input_element_size) + XNN_EXTRA_BYTES; |
| deconvolution_op->zero_buffer = xnn_allocate_simd_memory(zero_size); |
| if (deconvolution_op->zero_buffer == NULL) { |
| xnn_log_error( |
| "failed to allocate %zu bytes for %s operator zero padding", |
| zero_size, xnn_operator_type_to_string(operator_type)); |
| goto error; |
| } |
| memset(deconvolution_op->zero_buffer, input_padding_byte, zero_size); |
| |
| deconvolution_op->padding_top = output_padding_top; |
| deconvolution_op->padding_right = output_padding_right; |
| deconvolution_op->padding_bottom = output_padding_bottom; |
| deconvolution_op->padding_left = output_padding_left; |
| |
| deconvolution_op->kernel_height = kernel_height; |
| deconvolution_op->kernel_width = kernel_width; |
| deconvolution_op->stride_height = stride_height; |
| deconvolution_op->stride_width = stride_width; |
| deconvolution_op->dilation_height = dilation_height; |
| deconvolution_op->dilation_width = dilation_width; |
| deconvolution_op->groups = groups; |
| deconvolution_op->group_input_channels = group_input_channels; |
| deconvolution_op->group_output_channels = group_output_channels; |
| deconvolution_op->input_pixel_stride = input_pixel_stride; |
| deconvolution_op->output_pixel_stride = output_pixel_stride; |
| |
| memcpy(&deconvolution_op->params, params, params_size); |
| deconvolution_op->type = operator_type; |
| deconvolution_op->ukernel.type = ukernel_type; |
| deconvolution_op->ukernel.igemm = (struct xnn_ukernel_igemm) { |
| .general_case = gemm_ukernels->igemm, |
| .gemm_case = gemm_ukernels->gemm, |
| .mr = mr, |
| .nr = nr, |
| .kr = kr, |
| }; |
| |
| if (flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) { |
| if ((stride_height | stride_width) == 1) { |
| // Padding can be computed statically |
| const uint32_t padding_height = (kernel_height - 1) * dilation_height; |
| const uint32_t padding_width = (kernel_width - 1) * dilation_width; |
| |
| const uint32_t padding_top = padding_height / 2; |
| const uint32_t padding_left = padding_width / 2; |
| |
| deconvolution_op->padding_top = padding_top; |
| deconvolution_op->padding_left = padding_left; |
| deconvolution_op->padding_bottom = padding_height - padding_top; |
| deconvolution_op->padding_right = padding_width - padding_left; |
| } else { |
| deconvolution_op->flags = XNN_FLAG_TENSORFLOW_SAME_PADDING; |
| } |
| } |
| |
| deconvolution_op->state = xnn_run_state_invalid; |
| |
| *deconvolution_op_out = deconvolution_op; |
| return xnn_status_success; |
| |
| error: |
| xnn_delete_operator(deconvolution_op); |
| return status; |
| } |
| |
| enum xnn_status xnn_create_deconvolution2d_nhwc_q8( |
| uint32_t output_padding_top, |
| uint32_t output_padding_right, |
| uint32_t output_padding_bottom, |
| uint32_t output_padding_left, |
| uint32_t kernel_height, |
| uint32_t kernel_width, |
| uint32_t stride_height, |
| uint32_t stride_width, |
| uint32_t dilation_height, |
| uint32_t dilation_width, |
| uint32_t groups, |
| size_t group_input_channels, |
| size_t group_output_channels, |
| size_t input_pixel_stride, |
| size_t output_pixel_stride, |
| uint8_t input_zero_point, |
| float input_scale, |
| uint8_t kernel_zero_point, |
| float kernel_scale, |
| const uint8_t* kernel, |
| const int32_t* bias, |
| uint8_t output_zero_point, |
| float output_scale, |
| uint8_t output_min, |
| uint8_t output_max, |
| uint32_t flags, |
| xnn_operator_t* deconvolution_op_out) |
| { |
| if (input_scale <= 0.0f || !isnormal(input_scale)) { |
| xnn_log_error( |
| "failed to create %s operator with %.7g input scale: scale must be finite, normalized, and positive", |
| xnn_operator_type_to_string(xnn_operator_type_deconvolution_nhwc_q8), input_scale); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (kernel_scale <= 0.0f || !isnormal(kernel_scale)) { |
| xnn_log_error( |
| "failed to create %s operator with %.7g kernel scale: scale must be finite, normalized, and positive", |
| xnn_operator_type_to_string(xnn_operator_type_deconvolution_nhwc_q8), kernel_scale); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_scale <= 0.0f || !isnormal(output_scale)) { |
| xnn_log_error( |
| "failed to create %s operator with %.7g output scale: scale must be finite, normalized, and positive", |
| xnn_operator_type_to_string(xnn_operator_type_deconvolution_nhwc_q8), output_scale); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_min >= output_max) { |
| xnn_log_error( |
| "failed to create %s operator with [%" PRIu8 ", %" PRIu8 "] output range: range min must be below range max", |
| xnn_operator_type_to_string(xnn_operator_type_deconvolution_nhwc_q8), output_min, output_max); |
| return xnn_status_invalid_parameter; |
| } |
| |
| const float requantization_scale = input_scale * kernel_scale / output_scale; |
| if (requantization_scale >= 1.0f) { |
| xnn_log_error( |
| "failed to create %s operator with %.7g input scale, %.7g kernel scale, and %.7g output scale: " |
| "requantization scale %.7g is greater or equal to 1.0", |
| xnn_operator_type_to_string(xnn_operator_type_deconvolution_nhwc_q8), |
| input_scale, kernel_scale, output_scale, requantization_scale); |
| return xnn_status_unsupported_parameter; |
| } |
| |
| const union xnn_q8_gemm_params params = xnn_init_q8_gemm_params( |
| input_zero_point, kernel_zero_point, requantization_scale, output_zero_point, output_min, output_max); |
| const struct xnn_q8_packing_params packing_params = { |
| .input_zero_point = input_zero_point, |
| .kernel_zero_point = kernel_zero_point, |
| }; |
| return create_deconvolution2d_nhwc( |
| output_padding_top, output_padding_right, output_padding_bottom, output_padding_left, |
| kernel_height, kernel_width, |
| stride_height, stride_width, |
| dilation_height, dilation_width, |
| groups, group_input_channels, group_output_channels, |
| input_pixel_stride, output_pixel_stride, |
| kernel, bias, flags, |
| 0 /* log2(sizeof(input element)) = log2(sizeof(uint8_t)) */, |
| 0 /* log2(sizeof(filter element)) = log2(sizeof(uint8_t)) */, |
| sizeof(int32_t) /* sizeof(bias element) */, |
| (xnn_pack_conv_goki_w_function) xnn_pack_q8_conv_goki_w, |
| (xnn_pack_deconv_goki_w_function) xnn_pack_q8_deconv_goki_w, |
| &packing_params, input_zero_point /* input padding byte */, kernel_zero_point /* packed weights padding byte */, |
| ¶ms, sizeof(params), |
| &xnn_params.q8.gemm, &xnn_params.q8.gemm.minmax, |
| xnn_operator_type_deconvolution_nhwc_q8, |
| deconvolution_op_out); |
| } |
| |
| enum xnn_status xnn_create_deconvolution2d_nhwc_f32( |
| uint32_t output_padding_top, |
| uint32_t output_padding_right, |
| uint32_t output_padding_bottom, |
| uint32_t output_padding_left, |
| uint32_t kernel_height, |
| uint32_t kernel_width, |
| uint32_t stride_height, |
| uint32_t stride_width, |
| uint32_t dilation_height, |
| uint32_t dilation_width, |
| uint32_t groups, |
| size_t group_input_channels, |
| size_t group_output_channels, |
| size_t input_pixel_stride, |
| size_t output_pixel_stride, |
| const float* kernel, |
| const float* bias, |
| float output_min, |
| float output_max, |
| uint32_t flags, |
| xnn_operator_t* deconvolution_op_out) |
| { |
| if (isnan(output_min)) { |
| xnn_log_error( |
| "failed to create %s operator with NaN output lower bound: lower bound must be non-NaN", |
| xnn_operator_type_to_string(xnn_operator_type_deconvolution_nhwc_f32)); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (isnan(output_max)) { |
| xnn_log_error( |
| "failed to create %s operator with NaN output upper bound: upper bound must be non-NaN", |
| xnn_operator_type_to_string(xnn_operator_type_deconvolution_nhwc_f32)); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_min >= output_max) { |
| xnn_log_error( |
| "failed to create %s operator with [%.7g, %.7g] output range: lower bound must be below upper bound", |
| xnn_operator_type_to_string(xnn_operator_type_deconvolution_nhwc_f32), output_min, output_max); |
| return xnn_status_invalid_parameter; |
| } |
| |
| const struct gemm_parameters* gemm_parameters = &xnn_params.f32.gemm; |
| if (gemm_parameters->nr > group_output_channels) { |
| // Default micro-kernel is suboptimal. Try to find a better micro-kernel. |
| if (xnn_params.f32.gemm2.minmax.igemm.function[XNN_UARCH_DEFAULT] != NULL) { |
| gemm_parameters = &xnn_params.f32.gemm2; |
| } |
| } |
| const struct gemm_fused_ukernels* gemm_ukernels = &gemm_parameters->minmax; |
| const bool linear_activation = (output_max == INFINITY) && (output_min == -output_max); |
| if (linear_activation && gemm_parameters->linear.gemm.function[XNN_UARCH_DEFAULT] != NULL) { |
| gemm_ukernels = &gemm_parameters->linear; |
| } |
| |
| const union xnn_f32_minmax_params params = xnn_init_f32_minmax_params(output_min, output_max); |
| return create_deconvolution2d_nhwc( |
| output_padding_top, output_padding_right, output_padding_bottom, output_padding_left, |
| kernel_height, kernel_width, |
| stride_height, stride_width, |
| dilation_height, dilation_width, |
| groups, group_input_channels, group_output_channels, |
| input_pixel_stride, output_pixel_stride, |
| kernel, bias, flags, |
| 2 /* log2(sizeof(input element)) = log2(sizeof(float)) */, |
| 2 /* log2(sizeof(filter element)) = log2(sizeof(float)) */, |
| sizeof(float) /* sizeof(bias element) */, |
| (xnn_pack_conv_goki_w_function) xnn_pack_f32_conv_goki_w, |
| (xnn_pack_deconv_goki_w_function) xnn_pack_f32_deconv_goki_w, |
| NULL /* packing params */, 0 /* input padding byte */, 0 /* packed weights padding byte */, |
| ¶ms, sizeof(params), |
| gemm_parameters, gemm_ukernels, |
| xnn_operator_type_deconvolution_nhwc_f32, |
| deconvolution_op_out); |
| } |
| |
| static enum xnn_status setup_conv_path( |
| xnn_operator_t deconvolution_op, |
| size_t batch_size, |
| size_t input_height, |
| size_t input_width, |
| const void* input, |
| size_t output_height, |
| size_t output_width, |
| void* output, |
| uint32_t log2_input_element_size, |
| uint32_t log2_filter_element_size, |
| uint32_t bias_element_size, |
| uint32_t log2_output_element_size, |
| const void* params, |
| size_t params_size, |
| size_t num_threads) |
| { |
| assert(deconvolution_op->ukernel.type == xnn_ukernel_type_igemm); |
| |
| const size_t kernel_height = deconvolution_op->kernel_height; |
| const size_t kernel_width = deconvolution_op->kernel_width; |
| const size_t kernel_size = kernel_height * kernel_width; |
| |
| const size_t groups = deconvolution_op->groups; |
| const size_t output_size = output_height * output_width; |
| const size_t mr = deconvolution_op->ukernel.igemm.mr; |
| const size_t tiled_output_size = round_up(output_size, mr); |
| const size_t indirection_buffer_size = sizeof(void*) * kernel_size * tiled_output_size; |
| |
| if (input_height != deconvolution_op->last_input_height || |
| input_width != deconvolution_op->last_input_width) |
| { |
| const void** indirection_buffer = (const void**) xnn_reallocate_memory(deconvolution_op->indirection_buffer, indirection_buffer_size); |
| if (indirection_buffer == NULL) { |
| xnn_log_error( |
| "failed to allocate %zu bytes for %s operator indirection buffer", |
| indirection_buffer_size, xnn_operator_type_to_string(deconvolution_op->type)); |
| return xnn_status_out_of_memory; |
| } |
| deconvolution_op->indirection_buffer = indirection_buffer; |
| deconvolution_op->last_input = input; |
| deconvolution_op->last_input_height = input_height; |
| deconvolution_op->last_input_width = input_width; |
| |
| xnn_indirection_init_deconv2d(deconvolution_op, mr, log2_input_element_size); |
| } |
| |
| const size_t group_input_channels = deconvolution_op->group_input_channels; |
| const size_t group_output_channels = deconvolution_op->group_output_channels; |
| const uint32_t nr = deconvolution_op->ukernel.igemm.nr; |
| const size_t w_stride = bias_element_size + |
| (round_up_po2(group_input_channels, deconvolution_op->ukernel.igemm.kr) * kernel_size << log2_filter_element_size); |
| deconvolution_op->context.igemm = (struct igemm_context) { |
| .ks = kernel_size, |
| .ks_scaled = kernel_size * mr * sizeof(void*), |
| .kc = group_input_channels << log2_input_element_size, |
| .w_stride = w_stride, |
| .indirect_a = deconvolution_op->indirection_buffer, |
| .a_offset = (size_t) ((uintptr_t) input - (uintptr_t) deconvolution_op->last_input), |
| .zero = deconvolution_op->zero_buffer, |
| .packed_w = deconvolution_op->packed_weights, |
| .c = deconvolution_op->output, |
| .cm_stride = deconvolution_op->output_pixel_stride << log2_output_element_size, |
| .cn_stride = nr << log2_output_element_size, |
| .ga_stride = group_input_channels << log2_input_element_size, |
| .gw_stride = w_stride * round_up(group_output_channels, nr), |
| .gc_stride = group_output_channels << log2_output_element_size, |
| .ba_stride = input_height * input_width * deconvolution_op->input_pixel_stride << log2_input_element_size, |
| .bc_stride = output_size * deconvolution_op->output_pixel_stride << log2_output_element_size, |
| .log2_csize = log2_output_element_size, |
| .ukernel = deconvolution_op->ukernel.igemm.general_case, |
| }; |
| if (output_size == 1 && deconvolution_op->ukernel.igemm.mr1_case.function[XNN_UARCH_DEFAULT] != NULL) { |
| deconvolution_op->context.igemm.ukernel = deconvolution_op->ukernel.igemm.mr1_case; |
| } |
| memcpy(&deconvolution_op->context.igemm.params, params, params_size); |
| |
| size_t nc = group_output_channels; |
| if (num_threads > 1) { |
| const size_t num_other_tiles = groups * batch_size * divide_round_up(output_size, mr); |
| const size_t target_tiles_per_thread = 5; |
| const size_t max_nc = divide_round_up(group_output_channels * num_other_tiles, num_threads * target_tiles_per_thread); |
| if (max_nc < nc) { |
| nc = min(nc, divide_round_up(nc, max_nc * nr) * nr); |
| } |
| } |
| if (groups == 1) { |
| deconvolution_op->compute.type = xnn_parallelization_type_3d_tile_2d; |
| deconvolution_op->compute.task_3d_tile_2d = (pthreadpool_task_3d_tile_2d_t) xnn_compute_igemm; |
| deconvolution_op->compute.range[0] = batch_size; |
| deconvolution_op->compute.range[1] = output_size; |
| deconvolution_op->compute.range[2] = group_output_channels; |
| deconvolution_op->compute.tile[0] = mr; |
| deconvolution_op->compute.tile[1] = nc; |
| } else { |
| deconvolution_op->compute.type = xnn_parallelization_type_4d_tile_2d; |
| deconvolution_op->compute.task_4d_tile_2d = (pthreadpool_task_4d_tile_2d_t) xnn_compute_grouped_igemm; |
| deconvolution_op->compute.range[0] = batch_size; |
| deconvolution_op->compute.range[1] = groups; |
| deconvolution_op->compute.range[2] = output_size; |
| deconvolution_op->compute.range[3] = group_output_channels; |
| deconvolution_op->compute.tile[0] = mr; |
| deconvolution_op->compute.tile[1] = nc; |
| } |
| deconvolution_op->state = xnn_run_state_ready; |
| return xnn_status_success; |
| } |
| |
| static enum xnn_status setup_subconv2d_path( |
| xnn_operator_t deconvolution_op, |
| size_t batch_size, |
| size_t input_height, |
| size_t input_width, |
| const void* input, |
| size_t output_height, |
| size_t output_width, |
| void* output, |
| uint32_t log2_input_element_size, |
| uint32_t log2_filter_element_size, |
| uint32_t bias_element_size, |
| uint32_t log2_output_element_size, |
| const void* params, |
| size_t params_size, |
| size_t num_threads, |
| bool use_gemm) |
| { |
| assert(deconvolution_op->ukernel.type == xnn_ukernel_type_subconv2d); |
| |
| const size_t kernel_height = deconvolution_op->kernel_height; |
| const size_t kernel_width = deconvolution_op->kernel_width; |
| const size_t kernel_size = kernel_height * kernel_width; |
| const size_t stride_height = deconvolution_op->stride_height; |
| const size_t stride_width = deconvolution_op->stride_width; |
| |
| const size_t groups = deconvolution_op->groups; |
| const size_t output_size = output_height * output_width; |
| const size_t mr = deconvolution_op->ukernel.igemm.mr; |
| |
| const size_t input_pixel_stride = deconvolution_op->input_pixel_stride << log2_input_element_size; |
| const size_t output_pixel_stride = deconvolution_op->output_pixel_stride << log2_output_element_size; |
| |
| const bool any_size_change = |
| input_height != deconvolution_op->last_input_height || |
| input_width != deconvolution_op->last_input_width || |
| output_height != deconvolution_op->last_output_height || |
| output_width != deconvolution_op->last_output_width; |
| |
| if (any_size_change || output != deconvolution_op->last_output) { |
| // Initialize subconvolution parameters which depend on output dimensions or MR. |
| struct subconvolution_params* subconvolution_params = deconvolution_op->subconvolution_buffer; |
| const size_t modulo_padding_top = deconvolution_op->padding_top % stride_height; |
| const size_t modulo_padding_left = deconvolution_op->padding_left % stride_width; |
| for (size_t offset_y = 0; offset_y < stride_height; offset_y++) { |
| for (size_t offset_x = 0; offset_x < stride_width; offset_x++) { |
| const size_t output_x_start = subtract_modulo(offset_x, modulo_padding_left, stride_width); |
| const size_t output_y_start = subtract_modulo(offset_y, modulo_padding_top, stride_height); |
| subconvolution_params->scaled_kernel_size = mr * subconvolution_params->indirection_x_stride; |
| subconvolution_params->slice_width = divide_round_up(output_width - output_x_start, stride_width); |
| subconvolution_params->slice_height = divide_round_up(output_height - output_y_start, stride_height); |
| subconvolution_params->output = |
| (void*) ((uintptr_t) output + ((output_y_start * output_width + output_x_start) * output_pixel_stride)); |
| ++subconvolution_params; |
| } |
| } |
| deconvolution_op->last_output = output; |
| } |
| |
| if (any_size_change) { |
| if (!use_gemm) { |
| const size_t indirection_buffer_size = sizeof(void*) * |
| kernel_size * output_height * stride_width * round_up(divide_round_up(output_width, stride_width), mr); |
| |
| const void** indirection_buffer = |
| (const void**) xnn_reallocate_memory(deconvolution_op->indirection_buffer, indirection_buffer_size); |
| if (indirection_buffer == NULL) { |
| xnn_log_error( |
| "failed to allocate %zu bytes for %s operator indirection buffer", |
| indirection_buffer_size, xnn_operator_type_to_string(deconvolution_op->type)); |
| return xnn_status_out_of_memory; |
| } |
| deconvolution_op->indirection_buffer = indirection_buffer; |
| deconvolution_op->last_input = input; |
| |
| xnn_indirection_init_subconv2d(deconvolution_op, mr, log2_input_element_size); |
| } |
| deconvolution_op->last_input_height = input_height; |
| deconvolution_op->last_input_width = input_width; |
| deconvolution_op->last_output_height = output_height; |
| deconvolution_op->last_output_width = output_width; |
| } |
| |
| const size_t group_input_channels = deconvolution_op->group_input_channels; |
| const size_t group_output_channels = deconvolution_op->group_output_channels; |
| const uint32_t nr = deconvolution_op->ukernel.igemm.nr; |
| const uint32_t kr = deconvolution_op->ukernel.igemm.kr; |
| const size_t w_stride = stride_height * stride_width * bias_element_size + |
| (round_up_po2(group_input_channels, kr) * kernel_size << log2_filter_element_size); |
| if (use_gemm) { |
| deconvolution_op->context.subgemm = (struct subgemm_context) { |
| .subconvolution_params = deconvolution_op->subconvolution_buffer, |
| .kc = group_input_channels << log2_input_element_size, |
| .a = input, |
| .ax_stride = input_pixel_stride, |
| .ay_stride = input_width * input_pixel_stride, |
| .cx_stride = stride_width * output_pixel_stride, |
| .cy_stride = stride_height * output_width * output_pixel_stride, |
| .cn_stride = nr << log2_output_element_size, |
| .ga_stride = group_input_channels << log2_input_element_size, |
| .gw_stride = w_stride * round_up(group_output_channels, nr), |
| .gc_stride = group_output_channels << log2_output_element_size, |
| .ba_stride = input_height * input_width * input_pixel_stride, |
| .bc_stride = output_size * output_pixel_stride, |
| .log2_csize = log2_output_element_size, |
| .ukernel = deconvolution_op->ukernel.igemm.gemm_case, |
| }; |
| memcpy(&deconvolution_op->context.subgemm.params, params, params_size); |
| } else { |
| deconvolution_op->context.subconv = (struct subconv_context) { |
| .subconvolution_params = deconvolution_op->subconvolution_buffer, |
| .kc = group_input_channels << log2_input_element_size, |
| .a_offset = (size_t) ((uintptr_t) input - (uintptr_t) deconvolution_op->last_input), |
| .zero = deconvolution_op->zero_buffer, |
| .cx_stride = stride_width * output_pixel_stride, |
| .cy_stride = stride_height * output_width * output_pixel_stride, |
| .cn_stride = nr << log2_output_element_size, |
| .ga_stride = group_input_channels << log2_input_element_size, |
| .gw_stride = w_stride * round_up(group_output_channels, nr), |
| .gc_stride = group_output_channels << log2_output_element_size, |
| .ba_stride = input_height * input_width * input_pixel_stride, |
| .bc_stride = output_size * output_pixel_stride, |
| .log2_csize = log2_output_element_size, |
| .ukernel = deconvolution_op->ukernel.igemm.general_case, |
| }; |
| memcpy(&deconvolution_op->context.subconv.params, params, params_size); |
| } |
| |
| const size_t output_height_positions = divide_round_up(output_height, stride_height); |
| const size_t output_width_positions = divide_round_up(output_width, stride_width); |
| |
| size_t nc = group_output_channels; |
| if (num_threads > 1) { |
| const size_t num_other_tiles = groups * stride_height * stride_width * |
| output_height_positions * divide_round_up(output_width_positions, mr); |
| const size_t target_tiles_per_thread = 5; |
| const size_t max_nc = divide_round_up(group_output_channels * num_other_tiles, num_threads * target_tiles_per_thread); |
| if (max_nc < nc) { |
| nc = min(nc, divide_round_up(nc, max_nc * nr) * nr); |
| } |
| } |
| |
| if (groups == 1) { |
| deconvolution_op->compute.type = xnn_parallelization_type_5d_tile_2d; |
| deconvolution_op->compute.task_5d_tile_2d = use_gemm ? |
| (pthreadpool_task_5d_tile_2d_t) xnn_compute_subgemm2d : (pthreadpool_task_5d_tile_2d_t) xnn_compute_subconv2d; |
| deconvolution_op->compute.range[0] = batch_size; |
| deconvolution_op->compute.range[1] = stride_height * stride_width; |
| deconvolution_op->compute.range[2] = divide_round_up(output_height, stride_height); |
| deconvolution_op->compute.range[3] = divide_round_up(output_width, stride_width); |
| deconvolution_op->compute.range[4] = group_output_channels; |
| deconvolution_op->compute.tile[0] = mr; |
| deconvolution_op->compute.tile[1] = nc; |
| } else { |
| deconvolution_op->compute.type = xnn_parallelization_type_6d_tile_2d; |
| deconvolution_op->compute.task_6d_tile_2d = use_gemm ? |
| (pthreadpool_task_6d_tile_2d_t) xnn_compute_grouped_subgemm2d : (pthreadpool_task_6d_tile_2d_t) xnn_compute_grouped_subconv2d; |
| deconvolution_op->compute.range[0] = batch_size; |
| deconvolution_op->compute.range[1] = groups; |
| deconvolution_op->compute.range[2] = stride_height * stride_width; |
| deconvolution_op->compute.range[3] = divide_round_up(output_height, stride_height); |
| deconvolution_op->compute.range[4] = divide_round_up(output_width, stride_width); |
| deconvolution_op->compute.range[5] = group_output_channels; |
| deconvolution_op->compute.tile[0] = mr; |
| deconvolution_op->compute.tile[1] = nc; |
| } |
| |
| deconvolution_op->state = xnn_run_state_ready; |
| return xnn_status_success; |
| } |
| |
| static enum xnn_status setup_deconvolution2d_nhwc( |
| xnn_operator_t deconvolution_op, |
| size_t batch_size, |
| size_t input_height, |
| size_t input_width, |
| uint32_t adjustment_height, |
| uint32_t adjustment_width, |
| const void* input, |
| void* output, |
| uint32_t log2_input_element_size, |
| uint32_t log2_filter_element_size, |
| uint32_t bias_element_size, |
| uint32_t log2_output_element_size, |
| const void* params, |
| size_t params_size, |
| size_t num_threads) |
| { |
| deconvolution_op->state = xnn_run_state_invalid; |
| |
| if ((xnn_params.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) { |
| xnn_log_error("failed to setup %s operator: XNNPACK is not initialized", |
| xnn_operator_type_to_string(deconvolution_op->type)); |
| return xnn_status_uninitialized; |
| } |
| |
| if (input_width == 0 || input_height == 0) { |
| xnn_log_error( |
| "failed to setup %s operator with %zux%zu input: input dimensions must be non-zero", |
| xnn_operator_type_to_string(deconvolution_op->type), input_width, input_height); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (adjustment_height >= deconvolution_op->stride_height) { |
| xnn_log_error( |
| "failed to setup %s operator with %" PRIu32 " height adjustment: " |
| "height adjustment must be smaller than height stride (%" PRIu32 ")", |
| xnn_operator_type_to_string(deconvolution_op->type), adjustment_height, deconvolution_op->stride_height); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (adjustment_width >= deconvolution_op->stride_width) { |
| xnn_log_error( |
| "failed to setup %s operator with %" PRIu32 " width adjustment: " |
| "width adjustment must be smaller than width stride (%" PRIu32 ")", |
| xnn_operator_type_to_string(deconvolution_op->type), adjustment_width, deconvolution_op->stride_width); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (batch_size == 0) { |
| deconvolution_op->state = xnn_run_state_skip; |
| return xnn_status_success; |
| } |
| |
| deconvolution_op->batch_size = batch_size; |
| deconvolution_op->input_height = input_height; |
| deconvolution_op->input_width = input_width; |
| deconvolution_op->input = input; |
| deconvolution_op->output = output; |
| |
| if (deconvolution_op->flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) { |
| // Recompute padding for the input size. |
| const uint32_t dilated_kernel_height_minus_1 = (deconvolution_op->kernel_height - 1) * deconvolution_op->dilation_height; |
| const uint32_t dilated_kernel_width_minus_1 = (deconvolution_op->kernel_width - 1) * deconvolution_op->dilation_width; |
| |
| const size_t total_padding_height = doz(dilated_kernel_height_minus_1, (input_height - 1) % deconvolution_op->stride_height); |
| const size_t total_padding_width = doz(dilated_kernel_width_minus_1, (input_width - 1) % deconvolution_op->stride_width); |
| |
| const uint32_t padding_top = deconvolution_op->padding_top = total_padding_height / 2; |
| const uint32_t padding_left = deconvolution_op->padding_left = total_padding_width / 2; |
| deconvolution_op->padding_bottom = total_padding_height - padding_top; |
| deconvolution_op->padding_right = total_padding_width - padding_left; |
| } |
| |
| const size_t output_height = deconvolution_op->output_height = compute_output_dimension( |
| input_height, deconvolution_op->padding_top + deconvolution_op->padding_bottom, |
| adjustment_height, deconvolution_op->kernel_height, deconvolution_op->dilation_height, deconvolution_op->stride_height); |
| const size_t output_width = deconvolution_op->output_width = compute_output_dimension( |
| input_width, deconvolution_op->padding_left + deconvolution_op->padding_right, |
| adjustment_width, deconvolution_op->kernel_width, deconvolution_op->dilation_width, deconvolution_op->stride_width); |
| |
| switch (deconvolution_op->ukernel.type) { |
| case xnn_ukernel_type_igemm: |
| return setup_conv_path( |
| deconvolution_op, |
| batch_size, |
| input_height, input_width, input, |
| output_height, output_width, output, |
| log2_input_element_size, log2_filter_element_size, bias_element_size, log2_output_element_size, |
| params, params_size, num_threads); |
| case xnn_ukernel_type_subconv2d: |
| { |
| const bool no_padding = (deconvolution_op->padding_top | deconvolution_op->padding_right | deconvolution_op->padding_bottom | deconvolution_op->padding_left) == 0; |
| const bool no_adjustment = (adjustment_height | adjustment_width) == 0; |
| const bool use_gemm = no_padding && no_adjustment && |
| deconvolution_op->kernel_height == deconvolution_op->stride_height && |
| deconvolution_op->kernel_width == deconvolution_op->stride_width && |
| deconvolution_op->ukernel.igemm.gemm_case.function[XNN_UARCH_DEFAULT] != NULL; |
| return setup_subconv2d_path( |
| deconvolution_op, |
| batch_size, |
| input_height, input_width, input, |
| output_height, output_width, output, |
| log2_input_element_size, log2_filter_element_size, bias_element_size, log2_output_element_size, |
| params, params_size, num_threads, use_gemm); |
| } |
| default: |
| XNN_UNREACHABLE; |
| } |
| } |
| |
| enum xnn_status xnn_setup_deconvolution2d_nhwc_q8( |
| xnn_operator_t deconvolution_op, |
| size_t batch_size, |
| size_t input_height, |
| size_t input_width, |
| uint32_t adjustment_height, |
| uint32_t adjustment_width, |
| const uint8_t* input, |
| uint8_t* output, |
| pthreadpool_t threadpool) |
| { |
| if (deconvolution_op->type != xnn_operator_type_deconvolution_nhwc_q8) { |
| xnn_log_error("failed to setup operator: operator type mismatch (expected %s, got %s)", |
| xnn_operator_type_to_string(xnn_operator_type_deconvolution_nhwc_q8), |
| xnn_operator_type_to_string(deconvolution_op->type)); |
| return xnn_status_invalid_parameter; |
| } |
| |
| return setup_deconvolution2d_nhwc( |
| deconvolution_op, |
| batch_size, input_height, input_width, |
| adjustment_height, adjustment_width, |
| input, output, |
| 0 /* log2(sizeof(input element)) = log2(sizeof(uint8_t)) */, |
| 0 /* log2(sizeof(filter element)) = log2(sizeof(uint8_t)) */, |
| sizeof(int32_t) /* sizeof(bias element) */, |
| 0 /* log2(sizeof(output element)) = log2(sizeof(uint8_t)) */, |
| &deconvolution_op->params.q8_gemm, sizeof(deconvolution_op->params.q8_gemm), |
| pthreadpool_get_threads_count(threadpool)); |
| } |
| |
| enum xnn_status xnn_setup_deconvolution2d_nhwc_f32( |
| xnn_operator_t deconvolution_op, |
| size_t batch_size, |
| size_t input_height, |
| size_t input_width, |
| uint32_t adjustment_height, |
| uint32_t adjustment_width, |
| const float* input, |
| float* output, |
| pthreadpool_t threadpool) |
| { |
| if (deconvolution_op->type != xnn_operator_type_deconvolution_nhwc_f32) { |
| xnn_log_error("failed to setup operator: operator type mismatch (expected %s, got %s)", |
| xnn_operator_type_to_string(xnn_operator_type_deconvolution_nhwc_f32), |
| xnn_operator_type_to_string(deconvolution_op->type)); |
| return xnn_status_invalid_parameter; |
| } |
| |
| return setup_deconvolution2d_nhwc( |
| deconvolution_op, |
| batch_size, input_height, input_width, |
| adjustment_height, adjustment_width, |
| input, output, |
| 2 /* log2(sizeof(input element)) = log2(sizeof(float)) */, |
| 2 /* log2(sizeof(filter element)) = log2(sizeof(float)) */, |
| sizeof(float) /* sizeof(bias element) */, |
| 2 /* log2(sizeof(output element)) = log2(sizeof(float)) */, |
| &deconvolution_op->params.f32_minmax, sizeof(deconvolution_op->params.f32_minmax), |
| pthreadpool_get_threads_count(threadpool)); |
| } |