| // 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 <math.h> |
| #include <stdbool.h> |
| #include <stddef.h> |
| #include <stdint.h> |
| #include <stdlib.h> |
| #include <string.h> |
| |
| #include <xnnpack.h> |
| #include <xnnpack/allocator.h> |
| #include <xnnpack/operator.h> |
| #include <xnnpack/common.h> |
| #include <xnnpack/log.h> |
| #include <xnnpack/math.h> |
| #include <xnnpack/params-init.h> |
| #include <xnnpack/params.h> |
| #include <xnnpack/indirection.h> |
| |
| |
| static inline size_t compute_output_dimension( |
| size_t padded_input_dimension, |
| size_t pooling_dimension, |
| size_t stride_dimension) |
| { |
| return (padded_input_dimension - pooling_dimension) / stride_dimension + 1; |
| } |
| |
| static inline size_t compute_output_dimension_with_tf_same_padding( |
| size_t input_dimension, |
| size_t stride_dimension) |
| { |
| return divide_round_up(input_dimension, stride_dimension); |
| } |
| |
| enum xnn_status xnn_create_average_pooling2d_nhwc_q8( |
| uint32_t input_padding_top, |
| uint32_t input_padding_right, |
| uint32_t input_padding_bottom, |
| uint32_t input_padding_left, |
| uint32_t pooling_height, |
| uint32_t pooling_width, |
| uint32_t stride_height, |
| uint32_t stride_width, |
| size_t channels, |
| size_t input_pixel_stride, |
| size_t output_pixel_stride, |
| uint8_t input_zero_point, |
| float input_scale, |
| uint8_t output_zero_point, |
| float output_scale, |
| uint8_t output_min, |
| uint8_t output_max, |
| uint32_t flags, |
| xnn_operator_t* average_pooling_op_out) |
| { |
| xnn_operator_t average_pooling_op = NULL; |
| enum xnn_status status = xnn_status_uninitialized; |
| |
| if (!xnn_params.initialized) { |
| xnn_log_error("failed to create %s operator: XNNPACK is not initialized", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_q8)); |
| goto error; |
| } |
| |
| status = xnn_status_invalid_parameter; |
| |
| const uint32_t pooling_size = pooling_height * pooling_width; |
| if (pooling_size == 0) { |
| xnn_log_error( |
| "failed to create %s operator with %" PRIu32 "x%" PRIu32 " pooling size: " |
| "pooling size dimensions must be non-zero", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_q8), pooling_width, pooling_height); |
| goto error; |
| } |
| |
| if (pooling_size == 1) { |
| xnn_log_error( |
| "failed to create %s operator with 1 pooling element: 1x1 pooling is meaningless", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_q8)); |
| goto error; |
| } |
| |
| if (stride_height == 0 || stride_width == 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(xnn_operator_type_average_pooling_nhwc_q8), stride_width, stride_height); |
| goto error; |
| } |
| |
| if (channels == 0) { |
| xnn_log_error( |
| "failed to create %s operator with %zu channels: number of channels must be non-zero", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_q8), channels); |
| goto error; |
| } |
| |
| if (input_pixel_stride < 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 channels (%zu)", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_q8), input_pixel_stride, channels); |
| goto error; |
| } |
| |
| if (output_pixel_stride < 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 channels (%zu)", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_q8), output_pixel_stride, channels); |
| goto error; |
| } |
| |
| 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_average_pooling_nhwc_q8), input_scale); |
| goto error; |
| } |
| |
| 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_average_pooling_nhwc_q8), output_scale); |
| goto error; |
| } |
| |
| 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_average_pooling_nhwc_q8), output_min, output_max); |
| goto error; |
| } |
| |
| const bool any_padding = (input_padding_left | input_padding_top | input_padding_right | input_padding_bottom) != 0; |
| if ((flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) != 0) { |
| if (any_padding) { |
| 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(xnn_operator_type_average_pooling_nhwc_q8), |
| input_padding_top, input_padding_left, input_padding_bottom, input_padding_right); |
| goto error; |
| } |
| } |
| |
| status = xnn_status_unsupported_parameter; |
| |
| const float input_output_scale = input_scale / output_scale; |
| if (input_output_scale < 0x1.0p-8f || input_output_scale >= 0x1.0p+8f) { |
| xnn_log_error( |
| "failed to create %s operator with %.7g input scale and %.7g output scale: " |
| "input-to-output scale ratio (%.7f) must be in [2**-8, 2**8) range", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_q8), |
| input_scale, output_scale, input_output_scale); |
| goto error; |
| } |
| |
| if (pooling_size >= 16777216) { |
| xnn_log_error( |
| "failed to create %s operator with %"PRIu32" (%" PRIu32 "x%" PRIu32 ") pooling elements: " |
| "the number of elements in the pooling area must be below 2**24", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_q8), |
| pooling_size, pooling_width, pooling_height); |
| goto error; |
| } |
| |
| status = xnn_status_out_of_memory; |
| |
| average_pooling_op = xnn_allocate_zero_simd_memory(sizeof(struct xnn_operator)); |
| if (average_pooling_op == NULL) { |
| xnn_log_error( |
| "failed to allocate %zu bytes for %s operator descriptor", |
| sizeof(struct xnn_operator), xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_q8)); |
| goto error; |
| } |
| |
| const size_t zero_bytes = channels * sizeof(uint8_t) + XNN_EXTRA_BYTES; |
| void* zero_buffer = xnn_allocate_simd_memory(zero_bytes); |
| if (zero_buffer == NULL) { |
| xnn_log_error( |
| "failed to allocate %zu bytes for %s operator zero padding", |
| zero_bytes, xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_q8)); |
| goto error; |
| } |
| memset(zero_buffer, input_zero_point, channels * sizeof(uint8_t)); |
| average_pooling_op->zero_buffer = zero_buffer; |
| |
| average_pooling_op->padding_top = input_padding_top; |
| average_pooling_op->padding_right = input_padding_right; |
| average_pooling_op->padding_bottom = input_padding_bottom; |
| average_pooling_op->padding_left = input_padding_left; |
| |
| average_pooling_op->kernel_height = pooling_height; |
| average_pooling_op->kernel_width = pooling_width; |
| average_pooling_op->stride_height = stride_height; |
| average_pooling_op->stride_width = stride_width; |
| average_pooling_op->dilation_height = 1; |
| average_pooling_op->dilation_width = 1; |
| average_pooling_op->channels = channels; |
| average_pooling_op->input_pixel_stride = input_pixel_stride; |
| average_pooling_op->output_pixel_stride = output_pixel_stride; |
| |
| average_pooling_op->input_zero_point = input_zero_point; |
| average_pooling_op->output_zero_point = output_zero_point; |
| average_pooling_op->input_scale = input_scale; |
| average_pooling_op->output_scale = output_scale; |
| average_pooling_op->output_min = output_min; |
| average_pooling_op->output_max = output_max; |
| |
| // Number of rows read in the AVGPOOL micro-kernel. |
| const size_t avgpool_nrows = |
| round_up(doz(pooling_size, xnn_params.q8.avgpool.mr), xnn_params.q8.avgpool.qr) + xnn_params.q8.avgpool.mr; |
| average_pooling_op->params.q8_avgpool = |
| xnn_init_q8_avgpool_params( |
| (int32_t) -((uint32_t) input_zero_point * (uint32_t) avgpool_nrows), |
| input_scale / (output_scale * (float) pooling_size), |
| output_zero_point, output_min, output_max); |
| |
| average_pooling_op->type = xnn_operator_type_average_pooling_nhwc_q8; |
| average_pooling_op->ukernel.type = xnn_ukernel_type_average_pooling; |
| average_pooling_op->flags = flags; |
| |
| *average_pooling_op_out = average_pooling_op; |
| return xnn_status_success; |
| |
| error: |
| xnn_delete_operator(average_pooling_op); |
| return status; |
| } |
| |
| enum xnn_status xnn_create_average_pooling2d_nhwc_f32( |
| uint32_t input_padding_top, |
| uint32_t input_padding_right, |
| uint32_t input_padding_bottom, |
| uint32_t input_padding_left, |
| uint32_t pooling_height, |
| uint32_t pooling_width, |
| uint32_t stride_height, |
| uint32_t stride_width, |
| size_t channels, |
| size_t input_pixel_stride, |
| size_t output_pixel_stride, |
| float output_min, |
| float output_max, |
| uint32_t flags, |
| xnn_operator_t* average_pooling_op_out) |
| { |
| xnn_operator_t average_pooling_op = NULL; |
| enum xnn_status status = xnn_status_uninitialized; |
| |
| if (!xnn_params.initialized) { |
| xnn_log_error("failed to create %s operator: XNNPACK is not initialized", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32)); |
| goto error; |
| } |
| |
| status = xnn_status_invalid_parameter; |
| |
| const uint32_t pooling_size = pooling_height * pooling_width; |
| if (pooling_size == 0) { |
| xnn_log_error( |
| "failed to create %s operator with %" PRIu32 "x%" PRIu32 " pooling size: " |
| "pooling size dimensions must be non-zero", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32), pooling_width, pooling_height); |
| goto error; |
| } |
| |
| if (pooling_size == 1) { |
| xnn_log_error( |
| "failed to create %s operator with 1 pooling element: 1x1 pooling is meaningless", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32)); |
| goto error; |
| } |
| |
| if (stride_height == 0 || stride_width == 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(xnn_operator_type_average_pooling_nhwc_f32), stride_width, stride_height); |
| goto error; |
| } |
| |
| if (channels == 0) { |
| xnn_log_error( |
| "failed to create %s operator with %zu channels: number of channels must be non-zero", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32), channels); |
| goto error; |
| } |
| |
| if (input_pixel_stride < 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 channels (%zu)", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32), input_pixel_stride, channels); |
| goto error; |
| } |
| |
| if (output_pixel_stride < 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 channels (%zu)", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32), output_pixel_stride, channels); |
| goto error; |
| } |
| |
| 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_average_pooling_nhwc_f32)); |
| goto error; |
| } |
| |
| 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_average_pooling_nhwc_f32)); |
| goto error; |
| } |
| |
| 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_average_pooling_nhwc_f32), output_min, output_max); |
| goto error; |
| } |
| |
| const bool any_padding = (input_padding_left | input_padding_top | input_padding_right | input_padding_bottom) != 0; |
| if ((flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) != 0) { |
| if (any_padding) { |
| 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(xnn_operator_type_average_pooling_nhwc_f32), |
| input_padding_top, input_padding_left, input_padding_bottom, input_padding_right); |
| goto error; |
| } |
| } |
| |
| status = xnn_status_out_of_memory; |
| |
| average_pooling_op = xnn_allocate_zero_simd_memory(sizeof(struct xnn_operator)); |
| if (average_pooling_op == NULL) { |
| xnn_log_error( |
| "failed to allocate %zu bytes for %s operator descriptor", |
| sizeof(struct xnn_operator), xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32)); |
| goto error; |
| } |
| |
| const size_t zero_bytes = channels * sizeof(float) + XNN_EXTRA_BYTES; |
| void* zero_buffer = xnn_allocate_zero_simd_memory(zero_bytes); |
| if (zero_buffer == NULL) { |
| xnn_log_error( |
| "failed to allocate %zu bytes for %s operator zero padding", |
| zero_bytes, xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32)); |
| goto error; |
| } |
| average_pooling_op->zero_buffer = zero_buffer; |
| |
| average_pooling_op->padding_top = input_padding_top; |
| average_pooling_op->padding_right = input_padding_right; |
| average_pooling_op->padding_bottom = input_padding_bottom; |
| average_pooling_op->padding_left = input_padding_left; |
| |
| average_pooling_op->kernel_height = pooling_height; |
| average_pooling_op->kernel_width = pooling_width; |
| average_pooling_op->stride_height = stride_height; |
| average_pooling_op->stride_width = stride_width; |
| average_pooling_op->dilation_height = 1; |
| average_pooling_op->dilation_width = 1; |
| average_pooling_op->channels = channels; |
| average_pooling_op->input_pixel_stride = input_pixel_stride; |
| average_pooling_op->output_pixel_stride = output_pixel_stride; |
| |
| average_pooling_op->type = xnn_operator_type_average_pooling_nhwc_f32; |
| average_pooling_op->params.f32_scaleminmax = |
| xnn_init_f32_scaleminmax_params(1.0f / (float) pooling_size, output_min, output_max); |
| const bool tf_same_padding = (flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) != 0; |
| if (any_padding || tf_same_padding) { |
| average_pooling_op->params.f32_minmax = |
| xnn_init_f32_minmax_params(output_min, output_max); |
| average_pooling_op->ukernel.type = xnn_ukernel_type_pixelwise_average_pooling; |
| } else { |
| average_pooling_op->ukernel.type = xnn_ukernel_type_average_pooling; |
| } |
| average_pooling_op->flags = flags; |
| |
| *average_pooling_op_out = average_pooling_op; |
| return xnn_status_success; |
| |
| error: |
| xnn_delete_operator(average_pooling_op); |
| return status; |
| } |
| |
| static enum xnn_status setup_average_pooling2d( |
| xnn_operator_t average_pooling_op, |
| size_t batch_size, |
| size_t input_height, |
| size_t input_width, |
| const void* input, |
| void* output, |
| uint32_t log2_input_element_size, |
| uint32_t log2_output_element_size, |
| struct avgpool_parameters avgpool[restrict XNN_MIN_ELEMENTS(1)], |
| struct pavgpool_parameters pavgpool[restrict 1], |
| struct gavgpool_parameters gavgpool[restrict XNN_MIN_ELEMENTS(1)], |
| const void* params, |
| size_t params_size, |
| const void* global_params, |
| size_t global_params_size, |
| size_t num_threads, |
| bool is_pixelwise) |
| { |
| assert(!is_pixelwise || pavgpool != NULL); |
| |
| average_pooling_op->state = xnn_run_state_invalid; |
| |
| if (!xnn_params.initialized) { |
| xnn_log_error("failed to setup %s operator: XNNPACK is not initialized", |
| xnn_operator_type_to_string(average_pooling_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(average_pooling_op->type), input_width, input_height); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (batch_size == 0) { |
| average_pooling_op->state = xnn_run_state_skip; |
| return xnn_status_success; |
| } |
| |
| average_pooling_op->input_height = input_height; |
| average_pooling_op->input_width = input_width; |
| average_pooling_op->input = input; |
| |
| const bool tf_same_padding = (average_pooling_op->flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) != 0; |
| if (tf_same_padding) { |
| average_pooling_op->output_height = compute_output_dimension_with_tf_same_padding( |
| input_height, average_pooling_op->stride_height); |
| average_pooling_op->output_width = compute_output_dimension_with_tf_same_padding( |
| input_width, average_pooling_op->stride_width); |
| |
| const uint32_t kernel_height = average_pooling_op->kernel_height; |
| const uint32_t kernel_width = average_pooling_op->kernel_width; |
| const uint32_t total_padding_height = |
| (average_pooling_op->output_height - 1) * average_pooling_op->stride_height + kernel_height - input_height; |
| const uint32_t total_padding_width = |
| (average_pooling_op->output_width - 1) * average_pooling_op->stride_width + kernel_width - input_width; |
| average_pooling_op->padding_top = total_padding_height / 2; |
| average_pooling_op->padding_left = total_padding_width / 2; |
| average_pooling_op->padding_bottom = total_padding_height - average_pooling_op->padding_top; |
| average_pooling_op->padding_right = total_padding_width - average_pooling_op->padding_left; |
| } else { |
| average_pooling_op->output_height = compute_output_dimension( |
| average_pooling_op->padding_top + input_height + average_pooling_op->padding_bottom, |
| average_pooling_op->kernel_height, |
| average_pooling_op->stride_height); |
| average_pooling_op->output_width = compute_output_dimension( |
| average_pooling_op->padding_left + input_width + average_pooling_op->padding_right, |
| average_pooling_op->kernel_width, |
| average_pooling_op->stride_width); |
| } |
| average_pooling_op->output = output; |
| |
| const size_t output_height = average_pooling_op->output_height; |
| const size_t output_width = average_pooling_op->output_width; |
| const size_t padded_input_width = average_pooling_op->padding_left + input_width + average_pooling_op->padding_right; |
| const size_t padded_input_height = average_pooling_op->padding_top + input_height + average_pooling_op->padding_bottom; |
| if (padded_input_width == average_pooling_op->kernel_width && padded_input_height == average_pooling_op->kernel_height) { |
| // Global average pooling |
| const size_t input_elements = input_height * input_width; |
| const size_t input_stride_in_bytes = average_pooling_op->input_pixel_stride << log2_input_element_size; |
| const size_t channels = average_pooling_op->channels; |
| average_pooling_op->context.global_average_pooling_nwc = (struct global_average_pooling_nwc_context) { |
| .input = input, |
| .zero = average_pooling_op->zero_buffer, |
| .input_pixel_stride = input_stride_in_bytes, |
| .input_batch_stride = input_stride_in_bytes * input_elements, |
| .input_elements = input_elements, |
| .channels = channels, |
| .output = output, |
| .output_batch_stride = average_pooling_op->output_pixel_stride << log2_output_element_size, |
| }; |
| memcpy(&average_pooling_op->context.global_average_pooling_nwc.params, global_params, global_params_size); |
| average_pooling_op->compute.type = xnn_parallelization_type_1d; |
| average_pooling_op->compute.range[0] = batch_size; |
| |
| if (input_elements <= gavgpool->mr) { |
| average_pooling_op->compute.task_1d = (pthreadpool_task_1d_t) xnn_compute_global_average_pooling_nwc_unipass; |
| average_pooling_op->context.global_average_pooling_nwc.unipass_ukernel = gavgpool->up; |
| } else { |
| average_pooling_op->compute.task_1d = (pthreadpool_task_1d_t) xnn_compute_global_average_pooling_nwc_multipass; |
| average_pooling_op->context.global_average_pooling_nwc.multipass_ukernel = gavgpool->mp; |
| } |
| } else { |
| // Non-global average pooling |
| const size_t pooling_height = average_pooling_op->kernel_height; |
| const size_t pooling_width = average_pooling_op->kernel_width; |
| const size_t pooling_size = pooling_height * pooling_width; |
| |
| const uint32_t mr = is_pixelwise ? pavgpool->mr : avgpool->mr; |
| |
| const size_t step_width = min(average_pooling_op->stride_width, pooling_width); |
| const size_t step_height = pooling_size + (output_width - 1) * step_width * pooling_height; |
| |
| const size_t last_input_height = average_pooling_op->last_input_height; |
| const size_t last_input_width = average_pooling_op->last_input_width; |
| if (input_height != last_input_height || input_width != last_input_width) { |
| // Micro-kernel may read up to (mr - 1) elements after the end of indirection buffer. |
| const size_t indirection_buffer_size = sizeof(void*) * ((mr - 1) + batch_size * output_height * step_height); |
| |
| const void** indirection_buffer = |
| (const void**) xnn_reallocate_memory(average_pooling_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(average_pooling_op->type)); |
| return xnn_status_out_of_memory; |
| } |
| average_pooling_op->indirection_buffer = indirection_buffer; |
| |
| // Indirection buffer always setup for batch size 1, larger batch size supported through input_offset argument |
| average_pooling_op->batch_size = 1; |
| xnn_indirection_init_dwconv2d( |
| average_pooling_op, 0, step_height, step_width, log2_input_element_size); |
| |
| average_pooling_op->last_input = input; |
| average_pooling_op->last_input_height = input_height; |
| average_pooling_op->last_input_width = input_width; |
| } |
| |
| const size_t channels = average_pooling_op->channels; |
| |
| const size_t indirect_input_height_stride = step_height * sizeof(void*); |
| const size_t output_width_stride = average_pooling_op->output_pixel_stride << log2_output_element_size; |
| const size_t output_height_stride = output_width * output_width_stride; |
| |
| if (is_pixelwise) { |
| /* This part is specific to FP32, needs revision if Q8 gets a PAVGPOOL micro-kernel */ |
| if (input_height != last_input_height || input_width != last_input_width) { |
| const size_t pixelwise_buffer_size = output_height * output_width * sizeof(float); |
| float* pixelwise_buffer = |
| (float*) xnn_reallocate_memory(average_pooling_op->pixelwise_buffer, pixelwise_buffer_size); |
| if (pixelwise_buffer == NULL) { |
| xnn_log_error("failed to allocate %zu bytes for %s operator pixelwise buffer", |
| pixelwise_buffer_size, xnn_operator_type_to_string(average_pooling_op->type)); |
| return xnn_status_out_of_memory; |
| } |
| average_pooling_op->pixelwise_buffer = pixelwise_buffer; |
| |
| float* pixelwise_pointer = pixelwise_buffer; |
| for (size_t output_y = 0; output_y < output_height; output_y++) { |
| const size_t input_y_start = doz(output_y * average_pooling_op->stride_height, average_pooling_op->padding_top); |
| const size_t input_y_end = |
| min(doz(output_y * average_pooling_op->stride_height + average_pooling_op->kernel_height, average_pooling_op->padding_top), input_height); |
| const uint32_t input_y_range = (uint32_t) (input_y_end - input_y_start); |
| for (size_t output_x = 0; output_x < output_width; output_x++) { |
| const size_t input_x_start = doz(output_x * average_pooling_op->stride_width, average_pooling_op->padding_left); |
| const size_t input_x_end = |
| min(doz(output_x * average_pooling_op->stride_width + average_pooling_op->kernel_width, average_pooling_op->padding_left), input_width); |
| const uint32_t input_x_range = (uint32_t) (input_x_end - input_x_start); |
| *pixelwise_pointer++ = 1.0f / ((float) (int32_t) (input_y_range * input_x_range)); |
| } |
| } |
| } |
| |
| const uint32_t qr = pavgpool->qr; |
| const size_t multipass_adjustment = |
| pooling_size > mr ? round_up(pooling_size - mr, qr) + mr - qr : 0; |
| average_pooling_op->context.pixelwise_average_pooling = (struct pixelwise_average_pooling_context) { |
| .indirect_input = average_pooling_op->indirection_buffer, |
| .indirect_input_height_stride = indirect_input_height_stride, |
| .input_batch_stride = input_height * input_width * average_pooling_op->input_pixel_stride << log2_input_element_size, |
| .input_offset = (size_t) ((uintptr_t) input - (uintptr_t) average_pooling_op->last_input), |
| .pixelwise_buffer = average_pooling_op->pixelwise_buffer, |
| .pixelwise_buffer_height_stride = output_width * sizeof(float), |
| .output = output, |
| .output_batch_stride = output_height * output_height_stride, |
| .output_height_stride = output_height_stride, |
| .output_width = output_width, |
| .pooling_size = pooling_size, |
| .channels = channels, |
| .zero = average_pooling_op->zero_buffer, |
| .input_increment = (pooling_height * step_width - multipass_adjustment) * sizeof(void*), |
| .output_increment = output_width_stride - (channels << log2_output_element_size), |
| }; |
| memcpy(&average_pooling_op->context.pixelwise_average_pooling.params, params, params_size); |
| if (pooling_size <= mr) { |
| average_pooling_op->context.pixelwise_average_pooling.unipass_ukernel = pavgpool->up; |
| average_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_pixelwise_average_pooling_unipass; |
| } else { |
| average_pooling_op->context.pixelwise_average_pooling.multipass_ukernel = pavgpool->mp; |
| average_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_pixelwise_average_pooling_multipass; |
| } |
| } else { |
| const uint32_t qr = avgpool->qr; |
| const size_t multipass_adjustment = |
| pooling_size > mr ? round_up(pooling_size - mr, qr) + mr - qr : 0; |
| average_pooling_op->context.average_pooling = (struct average_pooling_context) { |
| .indirect_input = average_pooling_op->indirection_buffer, |
| .indirect_input_height_stride = indirect_input_height_stride, |
| .input_offset = (size_t) ((uintptr_t) input - (uintptr_t) average_pooling_op->last_input), |
| .input_batch_stride = input_height * input_width * average_pooling_op->input_pixel_stride << log2_input_element_size, |
| .output = output, |
| .output_batch_stride = output_height * output_height_stride, |
| .output_height_stride = output_height_stride, |
| .output_width = output_width, |
| .pooling_size = pooling_size, |
| .channels = channels, |
| .zero = average_pooling_op->zero_buffer, |
| .input_increment = (pooling_height * step_width - multipass_adjustment) * sizeof(void*), |
| .output_increment = output_width_stride - (channels << log2_output_element_size), |
| .params.f32 = average_pooling_op->params.f32_scaleminmax, |
| }; |
| memcpy(&average_pooling_op->context.average_pooling.params, params, params_size); |
| if (pooling_size <= mr) { |
| average_pooling_op->context.average_pooling.unipass_ukernel = avgpool->up; |
| average_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_average_pooling_unipass; |
| } else { |
| average_pooling_op->context.average_pooling.multipass_ukernel = avgpool->mp; |
| average_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_average_pooling_multipass; |
| } |
| } |
| average_pooling_op->compute.type = xnn_parallelization_type_2d; |
| average_pooling_op->compute.range[0] = batch_size; |
| average_pooling_op->compute.range[1] = output_height; |
| } |
| average_pooling_op->state = xnn_run_state_ready; |
| |
| return xnn_status_success; |
| } |
| |
| enum xnn_status xnn_setup_average_pooling2d_nhwc_q8( |
| xnn_operator_t average_pooling_op, |
| size_t batch_size, |
| size_t input_height, |
| size_t input_width, |
| const uint8_t* input, |
| uint8_t* output, |
| pthreadpool_t threadpool) |
| { |
| if (average_pooling_op->type != xnn_operator_type_average_pooling_nhwc_q8) { |
| xnn_log_error("failed to setup operator: operator type mismatch (expected %s, got %s)", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_q8), |
| xnn_operator_type_to_string(average_pooling_op->type)); |
| return xnn_status_invalid_parameter; |
| } |
| |
| assert(average_pooling_op->ukernel.type == xnn_ukernel_type_average_pooling); |
| |
| // Number of rows read in the GAVGPOOL micro-kernel. |
| const size_t input_size = input_height * input_width; |
| const size_t pooling_size = average_pooling_op->kernel_height * average_pooling_op->kernel_width; |
| const size_t gavgpool_nrows = round_up(input_size, xnn_params.q8.gavgpool.mr); |
| average_pooling_op->params.q8_gavgpool = |
| xnn_init_q8_avgpool_params( |
| (int32_t) -((uint32_t) average_pooling_op->input_zero_point * (uint32_t) gavgpool_nrows), |
| average_pooling_op->input_scale / (average_pooling_op->output_scale * (float) pooling_size), |
| average_pooling_op->output_zero_point, |
| average_pooling_op->output_min, |
| average_pooling_op->output_max); |
| |
| return setup_average_pooling2d( |
| average_pooling_op, |
| batch_size, input_height, input_width, |
| input, output, |
| 0 /* log2(sizeof(input element)) = log2(sizeof(uint8_t)) */, |
| 0 /* log2(sizeof(output element)) = log2(sizeof(uint8_t)) */, |
| &xnn_params.q8.avgpool, |
| NULL /* no PAVGPOOL micro-kernel */, |
| &xnn_params.q8.gavgpool, |
| &average_pooling_op->params.q8_avgpool, |
| sizeof(average_pooling_op->params.q8_avgpool), |
| &average_pooling_op->params.q8_gavgpool, |
| sizeof(average_pooling_op->params.q8_gavgpool), |
| pthreadpool_get_threads_count(threadpool), |
| false /* pixelwise not supported */); |
| } |
| |
| enum xnn_status xnn_setup_average_pooling2d_nhwc_f32( |
| xnn_operator_t average_pooling_op, |
| size_t batch_size, |
| size_t input_height, |
| size_t input_width, |
| const float* input, |
| float* output, |
| pthreadpool_t threadpool) |
| { |
| if (average_pooling_op->type != xnn_operator_type_average_pooling_nhwc_f32) { |
| xnn_log_error("failed to setup operator: operator type mismatch (expected %s, got %s)", |
| xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32), |
| xnn_operator_type_to_string(average_pooling_op->type)); |
| return xnn_status_invalid_parameter; |
| } |
| |
| assert(average_pooling_op->ukernel.type == xnn_ukernel_type_average_pooling || |
| average_pooling_op->ukernel.type == xnn_ukernel_type_pixelwise_average_pooling); |
| |
| const bool is_pixelwise = average_pooling_op->ukernel.type == xnn_ukernel_type_pixelwise_average_pooling; |
| if (is_pixelwise) { |
| const size_t input_size = input_height * input_width; |
| xnn_update_f32_scaleminmax_params(&average_pooling_op->params.f32_scaleminmax, 1.0f / (float) input_size); |
| } |
| |
| return setup_average_pooling2d( |
| average_pooling_op, |
| batch_size, input_height, input_width, |
| input, output, |
| 2 /* log2(sizeof(input element)) = log2(sizeof(float)) */, |
| 2 /* log2(sizeof(output element)) = log2(sizeof(float)) */, |
| &xnn_params.f32.avgpool, |
| &xnn_params.f32.pavgpool, |
| &xnn_params.f32.gavgpool, |
| is_pixelwise ? (const void*) &average_pooling_op->params.f32_minmax : (const void*) &average_pooling_op->params.f32_scaleminmax, |
| is_pixelwise ? sizeof(average_pooling_op->params.f32_minmax) : sizeof(average_pooling_op->params.f32_scaleminmax), |
| &average_pooling_op->params.f32_scaleminmax, |
| sizeof(average_pooling_op->params.f32_scaleminmax), |
| pthreadpool_get_threads_count(threadpool), |
| is_pixelwise); |
| } |