| // 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 kernel_dimension) |
| { |
| return padded_input_dimension / kernel_dimension; |
| } |
| |
| static const struct argmaxpool_parameters* select_ukernel( |
| size_t pooling_size, |
| const struct argmaxpool_parameters* ukernel) |
| { |
| while (ukernel->qr == 0 && ukernel->mr < pooling_size) { |
| ukernel++; |
| } |
| return ukernel; |
| } |
| |
| enum xnn_status xnn_create_argmax_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, |
| 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* argmax_pooling_op_out) |
| { |
| xnn_operator_t argmax_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_argmax_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_argmax_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_argmax_pooling_nhwc_f32)); |
| 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_argmax_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_argmax_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_argmax_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_argmax_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_argmax_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_argmax_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_argmax_pooling_nhwc_f32), |
| input_padding_top, input_padding_left, input_padding_bottom, input_padding_right); |
| goto error; |
| } |
| } |
| |
| status = xnn_status_out_of_memory; |
| |
| argmax_pooling_op = xnn_allocate_zero_simd_memory(sizeof(struct xnn_operator)); |
| if (argmax_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_argmax_pooling_nhwc_f32)); |
| goto error; |
| } |
| |
| argmax_pooling_op->padding_top = input_padding_top; |
| argmax_pooling_op->padding_right = input_padding_right; |
| argmax_pooling_op->padding_bottom = input_padding_bottom; |
| argmax_pooling_op->padding_left = input_padding_left; |
| |
| argmax_pooling_op->kernel_height = pooling_height; |
| argmax_pooling_op->kernel_width = pooling_width; |
| argmax_pooling_op->stride_height = pooling_height; |
| argmax_pooling_op->stride_width = pooling_width; |
| argmax_pooling_op->dilation_height = 1; |
| argmax_pooling_op->dilation_width = 1; |
| argmax_pooling_op->channels = channels; |
| argmax_pooling_op->input_pixel_stride = input_pixel_stride; |
| argmax_pooling_op->output_pixel_stride = output_pixel_stride; |
| |
| argmax_pooling_op->params.f32_minmax = xnn_init_f32_minmax_params(output_min, output_max); |
| |
| argmax_pooling_op->type = xnn_operator_type_argmax_pooling_nhwc_f32; |
| argmax_pooling_op->ukernel.type = xnn_ukernel_type_argmax_pooling; |
| argmax_pooling_op->flags = flags; |
| |
| argmax_pooling_op->state = xnn_run_state_invalid; |
| |
| *argmax_pooling_op_out = argmax_pooling_op; |
| return xnn_status_success; |
| |
| error: |
| xnn_delete_operator(argmax_pooling_op); |
| return status; |
| } |
| |
| enum xnn_status xnn_setup_argmax_pooling2d_nhwc_f32( |
| xnn_operator_t argmax_pooling_op, |
| size_t batch_size, |
| size_t input_height, |
| size_t input_width, |
| const float* input, |
| float* output, |
| uint32_t* index, |
| pthreadpool_t threadpool) |
| { |
| if (argmax_pooling_op->type != xnn_operator_type_argmax_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_argmax_pooling_nhwc_f32), |
| xnn_operator_type_to_string(argmax_pooling_op->type)); |
| return xnn_status_invalid_parameter; |
| } |
| argmax_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(xnn_operator_type_argmax_pooling_nhwc_f32)); |
| 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(xnn_operator_type_argmax_pooling_nhwc_f32), input_width, input_height); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (batch_size == 0) { |
| argmax_pooling_op->state = xnn_run_state_skip; |
| return xnn_status_success; |
| } |
| |
| argmax_pooling_op->batch_size = batch_size; |
| argmax_pooling_op->input_height = input_height; |
| argmax_pooling_op->input_width = input_width; |
| argmax_pooling_op->input = input; |
| |
| const size_t pooling_height = argmax_pooling_op->kernel_height; |
| const size_t pooling_width = argmax_pooling_op->kernel_width; |
| |
| if (argmax_pooling_op->flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) { |
| argmax_pooling_op->output_height = divide_round_up(input_height, pooling_height); |
| argmax_pooling_op->output_width = divide_round_up(input_width, pooling_width); |
| |
| const uint32_t padding_height = argmax_pooling_op->output_height * pooling_height - input_height; |
| const uint32_t padding_width = argmax_pooling_op->output_width * pooling_width - input_width; |
| argmax_pooling_op->padding_top = padding_height / 2; |
| argmax_pooling_op->padding_left = padding_width / 2; |
| argmax_pooling_op->padding_bottom = padding_height - argmax_pooling_op->padding_top; |
| argmax_pooling_op->padding_right = padding_width - argmax_pooling_op->padding_left; |
| } else { |
| argmax_pooling_op->output_height = compute_output_dimension( |
| argmax_pooling_op->padding_top + input_height + argmax_pooling_op->padding_bottom, |
| argmax_pooling_op->kernel_height); |
| argmax_pooling_op->output_width = compute_output_dimension( |
| argmax_pooling_op->padding_left + input_width + argmax_pooling_op->padding_right, |
| argmax_pooling_op->kernel_width); |
| } |
| |
| const size_t pooling_size = pooling_height * pooling_width; |
| const size_t output_height = argmax_pooling_op->output_height; |
| const size_t output_width = argmax_pooling_op->output_width; |
| const struct argmaxpool_parameters* ukernel = select_ukernel(pooling_size, xnn_params.f32.argmaxpool); |
| const uint32_t mr = ukernel->mr; |
| |
| const size_t step_width = pooling_width; |
| const size_t step_height = pooling_size + (output_width - 1) * step_width * pooling_height; |
| |
| if (input_height != argmax_pooling_op->last_input_height || |
| input_width != argmax_pooling_op->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) + output_height * step_height); |
| |
| const void** indirection_buffer = |
| (const void**) xnn_reallocate_memory(argmax_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(xnn_operator_type_argmax_pooling_nhwc_f32)); |
| return xnn_status_out_of_memory; |
| } |
| argmax_pooling_op->indirection_buffer = indirection_buffer; |
| |
| xnn_indirection_init_maxpool2d(argmax_pooling_op, step_height, step_width, 2 /* log2(sizeof(float)) */); |
| |
| argmax_pooling_op->last_input = input; |
| argmax_pooling_op->last_input_height = input_height; |
| argmax_pooling_op->last_input_width = input_width; |
| } |
| |
| const size_t channels = argmax_pooling_op->channels; |
| |
| const size_t indirect_input_height_stride = step_height * sizeof(void*); |
| const size_t output_width_stride = argmax_pooling_op->output_pixel_stride * sizeof(float); |
| const size_t output_height_stride = output_width * output_width_stride; |
| const size_t index_height_stride = output_width * channels * sizeof(uint32_t); |
| |
| const uint32_t qr = ukernel->qr; |
| const size_t multipass_adjustment = qr == 0 ? 0 : round_up(pooling_size - mr, qr) + mr - qr; |
| argmax_pooling_op->context.argmax_pooling = (struct argmax_pooling_context) { |
| .indirect_input = argmax_pooling_op->indirection_buffer, |
| .indirect_input_height_stride = indirect_input_height_stride, |
| .input_offset = (size_t) ((uintptr_t) input - (uintptr_t) argmax_pooling_op->last_input), |
| .input_batch_stride = input_height * input_width * argmax_pooling_op->input_pixel_stride * sizeof(float), |
| .output = output, |
| .output_batch_stride = output_height * output_height_stride, |
| .output_height_stride = output_height_stride, |
| .output_width = output_width, |
| .index = index, |
| .index_batch_stride = output_height * index_height_stride, |
| .index_height_stride = index_height_stride, |
| .pooling_size = pooling_size, |
| .channels = channels, |
| .input_increment = (pooling_height * step_width - multipass_adjustment) * sizeof(void*), |
| .output_increment = output_width_stride - channels * sizeof(float), |
| .params.f32 = argmax_pooling_op->params.f32_minmax, |
| }; |
| argmax_pooling_op->compute.type = xnn_parallelization_type_2d; |
| argmax_pooling_op->compute.range[0] = batch_size; |
| argmax_pooling_op->compute.range[1] = output_height; |
| |
| if (pooling_size <= mr) { |
| argmax_pooling_op->context.argmax_pooling.unipass_ukernel = ukernel->up; |
| argmax_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_argmax_pooling_unipass; |
| } else { |
| argmax_pooling_op->context.argmax_pooling.multipass_ukernel = ukernel->mp; |
| argmax_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_argmax_pooling_multipass; |
| } |
| argmax_pooling_op->state = xnn_run_state_ready; |
| |
| return xnn_status_success; |
| } |
| |