// 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 #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include 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_qu8( 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.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) { xnn_log_error("failed to create %s operator: XNNPACK is not initialized", xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8)); 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_qu8), 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_qu8)); 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_qu8), stride_width, stride_height); goto error; } if (stride_height > pooling_height) { xnn_log_error( "failed to define %s operator with %" PRIu32 " stride height: must be less than pooling height %" PRIu32, xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8), stride_height, pooling_height); return xnn_status_invalid_parameter; } if (stride_width > pooling_width) { xnn_log_error( "failed to define %s operator with %" PRIu32 " stride width: must be less than pooling width %" PRIu32, xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8), stride_width, pooling_width); return xnn_status_invalid_parameter; } 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_qu8), 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_qu8), 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_qu8), 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_qu8), 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_qu8), 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_qu8), 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_qu8), 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_qu8), 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_qu8), 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_qu8)); 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_qu8)); 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 = (int32_t) (uint32_t) input_zero_point; average_pooling_op->input_scale = input_scale; average_pooling_op->output_scale = output_scale; // Number of rows read in the AVGPOOL micro-kernel. const size_t avgpool_nrows = round_up(doz(pooling_size, xnn_params.qu8.avgpool.primary_tile), xnn_params.qu8.avgpool.incremental_tile) + xnn_params.qu8.avgpool.primary_tile; const float requantization_scale = input_scale / (output_scale * (float) pooling_size); xnn_params.qu8.avgpool.init.qu8(&average_pooling_op->params.qu8_avgpool, (int32_t) -((uint32_t) input_zero_point * (uint32_t) avgpool_nrows), requantization_scale, output_zero_point, output_min, output_max); xnn_params.qu8.gavgpool.init.qu8(&average_pooling_op->params.qu8_gavgpool, 0 /* bias */, requantization_scale, output_zero_point, output_min, output_max); average_pooling_op->type = xnn_operator_type_average_pooling_nhwc_qu8; 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_f16( 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.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) { xnn_log_error("failed to create %s operator: XNNPACK is not initialized", xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f16)); goto error; } status = xnn_status_unsupported_hardware; if ((xnn_params.init_flags & XNN_INIT_FLAG_F16) != XNN_INIT_FLAG_F16) { xnn_log_error( "failed to create %s operator: operations on data type are not supported", xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f16)); 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_f16), 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_f16)); 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_f16), stride_width, stride_height); goto error; } if (stride_height > pooling_height) { xnn_log_error( "failed to define %s operator with %" PRIu32 " stride height: must be less than pooling height %" PRIu32, xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f16), stride_height, pooling_height); return xnn_status_invalid_parameter; } if (stride_width > pooling_width) { xnn_log_error( "failed to define %s operator with %" PRIu32 " stride width: must be less than pooling width %" PRIu32, xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f16), stride_width, pooling_width); return xnn_status_invalid_parameter; } 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_f16), 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_f16), 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_f16), 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_f16)); 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_f16)); goto error; } const uint16_t fp16_output_min = fp16_ieee_from_fp32_value(output_min); const uint16_t fp16_output_max = fp16_ieee_from_fp32_value(output_max); const float rounded_output_min = fp16_ieee_to_fp32_value(fp16_output_min); const float rounded_output_max = fp16_ieee_to_fp32_value(fp16_output_max); if (rounded_output_min >= rounded_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_f16), rounded_output_min, rounded_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_f16), 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_f16)); goto error; } const size_t zero_bytes = channels * sizeof(uint16_t) + 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_f16)); 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_f16; xnn_params.f16.avgpool.init.f16(&average_pooling_op->params.f16_scaleminmax, fp16_ieee_from_fp32_value(1.0f / (float) (int32_t) pooling_size), fp16_output_min, fp16_output_max); const bool tf_same_padding = (flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) != 0; if (any_padding || tf_same_padding) { xnn_params.f16.pavgpool.init.f16(&average_pooling_op->params.f16_minmax, fp16_output_min, fp16_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; } 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.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) { 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 (stride_height > pooling_height) { xnn_log_error( "failed to define %s operator with %" PRIu32 " stride height: must be less than pooling height %" PRIu32, xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32), stride_height, pooling_height); return xnn_status_invalid_parameter; } if (stride_width > pooling_width) { xnn_log_error( "failed to define %s operator with %" PRIu32 " stride width: must be less than pooling width %" PRIu32, xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32), stride_width, pooling_width); return xnn_status_invalid_parameter; } 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; xnn_params.f32.avgpool.init.f32(&average_pooling_op->params.f32_scaleminmax, 1.0f / (float) (int32_t) pooling_size, output_min, output_max); const bool tf_same_padding = (flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) != 0; if (any_padding || tf_same_padding) { xnn_params.f32.pavgpool.init.f32(&average_pooling_op->params.f32_minmax, 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_data_element_size, uint32_t log2_weight_element_size, xnn_indirection_init_pavgpool2d_fn indirection_init_pavgpool2d, 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 && indirection_init_pavgpool2d != NULL); average_pooling_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(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 = xnn_compute_convolution_output_dimension( average_pooling_op->padding_top + input_height + average_pooling_op->padding_bottom, average_pooling_op->kernel_height, 1, average_pooling_op->stride_height); average_pooling_op->output_width = xnn_compute_convolution_output_dimension( average_pooling_op->padding_left + input_width + average_pooling_op->padding_right, average_pooling_op->kernel_width, 1, 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_data_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_data_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->row_tile) { 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->unipass; } 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->multipass; } } 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 primary_tile = is_pixelwise ? pavgpool->primary_tile : avgpool->primary_tile; 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 (primary_tile - 1) elements after the end of indirection buffer. const size_t indirection_buffer_size = sizeof(void*) * ((primary_tile - 1) + 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; xnn_indirection_init_dwconv2d(average_pooling_op, step_height, step_width, primary_tile, log2_data_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_data_element_size; const size_t output_height_stride = output_width * output_width_stride; if (is_pixelwise) { assert(indirection_init_pavgpool2d != NULL); if (input_height != last_input_height || input_width != last_input_width) { const size_t pixelwise_buffer_size = (output_height * output_width) << log2_weight_element_size; void* pixelwise_buffer = 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; indirection_init_pavgpool2d( input_height, input_width, output_height, output_width, average_pooling_op->kernel_height, average_pooling_op->kernel_width, average_pooling_op->stride_height, average_pooling_op->stride_width, average_pooling_op->padding_top, average_pooling_op->padding_left, pixelwise_buffer); } const uint32_t incremental_tile = pavgpool->incremental_tile; const size_t multipass_adjustment = pooling_size > primary_tile ? round_up(pooling_size - primary_tile, incremental_tile) + primary_tile - incremental_tile : 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_data_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 << log2_data_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_data_element_size), }; memcpy(&average_pooling_op->context.pixelwise_average_pooling.params, params, params_size); if (pooling_size <= primary_tile) { average_pooling_op->context.pixelwise_average_pooling.unipass_ukernel = pavgpool->unipass; 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->multipass; average_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_pixelwise_average_pooling_multipass; } } else { const uint32_t incremental_tile = avgpool->incremental_tile; const size_t multipass_adjustment = pooling_size > primary_tile ? round_up(pooling_size - primary_tile, incremental_tile) + primary_tile - incremental_tile : 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_data_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_data_element_size), .params.f32 = average_pooling_op->params.f32_scaleminmax, }; memcpy(&average_pooling_op->context.average_pooling.params, params, params_size); if (pooling_size <= primary_tile) { average_pooling_op->context.average_pooling.unipass_ukernel = avgpool->unipass; 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->multipass; 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_qu8( 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_qu8) { 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_qu8), 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.qu8.gavgpool.row_tile); xnn_params.qu8.gavgpool.update.qu8( &average_pooling_op->params.qu8_gavgpool, -(average_pooling_op->input_zero_point * (int32_t) gavgpool_nrows), average_pooling_op->input_scale / (average_pooling_op->output_scale * (float) pooling_size)); return setup_average_pooling2d( average_pooling_op, batch_size, input_height, input_width, input, output, 0 /* log2(sizeof(data element)) = log2(sizeof(uint8_t)) */, 0 /* log2(sizeof(weight element)) = log2(sizeof(uint8_t)) */, NULL /* indirection_init_pavgpool2d */, &xnn_params.qu8.avgpool, NULL /* no PAVGPOOL micro-kernel */, &xnn_params.qu8.gavgpool, &average_pooling_op->params.qu8_avgpool, sizeof(average_pooling_op->params.qu8_avgpool), &average_pooling_op->params.qu8_gavgpool, sizeof(average_pooling_op->params.qu8_gavgpool), pthreadpool_get_threads_count(threadpool), false /* pixelwise not supported */); } enum xnn_status xnn_setup_average_pooling2d_nhwc_f16( xnn_operator_t average_pooling_op, size_t batch_size, size_t input_height, size_t input_width, const void* input, void* output, pthreadpool_t threadpool) { if (average_pooling_op->type != xnn_operator_type_average_pooling_nhwc_f16) { 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_f16), 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 void* pooling_params = &average_pooling_op->params.f16_scaleminmax; size_t pooling_params_size = sizeof(average_pooling_op->params.f16_scaleminmax); 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_params.f16.gavgpool.update.f16(&average_pooling_op->params.f16_scaleminmax, fp16_ieee_from_fp32_value(1.0f / (float) (int32_t) input_size)); pooling_params = &average_pooling_op->params.f16_minmax; pooling_params_size = sizeof(average_pooling_op->params.f16_minmax); } return setup_average_pooling2d( average_pooling_op, batch_size, input_height, input_width, input, output, 1 /* log2(sizeof(data element)) = log2(sizeof(half)) */, 1 /* log2(sizeof(weight element)) = log2(sizeof(half)) */, (xnn_indirection_init_pavgpool2d_fn) xnn_indirection_init_pavgpool2d_f16, &xnn_params.f16.avgpool, &xnn_params.f16.pavgpool, &xnn_params.f16.gavgpool, pooling_params, pooling_params_size, &average_pooling_op->params.f16_scaleminmax, sizeof(average_pooling_op->params.f16_scaleminmax), pthreadpool_get_threads_count(threadpool), is_pixelwise); } 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 void* pooling_params = &average_pooling_op->params.f32_scaleminmax; size_t pooling_params_size = sizeof(average_pooling_op->params.f32_scaleminmax); 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_params.f32.gavgpool.update.f32(&average_pooling_op->params.f32_scaleminmax, 1.0f / (float) (int32_t) input_size); pooling_params = &average_pooling_op->params.f32_minmax; pooling_params_size = sizeof(average_pooling_op->params.f32_minmax); } return setup_average_pooling2d( average_pooling_op, batch_size, input_height, input_width, input, output, 2 /* log2(sizeof(data element)) = log2(sizeof(float)) */, 2 /* log2(sizeof(weight element)) = log2(sizeof(float)) */, (xnn_indirection_init_pavgpool2d_fn) xnn_indirection_init_pavgpool2d_f32, &xnn_params.f32.avgpool, &xnn_params.f32.pavgpool, &xnn_params.f32.gavgpool, pooling_params, pooling_params_size, &average_pooling_op->params.f32_scaleminmax, sizeof(average_pooling_op->params.f32_scaleminmax), pthreadpool_get_threads_count(threadpool), is_pixelwise); }