// 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); } static enum xnn_status create_max_pooling2d_nhwc( 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, uint32_t dilation_height, uint32_t dilation_width, size_t channels, size_t input_pixel_stride, size_t output_pixel_stride, uint32_t flags, const void* params, size_t params_size, uint32_t datatype_init_flags, enum xnn_operator_type operator_type, xnn_operator_t* max_pooling_op_out) { xnn_operator_t max_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 setup %s operator: XNNPACK is not initialized", xnn_operator_type_to_string(operator_type)); return xnn_status_uninitialized; } status = xnn_status_unsupported_hardware; if ((xnn_params.init_flags & datatype_init_flags) != datatype_init_flags) { xnn_log_error( "failed to create %s operator: operations on data type are not supported", xnn_operator_type_to_string(operator_type)); 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(operator_type), 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(operator_type)); 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(operator_type), stride_width, stride_height); goto error; } if (dilation_height == 0 || dilation_width == 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 (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(operator_type), 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(operator_type), 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(operator_type), 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(operator_type), 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(operator_type), output_pixel_stride, channels); 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(operator_type), input_padding_top, input_padding_left, input_padding_bottom, input_padding_right); goto error; } } status = xnn_status_out_of_memory; max_pooling_op = xnn_allocate_zero_simd_memory(sizeof(struct xnn_operator)); if (max_pooling_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; } max_pooling_op->padding_top = input_padding_top; max_pooling_op->padding_right = input_padding_right; max_pooling_op->padding_bottom = input_padding_bottom; max_pooling_op->padding_left = input_padding_left; max_pooling_op->kernel_height = pooling_height; max_pooling_op->kernel_width = pooling_width; max_pooling_op->stride_height = stride_height; max_pooling_op->stride_width = stride_width; max_pooling_op->dilation_height = dilation_height; max_pooling_op->dilation_width = dilation_width; max_pooling_op->channels = channels; max_pooling_op->input_pixel_stride = input_pixel_stride; max_pooling_op->output_pixel_stride = output_pixel_stride; memcpy(&max_pooling_op->params, params, params_size); max_pooling_op->type = operator_type; max_pooling_op->flags = flags; max_pooling_op->state = xnn_run_state_invalid; *max_pooling_op_out = max_pooling_op; return xnn_status_success; error: xnn_delete_operator(max_pooling_op); return status; } static enum xnn_status setup_max_pooling2d_nhwc( xnn_operator_t max_pooling_op, enum xnn_operator_type expected_operator_type, 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 maxpool_parameters maxpool[restrict XNN_MIN_ELEMENTS(1)], const void* params, size_t params_size, size_t num_threads) { if (max_pooling_op->type != expected_operator_type) { xnn_log_error("failed to setup operator: operator type mismatch (expected %s, got %s)", xnn_operator_type_to_string(expected_operator_type), xnn_operator_type_to_string(max_pooling_op->type)); return xnn_status_invalid_parameter; } max_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(max_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(max_pooling_op->type), input_width, input_height); return xnn_status_invalid_parameter; } if (batch_size == 0) { max_pooling_op->state = xnn_run_state_skip; return xnn_status_success; } max_pooling_op->input_height = input_height; max_pooling_op->input_width = input_width; max_pooling_op->input = input; if (max_pooling_op->flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) { max_pooling_op->output_height = compute_output_dimension_with_tf_same_padding( input_height, max_pooling_op->stride_height); max_pooling_op->output_width = compute_output_dimension_with_tf_same_padding( input_width, max_pooling_op->stride_width); const uint32_t effective_kernel_height = (max_pooling_op->kernel_height - 1) * max_pooling_op->dilation_height + 1; const uint32_t effective_kernel_width = (max_pooling_op->kernel_width - 1) * max_pooling_op->dilation_width + 1; const uint32_t total_padding_height = doz((max_pooling_op->output_height - 1) * max_pooling_op->stride_height + effective_kernel_height, input_height); const uint32_t total_padding_width = doz((max_pooling_op->output_width - 1) * max_pooling_op->stride_width + effective_kernel_width, input_width); max_pooling_op->padding_top = total_padding_height / 2; max_pooling_op->padding_left = total_padding_width / 2; max_pooling_op->padding_bottom = total_padding_height - max_pooling_op->padding_top; max_pooling_op->padding_right = total_padding_width - max_pooling_op->padding_left; } else { max_pooling_op->output_height = xnn_compute_convolution_output_dimension( max_pooling_op->padding_top + input_height + max_pooling_op->padding_bottom, max_pooling_op->kernel_height, max_pooling_op->dilation_height, max_pooling_op->stride_height); max_pooling_op->output_width = xnn_compute_convolution_output_dimension( max_pooling_op->padding_left + input_width + max_pooling_op->padding_right, max_pooling_op->kernel_width, max_pooling_op->dilation_width, max_pooling_op->stride_width); } const size_t pooling_height = max_pooling_op->kernel_height; const size_t pooling_width = max_pooling_op->kernel_width; const size_t pooling_size = pooling_height * pooling_width; const size_t output_height = max_pooling_op->output_height; const size_t output_width = max_pooling_op->output_width; const uint32_t mr = maxpool->mr; const size_t step_width = max_pooling_op->dilation_width > 1 ? pooling_width : min(max_pooling_op->stride_width, pooling_width); const size_t step_height = pooling_size + (output_width - 1) * step_width * pooling_height; if (input_height != max_pooling_op->last_input_height || input_width != max_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(max_pooling_op->indirection_buffer, indirection_buffer_size); if (indirection_buffer == NULL) { xnn_log_error("failed to allocate %zu bytes for indirection buffer", indirection_buffer_size); return xnn_status_out_of_memory; } max_pooling_op->indirection_buffer = indirection_buffer; xnn_indirection_init_maxpool2d(max_pooling_op, step_height, step_width, log2_input_element_size); max_pooling_op->last_input = input; max_pooling_op->last_input_height = input_height; max_pooling_op->last_input_width = input_width; } const uint32_t qr = maxpool->qr; const size_t channels = max_pooling_op->channels; const size_t indirect_input_height_stride = step_height * sizeof(void*); const size_t output_width_stride = max_pooling_op->output_pixel_stride << log2_output_element_size; const size_t output_height_stride = output_width * output_width_stride; const size_t multipass_adjustment = round_up(doz(pooling_size, mr), qr) + mr; max_pooling_op->context.max_pooling = (struct max_pooling_context) { .indirect_input = max_pooling_op->indirection_buffer, .indirect_input_height_stride = indirect_input_height_stride, .input_offset = (size_t) ((uintptr_t) input - (uintptr_t) max_pooling_op->last_input), .input_batch_stride = (input_height * input_width * max_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, .input_increment = (pooling_height * step_width - multipass_adjustment) * sizeof(void*), .output_increment = output_width_stride - (channels << log2_output_element_size), .ukernel = maxpool->ukernel, }; memcpy(&max_pooling_op->context.max_pooling.params, params, params_size); max_pooling_op->compute.type = xnn_parallelization_type_2d; max_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_max_pooling; max_pooling_op->compute.range[0] = batch_size; max_pooling_op->compute.range[1] = output_height; max_pooling_op->state = xnn_run_state_ready; return xnn_status_success; } enum xnn_status xnn_create_max_pooling2d_nhwc_s8( 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, uint32_t dilation_height, uint32_t dilation_width, size_t channels, size_t input_pixel_stride, size_t output_pixel_stride, int8_t output_min, int8_t output_max, uint32_t flags, xnn_operator_t* max_pooling_op_out) { if (output_min >= output_max) { xnn_log_error( "failed to create %s operator with [%" PRId8 ", %" PRId8 "] output range: range min must be below range max", xnn_operator_type_to_string(xnn_operator_type_max_pooling_nhwc_s8), output_min, output_max); return xnn_status_invalid_parameter; } union xnn_s8_minmax_params params; xnn_params.s8.maxpool.init.s8(¶ms, output_min, output_max); return create_max_pooling2d_nhwc( input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, pooling_height, pooling_width, stride_height, stride_width, dilation_height, dilation_width, channels, input_pixel_stride, output_pixel_stride, flags, ¶ms, sizeof(params), XNN_INIT_FLAG_S8, xnn_operator_type_max_pooling_nhwc_s8, max_pooling_op_out); } enum xnn_status xnn_create_max_pooling2d_nhwc_u8( 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, uint32_t dilation_height, uint32_t dilation_width, size_t channels, size_t input_pixel_stride, size_t output_pixel_stride, uint8_t output_min, uint8_t output_max, uint32_t flags, xnn_operator_t* max_pooling_op_out) { 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_max_pooling_nhwc_u8), output_min, output_max); return xnn_status_invalid_parameter; } union xnn_u8_minmax_params params; xnn_params.u8.maxpool.init.u8(¶ms, output_min, output_max); return create_max_pooling2d_nhwc( input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, pooling_height, pooling_width, stride_height, stride_width, dilation_height, dilation_width, channels, input_pixel_stride, output_pixel_stride, flags, ¶ms, sizeof(params), XNN_INIT_FLAG_U8, xnn_operator_type_max_pooling_nhwc_u8, max_pooling_op_out); } enum xnn_status xnn_create_max_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, uint32_t dilation_height, uint32_t dilation_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* max_pooling_op_out) { if (isnan(output_min)) { xnn_log_error( "failed to create %s with NaN output lower bound: lower bound must be non-NaN", xnn_operator_type_to_string(xnn_operator_type_max_pooling_nhwc_f32)); return xnn_status_invalid_parameter; } if (isnan(output_max)) { xnn_log_error( "failed to create %s with NaN output upper bound: upper bound must be non-NaN", xnn_operator_type_to_string(xnn_operator_type_max_pooling_nhwc_f32)); return xnn_status_invalid_parameter; } if (output_min >= output_max) { xnn_log_error( "failed to create %s with [%.7g, %.7g] output range: lower bound must be below upper bound", xnn_operator_type_to_string(xnn_operator_type_max_pooling_nhwc_f32), output_min, output_max); return xnn_status_invalid_parameter; } union xnn_f32_minmax_params params; xnn_params.f32.maxpool.init.f32(¶ms, output_min, output_max); return create_max_pooling2d_nhwc( input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, pooling_height, pooling_width, stride_height, stride_width, dilation_height, dilation_width, channels, input_pixel_stride, output_pixel_stride, flags, ¶ms, sizeof(params), XNN_INIT_FLAG_F32, xnn_operator_type_max_pooling_nhwc_f32, max_pooling_op_out); } enum xnn_status xnn_create_max_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, uint32_t dilation_height, uint32_t dilation_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* max_pooling_op_out) { if (isnan(output_min)) { xnn_log_error( "failed to create %s with NaN output lower bound: lower bound must be non-NaN", xnn_operator_type_to_string(xnn_operator_type_max_pooling_nhwc_f16)); return xnn_status_invalid_parameter; } if (isnan(output_max)) { xnn_log_error( "failed to create %s with NaN output upper bound: upper bound must be non-NaN", xnn_operator_type_to_string(xnn_operator_type_max_pooling_nhwc_f16)); return xnn_status_invalid_parameter; } const uint16_t output_min_as_half = fp16_ieee_from_fp32_value(output_min); const uint16_t output_max_as_half = fp16_ieee_from_fp32_value(output_max); output_min = fp16_ieee_to_fp32_value(output_min_as_half); output_max = fp16_ieee_to_fp32_value(output_max_as_half); 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_max_pooling_nhwc_f16), output_min, output_max); return xnn_status_invalid_parameter; } union xnn_f16_minmax_params params; if (xnn_params.f16.maxpool.init.f16 != NULL) { xnn_params.f16.maxpool.init.f16(¶ms, output_min_as_half, output_max_as_half); } return create_max_pooling2d_nhwc( input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, pooling_height, pooling_width, stride_height, stride_width, dilation_height, dilation_width, channels, input_pixel_stride, output_pixel_stride, flags, ¶ms, sizeof(params), XNN_INIT_FLAG_F16, xnn_operator_type_max_pooling_nhwc_f16, max_pooling_op_out); } enum xnn_status xnn_setup_max_pooling2d_nhwc_s8( xnn_operator_t max_pooling_op, size_t batch_size, size_t input_height, size_t input_width, const int8_t* input, int8_t* output, pthreadpool_t threadpool) { return setup_max_pooling2d_nhwc( max_pooling_op, xnn_operator_type_max_pooling_nhwc_s8, batch_size, input_height, input_width, input, output, 0 /* log2(sizeof(input element)) = log2(sizeof(int8_t)) */, 0 /* log2(sizeof(output element)) = log2(sizeof(int8_t)) */, &xnn_params.s8.maxpool, &max_pooling_op->params.s8_minmax, sizeof(max_pooling_op->params.s8_minmax), pthreadpool_get_threads_count(threadpool)); } enum xnn_status xnn_setup_max_pooling2d_nhwc_u8( xnn_operator_t max_pooling_op, size_t batch_size, size_t input_height, size_t input_width, const uint8_t* input, uint8_t* output, pthreadpool_t threadpool) { return setup_max_pooling2d_nhwc( max_pooling_op, xnn_operator_type_max_pooling_nhwc_u8, 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.u8.maxpool, &max_pooling_op->params.u8_minmax, sizeof(max_pooling_op->params.u8_minmax), pthreadpool_get_threads_count(threadpool)); } enum xnn_status xnn_setup_max_pooling2d_nhwc_f16( xnn_operator_t max_pooling_op, size_t batch_size, size_t input_height, size_t input_width, const void* input, void* output, pthreadpool_t threadpool) { return setup_max_pooling2d_nhwc( max_pooling_op, xnn_operator_type_max_pooling_nhwc_f16, batch_size, input_height, input_width, input, output, 1 /* log2(sizeof(input element)) = log2(sizeof(uint16_t)) */, 1 /* log2(sizeof(output element)) = log2(sizeof(uint16_t)) */, &xnn_params.f16.maxpool, &max_pooling_op->params.f16_minmax, sizeof(max_pooling_op->params.f16_minmax), pthreadpool_get_threads_count(threadpool)); } enum xnn_status xnn_setup_max_pooling2d_nhwc_f32( xnn_operator_t max_pooling_op, size_t batch_size, size_t input_height, size_t input_width, const float* input, float* output, pthreadpool_t threadpool) { return setup_max_pooling2d_nhwc( max_pooling_op, xnn_operator_type_max_pooling_nhwc_f32, 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.maxpool, &max_pooling_op->params.f32_minmax, sizeof(max_pooling_op->params.f32_minmax), pthreadpool_get_threads_count(threadpool)); }