// Copyright 2020 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 <inttypes.h> #include <stddef.h> #include <stdint.h> #include "xnnpack.h" #include "xnnpack/common.h" #include "xnnpack/log.h" #include "xnnpack/node-type.h" #include "xnnpack/operator-type.h" #include "xnnpack/operator.h" #include "xnnpack/requantization.h" #include "xnnpack/subgraph-validation.h" #include "xnnpack/subgraph.h" #include "pthreadpool.h" static enum xnn_status create_convolution_operator( const struct xnn_node* node, const struct xnn_value* values, size_t num_values, struct xnn_operator_data* opdata, struct xnn_code_cache* code_cache, xnn_weights_cache_t weights_cache) { … } static enum xnn_status reshape_convolution_operator( struct xnn_operator_data* opdata, struct xnn_value* values, size_t num_values, pthreadpool_t threadpool) { … } static enum xnn_status setup_convolution_operator( const struct xnn_operator_data* opdata, const struct xnn_value* values, size_t num_values, pthreadpool_t threadpool) { … } static inline enum xnn_compute_type validate_datatypes_with_bias( enum xnn_datatype input_datatype, enum xnn_datatype filter_datatype, enum xnn_datatype bias_datatype, enum xnn_datatype output_datatype) { … } static inline enum xnn_compute_type validate_datatypes_without_bias( enum xnn_datatype input_datatype, enum xnn_datatype filter_datatype, enum xnn_datatype output_datatype) { … } enum xnn_status xnn_define_depthwise_convolution_2d( xnn_subgraph_t subgraph, uint32_t input_padding_top, uint32_t input_padding_right, uint32_t input_padding_bottom, uint32_t input_padding_left, uint32_t kernel_height, uint32_t kernel_width, uint32_t subsampling_height, uint32_t subsampling_width, uint32_t dilation_height, uint32_t dilation_width, uint32_t depth_multiplier, size_t input_channels, float output_min, float output_max, uint32_t input_id, uint32_t filter_id, uint32_t bias_id, uint32_t output_id, uint32_t flags) { enum xnn_status status; if ((status = xnn_subgraph_check_xnnpack_initialized(xnn_node_type_depthwise_convolution_2d)) != xnn_status_success) { return status; } if (kernel_width == 0 || kernel_height == 0) { xnn_log_error( "failed to define %s operator with %" PRIu32 "x%" PRIu32 " kernel: kernel dimensions must be non-zero", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), kernel_width, kernel_height); return xnn_status_invalid_parameter; } if (subsampling_width == 0 || subsampling_height == 0) { xnn_log_error( "failed to define %s operator with %" PRIu32 "x%" PRIu32 " subsampling: subsampling dimensions must be non-zero", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), subsampling_width, subsampling_height); return xnn_status_invalid_parameter; } if (dilation_width == 0 || dilation_height == 0) { xnn_log_error( "failed to define %s operator with %" PRIu32 "x%" PRIu32 " dilation: dilation dimensions must be non-zero", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), dilation_width, dilation_height); return xnn_status_invalid_parameter; } if (depth_multiplier == 0) { xnn_log_error( "failed to define %s operator with %" PRIu32 " depth multiplier: depth multiplier must be non-zero", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), depth_multiplier); return xnn_status_invalid_parameter; } if (input_channels == 0) { xnn_log_error( "failed to define %s operator with %zu input channels: number of channels must be non-zero", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), input_channels); return xnn_status_invalid_parameter; } status = xnn_subgraph_check_output_min_max(xnn_node_type_depthwise_convolution_2d, output_min, output_max); if (status != xnn_status_success) { return status; } const uint32_t supported_flags = XNN_FLAG_TENSORFLOW_SAME_PADDING | XNN_FLAG_TRANSIENT_INDIRECTION_BUFFER; const uint32_t invalid_flags = flags & ~supported_flags; if (invalid_flags != 0) { xnn_log_error( "failed to define %s operator with 0x%08" PRIx32 " flags: invalid flags 0x%08" PRIx32, xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), flags, invalid_flags); return xnn_status_invalid_parameter; } 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 && any_padding) { xnn_log_error( "failed to define %s operator with %" PRIu32 "+%" PRIu32 "x%" PRIu32 "+%" PRIu32" padding: " "TensorFlow SAME padding can't be combined with explicit padding specification", xnn_node_type_to_string(xnn_node_type_convolution_2d), input_padding_top, input_padding_left, input_padding_bottom, input_padding_right); return xnn_status_invalid_parameter; } // Convert TensorFlow SAME padding to explicit padding specification whenever possible if ((flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) != 0 && (subsampling_height | subsampling_width) == 1) { flags &= ~XNN_FLAG_TENSORFLOW_SAME_PADDING; const uint32_t padding_height = (kernel_height - 1) * dilation_height; const uint32_t padding_width = (kernel_width - 1) * dilation_width; input_padding_left = padding_width / 2; input_padding_top = padding_height / 2; input_padding_right = padding_width - input_padding_left; input_padding_bottom = padding_height - input_padding_top; } if ((status = xnn_subgraph_check_input_node_id(xnn_node_type_depthwise_convolution_2d, input_id, subgraph->num_values)) != xnn_status_success) { return status; } const struct xnn_value* input_value = &subgraph->values[input_id]; status = xnn_subgraph_check_input_type_dense(xnn_node_type_depthwise_convolution_2d, input_id, input_value); if (status != xnn_status_success) { return status; } switch (input_value->datatype) { case xnn_datatype_fp16: case xnn_datatype_fp32: case xnn_datatype_qint8: case xnn_datatype_quint8: break; default: xnn_log_error( "failed to define %s operator with input ID #%" PRIu32 ": unsupported Value datatype %s (%d)", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), input_id, xnn_datatype_to_string(input_value->datatype), input_value->datatype); return xnn_status_invalid_parameter; } if (filter_id >= subgraph->num_values) { xnn_log_error( "failed to define %s operator with filter ID #%" PRIu32 ": invalid Value ID", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), filter_id); return xnn_status_invalid_parameter; } const struct xnn_value* filter_value = &subgraph->values[filter_id]; if (filter_value->type != xnn_value_type_dense_tensor) { xnn_log_error( "failed to define %s operator with filter ID #%" PRIu32 ": unsupported Value type %d (expected dense tensor)", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), filter_id, filter_value->type); return xnn_status_invalid_parameter; } if (filter_value->data == NULL) { xnn_log_error( "failed to define %s operator with filter ID #%" PRIu32 ": non-static Value", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), filter_id); return xnn_status_invalid_parameter; } switch (filter_value->datatype) { case xnn_datatype_fp16: case xnn_datatype_fp32: break; case xnn_datatype_qint8: if (filter_value->quantization.zero_point != 0) { xnn_log_error( "failed to define %s operator with filter ID #%" PRIu32 ": unsupported quantization zero point %" PRId32 " for datatype %s", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), filter_id, filter_value->quantization.zero_point, xnn_datatype_to_string(filter_value->datatype)); return xnn_status_invalid_parameter; } break; case xnn_datatype_qcint8: break; case xnn_datatype_quint8: break; default: xnn_log_error( "failed to define %s operator with filter ID #%" PRIu32 ": unsupported Value datatype %s (%d)", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), filter_id, xnn_datatype_to_string(filter_value->datatype), filter_value->datatype); return xnn_status_invalid_parameter; } const struct xnn_value* bias_value = NULL; if (bias_id != XNN_INVALID_VALUE_ID) { if (bias_id >= subgraph->num_values) { xnn_log_error( "failed to define %s operator with bias ID #%" PRIu32 ": invalid Value ID", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), bias_id); return xnn_status_invalid_parameter; } bias_value = &subgraph->values[bias_id]; if (bias_value->type != xnn_value_type_dense_tensor) { xnn_log_error( "failed to define %s operator with bias ID #%" PRIu32 ": unsupported Value type %d (expected dense tensor)", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), bias_id, bias_value->type); return xnn_status_invalid_parameter; } if (bias_value->data == NULL) { xnn_log_error( "failed to define %s operator with bias ID #%" PRIu32 ": non-static Value", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), bias_id); return xnn_status_invalid_parameter; } switch (bias_value->datatype) { case xnn_datatype_fp16: case xnn_datatype_fp32: case xnn_datatype_qint32: case xnn_datatype_qcint32: break; default: xnn_log_error( "failed to define %s operator with bias ID #%" PRIu32 ": unsupported Value datatype %s (%d)", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), bias_id, xnn_datatype_to_string(bias_value->datatype), bias_value->datatype); return xnn_status_invalid_parameter; } } status = xnn_subgraph_check_output_node_id(xnn_node_type_depthwise_convolution_2d, output_id, subgraph->num_values); if (status != xnn_status_success) { return status; } const struct xnn_value* output_value = &subgraph->values[output_id]; status = xnn_subgraph_check_output_type_dense(xnn_node_type_depthwise_convolution_2d, output_id, output_value); if (status != xnn_status_success) { return status; } switch (output_value->datatype) { case xnn_datatype_fp16: case xnn_datatype_fp32: case xnn_datatype_qint8: case xnn_datatype_quint8: break; default: xnn_log_error( "failed to define %s operator with output ID #%" PRIu32 ": unsupported Value datatype %s (%d)", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), output_id, xnn_datatype_to_string(output_value->datatype), output_value->datatype); return xnn_status_invalid_parameter; } enum xnn_compute_type compute_type = xnn_compute_type_invalid; if (bias_value != NULL) { compute_type = validate_datatypes_with_bias( input_value->datatype, filter_value->datatype, bias_value->datatype, output_value->datatype); if (compute_type == xnn_compute_type_invalid) { xnn_log_error( "failed to define %s operator with input ID #%" PRIu32 ", filter ID #%" PRIu32 ", bias ID #%" PRIu32 ", and output ID #%" PRIu32 ": mismatching datatypes across input (%s), filter (%s), bias (%s), and output (%s)", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), input_id, filter_id, bias_id, output_id, xnn_datatype_to_string(input_value->datatype), xnn_datatype_to_string(filter_value->datatype), xnn_datatype_to_string(bias_value->datatype), xnn_datatype_to_string(output_value->datatype)); return xnn_status_invalid_parameter; } } else { compute_type = validate_datatypes_without_bias(input_value->datatype, filter_value->datatype, output_value->datatype); if (compute_type == xnn_compute_type_invalid) { xnn_log_error( "failed to define %s operator with input ID #%" PRIu32 ", filter ID #%" PRIu32 ", and output ID #%" PRIu32 ": mismatching datatypes across input (%s), filter (%s), and output (%s)", xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), input_id, filter_id, output_id, xnn_datatype_to_string(input_value->datatype), xnn_datatype_to_string(filter_value->datatype), xnn_datatype_to_string(output_value->datatype)); return xnn_status_invalid_parameter; } } if (filter_value->datatype == xnn_datatype_qcint8) { if (filter_value->quantization.channel_dimension != filter_value->shape.num_dims - 1) { xnn_log_error( "failed to define %s operator with filter ID #%" PRIu32 ": invalid channel dimension %zu", xnn_node_type_to_string(xnn_node_type_convolution_2d), input_id, filter_value->quantization.channel_dimension); return xnn_status_invalid_parameter; } if (bias_value != NULL) { assert(bias_value->datatype == xnn_datatype_qcint32); if (bias_value->quantization.channel_dimension != 0) { xnn_log_error( "failed to define %s operator with bias ID #%" PRIu32 ": invalid channel dimension %zu", xnn_node_type_to_string(xnn_node_type_convolution_2d), bias_id, bias_value->quantization.channel_dimension); return xnn_status_invalid_parameter; } } } struct xnn_node* node = xnn_subgraph_new_node(subgraph); if (node == NULL) { return xnn_status_out_of_memory; } node->type = xnn_node_type_depthwise_convolution_2d; node->compute_type = compute_type; node->params.depthwise_convolution_2d.input_padding_top = input_padding_top; node->params.depthwise_convolution_2d.input_padding_right = input_padding_right; node->params.depthwise_convolution_2d.input_padding_bottom = input_padding_bottom; node->params.depthwise_convolution_2d.input_padding_left = input_padding_left; node->params.depthwise_convolution_2d.kernel_height = kernel_height; node->params.depthwise_convolution_2d.kernel_width = kernel_width; node->params.depthwise_convolution_2d.subsampling_height = subsampling_height; node->params.depthwise_convolution_2d.subsampling_width = subsampling_width; node->params.depthwise_convolution_2d.dilation_height = dilation_height; node->params.depthwise_convolution_2d.dilation_width = dilation_width; node->params.depthwise_convolution_2d.depth_multiplier = depth_multiplier; node->params.depthwise_convolution_2d.input_channels = input_channels; node->activation.output_min = output_min; node->activation.output_max = output_max; node->num_inputs = 2 + (size_t) (bias_id != XNN_INVALID_VALUE_ID); node->inputs[0] = input_id; node->inputs[1] = filter_id; node->inputs[2] = bias_id; node->num_outputs = 1; node->outputs[0] = output_id; node->flags = flags; node->create = create_convolution_operator; node->reshape = reshape_convolution_operator; node->setup = setup_convolution_operator; return xnn_status_success; };
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