| // 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 <math.h> |
| #include <stddef.h> |
| #include <stdint.h> |
| #include <stdlib.h> |
| |
| #include <xnnpack.h> |
| #include <xnnpack/allocator.h> |
| #include <xnnpack/log.h> |
| #include <xnnpack/math.h> |
| #include <xnnpack/params.h> |
| #include <xnnpack/subgraph.h> |
| |
| |
| enum xnn_status xnn_create_subgraph( |
| uint32_t external_value_ids, |
| uint32_t flags, |
| xnn_subgraph_t* subgraph_out) |
| { |
| struct xnn_subgraph* subgraph = NULL; |
| enum xnn_status status = xnn_status_uninitialized; |
| |
| if (!xnn_params.initialized) { |
| xnn_log_error("failed to create subgraph: XNNPACK is not initialized"); |
| goto error; |
| } |
| |
| status = xnn_status_out_of_memory; |
| |
| subgraph = xnn_allocate_zero_memory(sizeof(struct xnn_subgraph)); |
| if (subgraph == NULL) { |
| xnn_log_error("failed to allocate %zu bytes for subgraph descriptor", sizeof(struct xnn_subgraph)); |
| goto error; |
| } |
| |
| subgraph->external_value_ids = external_value_ids; |
| |
| subgraph->values = xnn_allocate_zero_memory(external_value_ids * sizeof(struct xnn_value)); |
| if (subgraph->values == NULL) { |
| xnn_log_error("failed to allocate %zu bytes for subgraph values", external_value_ids * sizeof(struct xnn_value)); |
| goto error; |
| } |
| for (size_t i = 0; i < external_value_ids; i++) { |
| subgraph->values[i].id = i; |
| } |
| subgraph->num_values = external_value_ids; |
| subgraph->num_reserved_values = external_value_ids; |
| |
| *subgraph_out = subgraph; |
| return xnn_status_success; |
| |
| error: |
| xnn_delete_subgraph(subgraph); |
| return status; |
| } |
| |
| |
| struct xnn_value* xnn_subgraph_new_internal_value(xnn_subgraph_t subgraph) |
| { |
| struct xnn_value* values = subgraph->values; |
| const size_t size = subgraph->num_values; |
| const size_t capacity = subgraph->num_reserved_values; |
| if (capacity < size + 1) { |
| const size_t new_capacity = max(min(capacity * 2, capacity + 512), capacity + 64); |
| assert(new_capacity >= size + 1); |
| values = xnn_reallocate_memory(values, new_capacity * sizeof(struct xnn_value)); |
| if (values == NULL) { |
| xnn_log_error("failed to allocate %zu bytes for subgraph values", |
| capacity * sizeof(struct xnn_value)); |
| return values; |
| } |
| |
| memset(values + size, 0, (new_capacity - size) * sizeof(struct xnn_value)); |
| subgraph->num_reserved_values = new_capacity; |
| subgraph->values = values; |
| } |
| subgraph->num_values = size + 1; |
| struct xnn_value* new_value = values + size; |
| new_value->id = size; |
| return new_value; |
| } |
| |
| struct xnn_node* xnn_subgraph_new_node(xnn_subgraph_t subgraph) |
| { |
| struct xnn_node* nodes = subgraph->nodes; |
| const size_t size = subgraph->num_nodes; |
| const size_t capacity = subgraph->num_reserved_nodes; |
| |
| if (capacity < size + 1) { |
| const size_t new_capacity = max(min(capacity * 2, capacity + 512), capacity + 64); |
| assert(new_capacity >= size + 1); |
| nodes = xnn_reallocate_memory(nodes, new_capacity * sizeof(struct xnn_node)); |
| if (nodes == NULL) { |
| xnn_log_error("failed to allocate %zu bytes for subgraph nodes", |
| capacity * sizeof(struct xnn_node)); |
| return nodes; |
| } |
| |
| memset(nodes + size, 0, (new_capacity - size) * sizeof(struct xnn_node)); |
| subgraph->num_reserved_nodes = new_capacity; |
| subgraph->nodes = nodes; |
| } |
| subgraph->num_nodes = size + 1; |
| struct xnn_node* new_node = nodes + size; |
| new_node->id = size; |
| return new_node; |
| } |
| |
| enum xnn_status xnn_define_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 groups, |
| size_t group_input_channels, |
| size_t group_output_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) |
| { |
| if (!xnn_params.initialized) { |
| xnn_log_error("failed to define Convolution operator: XNNPACK is not initialized"); |
| return xnn_status_uninitialized; |
| } |
| |
| if (kernel_width == 0 || kernel_height == 0) { |
| xnn_log_error( |
| "failed to define Convolution operator with %" PRIu32 "x%" PRIu32 " kernel: kernel dimensions must be non-zero", |
| kernel_width, kernel_height); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (subsampling_width == 0 || subsampling_height == 0) { |
| xnn_log_error( |
| "failed to define Convolution operator with %" PRIu32 "x%" PRIu32 " subsampling: " |
| "subsampling dimensions must be non-zero", |
| subsampling_width, subsampling_height); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (dilation_width == 0 || dilation_height == 0) { |
| xnn_log_error( |
| "failed to define Convolution operator with %" PRIu32 "x%" PRIu32 " dilation: " |
| "dilation dimensions must be non-zero", |
| dilation_width, dilation_height); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (groups == 0) { |
| xnn_log_error( |
| "failed to define Convolution operator with %" PRIu32 " groups: number of groups must be non-zero", groups); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (group_input_channels == 0) { |
| xnn_log_error( |
| "failed to define Convolution operator with %zu input channels per group: " |
| "number of channels must be non-zero", |
| group_input_channels); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (group_output_channels == 0) { |
| xnn_log_error( |
| "failed to define Convolution operator with %zu output channels per group: " |
| "number of channels must be non-zero", |
| group_output_channels); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (isnan(output_min)) { |
| xnn_log_error( |
| "failed to define Convolution operator with NaN output lower bound: lower bound must be non-NaN"); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (isnan(output_max)) { |
| xnn_log_error( |
| "failed to define Convolution operator with NaN output upper bound: upper bound must be non-NaN"); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_min >= output_max) { |
| xnn_log_error( |
| "failed to define Convolution operator with [%.7g, %.7g] output range: " |
| "lower bound must be below upper bound", |
| output_min, output_max); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (input_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Convolution operator with input ID #%" PRIu32 ": invalid Value ID", |
| input_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (filter_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Convolution operator with filter ID #%" PRIu32 ": invalid Value ID", |
| filter_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (bias_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Convolution operator with bias ID #%" PRIu32 ": invalid Value ID", |
| bias_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Convolution operator with output ID #%" PRIu32 ": invalid Value ID", |
| output_id); |
| 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_convolution_2d; |
| node->params.convolution_2d.input_padding_top = input_padding_top; |
| node->params.convolution_2d.input_padding_right = input_padding_right; |
| node->params.convolution_2d.input_padding_bottom = input_padding_bottom; |
| node->params.convolution_2d.input_padding_left = input_padding_left; |
| node->params.convolution_2d.kernel_height = kernel_height; |
| node->params.convolution_2d.kernel_width = kernel_width; |
| node->params.convolution_2d.subsampling_height = subsampling_height; |
| node->params.convolution_2d.subsampling_width = subsampling_width; |
| node->params.convolution_2d.dilation_height = dilation_height; |
| node->params.convolution_2d.dilation_width = dilation_width; |
| node->params.convolution_2d.groups = groups; |
| node->params.convolution_2d.group_input_channels = group_input_channels; |
| node->params.convolution_2d.group_output_channels = group_output_channels; |
| node->activation.output_min = output_min; |
| node->activation.output_max = output_max; |
| node->num_inputs = 3; |
| node->inputs.raw[0] = input_id; |
| node->inputs.raw[1] = filter_id; |
| node->inputs.raw[2] = bias_id; |
| node->num_outputs = 1; |
| node->outputs.raw[0] = output_id; |
| node->flags = flags; |
| |
| return xnn_status_success; |
| }; |
| |
| 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) |
| { |
| if (!xnn_params.initialized) { |
| xnn_log_error("failed to define Depthwise Convolution operator: XNNPACK is not initialized"); |
| return xnn_status_uninitialized; |
| } |
| |
| if (kernel_width == 0 || kernel_height == 0) { |
| xnn_log_error( |
| "failed to define Depthwise Convolution operator with %" PRIu32 "x%" PRIu32 " kernel: kernel dimensions must be non-zero", |
| kernel_width, kernel_height); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (subsampling_width == 0 || subsampling_height == 0) { |
| xnn_log_error( |
| "failed to define Depthwise Convolution operator with %" PRIu32 "x%" PRIu32 " subsampling: " |
| "subsampling dimensions must be non-zero", |
| subsampling_width, subsampling_height); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (dilation_width == 0 || dilation_height == 0) { |
| xnn_log_error( |
| "failed to define Depthwise Convolution operator with %" PRIu32 "x%" PRIu32 " dilation: " |
| "dilation dimensions must be non-zero", |
| dilation_width, dilation_height); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (depth_multiplier == 0) { |
| xnn_log_error( |
| "failed to define Depthwise Convolution operator with %" PRIu32 " depth multiplier: " |
| "depth multiplier must be non-zero", |
| depth_multiplier); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (input_channels == 0) { |
| xnn_log_error( |
| "failed to define Depthwise Convolution operator with %zu input channels: " |
| "number of channels must be non-zero", |
| input_channels); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (isnan(output_min)) { |
| xnn_log_error( |
| "failed to define Depthwise Convolution operator with NaN output lower bound: lower bound must be non-NaN"); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (isnan(output_max)) { |
| xnn_log_error( |
| "failed to define Depthwise Convolution operator with NaN output upper bound: upper bound must be non-NaN"); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_min >= output_max) { |
| xnn_log_error( |
| "failed to define Depthwise Convolution operator with [%.7g, %.7g] output range: " |
| "lower bound must be below upper bound", |
| output_min, output_max); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (input_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Depthwise Convolution operator with input ID #%" PRIu32 ": invalid Value ID", |
| input_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (filter_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Depthwise Convolution operator with filter ID #%" PRIu32 ": invalid Value ID", |
| filter_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (bias_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Depthwise Convolution operator with bias ID #%" PRIu32 ": invalid Value ID", |
| bias_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Depthwise Convolution operator with output ID #%" PRIu32 ": invalid Value ID", |
| output_id); |
| 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->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 = 3; |
| node->inputs.raw[0] = input_id; |
| node->inputs.raw[1] = filter_id; |
| node->inputs.raw[2] = bias_id; |
| node->num_outputs = 1; |
| node->outputs.raw[0] = output_id; |
| node->flags = flags; |
| |
| return xnn_status_success; |
| }; |
| |
| enum xnn_status xnn_define_add2( |
| xnn_subgraph_t subgraph, |
| float output_min, |
| float output_max, |
| uint32_t input1_id, |
| uint32_t input2_id, |
| uint32_t output_id, |
| uint32_t flags) |
| { |
| if (!xnn_params.initialized) { |
| xnn_log_error("failed to define Add2 operator: XNNPACK is not initialized"); |
| return xnn_status_uninitialized; |
| } |
| |
| if (isnan(output_min)) { |
| xnn_log_error( |
| "failed to define Add2 operator with NaN output lower bound: lower bound must be non-NaN"); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (isnan(output_max)) { |
| xnn_log_error( |
| "failed to define Add2 operator with NaN output upper bound: upper bound must be non-NaN"); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_min >= output_max) { |
| xnn_log_error( |
| "failed to define Add2 operator with [%.7g, %.7g] output range: " |
| "lower bound must be below upper bound", |
| output_min, output_max); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (input1_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Add2 operator with the first input ID #%" PRIu32 ": invalid Value ID", |
| input1_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (input2_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Add2 operator with the second input ID #%" PRIu32 ": invalid Value ID", |
| input2_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Add2 operator with output ID #%" PRIu32 ": invalid Value ID", |
| output_id); |
| 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_add2; |
| node->activation.output_min = output_min; |
| node->activation.output_max = output_max; |
| node->num_inputs = 2; |
| node->inputs.raw[0] = input1_id; |
| node->inputs.raw[1] = input2_id; |
| node->num_outputs = 1; |
| node->outputs.raw[0] = output_id; |
| node->flags = flags; |
| |
| return xnn_status_success; |
| } |
| |
| enum xnn_status xnn_define_multiply2( |
| xnn_subgraph_t subgraph, |
| float output_min, |
| float output_max, |
| uint32_t input1_id, |
| uint32_t input2_id, |
| uint32_t output_id, |
| uint32_t flags) |
| { |
| if (!xnn_params.initialized) { |
| xnn_log_error("failed to define Multiply2 operator: XNNPACK is not initialized"); |
| return xnn_status_uninitialized; |
| } |
| |
| if (isnan(output_min)) { |
| xnn_log_error( |
| "failed to define Multiply2 operator with NaN output lower bound: lower bound must be non-NaN"); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (isnan(output_max)) { |
| xnn_log_error( |
| "failed to define Multiply2 operator with NaN output upper bound: upper bound must be non-NaN"); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_min >= output_max) { |
| xnn_log_error( |
| "failed to define Multiply2 operator with [%.7g, %.7g] output range: " |
| "lower bound must be below upper bound", |
| output_min, output_max); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (input1_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Multiply2 operator with the first input ID #%" PRIu32 ": invalid Value ID", |
| input1_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (input2_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Multiply2 operator with the second input ID #%" PRIu32 ": invalid Value ID", |
| input2_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Multiply2 operator with output ID #%" PRIu32 ": invalid Value ID", |
| output_id); |
| 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_multiply2; |
| node->activation.output_min = output_min; |
| node->activation.output_max = output_max; |
| node->num_inputs = 2; |
| node->inputs.raw[0] = input1_id; |
| node->inputs.raw[1] = input2_id; |
| node->num_outputs = 1; |
| node->outputs.raw[0] = output_id; |
| node->flags = flags; |
| |
| return xnn_status_success; |
| } |
| |
| enum xnn_status xnn_define_prelu( |
| xnn_subgraph_t subgraph, |
| uint32_t input_id, |
| uint32_t slope_id, |
| uint32_t output_id, |
| uint32_t flags) |
| { |
| if (!xnn_params.initialized) { |
| xnn_log_error("failed to define PReLU operator: XNNPACK is not initialized"); |
| return xnn_status_uninitialized; |
| } |
| |
| if (input_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define PReLU operator with input ID #%" PRIu32 ": invalid Value ID", |
| input_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (slope_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define PReLU operator with slope ID #%" PRIu32 ": invalid Value ID", |
| slope_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define PReLU operator with output ID #%" PRIu32 ": invalid Value ID", |
| output_id); |
| 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_prelu; |
| node->num_inputs = 2; |
| node->inputs.raw[0] = input_id; |
| node->inputs.raw[1] = slope_id; |
| node->num_outputs = 1; |
| node->outputs.raw[0] = output_id; |
| node->flags = flags; |
| |
| return xnn_status_success; |
| } |
| |
| enum xnn_status xnn_define_clamp( |
| xnn_subgraph_t subgraph, |
| float output_min, |
| float output_max, |
| uint32_t input_id, |
| uint32_t output_id, |
| uint32_t flags) |
| { |
| if (!xnn_params.initialized) { |
| xnn_log_error("failed to define Clamp operator: XNNPACK is not initialized"); |
| return xnn_status_uninitialized; |
| } |
| |
| if (input_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Clamp operator with input ID #%" PRIu32 ": invalid Value ID", |
| input_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Clamp operator with output ID #%" PRIu32 ": invalid Value ID", |
| output_id); |
| 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_clamp; |
| node->activation.output_min = output_min; |
| node->activation.output_max = output_max; |
| node->num_inputs = 1; |
| node->inputs.raw[0] = input_id; |
| node->num_outputs = 1; |
| node->outputs.raw[0] = output_id; |
| node->flags = flags; |
| |
| return xnn_status_success; |
| } |
| |
| enum xnn_status xnn_define_hardswish( |
| xnn_subgraph_t subgraph, |
| uint32_t input_id, |
| uint32_t output_id, |
| uint32_t flags) |
| { |
| if (!xnn_params.initialized) { |
| xnn_log_error("failed to define HardSwish operator: XNNPACK is not initialized"); |
| return xnn_status_uninitialized; |
| } |
| |
| if (input_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define HardSwish operator with input ID #%" PRIu32 ": invalid Value ID", |
| input_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define HardSwish operator with output ID #%" PRIu32 ": invalid Value ID", |
| output_id); |
| 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_hardswish; |
| node->num_inputs = 1; |
| node->inputs.raw[0] = input_id; |
| node->num_outputs = 1; |
| node->outputs.raw[0] = output_id; |
| node->flags = flags; |
| |
| return xnn_status_success; |
| } |
| |
| enum xnn_status xnn_define_sigmoid( |
| xnn_subgraph_t subgraph, |
| uint32_t input_id, |
| uint32_t output_id, |
| uint32_t flags) |
| { |
| if (!xnn_params.initialized) { |
| xnn_log_error("failed to define Sigmoid operator: XNNPACK is not initialized"); |
| return xnn_status_uninitialized; |
| } |
| |
| if (input_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Sigmoid operator with input ID #%" PRIu32 ": invalid Value ID", |
| input_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define Sigmoid operator with output ID #%" PRIu32 ": invalid Value ID", |
| output_id); |
| 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_sigmoid; |
| node->num_inputs = 1; |
| node->inputs.raw[0] = input_id; |
| node->num_outputs = 1; |
| node->outputs.raw[0] = output_id; |
| node->flags = flags; |
| |
| return xnn_status_success; |
| } |
| |
| enum xnn_status xnn_define_softmax( |
| xnn_subgraph_t subgraph, |
| uint32_t input_id, |
| uint32_t output_id, |
| uint32_t flags) |
| { |
| if (!xnn_params.initialized) { |
| xnn_log_error("failed to define SoftMax operator: XNNPACK is not initialized"); |
| return xnn_status_uninitialized; |
| } |
| |
| if (input_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define SoftMax operator with input ID #%" PRIu32 ": invalid Value ID", |
| input_id); |
| return xnn_status_invalid_parameter; |
| } |
| |
| if (output_id >= subgraph->num_values) { |
| xnn_log_error( |
| "failed to define SoftMax operator with output ID #%" PRIu32 ": invalid Value ID", |
| output_id); |
| 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_softmax; |
| node->num_inputs = 1; |
| node->inputs.raw[0] = input_id; |
| node->num_outputs = 1; |
| node->outputs.raw[0] = output_id; |
| node->flags = flags; |
| |
| return xnn_status_success; |
| } |
| |
| enum xnn_status xnn_delete_subgraph( |
| xnn_subgraph_t subgraph) |
| { |
| if (subgraph != NULL) { |
| memset(subgraph->nodes, 0, sizeof(struct xnn_node) * subgraph->num_nodes); |
| xnn_release_memory(subgraph->nodes); |
| |
| memset(subgraph->values, 0, sizeof(struct xnn_value) * subgraph->num_values); |
| xnn_release_memory(subgraph->values); |
| |
| memset(subgraph, 0, sizeof(struct xnn_subgraph)); |
| xnn_release_memory(subgraph); |
| } |
| return xnn_status_success; |
| } |