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Merged
copybara-service merged 1 commit into from
Mar 26, 2026
Merged

Automated code review of src/subgraph/ by Gemini for potential bugs #9726

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16 changes: 12 additions & 4 deletions src/subgraph/argmax-pooling-2d.c
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Original file line number Diff line number Diff line change
Expand Up @@ -50,10 +50,18 @@ static enum xnn_status reshape_argmax_pooling_operator(
{
const uint32_t input_id = opdata->inputs[0];
assert(input_id < num_values);
const size_t batch_size = values[input_id].shape.dim[0];
const size_t input_height = values[input_id].shape.dim[1];
const size_t input_width = values[input_id].shape.dim[2];
const size_t channel_dim = values[input_id].shape.dim[3];
const struct xnn_runtime_value* input_value = values + input_id;
if (input_value->shape.num_dims != 4) {
xnn_log_error("failed to reshape %s operator with input ID #%" PRIu32
": number of dimensions (%zu) must be 4",
xnn_node_type_to_string(xnn_node_type_argmax_pooling_2d),
input_id, input_value->shape.num_dims);
return xnn_status_invalid_parameter;
}
const size_t batch_size = input_value->shape.dim[0];
const size_t input_height = input_value->shape.dim[1];
const size_t input_width = input_value->shape.dim[2];
const size_t channel_dim = input_value->shape.dim[3];

size_t output_height, output_width;
enum xnn_status status = xnn_reshape_argmax_pooling2d_nhwc_f32(
Expand Down
7 changes: 7 additions & 0 deletions src/subgraph/average-pooling-2d.c
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Expand Up @@ -83,6 +83,13 @@ static enum xnn_status reshape_average_pooling_operator(
assert(output_id < num_values);

const struct xnn_runtime_value* input_value = values + input_id;
if (input_value->shape.num_dims != 4) {
xnn_log_error("failed to reshape %s operator with input ID #%" PRIu32
": number of dimensions (%zu) must be 4",
xnn_node_type_to_string(xnn_node_type_average_pooling_2d),
input_id, input_value->shape.num_dims);
return xnn_status_invalid_parameter;
}
struct xnn_runtime_value* output_value = values + output_id;

const size_t batch_size = input_value->shape.dim[0];
Expand Down
2 changes: 1 addition & 1 deletion src/subgraph/batch-matrix-multiply.c
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Expand Up @@ -573,7 +573,7 @@ enum xnn_status xnn_define_batch_matrix_multiply(xnn_subgraph_t subgraph,
const struct xnn_value* input2_value = &subgraph->values[input2_id];

status = xnn_subgraph_check_input_type_dense(
xnn_node_type_batch_matrix_multiply, input2_id, input1_value);
xnn_node_type_batch_matrix_multiply, input2_id, input2_value);
if (status != xnn_status_success) {
return status;
}
Expand Down
2 changes: 1 addition & 1 deletion src/subgraph/binary.c
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Expand Up @@ -83,7 +83,7 @@ static enum xnn_status reshape_binary_operator(
}
shape2.dim[0] = values[input2_id].shape.dim[0];
shape2.dim[1] = values[input2_id].shape.dim[values[input2_id].shape.num_dims - 1];
if (values[input1_id].shape.num_dims > 2) {
if (values[input2_id].shape.num_dims > 2) {
memcpy(&shape2.dim[2], &values[input2_id].shape.dim[1], (values[input2_id].shape.num_dims - 2) * sizeof(size_t));
}
} else {
Expand Down
27 changes: 18 additions & 9 deletions src/subgraph/convolution-2d.c
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Expand Up @@ -34,7 +34,7 @@ enum xnn_status create_nchw_convolution(
enum xnn_status status;
const enum xnn_datatype filter_datatype = values[filter_id].datatype;
const enum xnn_datatype bias_datatype = bias_id != XNN_INVALID_VALUE_ID
? values[filter_id].datatype
? values[bias_id].datatype
: xnn_datatype_invalid;
const enum xnn_datatype output_datatype = values[output_id].datatype;
switch (filter_datatype) {
Expand Down Expand Up @@ -142,7 +142,7 @@ static enum xnn_status create_convolution_operator(
: values[input_id].datatype;
const enum xnn_datatype filter_datatype = values[filter_id].datatype;
const enum xnn_datatype bias_datatype = bias_id != XNN_INVALID_VALUE_ID
? values[filter_id].datatype
? values[bias_id].datatype
: xnn_datatype_invalid;
const enum xnn_datatype output_datatype = values[output_id].datatype;
if (values[output_id].flags & XNN_VALUE_FLAG_LAYOUT_NCHW) {
Expand Down Expand Up @@ -257,7 +257,7 @@ static enum xnn_status create_convolution_operator(
node->activation.output_max, node->flags, weights_cache,
&opdata->operator_objects[0]);
break;
case xnn_datatype_pfp32:
case xnn_datatype_pfp32:
status = xnn_create_convolution2d_nhwc_pf32(
node->params.convolution_2d.input_padding_top,
node->params.convolution_2d.input_padding_right,
Expand Down Expand Up @@ -668,9 +668,18 @@ enum xnn_status reshape_convolution_operator(struct xnn_operator_data* opdata,
const uint32_t output_id = opdata->outputs[0];
assert(output_id < num_values);

const size_t batch_size = values[input_id].shape.dim[0];
const size_t input_height = values[input_id].shape.dim[1];
const size_t input_width = values[input_id].shape.dim[2];
const struct xnn_runtime_value* input_value = values + input_id;
if (input_value->shape.num_dims < 3) {
xnn_log_error("failed to reshape %s operator with input ID #%" PRIu32
": number of dimensions (%zu) must be at least 3",
xnn_node_type_to_string(xnn_node_type_convolution_2d),
input_id, input_value->shape.num_dims);
return xnn_status_invalid_parameter;
}

const size_t batch_size = input_value->shape.dim[0];
const size_t input_height = input_value->shape.dim[1];
const size_t input_width = input_value->shape.dim[2];

size_t output_height, output_width;
enum xnn_status status = xnn_status_invalid_state;
Expand All @@ -695,7 +704,7 @@ enum xnn_status reshape_convolution_operator(struct xnn_operator_data* opdata,
status = xnn_reshape_convolution2d_nhwc_pf32(
opdata->operator_objects[0], batch_size, input_height, input_width,
&opdata->workspace_size, &output_height, &output_width, threadpool);
break;
break;
case xnn_operator_type_convolution_nhwc_f16:
status = xnn_reshape_convolution2d_nhwc_f16(
opdata->operator_objects[0], batch_size, input_height, input_width,
Expand Down Expand Up @@ -816,7 +825,7 @@ enum xnn_status setup_convolution_operator(
return xnn_setup_convolution2d_nhwc_pf32(opdata->operator_objects[0],
opdata->workspace, input_data,
output_data);
break;
break;
case xnn_operator_type_convolution_nhwc_f16:
return xnn_setup_convolution2d_nhwc_f16(opdata->operator_objects[0],
opdata->workspace, input_data,
Expand Down Expand Up @@ -1325,7 +1334,7 @@ enum xnn_status xnn_define_convolution_2d(
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);
filter_id, filter_value->quantization.channel_dimension);
return xnn_status_invalid_parameter;
}

Expand Down
20 changes: 17 additions & 3 deletions src/subgraph/copy.c
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Expand Up @@ -100,6 +100,14 @@ static enum xnn_status resize_copy_output_tensor(
const size_t input_num_elements = xnn_shape_multiply_all_dims(&input->shape);
if (output_axis_dynamic < XNN_MAX_TENSOR_DIMS) {
const size_t output_num_elements = xnn_shape_multiply_all_dims(&output->shape);
if (output_num_elements == 0) {
xnn_log_error("failed to reshape %s operator with input ID #%" PRIu32
" and output ID #%" PRIu32
": output number of elements must be non-zero",
xnn_node_type_to_string(xnn_node_type_static_reshape),
input_id, output_id);
return xnn_status_invalid_parameter;
}
const size_t inferred_dim = input_num_elements / output_num_elements;
if (inferred_dim * output_num_elements != input_num_elements) {
xnn_log_error(
Expand Down Expand Up @@ -150,6 +158,12 @@ static enum xnn_status resize_expand_dims_output_tensor(
const size_t *new_axes = opdata->reshape_dims;
const size_t num_output_dims = input_shape->num_dims + opdata->num_reshape_dims;
if (num_output_dims > XNN_MAX_TENSOR_DIMS) {
xnn_log_error("failed to expand dims in %s operator with input ID #%" PRIu32
" and output ID #%" PRIu32
": number of output dimensions, %zu, is larger than the "
"maximum number of dimensions, %zu",
xnn_node_type_to_string(xnn_node_type_static_expand_dims),
input_id, output_id, num_output_dims, (size_t)XNN_MAX_TENSOR_DIMS);
return xnn_status_invalid_parameter;
}
size_t input_iter = 0;
Expand Down Expand Up @@ -198,7 +212,7 @@ static enum xnn_status resize_fuse_dims_output_tensor(
" and output ID #%" PRIu32
": number of input dimensions, %zu, is smaller than the "
"fused dimensions, %zu-%zu",
xnn_node_type_to_string(xnn_node_type_static_reshape),
xnn_node_type_to_string(xnn_node_type_fuse_dims),
input_id, output_id, input_shape->num_dims, first_dim,
first_dim + num_dims);
return xnn_status_invalid_parameter;
Expand Down Expand Up @@ -246,7 +260,7 @@ static enum xnn_status resize_split_dims_output_tensor(
" and output ID #%" PRIu32
": number of output dimensions, %zu, is larger than the "
"maximum number of dimensions, %zu",
xnn_node_type_to_string(xnn_node_type_static_reshape),
xnn_node_type_to_string(xnn_node_type_split_dims),
input_id, output_id, input_shape->num_dims - 1 + num_dims,
(size_t)XNN_MAX_TENSOR_DIMS);
return xnn_status_invalid_parameter;
Expand All @@ -264,7 +278,7 @@ static enum xnn_status resize_split_dims_output_tensor(
" and output ID #%" PRIu32
": product of defined splits, %zu, does not divide the split "
"input dimension, %zu",
xnn_node_type_to_string(xnn_node_type_static_reshape),
xnn_node_type_to_string(xnn_node_type_split_dims),
input_id, output_id, count, input_shape->dim[axis]);
return xnn_status_invalid_parameter;
}
Expand Down
16 changes: 11 additions & 5 deletions src/subgraph/deconvolution-2d.c
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Expand Up @@ -63,7 +63,7 @@ static enum xnn_status create_deconvolution_operator(
: values[input_id].datatype;
const enum xnn_datatype filter_datatype = values[filter_id].datatype;
const enum xnn_datatype bias_datatype = bias_id != XNN_INVALID_VALUE_ID
? values[filter_id].datatype
? values[bias_id].datatype
: xnn_datatype_invalid;
const enum xnn_datatype output_datatype = values[output_id].datatype;
switch (output_datatype) {
Expand Down Expand Up @@ -405,6 +405,12 @@ static enum xnn_status reshape_deconvolution_operator(
size_t num_values, pthreadpool_t threadpool) {
const uint32_t input_id = opdata->inputs[0];
assert(input_id < num_values);
if (values[input_id].shape.num_dims != 3 && values[input_id].shape.num_dims != 4) {
xnn_log_error(
"failed to define %s operator with input ID #%" PRIu32 ": num_dims (%zu) must be 3 or 4",
xnn_node_type_to_string(xnn_node_type_deconvolution_2d), input_id, values[input_id].shape.num_dims);
return xnn_status_invalid_parameter;
}
const size_t batch_size = values[input_id].shape.dim[0];
const size_t input_height = values[input_id].shape.dim[1];
const size_t input_width = values[input_id].shape.dim[2];
Expand Down Expand Up @@ -755,7 +761,7 @@ enum xnn_status xnn_define_deconvolution_2d(
input_value->shape.num_dims) {
xnn_log_error("failed to define %s operator with input ID #%" PRIu32
": num_nonbatch_dims (%zu) must be < num_dims (%zu)",
xnn_node_type_to_string(xnn_node_type_convolution_2d),
xnn_node_type_to_string(xnn_node_type_deconvolution_2d),
input_id, input_value->quantization.num_nonbatch_dims,
input_value->shape.num_dims);
return xnn_status_invalid_parameter;
Expand Down Expand Up @@ -931,8 +937,8 @@ enum xnn_status xnn_define_deconvolution_2d(
if (filter_value->quantization.channel_dimension != 0) {
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);
xnn_node_type_to_string(xnn_node_type_deconvolution_2d),
filter_id, filter_value->quantization.channel_dimension);
return xnn_status_invalid_parameter;
}

Expand All @@ -943,7 +949,7 @@ enum xnn_status xnn_define_deconvolution_2d(
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),
xnn_node_type_to_string(xnn_node_type_deconvolution_2d),
bias_id, bias_value->quantization.channel_dimension);
return xnn_status_invalid_parameter;
}
Expand Down
7 changes: 7 additions & 0 deletions src/subgraph/depth-to-space-2d.c
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Expand Up @@ -91,6 +91,13 @@ static enum xnn_status reshape_depth_to_space_operator(
const uint32_t input_id = opdata->inputs[0];
assert(input_id < num_values);
const struct xnn_runtime_value* input_value = values + input_id;

if (input_value->shape.num_dims != 4) {
xnn_log_error(
"failed to reshape %s operator with input ID #%" PRIu32 ": number of dimensions (%zu) must be 4",
xnn_node_type_to_string(xnn_node_type_depth_to_space_2d), input_id, input_value->shape.num_dims);
return xnn_status_invalid_parameter;
}
const size_t batch_size = input_value->shape.dim[0];
const size_t input_height = input_value->shape.dim[1];
const size_t input_width = input_value->shape.dim[2];
Expand Down
21 changes: 15 additions & 6 deletions src/subgraph/depthwise-convolution-2d.c
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Expand Up @@ -59,7 +59,7 @@ static enum xnn_status create_depthwise_convolution_operator(
const enum xnn_datatype filter_datatype = values[filter_id].datatype;
const enum xnn_datatype output_datatype = values[output_id].datatype;
const enum xnn_datatype bias_datatype = bias_id != XNN_INVALID_VALUE_ID
? values[filter_id].datatype
? values[bias_id].datatype
: xnn_datatype_invalid;
if (values[output_id].flags & XNN_VALUE_FLAG_LAYOUT_NCHW) {
assert(values[input_id].flags & XNN_VALUE_FLAG_LAYOUT_NCHW);
Expand Down Expand Up @@ -395,9 +395,18 @@ static enum xnn_status reshape_depthwise_convolution_operator(
{
const uint32_t input_id = opdata->inputs[0];
assert(input_id < num_values);
const size_t batch_size = values[input_id].shape.dim[0];
const size_t input_height = values[input_id].shape.dim[1];
const size_t input_width = values[input_id].shape.dim[2];
const struct xnn_runtime_value* input_value = values + input_id;
if (input_value->shape.num_dims != 3 && input_value->shape.num_dims != 4) {
xnn_log_error("failed to reshape %s operator with input ID #%" PRIu32
": number of dimensions (%zu) must be 3 or 4",
xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d),
input_id, input_value->shape.num_dims);
return xnn_status_invalid_parameter;
}

const size_t batch_size = input_value->shape.dim[0];
const size_t input_height = input_value->shape.dim[1];
const size_t input_width = input_value->shape.dim[2];
enum xnn_status status = xnn_status_invalid_state;
const size_t old_workspace_size = opdata->workspace_size;
size_t output_height, output_width;
Expand Down Expand Up @@ -946,7 +955,7 @@ enum xnn_status xnn_define_depthwise_convolution_2d(
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);
xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), filter_id, filter_value->quantization.channel_dimension);
return xnn_status_invalid_parameter;
}

Expand All @@ -955,7 +964,7 @@ enum xnn_status xnn_define_depthwise_convolution_2d(
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);
xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), bias_id, bias_value->quantization.channel_dimension);
return xnn_status_invalid_parameter;
}
}
Expand Down
33 changes: 21 additions & 12 deletions src/subgraph/even-split.c
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Expand Up @@ -12,6 +12,7 @@
#include "src/xnnpack/allocation-type.h"
#include "src/xnnpack/common.h"
#include "src/xnnpack/datatype.h"
#include "src/xnnpack/internal.h"
#include "src/xnnpack/log.h"
#include "src/xnnpack/node-type.h"
#include "src/xnnpack/operator-type.h"
Expand Down Expand Up @@ -146,6 +147,13 @@ static enum xnn_status reshape_even_split_operator(
size_t batch_size = xnn_shape_multiply_leading_dims(&input_value->shape, axis);

size_t num_splits = opdata->num_outputs;
if (input_value->shape.dim[axis] % num_splits != 0) {
xnn_log_error(
"failed to reshape %s operator with the input ID #%" PRIu32
": split dimension (%zu) is not divisible by the number of splits (%zu)",
xnn_node_type_to_string(xnn_node_type_even_split), input_id, input_value->shape.dim[axis], num_splits);
return xnn_status_invalid_parameter;
}
const size_t axis_elements = input_value->shape.dim[axis] / num_splits;
const size_t old_workspace_size = opdata->workspace_size;
bool reallocation_required = false;
Expand Down Expand Up @@ -328,22 +336,23 @@ enum xnn_status xnn_define_even_split(
return status;
}

for (int i = 0; i < num_outputs; ++i) {
status = check_output_value(subgraph, split_dim, input_id, output_ids[i], "Nth", node_type);
if (status != xnn_status_success) {
return status;
}
if (num_outputs == 0) {
xnn_log_error(
"failed to define %s operator with 0 outputs",
xnn_node_type_to_string(node_type));
return xnn_status_invalid_parameter;
}

if (num_outputs > XNN_MAX_OUTPUTS) {
xnn_log_error(
"failed to define %s operator with %zu inputs: number of inputs (%zu) exceeds the supported maximum (%zu)",
xnn_node_type_to_string(node_type), num_outputs, num_outputs, (size_t) XNN_MAX_OUTPUTS);
"failed to define %s operator with %zu outputs: number of outputs exceeds the supported maximum (%zu)",
xnn_node_type_to_string(node_type), num_outputs, (size_t) XNN_MAX_OUTPUTS);
return xnn_status_invalid_parameter;
}

for (int i = 0; i < num_outputs; ++i) {
check_datatype_copyable(subgraph, input_id, output_ids[i], "Nth", node_type);
for (size_t i = 0; i < num_outputs; ++i) {
XNN_RETURN_IF_ERROR(check_output_value(subgraph, split_dim, input_id, output_ids[i], "Nth", node_type));
XNN_RETURN_IF_ERROR(check_datatype_copyable(subgraph, input_id, output_ids[i], "Nth", node_type));
}

struct xnn_node* node = xnn_subgraph_new_node(subgraph);
Expand All @@ -355,9 +364,9 @@ enum xnn_status xnn_define_even_split(
node->type = node_type;
node->num_inputs = 1;
node->inputs[0] = input_id;
node->num_outputs = num_outputs;
for(int i=0;i<num_outputs;++i){
node->outputs[i]=output_ids[i];
node->num_outputs = (uint32_t) num_outputs;
for (size_t i = 0; i < num_outputs; ++i) {
node->outputs[i] = output_ids[i];
}
node->create = create_even_split_operator;
node->reshape = reshape_even_split_operator;
Expand Down
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