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Avoid return without freeing a pointer #31078

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merged 1 commit into from Jul 27, 2019
Merged

Avoid return without freeing a pointer #31078

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5 changes: 4 additions & 1 deletion tensorflow/c/c_api_experimental.cc
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Expand Up @@ -598,7 +598,10 @@ struct TF_CheckpointReader : public tensorflow::checkpoint::CheckpointReader {
TF_CheckpointReader* TF_NewCheckpointReader(const char* filename,
TF_Status* status) {
TF_CheckpointReader* reader = new TF_CheckpointReader(filename, status);
if (!status->status.ok()) return nullptr;
if (!status->status.ok()) {
TF_DeleteCheckpointReader(reader);
return nullptr;
}
const auto& m = reader->GetVariableToDataTypeMap();
for (auto it = m.begin(); it != m.end(); ++it)
reader->variable_list.push_back(it->first);
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7 changes: 4 additions & 3 deletions tensorflow/c/eager/c_api.cc
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Expand Up @@ -683,10 +683,11 @@ TF_AttrType TFE_OpNameGetAttrType(TFE_Context* ctx,
TF_Status* status) {
TF_AttrType ret;
TFE_Op* op = TFE_NewOp(ctx, op_or_function_name, status);
if (!status->status.ok()) {
return TF_ATTR_INT; // Same dummy return as TFE_OpGetAttrType.
if (status->status.ok()) {
ret = TFE_OpGetAttrType(op, attr_name, is_list, status);
} else {
ret = TF_ATTR_INT; // Same dummy return as TFE_OpGetAttrType.
}
ret = TFE_OpGetAttrType(op, attr_name, is_list, status);
TFE_DeleteOp(op);
return ret;
}
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2 changes: 1 addition & 1 deletion tensorflow/core/kernels/stack.cc
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Expand Up @@ -184,10 +184,10 @@ void StackOp::Compute(OpKernelContext* ctx) {
ResourceMgr* rm = ctx->resource_manager();
OP_REQUIRES(ctx, rm != nullptr, errors::Internal("No resource manager."));
string key = strings::StrCat(kContainer, stack_name);
Stack* stack = new Stack(elem_type_, stack_name, size);
auto* step_container = ctx->step_container();
OP_REQUIRES(ctx, step_container != nullptr,
errors::Internal("No step container."));
Stack* stack = new Stack(elem_type_, stack_name, size);
OP_REQUIRES_OK(ctx, rm->Create(step_container->name(), key, stack));
if (IsRefType(ctx->expected_output_dtype(0))) {
// Create the stack handle.
Expand Down
16 changes: 13 additions & 3 deletions tensorflow/core/profiler/internal/tfprof_code.cc
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Expand Up @@ -295,13 +295,23 @@ class PprofProfileImpl : public PprofProfile {
io::ZlibOutputBuffer* zlib_output_buffer = new io::ZlibOutputBuffer(
file.get(), buf_size, buf_size, io::ZlibCompressionOptions::GZIP());
s = zlib_output_buffer->Init();
if (!s.ok()) return s;
if (!s.ok()) {
delete zlib_output_buffer;
return s;
}
s = zlib_output_buffer->Append(profile_pb.SerializeAsString());
if (!s.ok()) return s;
if (!s.ok()) {
delete zlib_output_buffer;
return s;
}
s = zlib_output_buffer->Close();
if (!s.ok()) return s;
if (!s.ok()) {
delete zlib_output_buffer;
return s;
}
fprintf(stdout, "\nRun pprof -png --nodecount=100 --sample_index=1 <%s>\n",
filename.c_str());
delete zlib_output_buffer;
return s;
}

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70 changes: 42 additions & 28 deletions tensorflow/lite/toco/import_tensorflow.cc
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Expand Up @@ -818,18 +818,15 @@ tensorflow::Status ConvertConvOperator(
reorder->output_axes_order = AxesOrder::kOHWI;
model->operators.emplace_back(reorder);
}
auto* conv = new ConvOperator;
conv->inputs = {input_name, reordered_weights_name};
conv->outputs = {node.name()};
if (!HasAttr(node, "strides")) {
return tensorflow::errors::InvalidArgument("Missing attribute 'strides'");
}
const auto& strides = GetListAttr(node, "strides");
TF_RETURN_IF_ERROR(ExpectValue(strides.i_size(), 4, "number of strides"));
TF_RETURN_IF_ERROR(ExpectValue(strides.i(0), 1, "strides(0)"));
TF_RETURN_IF_ERROR(ExpectValue(strides.i(3), 1, "strides(3)"));
conv->stride_height = strides.i(1);
conv->stride_width = strides.i(2);
int dilation_height_factor;
int dilation_width_factor;
if (HasAttr(node, "dilations")) {
const auto& dilations = GetListAttr(node, "dilations");
TF_RETURN_IF_ERROR(
Expand All @@ -841,21 +838,30 @@ tensorflow::Status ConvertConvOperator(
node.name(), "\" had dilations:[ ", dilations.i(0), ", ",
dilations.i(1), ", ", dilations.i(2), ", ", dilations.i(3), "]."));
}
conv->dilation_height_factor = dilations.i(1);
conv->dilation_width_factor = dilations.i(2);
dilation_height_factor = dilations.i(1);
dilation_width_factor = dilations.i(2);
} else {
conv->dilation_height_factor = 1;
conv->dilation_width_factor = 1;
dilation_height_factor = 1;
dilation_width_factor = 1;
}
const auto& padding = GetStringAttr(node, "padding");
PaddingType padding_type;
if (padding == "SAME") {
conv->padding.type = PaddingType::kSame;
padding_type = PaddingType::kSame;
} else if (padding == "VALID") {
conv->padding.type = PaddingType::kValid;
padding_type = PaddingType::kValid;
} else {
return tensorflow::errors::InvalidArgument(
"Bad padding (only SAME and VALID are supported)");
}
auto* conv = new ConvOperator;
conv->inputs = {input_name, reordered_weights_name};
conv->outputs = {node.name()};
conv->stride_height = strides.i(1);
conv->stride_width = strides.i(2);
conv->dilation_height_factor = dilation_height_factor;
conv->dilation_width_factor = dilation_width_factor;
conv->padding.type = padding_type;
model->operators.emplace_back(conv);

return tensorflow::Status::OK();
Expand Down Expand Up @@ -894,15 +900,12 @@ tensorflow::Status ConvertDepthwiseConvOperator(
reorder->output_axes_order = AxesOrder::k1HWO;
model->operators.emplace_back(reorder);
}
auto* conv = new DepthwiseConvOperator;
conv->inputs = {input_name, reordered_weights_name};
conv->outputs = {node.name()};
const auto& strides = GetListAttr(node, "strides");
CHECK_EQ(strides.i_size(), 4);
CHECK_EQ(strides.i(0), 1);
CHECK_EQ(strides.i(3), 1);
conv->stride_height = strides.i(1);
conv->stride_width = strides.i(2);
TF_RETURN_IF_ERROR(ExpectValue(strides.i_size(), 4, "number of strides"));
TF_RETURN_IF_ERROR(ExpectValue(strides.i(0), 1, "strides(0)"));
TF_RETURN_IF_ERROR(ExpectValue(strides.i(3), 1, "strides(3)"));
int dilation_height_factor;
int dilation_width_factor;
if (HasAttr(node, "dilations")) {
const auto& dilations = GetListAttr(node, "dilations");
TF_RETURN_IF_ERROR(
Expand All @@ -914,20 +917,30 @@ tensorflow::Status ConvertDepthwiseConvOperator(
node.name(), "\" had dilations:[ ", dilations.i(0), ", ",
dilations.i(1), ", ", dilations.i(2), ", ", dilations.i(3), "]."));
}
conv->dilation_height_factor = dilations.i(1);
conv->dilation_width_factor = dilations.i(2);
dilation_height_factor = dilations.i(1);
dilation_width_factor = dilations.i(2);
} else {
conv->dilation_height_factor = 1;
conv->dilation_width_factor = 1;
dilation_height_factor = 1;
dilation_width_factor = 1;
}
const auto& padding = GetStringAttr(node, "padding");
PaddingType padding_type;
if (padding == "SAME") {
conv->padding.type = PaddingType::kSame;
padding_type = PaddingType::kSame;
} else if (padding == "VALID") {
conv->padding.type = PaddingType::kValid;
padding_type = PaddingType::kValid;
} else {
LOG(FATAL) << "Bad padding (only SAME and VALID are supported)";
return tensorflow::errors::InvalidArgument(
"Bad padding (only SAME and VALID are supported)");
}
auto* conv = new DepthwiseConvOperator;
conv->inputs = {input_name, reordered_weights_name};
conv->outputs = {node.name()};
conv->stride_height = strides.i(1);
conv->stride_width = strides.i(2);
conv->dilation_height_factor = dilation_height_factor;
conv->dilation_width_factor = dilation_width_factor;
conv->padding.type = padding_type;
model->operators.emplace_back(conv);
return tensorflow::Status::OK();
}
Expand Down Expand Up @@ -2369,12 +2382,13 @@ tensorflow::Status ConvertUnidirectionalSequenceLstm(
const ModelFlags& model_flags, Model* model) {
DCHECK_EQ(node.op(), "UnidirectionalSequenceLstm");

auto* op = new UnidirectionalSequenceLstmOperator();
const auto& indices = GetListAttr(node, "_tflite_input_indices");
if (indices.i_size() != node.input().size()) {
return tensorflow::errors::InvalidArgument("Input size does not match.");
}

auto* op = new UnidirectionalSequenceLstmOperator();

// The input size needs to be the same as the TfLite UniDirectionalSequence
// Lstm implementation.
const int kInputsSize = 20;
Expand Down Expand Up @@ -2424,12 +2438,12 @@ tensorflow::Status ConvertUnidirectionalSequenceRnn(
const ModelFlags& model_flags, Model* model) {
DCHECK_EQ(node.op(), "UnidirectionalSequenceRnn");

auto* op = new UnidirectionalSequenceRnnOperator();
const auto& indices = GetListAttr(node, "_tflite_input_indices");
if (indices.i_size() != node.input().size()) {
return tensorflow::errors::InvalidArgument("Input size does not match.");
}

auto* op = new UnidirectionalSequenceRnnOperator();
for (const string& input : node.input()) {
op->inputs.push_back(input);
}
Expand Down