experiments/STT-tensorflow
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c, compiler, jit resolutions

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This commit is contained in:
Taré Gaskin 2020-07-26 20:42:00 +00:00
parent 3bb28df8d4
commit cb3cf00aad
18 changed files with 67 additions and 59 deletions
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10
tensorflow/c/eager/tape.h
View File

@ -573,7 +573,7 @@ Status InitialGradients(
gtl::ArraySlice<Gradient*> output_gradients, const TensorTape& tensor_tape,
const OpTape<BackwardFunction, TapeTensor>& op_tape,
std::unordered_map<int64, std::vector<Gradient*>>* result) {
for (int i = 0; i < target_tensor_ids.size(); ++i) {
for (int i = 0, end = target_tensor_ids.size(); i < end; ++i) {
const int64 id = target_tensor_ids[i];
if (output_gradients.empty() || output_gradients[i] == nullptr) {
auto tensor_it = tensor_tape.find(id);
@ -699,7 +699,7 @@ Status GradientTape<Gradient, BackwardFunction, TapeTensor>::ComputeGradient(
std::vector<Gradient*> out_gradients;
out_gradients.reserve(trace.output_tensor_info.size());
std::vector<int64> unneeded_gradients;
for (int i = 0; i < trace.input_tensor_id.size(); i++) {
for (int i = 0, end = trace.input_tensor_id.size(); i < end; i++) {
const auto& in_tensor_id = trace.input_tensor_id[i];
if (tensor_tape_.find(in_tensor_id) == tensor_tape_.end() &&
sources_set.find(in_tensor_id) == sources_set.end()) {
@ -709,7 +709,7 @@ Status GradientTape<Gradient, BackwardFunction, TapeTensor>::ComputeGradient(
bool any_gradient_nonzero = false;
std::vector<int> zero_indices;
for (int i = 0; i < trace.output_tensor_info.size(); ++i) {
for (int i = 0, end = trace.output_tensor_info.size(); i < end; ++i) {
const int64 id = trace.output_tensor_info[i].GetID();
auto grad_it = gradients.find(id);
if (grad_it == gradients.end()) {
@ -775,7 +775,7 @@ Status GradientTape<Gradient, BackwardFunction, TapeTensor>::ComputeGradient(
}
VLOG(1) << "Got " << in_gradients.size() << " in_gradients for "
<< trace.input_tensor_id.size() << " sources";
for (int i = 0; i < in_gradients.size(); ++i) {
for (int i = 0, end = in_gradients.size(); i < end; ++i) {
const int64 id = trace.input_tensor_id[i];
if (in_gradients[i] != nullptr) {
auto& unaggregated_grads = gradients[id];
@ -968,7 +968,7 @@ ForwardAccumulator<Gradient, BackwardFunction, TapeTensor>::ForwardpropFromTape(
targets.reserve(grad.size());
used_in_grads.reserve(grad.size());
std::unordered_map<int64, TapeTensor> sources_that_are_targets;
for (int grad_index = 0; grad_index < grad.size(); ++grad_index) {
for (int grad_index = 0, end = grad.size(); grad_index < end; ++grad_index) {
Gradient* grad_tensor = grad[grad_index];
if (grad_tensor != nullptr) {
int64 tensor_id = vspace_.TensorId(grad_tensor);

7
tensorflow/cc/framework/gradients.cc
View File

@ -425,7 +425,7 @@ Status SymbolicGradientBuilder::ProcessWhileLoop(Node* exit_node,
// Backprop along the in edges to the while loop (i.e. the inputs to the enter
// nodes)
DCHECK_EQ(dx.size(), while_ctx->enter_nodes().size());
for (int i = 0; i < dx.size(); ++i) {
for (int i = 0, end = dx.size(); i < end; ++i) {
Node* enter_node = while_ctx->enter_nodes()[i];
for (const Edge* e : enter_node->in_edges()) {
if (e->IsControlEdge()) continue;
@ -489,7 +489,7 @@ Status SymbolicGradientBuilder::AddGradients() {
// All loop-specific control flow ops should have been handled above
DCHECK(!n->IsEnter() && !n->IsNextIteration()) << n->DebugString();
const size_t num_no_grad = no_grad_dy_indices.size();
const int num_no_grad = no_grad_dy_indices.size();
if (IsPrimitiveOpWithNoGrad(n->type_string()) || num_no_grad == num_y) {
// No grad defined for this op, or all outputs returned 'NoGradient':
// Backprop 'NoGradient' along the in edges.
@ -525,7 +525,8 @@ Status SymbolicGradientBuilder::AddGradients() {
for (const Edge* e : n->in_edges()) {
if (e->IsControlEdge()) continue;
int dx_index = e->dst_input();
if (dx_index >= dx.size()) {
const int dx_size = dx.size();
if (dx_index >= dx_size) {
return errors::Internal(
"Invalid gradient output index: ", dx_index, " size: ", dx.size());
}

2
tensorflow/cc/framework/while_gradients.cc
View File

@ -34,7 +34,7 @@ Output ToOutput(OutputTensor output_tensor) {
std::vector<Output> ToOutputVector(
const std::vector<OutputTensor>& output_tensors) {
size_t n = output_tensors.size();
const int n = output_tensors.size();
std::vector<Output> result;
result.reserve(n);
for (int i = 0; i < n; ++i) result.push_back(ToOutput(output_tensors[i]));

16
tensorflow/compiler/aot/codegen.cc
View File

@ -172,7 +172,7 @@ string RewriteWithName(const string& name, string code,
Status GenArgMethods(const tf2xla::Config& config,
const xla::ProgramShapeProto& ps,
const CompileResult& compile_result, string* methods) {
size_t num_args = ps.parameters_size();
const int num_args = ps.parameters_size();
// feed_size() + variable_size() is the maximum number of args as an
// implementation may not create an argument for an unused variable.
if (config.feed_size() + config.variable_size() < num_args) {
@ -229,8 +229,9 @@ Status GenResultMethods(const tf2xla::Config& config,
int readonly_variables = absl::c_count_if(
config.variable(),
[](const tf2xla::Variable& var) { return var.readonly(); });
if (config.fetch_size() + config.variable_size() - readonly_variables !=
num_results) {
const int actual_num_results = config.fetch_size()
+ config.variable_size() - readonly_variables;
if (actual_num_results != num_results) {
return errors::InvalidArgument("mismatch between fetch_size(",
config.fetch_size(), ")+variable_size(",
config.variable_size(), ") and tuple_size(",
@ -273,7 +274,7 @@ Status GenResultMethods(const tf2xla::Config& config,
// Generate methods for variables.
Status GenVariableMethods(const tf2xla::Config& config,
const xla::ProgramShapeProto& ps, string* methods) {
size_t num_args = ps.parameters_size();
const int num_args = ps.parameters_size();
for (int i = config.feed_size(); i < num_args; ++i) {
std::vector<std::pair<string, string>> rewrites;
TF_RETURN_IF_ERROR(
@ -401,7 +402,8 @@ Status GenerateHeader(const CodegenOpts& opts, const tf2xla::Config& config,
::xla::cpu::CreateArgIndexTableFromBufferInfos(buffer_infos);
std::vector<string> buffer_infos_as_strings =
BufferInfosToCppExpression(buffer_infos);
if (result_index < 0 || result_index >= buffer_infos.size()) {
const int64 buffer_infos_size = buffer_infos.size();
if (result_index < 0 || result_index >= buffer_infos_size) {
return errors::InvalidArgument("result index: ", result_index,
" is outside the range of temp sizes: [0,",
buffer_infos.size(), ")");
@ -797,8 +799,8 @@ Status ParseCppClass(const string& cpp_class, string* class_name,
// Allow a fully qualified name that starts with "::".
parts.erase(parts.begin());
}
for (int i = 0; i < parts.size(); ++i) {
if (i < parts.size() - 1) {
for (int i = 0, end = parts.size(); i < end; ++i) {
if (i < end - 1) {
TF_RETURN_IF_ERROR(ValidateCppIdent(
parts[i], "in namespace component of cpp_class: " + cpp_class));
namespaces->push_back(parts[i]);

2
tensorflow/compiler/jit/build_xla_ops_pass.cc
View File

@ -452,7 +452,7 @@ Status PredicateInt32Inputs(const Scope& root, Node* n,
root.graph()->AddControlEdge(predicate_as_control.node(),
identity_n.operation.node());
for (int i = 0; i < int32_inputs.size(); i++) {
for (int32 i = 0, end = int32_inputs.size(); i < end; i++) {
TF_RETURN_IF_ERROR(root.graph()->UpdateEdge(identity_n[i].node(), i, n,
int32_inputs_input_idxs[i]));
}

2
tensorflow/compiler/jit/compilability_check_util.h
View File

@ -257,7 +257,7 @@ class RecursiveCompilabilityChecker {
UncompilableNodesMap* uncompilable_nodes_map);
// Make sure we don't recurse infinitely on recursive functions.
const int kMaxRecursionDepth = 10;
const size_t kMaxRecursionDepth = 10;
const OperationFilter& op_filter_;
const DeviceType& jit_device_type_;

6
tensorflow/compiler/jit/device_util.cc
View File

@ -26,8 +26,8 @@ using xla::StatusOr;
void DeviceSet::Insert(DeviceId device_id) {
int word_index = device_id.id() / kWordSize;
int bit_index = device_id.id() % kWordSize;
if (word_index >= storage_.size()) {
const int storage_size = storage_.size();
if (word_index >= storage_size) {
storage_.resize(word_index + 1, 0);
}
@ -39,7 +39,7 @@ void DeviceSet::UnionWith(const DeviceSet& other) {
storage_.resize(other.storage_.size(), 0);
}
for (int i = 0; i < other.storage_.size(); i++) {
for (int i = 0, end = other.storage_.size(); i < end; i++) {
storage_[i] |= other.storage_[i];
}
}

2
tensorflow/compiler/jit/device_util.h
View File

@ -72,7 +72,7 @@ class DeviceSet {
void ForEach(FnTy func) const {
// This is really a poor man's iterator, we should consider writing a proper
// iterator if this ends up being used widely.
for (int word_index = 0; word_index < storage_.size(); word_index++) {
for (int word_index = 0, end = storage_.size(); word_index < end; word_index++) {
uint64 word = storage_[word_index];
while (word != 0) {
uint64 only_lowest_bit_set = word & -word;

6
tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc
View File

@ -1132,7 +1132,8 @@ static Status GetArgTypes(const Graph& graph, DataTypeVector* types) {
if (n->type_string() == kArgOp) {
int index;
TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), "index", &index));
if (index < 0 || index >= types->size()) {
const int types_size = types->size();
if (index < 0 || index >= types_size) {
return errors::InvalidArgument("Invalid argument number");
}
(*types)[index] = n->output_type(0);
@ -1149,7 +1150,8 @@ static Status RenumberArguments(Graph* graph,
if (n->type_string() == kArgOp) {
int index;
TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), "index", &index));
if (index < 0 || index >= permutation.size()) {
const int permutation_size = permutation.size();
if (index < 0 || index >= permutation_size) {
return errors::InvalidArgument("Invalid argument number");
}
n->AddAttr("index", permutation[index]);

8
tensorflow/compiler/jit/encapsulate_util.cc
View File

@ -139,7 +139,7 @@ Status PreprocessDataEdgesBetweenOutsideCompilations(
// Remove the edge from host to outside compilation. Add a placeholder as
// outside compilation node input.
std::map<std::pair<string, int>, Node*> placeholders;
for (int i = 0; i < edges.size(); i++) {
for (int i = 0, end = edges.size(); i < end; i++) {
Node* dst = g->FindNodeId(edges[i].dst_node_id);
const Edge* e;
TF_RETURN_IF_ERROR(dst->input_edge(edges[i].dst_input, &e));
@ -185,7 +185,7 @@ Status PreprocessDataEdgesBetweenOutsideCompilations(
// Other edge in `edges` might have `e->dst()` as src or dst
// node. Before removing `e->dst()`, replace those edges with
// corresponding edges for `dst_replace_node`.
for (int j = i + 1; j < edges.size(); j++) {
for (int j = i + 1, end = edges.size(); j < end; j++) {
if (edges[j].dst_node_id == edges[i].dst_node_id) {
edges[j].dst_node_id = dst_replace_node->id();
}
@ -238,7 +238,7 @@ Status PostprocessDataEdgesBetweenOutsideCompilations(
g->AddControlEdge(original_node, e->dst());
g->RemoveEdge(e);
}
for (int i = 0; i < data_edges.size(); i++) {
for (int i = 0, end = data_edges.size(); i < end; i++) {
Node* dst = data_edges[i].dst;
NodeDef new_def = dst->def();
int dst_input = data_edges[i].dst_input;
@ -253,7 +253,7 @@ Status PostprocessDataEdgesBetweenOutsideCompilations(
// Other edges might have `dst` as dst node. Update those edges with
// `replace_node`.
for (int j = i + 1; j < data_edges.size(); j++) {
for (int j = i + 1, end = data_edges.size(); j < end; j++) {
if (data_edges[j].dst == dst) {
data_edges[j].dst = replace_node;
}

4
tensorflow/compiler/jit/encapsulate_xla_computations_pass.cc
View File

@ -351,14 +351,14 @@ Status RewriteSubgraph(const std::vector<OutputTensor>& arg_source_tensors,
if (!status.ok()) {
return status;
}
for (int i = 0; i < data_inputs.size(); ++i) {
for (int i = 0, end = data_inputs.size(); i < end; ++i) {
graph->AddEdge(data_inputs[i].first, data_inputs[i].second, xla_launch,
i);
}
for (Node* n : control_inputs) {
graph->AddControlEdge(n, xla_launch);
}
for (int i = 0; i < data_outputs.size(); ++i) {
for (int i = 0, end = data_outputs.size(); i < end; ++i) {
for (const auto& successor : data_outputs[i]) {
graph->AddEdge(xla_launch, i, successor.first, successor.second);
}

33
tensorflow/compiler/jit/extract_outside_compilation_pass.cc
View File

@ -95,7 +95,7 @@ Status GetArgDataTypes(const std::vector<Node*>& arg_nodes,
TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), "T", &dtype));
(*recv_at_host_dtypes)[index] = dtype;
}
for (int i = 0; i < recv_at_host_dtypes->size(); i++) {
for (int i = 0, end = recv_at_host_dtypes->size(); i < end; i++) {
if ((*recv_at_host_dtypes)[i] == DT_INVALID) {
return errors::Internal("Cannot get datatype for input ", i);
}
@ -160,7 +160,7 @@ xla::StatusOr<Node*> ReplaceArgNodesWithRecvAtHostNode(
}
// Rewrite dst nodes because their input changed.
for (int i = 0; i < out_edge_info.size(); i++) {
for (int i = 0, end = out_edge_info.size(); i < end; i++) {
const OutEdgeInfo edge = out_edge_info[i];
if (edge.dst_input == Graph::kControlSlot) {
continue;
@ -174,7 +174,7 @@ xla::StatusOr<Node*> ReplaceArgNodesWithRecvAtHostNode(
// Other edges might have `dst` as dst node as well. Update those edges
// with `dst_replace`.
for (int j = i + 1; j < out_edge_info.size(); j++) {
for (int j = i + 1, end = out_edge_info.size(); j < end; j++) {
if (out_edge_info[j].dst == dst) {
out_edge_info[j].dst = dst_replace;
}
@ -196,7 +196,7 @@ Status GetRetDataTypes(const std::vector<Node*>& ret_nodes,
TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), "T", &dtype));
(*send_from_host_dtypes)[index] = dtype;
}
for (int i = 0; i < send_from_host_dtypes->size(); i++) {
for (int i = 0, end = send_from_host_dtypes->size(); i < end; i++) {
if ((*send_from_host_dtypes)[i] == DT_INVALID) {
return errors::Internal("Cannot get datatype for output ", i);
}
@ -226,7 +226,8 @@ xla::StatusOr<Node*> BuildSendFromHostNode(
for (auto* n : ret_nodes) {
int index;
TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), "index", &index));
if (index < 0 || index >= send_from_host_dtypes.size()) {
const int send_from_host_dtypes_size = send_from_host_dtypes.size();
if (index < 0 || index >= send_from_host_dtypes_size) {
return errors::Internal("Invalid _Retval index: ", index);
}
for (auto edge : n->in_edges()) {
@ -360,8 +361,9 @@ xla::StatusOr<NodeDef> BuildXlaHostComputeNodeDef(
if (e->IsControlEdge()) {
continue;
}
if (e->dst_input() < 0 || e->dst_input() >= input_dtypes.size()) {
const int input_dtypes_size = input_dtypes.size();
if (e->dst_input() < 0 || e->dst_input() >= input_dtypes_size) {
return errors::Internal("Invalid dst_input: ", e->dst_input());
}
inputs[e->dst_input()] = NodeDefBuilder::NodeOut{
@ -500,7 +502,7 @@ void AddEdgesFromOutsideCompilationNodes(
const std::vector<DataType>& data_types,
const std::vector<Node*>& outside_compilation_nodes, Graph* g, Node* n) {
// Add edges from outside compilation nodes to While node.
for (int i = original_arg_count; i < data_types.size(); i++) {
for (int i = original_arg_count, end = data_types.size(); i < end; i++) {
Node* outside_compilation_node =
outside_compilation_nodes[i - original_arg_count];
g->AddEdge(outside_compilation_node, 0, n, i + arg_to_input_edge_offset);
@ -619,7 +621,7 @@ Status PostprocessLiftedArgsForWhile(
lifted_arg_nodes_and_outside_compilation_nodes.end(),
std::back_inserter(lifted_arg_nodes),
[](const std::pair<Node*, Node*>& pair) { return pair.first; });
for (int i = original_arg_count; i < data_types.size(); i++) {
for (int i = original_arg_count, end = data_types.size(); i < end; i++) {
TF_ASSIGN_OR_RETURN(Node * arg_node,
AddOutsideCompilationInputArgToFunctionBody(
*body_function_body, i, data_types[i]));
@ -648,7 +650,7 @@ Status PostprocessLiftedArgsForWhile(
AttrSlice(&cond_func.attr()), fld,
&cond_function_body));
for (int i = original_arg_count; i < data_types.size(); i++) {
for (int i = original_arg_count, end = data_types.size(); i < end; i++) {
xla::StatusOr<Node*> arg_node_or =
AddOutsideCompilationInputArgToFunctionBody(*cond_function_body, i,
data_types[i]);
@ -759,7 +761,7 @@ Status PostprocessLiftedArgsForIf(
data_types, outside_compilation_nodes, g,
n);
for (int i = original_arg_count; i < data_types.size(); ++i) {
for (int i = original_arg_count, end = data_types.size(); i < end; ++i) {
TF_ASSIGN_OR_RETURN(Node * then_branch_arg_node,
AddOutsideCompilationInputArgToFunctionBody(
*then_branch_function_body, i, data_types[i]));
@ -837,7 +839,7 @@ Status PostprocessLiftedArgsForCall(
lifted_arg_nodes_and_outside_compilation_nodes.end(),
std::back_inserter(lifted_arg_nodes),
[](const std::pair<Node*, Node*>& pair) { return pair.first; });
for (int i = original_arg_count; i < data_types.size(); ++i) {
for (int i = original_arg_count, end = data_types.size(); i < end; ++i) {
TF_ASSIGN_OR_RETURN(
Node * arg_node,
AddOutsideCompilationInputArgToFunctionBody(*fbody, i, data_types[i]));
@ -855,7 +857,7 @@ Status PostprocessLiftedArgsForCall(
// We need to recreate the node. Otherwise TF will not know n->num_inputs()
// has increased.
NodeDef node_def = n->def();
for (int i = original_arg_count; i < data_types.size(); i++) {
for (int i = original_arg_count, end = data_types.size(); i < end; i++) {
Node* outside_compilation_node =
lifted_arg_nodes_and_outside_compilation_nodes[i - original_arg_count]
.second;
@ -1803,8 +1805,9 @@ TF_ATTRIBUTE_NOINLINE Status ExtractOutsideCompilationForFuncCallNode(
if (e->IsControlEdge()) {
continue;
}
TF_RET_CHECK(e->dst_input() >= 0 && e->dst_input() < inputs.size());
const int input_size_check = e->dst_input() < inputs.size();
TF_RET_CHECK(e->dst_input() >= 0 && input_size_check);
inputs[e->dst_input()] =
NodeDefBuilder::NodeOut{e->src()->name(), e->src_output(),
e->src()->output_type(e->src_output())};

2
tensorflow/compiler/jit/graphcycles/graphcycles.cc
View File

@ -461,7 +461,7 @@ string GraphCycles::DebugString() const {
}
string result = "digraph {\n";
for (int i = 0; i < rep_->nodes_.size(); i++) {
for (int i = 0, end = rep_->nodes_.size(); i < end; i++) {
if (free_nodes_set.contains(i)) {
continue;
}

2
tensorflow/compiler/jit/increase_dynamism_for_auto_jit_pass.cc
View File

@ -194,7 +194,7 @@ Status ComputeSliceSize(const Scope& host_scope,
ConstantCache constant_pool(host_scope, control_deps);
std::vector<Output> slice_size;
for (int i = 0; i < slice_inputs.size_as_vector.size(); i++) {
for (int i = 0, end = slice_inputs.size_as_vector.size(); i < end; i++) {
if (slice_inputs.size_as_vector[i] >= 0) {
slice_size.push_back(
constant_pool.Get1DHostConstant(slice_inputs.size_as_vector[i]));

2
tensorflow/compiler/jit/shape_inference.cc
View File

@ -36,7 +36,7 @@ Status ShapeHandleToTensorShape(shape_inference::InferenceContext* context,
if (!context->RankKnown(handle)) return Status::OK();
std::vector<int64> dims(context->Rank(handle));
for (int32 i = 0; i < dims.size(); ++i) {
for (int32 i = 0, end = dims.size(); i < end; ++i) {
dims[i] = context->Value(context->Dim(handle, i));
}
return PartialTensorShape::MakePartialShape(dims.data(), dims.size(), shape);

2
tensorflow/compiler/jit/xla_cluster_util.cc
View File

@ -489,7 +489,7 @@ Status GetNodesRelatedToRefVariablesInDirection(
/*stable_comparator=*/NodeComparatorName());
}
int old_result_size;
size_t old_result_size;
int iterations = 0;
const int kMaxIterations = 10 * 1000;

12
tensorflow/compiler/jit/xla_compilation_cache.cc
View File

@ -97,7 +97,7 @@ bool XlaCompilationCache::Signature::operator==(const Signature& other) const {
if (arg_shapes != other.arg_shapes) return false;
if (arg_values.size() != other.arg_values.size()) return false;
for (int i = 0; i < arg_values.size(); ++i) {
for (int i = 0, end = arg_values.size(); i < end; ++i) {
if (arg_values[i].dtype() != other.arg_values[i].dtype() ||
arg_values[i].shape() != other.arg_values[i].shape() ||
arg_values[i].tensor_data() != other.arg_values[i].tensor_data()) {
@ -158,7 +158,7 @@ Status XlaCompilationCache::BuildExecutable(
std::vector<const xla::Shape*> argument_layouts(
result.xla_input_shapes.size());
for (int i = 0; i < result.xla_input_shapes.size(); ++i) {
for (int i = 0, end = result.xla_input_shapes.size(); i < end; ++i) {
argument_layouts[i] = &result.xla_input_shapes[i];
}
xla::ExecutableBuildOptions build_options;
@ -224,7 +224,7 @@ static xla::StatusOr<std::unique_ptr<Graph>> CreateGraph(
// Create dummy _Arg nodes. Link these to `node` and also via a control
// dependency edge to the _SOURCE node.
for (int64 i = 0; i < args.size(); ++i) {
for (int64 i = 0, end = args.size(); i < end; ++i) {
Node* node;
string arg_name = absl::StrCat("_arg", i);
Status status =
@ -240,7 +240,7 @@ static xla::StatusOr<std::unique_ptr<Graph>> CreateGraph(
}
// Similarly with return values, create dummy _Retval nodes fed by `node`.
for (int64 i = 0; i < result_types.size(); ++i) {
for (int64 i = 0, end = result_types.size(); i < end; ++i) {
Node* node;
string retval_name = absl::StrCat("_retval", i);
Status status = NodeBuilder(retval_name, FunctionLibraryDefinition::kRetOp)
@ -271,7 +271,7 @@ Status XlaCompilationCache::CompileSingleOp(
auto compile_op = [&](XlaCompiler* compiler,
XlaCompiler::CompilationResult* result) {
std::vector<DataType> result_dtypes(ctx->num_outputs());
for (int i = 0; i < result_dtypes.size(); ++i) {
for (int i = 0, end = result_dtypes.size(); i < end; ++i) {
result_dtypes[i] = ctx->expected_output_dtype(i);
}
@ -330,7 +330,7 @@ Status XlaCompilationCache::CompileImpl(
if (VLOG_IS_ON(2)) {
VLOG(2) << "num_inputs=" << args.size();
for (int i = 0; i < args.size(); i++) {
for (int i = 0, end = args.size(); i < end; i++) {
VLOG(3) << i << ": " << args[i].HumanString();
}
}

8
tensorflow/compiler/jit/xla_launch_util.cc
View File

@ -156,7 +156,7 @@ Status SnapshotResourceVariables(OpKernelContext* ctx,
absl::Span<const int> variable_indices,
absl::Span<VariableInfo const> variable_infos,
ResourceVarsSnapshot* result) {
for (int i = 0; i < variable_indices.size(); i++) {
for (int i = 0, end = variable_indices.size(); i < end; i++) {
Var* var = variable_infos[i].var();
(*result)[variable_indices[i]] =
var ? absl::make_optional(*var->tensor()) : absl::nullopt;
@ -206,7 +206,7 @@ XlaComputationLaunchContext::PopulateInputs(
xla::TransferManager* transfer_manager =
client_->backend().transfer_manager();
for (int i = 0; i < compilation_result->xla_input_shapes.size(); ++i) {
for (int i = 0, end = compilation_result->xla_input_shapes.size(); i < end; ++i) {
int arg_num = compilation_result->input_mapping[i];
CHECK_GE(arg_num, missing_ctx_input_prefix);
const xla::Shape& shape = compilation_result->xla_input_shapes[i];
@ -466,7 +466,7 @@ Status XlaComputationLaunchContext::PopulateOutputs(
// Copy XLA results to the OpOutputList.
int output_num = 0;
for (int i = 0; i < ctx->num_outputs(); ++i) {
for (int i = 0, end = ctx->num_outputs(); i < end; ++i) {
const TensorShape& shape = output_tensor_shapes[i];
const DataType& type = compilation_result->outputs[i].type;
VLOG(2) << "Populating output for retval " << i << " shape "
@ -514,7 +514,7 @@ Status XlaComputationLaunchContext::PopulateOutputs(
}
// Apply variable updates, if any.
for (int i = 0; i < compilation_result->resource_updates.size(); ++i) {
for (int i = 0, end = compilation_result->resource_updates.size(); i < end; ++i) {
const XlaCompiler::ResourceUpdate& write =
compilation_result->resource_updates[i];
int actual_input_index = write.input_index - missing_ctx_input_prefix;