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[ONNX] replace AT_ASSERT with TORCH_INTERNAL_ASSERT #84790

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[ONNX] replace AT_ASSERT with TORCH_INTERNAL_ASSERT #84790

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6 changes: 3 additions & 3 deletions torch/csrc/jit/passes/onnx/constant_fold.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -202,7 +202,7 @@ c10::optional<at::Tensor> runTorchSlice_opset10(
at::Tensor runTorchArange_opset11(
const Node* node,
const std::vector<at::Tensor>& inputTensorValues) {
AT_ASSERT(inputTensorValues.size() == 3);
TORCH_INTERNAL_ASSERT(inputTensorValues.size() == 3);
auto dtype = inputTensorValues[0].scalar_type();
at::Tensor updated_val;
switch (dtype) {
Expand Down Expand Up @@ -575,7 +575,7 @@ std::vector<at::Tensor> getValues(
"getValues: Unsupported kind of constant node found.");
}
}
AT_ASSERT(inputTensorValues.size() == numInputs);
TORCH_INTERNAL_ASSERT(inputTensorValues.size() == numInputs);
return inputTensorValues;
}

Expand Down Expand Up @@ -618,7 +618,7 @@ void ConstantFoldONNX(Block* b, ParamMap& paramsDict, int opset_version) {
"Constant folding not applied.");
return;
}
AT_ASSERT(b->param_node());
TORCH_INTERNAL_ASSERT(b->param_node());
auto valsToParamsMap = buildValueToParamsMap(b, paramsDict);
// Only the root block is constant-folded. Folding nested blocks is
// not supported for now.
Expand Down
2 changes: 1 addition & 1 deletion torch/csrc/jit/passes/onnx/function_substitution.cpp
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Expand Up @@ -85,7 +85,7 @@ void functionCallSubstitution(Block* block) {
Node* cur = *it++;
switch (cur->kind()) {
case prim::CallFunction: {
AT_ASSERT(cur->input(0)->node()->kind() == prim::Constant);
TORCH_INTERNAL_ASSERT(cur->input(0)->node()->kind() == prim::Constant);
auto function_constant = cur->input(0)->node();
auto fun_type =
function_constant->output()->type()->expect<FunctionType>();
Expand Down
8 changes: 4 additions & 4 deletions torch/csrc/jit/passes/onnx/pattern_conversion/pattern_conversion.cpp
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Expand Up @@ -130,7 +130,7 @@ std::unordered_map<int64_t, ConvertedIndex> MergeSliceAndSelectToIndices(
cur_dim++;
}

AT_ASSERT(cur_dim == dim);
TORCH_INTERNAL_ASSERT(cur_dim == dim);
if (node->kind() == aten::slice) {
auto size = CreateSizeOfDim(orig_data, dim, index_put_node);
auto index_tensor = ConvertSliceToIndex(node, size, index_put_node);
Expand Down Expand Up @@ -165,7 +165,7 @@ std::unordered_map<int64_t, ConvertedIndex> MergeSliceAndSelectToIndices(
}

// Each dimension should have its associated index tensor.
AT_ASSERT((int64_t)dim_index_map.size() == cur_dim);
TORCH_INTERNAL_ASSERT((int64_t)dim_index_map.size() == cur_dim);
return dim_index_map;
}

Expand All @@ -190,7 +190,7 @@ std::vector<Value*> ReshapeToAdvancedIndexingFormat(
size_t tensor_ind_count = 0;
for (const auto i : c10::irange(dim_index_map.size())) {
auto index_i = dim_index_map.find(i);
AT_ASSERT(index_i != dim_index_map.end());
TORCH_INTERNAL_ASSERT(index_i != dim_index_map.end());
if (index_i->second.orig_node_kind == aten::index) {
if (i < min_index_dim)
min_index_dim = i;
Expand All @@ -212,7 +212,7 @@ std::vector<Value*> ReshapeToAdvancedIndexingFormat(
for (const auto i : c10::irange(dim_index_map.size())) {
size_t ind_size = 0;
auto index_i = dim_index_map.find(i);
AT_ASSERT(index_i != dim_index_map.end());
TORCH_INTERNAL_ASSERT(index_i != dim_index_map.end());
Value* index = index_i->second.index;
switch (index_i->second.orig_node_kind) {
case aten::select:
Expand Down
8 changes: 4 additions & 4 deletions torch/csrc/jit/passes/onnx/peephole.cpp
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Expand Up @@ -56,11 +56,11 @@ bool isNopTranspose(const std::vector<int64_t>& perm) {
std::vector<int64_t> composeTransposes(
const std::vector<int64_t>& t1,
const std::vector<int64_t>& t2) {
AT_ASSERT(t1.size() == t2.size());
TORCH_INTERNAL_ASSERT(t1.size() == t2.size());
std::vector<int64_t> ret;
ret.reserve(t1.size());
for (const auto& i : t2) {
AT_ASSERT(i < int64_t(t1.size()));
TORCH_INTERNAL_ASSERT(i < int64_t(t1.size()));
ret.push_back(t1[i]);
}
return ret;
Expand Down Expand Up @@ -131,7 +131,7 @@ void fuseBroadcast(Block* b) {

auto broadcast_positions = getBroadcastPositions(n);
if (!broadcast_positions.empty()) {
AT_ASSERT(!n->hasAttribute(attr::axis));
TORCH_INTERNAL_ASSERT(!n->hasAttribute(attr::axis));
}

for (size_t position : broadcast_positions) {
Expand Down Expand Up @@ -627,7 +627,7 @@ static void speculateOps(Block* block) {
static void replaceInputWithList(Node* node, size_t i, ArrayRef<Value*> to) {
node->removeInput(i);
for (auto* to_val : to) {
AT_ASSERT(to_val->owningGraph() == node->owningGraph());
TORCH_INTERNAL_ASSERT(to_val->owningGraph() == node->owningGraph());
node->insertInput(i++, to_val);
}
}
Expand Down
2 changes: 1 addition & 1 deletion torch/csrc/jit/passes/onnx/scalar_type_analysis.cpp
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Expand Up @@ -75,7 +75,7 @@ static bool IsComparisonOp(const NodeKind& nkind) {
static TensorTypePtr CreateProfiledTensorTypeWithScalarType(
const TensorTypePtr& typePtr,
const c10::ScalarType& scalar_type) {
AT_ASSERT(typePtr != nullptr);
TORCH_INTERNAL_ASSERT(typePtr != nullptr);
return typePtr->withScalarType({scalar_type});
}

Expand Down