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Anurag Dixit
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chore: apply lint
Signed-off-by: Anurag Dixit <[email protected]>
1 parent dad4620 commit b7e8d1c

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2 files changed

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-97
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‎core/conversion/converters/impl/shuffle.cpp

Lines changed: 96 additions & 96 deletions
Original file line numberDiff line numberDiff line change
@@ -66,102 +66,102 @@ static auto shuffle_registrations TORCHTRT_UNUSED =
6666
}})
6767
.pattern(
6868
{"aten::unflatten.int(Tensor self, int dim, int[] sizes) -> (Tensor)",
69-
[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
70-
auto in = args[0].ITensorOrFreeze(ctx);
71-
auto dim = args[1].unwrapToInt();
72-
auto in_shape = util::toVec(in->getDimensions());
73-
std::vector<int64_t> new_shape;
74-
nvinfer1::ITensor* shape_tensor;
75-
if (ctx->input_is_dynamic) {
76-
/*
77-
* In case the dim is negative
78-
* If the dim in negative range is larger than in_shape,
79-
* then it should run into index out of bound error as expected
80-
*/
81-
if (dim < 0) {
82-
dim = in_shape.size() + dim;
83-
}
84-
std::cout << "Dynamic shape case" << std::endl;
85-
LOG_DEBUG("Using dynamic version of reshape layer");
86-
if (args[2].isITensorList()) {
87-
std::cout << "isTensorList case" << std::endl;
88-
LOG_DEBUG("Shape tensor is an ITensorList");
89-
auto expand_shape = args[2].unwrapToITensorList();
90-
auto shape_layer = ctx->net->addShape(*in);
91-
TORCHTRT_CHECK(shape_layer, "Unable to create shape layer from node: " << *n);
92-
auto shape_1d_tensor = shape_layer->getOutput(0);
93-
94-
std::vector<int> before_dim_indices_vector(dim);
95-
std::iota(before_dim_indices_vector.begin(), before_dim_indices_vector.end(), 0);
96-
97-
nvinfer1::ITensor* before_dim_gather_out = nullptr;
98-
if(before_dim_indices_vector.size()){
99-
at::Tensor before_dim_indices = torch::tensor(before_dim_indices_vector).to(torch::kI32);
100-
auto before_dim_indices_out = converters::tensor_to_const(ctx, before_dim_indices);
101-
auto before_dim_gather_layer = ctx->net->addGather(*shape_1d_tensor, *before_dim_indices_out, 0);
102-
TORCHTRT_CHECK(before_dim_gather_layer, "Unable to create gather layer from node: " << *n);
103-
before_dim_gather_out = before_dim_gather_layer->getOutput(0);
104-
}
105-
106-
std::vector<int> after_dim_indices_vector(in_shape.size() - (dim + 1));
107-
std::iota(after_dim_indices_vector.begin(), after_dim_indices_vector.end(), dim + 1);
108-
109-
nvinfer1::ITensor* after_dim_gather_out = nullptr;
110-
if(after_dim_indices_vector.size()){
111-
at::Tensor after_dim_indices = torch::tensor(after_dim_indices_vector).to(torch::kI32);
112-
auto after_dim_indices_out = converters::tensor_to_const(ctx, after_dim_indices);
113-
auto after_dim_gather_layer = ctx->net->addGather(*shape_1d_tensor, *after_dim_indices_out, 0);
114-
TORCHTRT_CHECK(after_dim_gather_layer, "Unable to create gather layer from node: " << *n);
115-
after_dim_gather_out = after_dim_gather_layer->getOutput(0);
116-
}
117-
118-
std::vector<nvinfer1::ITensor*> shape_tensors;
119-
if(before_dim_gather_out){
120-
shape_tensors.push_back(before_dim_gather_out);
121-
}
122-
for(auto new_shape_tensor : expand_shape){
123-
shape_tensors.push_back(new_shape_tensor);
124-
}
125-
if(after_dim_gather_out){
126-
shape_tensors.push_back(after_dim_gather_out);
127-
}
128-
129-
auto shape_cat_layer = ctx->net->addConcatenation(shape_tensors.data(), shape_tensors.size());
130-
TORCHTRT_CHECK(shape_cat_layer, "Unable to create cat layer from node: " << *n);
131-
shape_tensor = shape_cat_layer->getOutput(0);
132-
LOG_DEBUG("Shape tensor shape: " << shape_tensor->getDimensions());
133-
} else if (args[2].isIntList()) {
134-
auto shape_vec = args[2].unwrapToIntList().vec();
135-
// New shape
136-
new_shape.insert(new_shape.end(), in_shape.begin(), in_shape.begin() + dim);
137-
new_shape.insert(new_shape.end(), shape_vec.begin(), shape_vec.end());
138-
new_shape.insert(new_shape.end(), in_shape.begin() + dim + 1, in_shape.end());
139-
140-
shape_tensor = tensor_to_const(ctx, torch::tensor(new_shape).to(torch::kI32));
141-
} else {
142-
LOG_ERROR(
143-
"Invalid IValue type of " << args[2].ivalue_type()
144-
<< " detected for shape tensor from node: " << *n);
145-
}
146-
}
147-
else {
148-
new_shape = torch::unflatten(torch::rand(in_shape), dim, args[2].unwrapToIntList().vec()).sizes().vec();
149-
}
150-
auto shuffle = ctx->net->addShuffle(*in);
151-
shuffle->setName(util::node_info(n).c_str());
152-
TORCHTRT_CHECK(shuffle, "Unable to create shuffle layer from node: " << *n);
153-
154-
if (ctx->input_is_dynamic) {
155-
shuffle->setInput(1, *shape_tensor);
156-
} else {
157-
shuffle->setReshapeDimensions(util::toDims(new_shape));
158-
}
159-
160-
auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle->getOutput(0));
161-
LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
162-
163-
return true;
164-
}})
69+
[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
70+
auto in = args[0].ITensorOrFreeze(ctx);
71+
auto dim = args[1].unwrapToInt();
72+
auto in_shape = util::toVec(in->getDimensions());
73+
std::vector<int64_t> new_shape;
74+
nvinfer1::ITensor* shape_tensor;
75+
if (ctx->input_is_dynamic) {
76+
/*
77+
* In case the dim is negative
78+
* If the dim in negative range is larger than in_shape,
79+
* then it should run into index out of bound error as expected
80+
*/
81+
if (dim < 0) {
82+
dim = in_shape.size() + dim;
83+
}
84+
std::cout << "Dynamic shape case" << std::endl;
85+
LOG_DEBUG("Using dynamic version of reshape layer");
86+
if (args[2].isITensorList()) {
87+
std::cout << "isTensorList case" << std::endl;
88+
LOG_DEBUG("Shape tensor is an ITensorList");
89+
auto expand_shape = args[2].unwrapToITensorList();
90+
auto shape_layer = ctx->net->addShape(*in);
91+
TORCHTRT_CHECK(shape_layer, "Unable to create shape layer from node: " << *n);
92+
auto shape_1d_tensor = shape_layer->getOutput(0);
93+
94+
std::vector<int> before_dim_indices_vector(dim);
95+
std::iota(before_dim_indices_vector.begin(), before_dim_indices_vector.end(), 0);
96+
97+
nvinfer1::ITensor* before_dim_gather_out = nullptr;
98+
if (before_dim_indices_vector.size()) {
99+
at::Tensor before_dim_indices = torch::tensor(before_dim_indices_vector).to(torch::kI32);
100+
auto before_dim_indices_out = converters::tensor_to_const(ctx, before_dim_indices);
101+
auto before_dim_gather_layer = ctx->net->addGather(*shape_1d_tensor, *before_dim_indices_out, 0);
102+
TORCHTRT_CHECK(before_dim_gather_layer, "Unable to create gather layer from node: " << *n);
103+
before_dim_gather_out = before_dim_gather_layer->getOutput(0);
104+
}
105+
106+
std::vector<int> after_dim_indices_vector(in_shape.size() - (dim + 1));
107+
std::iota(after_dim_indices_vector.begin(), after_dim_indices_vector.end(), dim + 1);
108+
109+
nvinfer1::ITensor* after_dim_gather_out = nullptr;
110+
if (after_dim_indices_vector.size()) {
111+
at::Tensor after_dim_indices = torch::tensor(after_dim_indices_vector).to(torch::kI32);
112+
auto after_dim_indices_out = converters::tensor_to_const(ctx, after_dim_indices);
113+
auto after_dim_gather_layer = ctx->net->addGather(*shape_1d_tensor, *after_dim_indices_out, 0);
114+
TORCHTRT_CHECK(after_dim_gather_layer, "Unable to create gather layer from node: " << *n);
115+
after_dim_gather_out = after_dim_gather_layer->getOutput(0);
116+
}
117+
118+
std::vector<nvinfer1::ITensor*> shape_tensors;
119+
if (before_dim_gather_out) {
120+
shape_tensors.push_back(before_dim_gather_out);
121+
}
122+
for (auto new_shape_tensor : expand_shape) {
123+
shape_tensors.push_back(new_shape_tensor);
124+
}
125+
if (after_dim_gather_out) {
126+
shape_tensors.push_back(after_dim_gather_out);
127+
}
128+
129+
auto shape_cat_layer = ctx->net->addConcatenation(shape_tensors.data(), shape_tensors.size());
130+
TORCHTRT_CHECK(shape_cat_layer, "Unable to create cat layer from node: " << *n);
131+
shape_tensor = shape_cat_layer->getOutput(0);
132+
LOG_DEBUG("Shape tensor shape: " << shape_tensor->getDimensions());
133+
} else if (args[2].isIntList()) {
134+
auto shape_vec = args[2].unwrapToIntList().vec();
135+
// New shape
136+
new_shape.insert(new_shape.end(), in_shape.begin(), in_shape.begin() + dim);
137+
new_shape.insert(new_shape.end(), shape_vec.begin(), shape_vec.end());
138+
new_shape.insert(new_shape.end(), in_shape.begin() + dim + 1, in_shape.end());
139+
140+
shape_tensor = tensor_to_const(ctx, torch::tensor(new_shape).to(torch::kI32));
141+
} else {
142+
LOG_ERROR(
143+
"Invalid IValue type of " << args[2].ivalue_type()
144+
<< " detected for shape tensor from node: " << *n);
145+
}
146+
} else {
147+
new_shape =
148+
torch::unflatten(torch::rand(in_shape), dim, args[2].unwrapToIntList().vec()).sizes().vec();
149+
}
150+
auto shuffle = ctx->net->addShuffle(*in);
151+
shuffle->setName(util::node_info(n).c_str());
152+
TORCHTRT_CHECK(shuffle, "Unable to create shuffle layer from node: " << *n);
153+
154+
if (ctx->input_is_dynamic) {
155+
shuffle->setInput(1, *shape_tensor);
156+
} else {
157+
shuffle->setReshapeDimensions(util::toDims(new_shape));
158+
}
159+
160+
auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle->getOutput(0));
161+
LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
162+
163+
return true;
164+
}})
165165
.pattern(
166166
{"aten::reshape(Tensor self, int[] shape) -> (Tensor)",
167167
[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {

‎tests/cpp/test_dynamic_size.cpp

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -257,4 +257,4 @@ TEST(Converters, ATenUnflattenDynShapeITensorShapeCorrectlyLastDim) {
257257
auto trt_results = torch_tensorrt::tests::util::RunGraphEngineDynamic(g, params, {in}, true);
258258

259259
ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0], 2e-6));
260-
}
260+
}

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