feat: Added support for aten::unflatten converter #1808
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Signed-off-by: Anurag Dixit <[email protected]>
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There are some changes that do not conform to C++ style guidelines:
diff --git a/home/runner/work/TensorRT/TensorRT/core/conversion/converters/impl/shuffle.cpp b/tmp/changes.txt
index 314fe74..4ac9699 100644
--- a/home/runner/work/TensorRT/TensorRT/core/conversion/converters/impl/shuffle.cpp
+++ b/tmp/changes.txt
@@ -66,102 +66,102 @@ static auto shuffle_registrations TORCHTRT_UNUSED =
}})
.pattern(
{"aten::unflatten.int(Tensor self, int dim, int[] sizes) -> (Tensor)",
- [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
- auto in = args[0].ITensorOrFreeze(ctx);
- auto dim = args[1].unwrapToInt();
- auto in_shape = util::toVec(in->getDimensions());
- std::vector<int64_t> new_shape;
- nvinfer1::ITensor* shape_tensor;
- if (ctx->input_is_dynamic) {
- /*
- * In case the dim is negative
- * If the dim in negative range is larger than in_shape,
- * then it should run into index out of bound error as expected
- */
- if (dim < 0) {
- dim = in_shape.size() + dim;
- }
- std::cout << "Dynamic shape case" << std::endl;
- LOG_DEBUG("Using dynamic version of reshape layer");
- if (args[2].isITensorList()) {
- std::cout << "isTensorList case" << std::endl;
- LOG_DEBUG("Shape tensor is an ITensorList");
- auto expand_shape = args[2].unwrapToITensorList();
- auto shape_layer = ctx->net->addShape(*in);
- TORCHTRT_CHECK(shape_layer, "Unable to create shape layer from node: " << *n);
- auto shape_1d_tensor = shape_layer->getOutput(0);
-
- std::vector<int> before_dim_indices_vector(dim);
- std::iota(before_dim_indices_vector.begin(), before_dim_indices_vector.end(), 0);
-
- nvinfer1::ITensor* before_dim_gather_out = nullptr;
- if(before_dim_indices_vector.size()){
- at::Tensor before_dim_indices = torch::tensor(before_dim_indices_vector).to(torch::kI32);
- auto before_dim_indices_out = converters::tensor_to_const(ctx, before_dim_indices);
- auto before_dim_gather_layer = ctx->net->addGather(*shape_1d_tensor, *before_dim_indices_out, 0);
- TORCHTRT_CHECK(before_dim_gather_layer, "Unable to create gather layer from node: " << *n);
- before_dim_gather_out = before_dim_gather_layer->getOutput(0);
- }
-
- std::vector<int> after_dim_indices_vector(in_shape.size() - (dim + 1));
- std::iota(after_dim_indices_vector.begin(), after_dim_indices_vector.end(), dim + 1);
-
- nvinfer1::ITensor* after_dim_gather_out = nullptr;
- if(after_dim_indices_vector.size()){
- at::Tensor after_dim_indices = torch::tensor(after_dim_indices_vector).to(torch::kI32);
- auto after_dim_indices_out = converters::tensor_to_const(ctx, after_dim_indices);
- auto after_dim_gather_layer = ctx->net->addGather(*shape_1d_tensor, *after_dim_indices_out, 0);
- TORCHTRT_CHECK(after_dim_gather_layer, "Unable to create gather layer from node: " << *n);
- after_dim_gather_out = after_dim_gather_layer->getOutput(0);
- }
-
- std::vector<nvinfer1::ITensor*> shape_tensors;
- if(before_dim_gather_out){
- shape_tensors.push_back(before_dim_gather_out);
- }
- for(auto new_shape_tensor : expand_shape){
- shape_tensors.push_back(new_shape_tensor);
- }
- if(after_dim_gather_out){
- shape_tensors.push_back(after_dim_gather_out);
- }
-
- auto shape_cat_layer = ctx->net->addConcatenation(shape_tensors.data(), shape_tensors.size());
- TORCHTRT_CHECK(shape_cat_layer, "Unable to create cat layer from node: " << *n);
- shape_tensor = shape_cat_layer->getOutput(0);
- LOG_DEBUG("Shape tensor shape: " << shape_tensor->getDimensions());
- } else if (args[2].isIntList()) {
- auto shape_vec = args[2].unwrapToIntList().vec();
- // New shape
- new_shape.insert(new_shape.end(), in_shape.begin(), in_shape.begin() + dim);
- new_shape.insert(new_shape.end(), shape_vec.begin(), shape_vec.end());
- new_shape.insert(new_shape.end(), in_shape.begin() + dim + 1, in_shape.end());
-
- shape_tensor = tensor_to_const(ctx, torch::tensor(new_shape).to(torch::kI32));
- } else {
- LOG_ERROR(
- "Invalid IValue type of " << args[2].ivalue_type()
- << " detected for shape tensor from node: " << *n);
- }
- }
- else {
- new_shape = torch::unflatten(torch::rand(in_shape), dim, args[2].unwrapToIntList().vec()).sizes().vec();
- }
- auto shuffle = ctx->net->addShuffle(*in);
- shuffle->setName(util::node_info(n).c_str());
- TORCHTRT_CHECK(shuffle, "Unable to create shuffle layer from node: " << *n);
-
- if (ctx->input_is_dynamic) {
- shuffle->setInput(1, *shape_tensor);
- } else {
- shuffle->setReshapeDimensions(util::toDims(new_shape));
- }
-
- auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle->getOutput(0));
- LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
-
- return true;
- }})
+ [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+ auto in = args[0].ITensorOrFreeze(ctx);
+ auto dim = args[1].unwrapToInt();
+ auto in_shape = util::toVec(in->getDimensions());
+ std::vector<int64_t> new_shape;
+ nvinfer1::ITensor* shape_tensor;
+ if (ctx->input_is_dynamic) {
+ /*
+ * In case the dim is negative
+ * If the dim in negative range is larger than in_shape,
+ * then it should run into index out of bound error as expected
+ */
+ if (dim < 0) {
+ dim = in_shape.size() + dim;
+ }
+ std::cout << "Dynamic shape case" << std::endl;
+ LOG_DEBUG("Using dynamic version of reshape layer");
+ if (args[2].isITensorList()) {
+ std::cout << "isTensorList case" << std::endl;
+ LOG_DEBUG("Shape tensor is an ITensorList");
+ auto expand_shape = args[2].unwrapToITensorList();
+ auto shape_layer = ctx->net->addShape(*in);
+ TORCHTRT_CHECK(shape_layer, "Unable to create shape layer from node: " << *n);
+ auto shape_1d_tensor = shape_layer->getOutput(0);
+
+ std::vector<int> before_dim_indices_vector(dim);
+ std::iota(before_dim_indices_vector.begin(), before_dim_indices_vector.end(), 0);
+
+ nvinfer1::ITensor* before_dim_gather_out = nullptr;
+ if (before_dim_indices_vector.size()) {
+ at::Tensor before_dim_indices = torch::tensor(before_dim_indices_vector).to(torch::kI32);
+ auto before_dim_indices_out = converters::tensor_to_const(ctx, before_dim_indices);
+ auto before_dim_gather_layer = ctx->net->addGather(*shape_1d_tensor, *before_dim_indices_out, 0);
+ TORCHTRT_CHECK(before_dim_gather_layer, "Unable to create gather layer from node: " << *n);
+ before_dim_gather_out = before_dim_gather_layer->getOutput(0);
+ }
+
+ std::vector<int> after_dim_indices_vector(in_shape.size() - (dim + 1));
+ std::iota(after_dim_indices_vector.begin(), after_dim_indices_vector.end(), dim + 1);
+
+ nvinfer1::ITensor* after_dim_gather_out = nullptr;
+ if (after_dim_indices_vector.size()) {
+ at::Tensor after_dim_indices = torch::tensor(after_dim_indices_vector).to(torch::kI32);
+ auto after_dim_indices_out = converters::tensor_to_const(ctx, after_dim_indices);
+ auto after_dim_gather_layer = ctx->net->addGather(*shape_1d_tensor, *after_dim_indices_out, 0);
+ TORCHTRT_CHECK(after_dim_gather_layer, "Unable to create gather layer from node: " << *n);
+ after_dim_gather_out = after_dim_gather_layer->getOutput(0);
+ }
+
+ std::vector<nvinfer1::ITensor*> shape_tensors;
+ if (before_dim_gather_out) {
+ shape_tensors.push_back(before_dim_gather_out);
+ }
+ for (auto new_shape_tensor : expand_shape) {
+ shape_tensors.push_back(new_shape_tensor);
+ }
+ if (after_dim_gather_out) {
+ shape_tensors.push_back(after_dim_gather_out);
+ }
+
+ auto shape_cat_layer = ctx->net->addConcatenation(shape_tensors.data(), shape_tensors.size());
+ TORCHTRT_CHECK(shape_cat_layer, "Unable to create cat layer from node: " << *n);
+ shape_tensor = shape_cat_layer->getOutput(0);
+ LOG_DEBUG("Shape tensor shape: " << shape_tensor->getDimensions());
+ } else if (args[2].isIntList()) {
+ auto shape_vec = args[2].unwrapToIntList().vec();
+ // New shape
+ new_shape.insert(new_shape.end(), in_shape.begin(), in_shape.begin() + dim);
+ new_shape.insert(new_shape.end(), shape_vec.begin(), shape_vec.end());
+ new_shape.insert(new_shape.end(), in_shape.begin() + dim + 1, in_shape.end());
+
+ shape_tensor = tensor_to_const(ctx, torch::tensor(new_shape).to(torch::kI32));
+ } else {
+ LOG_ERROR(
+ "Invalid IValue type of " << args[2].ivalue_type()
+ << " detected for shape tensor from node: " << *n);
+ }
+ } else {
+ new_shape =
+ torch::unflatten(torch::rand(in_shape), dim, args[2].unwrapToIntList().vec()).sizes().vec();
+ }
+ auto shuffle = ctx->net->addShuffle(*in);
+ shuffle->setName(util::node_info(n).c_str());
+ TORCHTRT_CHECK(shuffle, "Unable to create shuffle layer from node: " << *n);
+
+ if (ctx->input_is_dynamic) {
+ shuffle->setInput(1, *shape_tensor);
+ } else {
+ shuffle->setReshapeDimensions(util::toDims(new_shape));
+ }
+
+ auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle->getOutput(0));
+ LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
+
+ return true;
+ }})
.pattern(
{"aten::reshape(Tensor self, int[] shape) -> (Tensor)",
[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
ERROR: Some files do not conform to style guidelinesSigned-off-by: Anurag Dixit <[email protected]>
There was a problem hiding this comment.
There are some changes that do not conform to C++ style guidelines:
diff --git a/home/runner/work/TensorRT/TensorRT/core/conversion/converters/impl/shuffle.cpp b/tmp/changes.txt
index 314fe74..4ac9699 100644
--- a/home/runner/work/TensorRT/TensorRT/core/conversion/converters/impl/shuffle.cpp
+++ b/tmp/changes.txt
@@ -66,102 +66,102 @@ static auto shuffle_registrations TORCHTRT_UNUSED =
}})
.pattern(
{"aten::unflatten.int(Tensor self, int dim, int[] sizes) -> (Tensor)",
- [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
- auto in = args[0].ITensorOrFreeze(ctx);
- auto dim = args[1].unwrapToInt();
- auto in_shape = util::toVec(in->getDimensions());
- std::vector<int64_t> new_shape;
- nvinfer1::ITensor* shape_tensor;
- if (ctx->input_is_dynamic) {
- /*
- * In case the dim is negative
- * If the dim in negative range is larger than in_shape,
- * then it should run into index out of bound error as expected
- */
- if (dim < 0) {
- dim = in_shape.size() + dim;
- }
- std::cout << "Dynamic shape case" << std::endl;
- LOG_DEBUG("Using dynamic version of reshape layer");
- if (args[2].isITensorList()) {
- std::cout << "isTensorList case" << std::endl;
- LOG_DEBUG("Shape tensor is an ITensorList");
- auto expand_shape = args[2].unwrapToITensorList();
- auto shape_layer = ctx->net->addShape(*in);
- TORCHTRT_CHECK(shape_layer, "Unable to create shape layer from node: " << *n);
- auto shape_1d_tensor = shape_layer->getOutput(0);
-
- std::vector<int> before_dim_indices_vector(dim);
- std::iota(before_dim_indices_vector.begin(), before_dim_indices_vector.end(), 0);
-
- nvinfer1::ITensor* before_dim_gather_out = nullptr;
- if(before_dim_indices_vector.size()){
- at::Tensor before_dim_indices = torch::tensor(before_dim_indices_vector).to(torch::kI32);
- auto before_dim_indices_out = converters::tensor_to_const(ctx, before_dim_indices);
- auto before_dim_gather_layer = ctx->net->addGather(*shape_1d_tensor, *before_dim_indices_out, 0);
- TORCHTRT_CHECK(before_dim_gather_layer, "Unable to create gather layer from node: " << *n);
- before_dim_gather_out = before_dim_gather_layer->getOutput(0);
- }
-
- std::vector<int> after_dim_indices_vector(in_shape.size() - (dim + 1));
- std::iota(after_dim_indices_vector.begin(), after_dim_indices_vector.end(), dim + 1);
-
- nvinfer1::ITensor* after_dim_gather_out = nullptr;
- if(after_dim_indices_vector.size()){
- at::Tensor after_dim_indices = torch::tensor(after_dim_indices_vector).to(torch::kI32);
- auto after_dim_indices_out = converters::tensor_to_const(ctx, after_dim_indices);
- auto after_dim_gather_layer = ctx->net->addGather(*shape_1d_tensor, *after_dim_indices_out, 0);
- TORCHTRT_CHECK(after_dim_gather_layer, "Unable to create gather layer from node: " << *n);
- after_dim_gather_out = after_dim_gather_layer->getOutput(0);
- }
-
- std::vector<nvinfer1::ITensor*> shape_tensors;
- if(before_dim_gather_out){
- shape_tensors.push_back(before_dim_gather_out);
- }
- for(auto new_shape_tensor : expand_shape){
- shape_tensors.push_back(new_shape_tensor);
- }
- if(after_dim_gather_out){
- shape_tensors.push_back(after_dim_gather_out);
- }
-
- auto shape_cat_layer = ctx->net->addConcatenation(shape_tensors.data(), shape_tensors.size());
- TORCHTRT_CHECK(shape_cat_layer, "Unable to create cat layer from node: " << *n);
- shape_tensor = shape_cat_layer->getOutput(0);
- LOG_DEBUG("Shape tensor shape: " << shape_tensor->getDimensions());
- } else if (args[2].isIntList()) {
- auto shape_vec = args[2].unwrapToIntList().vec();
- // New shape
- new_shape.insert(new_shape.end(), in_shape.begin(), in_shape.begin() + dim);
- new_shape.insert(new_shape.end(), shape_vec.begin(), shape_vec.end());
- new_shape.insert(new_shape.end(), in_shape.begin() + dim + 1, in_shape.end());
-
- shape_tensor = tensor_to_const(ctx, torch::tensor(new_shape).to(torch::kI32));
- } else {
- LOG_ERROR(
- "Invalid IValue type of " << args[2].ivalue_type()
- << " detected for shape tensor from node: " << *n);
- }
- }
- else {
- new_shape = torch::unflatten(torch::rand(in_shape), dim, args[2].unwrapToIntList().vec()).sizes().vec();
- }
- auto shuffle = ctx->net->addShuffle(*in);
- shuffle->setName(util::node_info(n).c_str());
- TORCHTRT_CHECK(shuffle, "Unable to create shuffle layer from node: " << *n);
-
- if (ctx->input_is_dynamic) {
- shuffle->setInput(1, *shape_tensor);
- } else {
- shuffle->setReshapeDimensions(util::toDims(new_shape));
- }
-
- auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle->getOutput(0));
- LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
-
- return true;
- }})
+ [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+ auto in = args[0].ITensorOrFreeze(ctx);
+ auto dim = args[1].unwrapToInt();
+ auto in_shape = util::toVec(in->getDimensions());
+ std::vector<int64_t> new_shape;
+ nvinfer1::ITensor* shape_tensor;
+ if (ctx->input_is_dynamic) {
+ /*
+ * In case the dim is negative
+ * If the dim in negative range is larger than in_shape,
+ * then it should run into index out of bound error as expected
+ */
+ if (dim < 0) {
+ dim = in_shape.size() + dim;
+ }
+ std::cout << "Dynamic shape case" << std::endl;
+ LOG_DEBUG("Using dynamic version of reshape layer");
+ if (args[2].isITensorList()) {
+ std::cout << "isTensorList case" << std::endl;
+ LOG_DEBUG("Shape tensor is an ITensorList");
+ auto expand_shape = args[2].unwrapToITensorList();
+ auto shape_layer = ctx->net->addShape(*in);
+ TORCHTRT_CHECK(shape_layer, "Unable to create shape layer from node: " << *n);
+ auto shape_1d_tensor = shape_layer->getOutput(0);
+
+ std::vector<int> before_dim_indices_vector(dim);
+ std::iota(before_dim_indices_vector.begin(), before_dim_indices_vector.end(), 0);
+
+ nvinfer1::ITensor* before_dim_gather_out = nullptr;
+ if (before_dim_indices_vector.size()) {
+ at::Tensor before_dim_indices = torch::tensor(before_dim_indices_vector).to(torch::kI32);
+ auto before_dim_indices_out = converters::tensor_to_const(ctx, before_dim_indices);
+ auto before_dim_gather_layer = ctx->net->addGather(*shape_1d_tensor, *before_dim_indices_out, 0);
+ TORCHTRT_CHECK(before_dim_gather_layer, "Unable to create gather layer from node: " << *n);
+ before_dim_gather_out = before_dim_gather_layer->getOutput(0);
+ }
+
+ std::vector<int> after_dim_indices_vector(in_shape.size() - (dim + 1));
+ std::iota(after_dim_indices_vector.begin(), after_dim_indices_vector.end(), dim + 1);
+
+ nvinfer1::ITensor* after_dim_gather_out = nullptr;
+ if (after_dim_indices_vector.size()) {
+ at::Tensor after_dim_indices = torch::tensor(after_dim_indices_vector).to(torch::kI32);
+ auto after_dim_indices_out = converters::tensor_to_const(ctx, after_dim_indices);
+ auto after_dim_gather_layer = ctx->net->addGather(*shape_1d_tensor, *after_dim_indices_out, 0);
+ TORCHTRT_CHECK(after_dim_gather_layer, "Unable to create gather layer from node: " << *n);
+ after_dim_gather_out = after_dim_gather_layer->getOutput(0);
+ }
+
+ std::vector<nvinfer1::ITensor*> shape_tensors;
+ if (before_dim_gather_out) {
+ shape_tensors.push_back(before_dim_gather_out);
+ }
+ for (auto new_shape_tensor : expand_shape) {
+ shape_tensors.push_back(new_shape_tensor);
+ }
+ if (after_dim_gather_out) {
+ shape_tensors.push_back(after_dim_gather_out);
+ }
+
+ auto shape_cat_layer = ctx->net->addConcatenation(shape_tensors.data(), shape_tensors.size());
+ TORCHTRT_CHECK(shape_cat_layer, "Unable to create cat layer from node: " << *n);
+ shape_tensor = shape_cat_layer->getOutput(0);
+ LOG_DEBUG("Shape tensor shape: " << shape_tensor->getDimensions());
+ } else if (args[2].isIntList()) {
+ auto shape_vec = args[2].unwrapToIntList().vec();
+ // New shape
+ new_shape.insert(new_shape.end(), in_shape.begin(), in_shape.begin() + dim);
+ new_shape.insert(new_shape.end(), shape_vec.begin(), shape_vec.end());
+ new_shape.insert(new_shape.end(), in_shape.begin() + dim + 1, in_shape.end());
+
+ shape_tensor = tensor_to_const(ctx, torch::tensor(new_shape).to(torch::kI32));
+ } else {
+ LOG_ERROR(
+ "Invalid IValue type of " << args[2].ivalue_type()
+ << " detected for shape tensor from node: " << *n);
+ }
+ } else {
+ new_shape =
+ torch::unflatten(torch::rand(in_shape), dim, args[2].unwrapToIntList().vec()).sizes().vec();
+ }
+ auto shuffle = ctx->net->addShuffle(*in);
+ shuffle->setName(util::node_info(n).c_str());
+ TORCHTRT_CHECK(shuffle, "Unable to create shuffle layer from node: " << *n);
+
+ if (ctx->input_is_dynamic) {
+ shuffle->setInput(1, *shape_tensor);
+ } else {
+ shuffle->setReshapeDimensions(util::toDims(new_shape));
+ }
+
+ auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle->getOutput(0));
+ LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
+
+ return true;
+ }})
.pattern(
{"aten::reshape(Tensor self, int[] shape) -> (Tensor)",
[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
diff --git a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_dynamic_size.cpp b/tmp/changes.txt
index 5870796..bd2bf90 100644
--- a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_dynamic_size.cpp
+++ b/tmp/changes.txt
@@ -5,7 +5,6 @@
#include "tests/util/util.h"
#include "torch/csrc/jit/ir/irparser.h"
-
TEST(Converters, ATenUnflattenDynShapeShapeCorrectly) {
const auto graph = R"IR(
graph(%x.1 : Tensor):
ERROR: Some files do not conform to style guidelines
|
Hi @andi4191! Thank you for your pull request. We require contributors to sign our Contributor License Agreement, and yours needs attention. You currently have a record in our system, but the CLA is no longer valid, and will need to be resubmitted. ProcessIn order for us to review and merge your suggested changes, please sign at https://code.facebook.com/cla. If you are contributing on behalf of someone else (eg your employer), the individual CLA may not be sufficient and your employer may need to sign the corporate CLA. Once the CLA is signed, our tooling will perform checks and validations. Afterwards, the pull request will be tagged with If you have received this in error or have any questions, please contact us at [email protected]. Thanks! |
7 tasks
|
Created a replica PR for tracking this #2097. Closing this. |
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Description
Added a converter support for aten::unflatten.
This PR is blocked by #1647
This PR should be merged after the above PR is merged.
Fixes # (issue)
Type of change
Please delete options that are not relevant and/or add your own.
Checklist: