feat!: Masking FX path for SBSA build [Do Not Merge yet] #1244
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There are some changes that do not conform to C++ style guidelines:
diff --git a/workspace/py/torch_tensorrt/csrc/register_tensorrt_classes.cpp b/tmp/changes.txt
index 9db567c..ba07250 100644
--- a/workspace/py/torch_tensorrt/csrc/register_tensorrt_classes.cpp
+++ b/tmp/changes.txt
@@ -26,11 +26,11 @@ void RegisterTRTCompileSpec() {
static auto TORCHTRT_UNUSED TRTInputSignatureTSRegistration =
torch::class_<torch_tensorrt::pyapi::InputSignature>("tensorrt", "_InputSignature")
.def(torch::init<>())
- .def("_set_signature_ivalue_torchbind",
- [](const c10::intrusive_ptr<torch_tensorrt::pyapi::InputSignature>& self,
- torch::jit::IValue ival) {
- self->signature_ivalue = ival;
- })
+ .def(
+ "_set_signature_ivalue_torchbind",
+ [](const c10::intrusive_ptr<torch_tensorrt::pyapi::InputSignature>& self, torch::jit::IValue ival) {
+ self->signature_ivalue = ival;
+ })
.def("__str__", &torch_tensorrt::pyapi::InputSignature::to_str);
ADD_FIELD_GET_SET_REGISTRATION(
diff --git a/workspace/core/conversion/evaluators/aten.cpp b/tmp/changes.txt
index ca9ff4d..aa58dc7 100644
--- a/workspace/core/conversion/evaluators/aten.cpp
+++ b/tmp/changes.txt
@@ -176,7 +176,7 @@ auto aten_registrations TORCHTRT_UNUSED =
int64_t start = 0;
auto startIVal = args.at(n->input(1)).IValue();
- if(!startIVal->isNone()){
+ if (!startIVal->isNone()) {
start = args.at(n->input(1)).unwrapToInt();
}
int64_t end = args.at(n->input(2)).unwrapToInt();
diff --git a/workspace/core/conversion/evaluators/eval_util.cpp b/tmp/changes.txt
index 742a4f4..c14f9a6 100644
--- a/workspace/core/conversion/evaluators/eval_util.cpp
+++ b/tmp/changes.txt
@@ -20,7 +20,6 @@ int64_t normalizeIndex(int64_t idx, int64_t list_size) {
return idx;
}
-
// TODO: Switch back to PyTorch canonical implimentation
c10::optional<torch::jit::IValue> toIValue(const torch::jit::Value* v) {
if (v->node()->kind() != torch::jit::prim::Constant || v->type()->cast<c10::FunctionType>()) {
diff --git a/workspace/core/conversion/evaluators/prim.cpp b/tmp/changes.txt
index 338c427..244c3e8 100644
--- a/workspace/core/conversion/evaluators/prim.cpp
+++ b/tmp/changes.txt
@@ -260,40 +260,39 @@ auto prim_registrations =
},
EvalOptions().validSchemas({"prim::shape(Tensor a) -> (int[])"})})
.evaluator({torch::jit::prim::TupleConstruct,
- [](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
- auto num_inputs = n->inputs().size();
- c10::IValue tuple = c10::ivalue::Tuple::create();
- switch (num_inputs) {
- case 0:
- tuple = c10::ivalue::Tuple::create();
- break;
- case 1:
- tuple = c10::ivalue::Tuple::create(std::move((*args.at(n->input(0)).IValue())));
- break;
- case 2: {
- tuple = c10::ivalue::Tuple::create(
- std::move(*(args.at(n->input(0)).IValue())),
- std::move(*(args.at(n->input(1)).IValue())));
- break;
- }
- case 3: {
- tuple = c10::ivalue::Tuple::create(
- std::move(*(args.at(n->input(0)).IValue())),
- std::move(*(args.at(n->input(1)).IValue())),
- std::move(*(args.at(n->input(2)).IValue())));
- break;
- }
- default: {
- std::vector<c10::IValue> elems;
- for (size_t i = 0; i < num_inputs; i++) {
- elems.push_back(*(args.at(n->input(i)).IValue()));
+ [](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
+ auto num_inputs = n->inputs().size();
+ c10::IValue tuple = c10::ivalue::Tuple::create();
+ switch (num_inputs) {
+ case 0:
+ tuple = c10::ivalue::Tuple::create();
+ break;
+ case 1:
+ tuple = c10::ivalue::Tuple::create(std::move((*args.at(n->input(0)).IValue())));
+ break;
+ case 2: {
+ tuple = c10::ivalue::Tuple::create(
+ std::move(*(args.at(n->input(0)).IValue())), std::move(*(args.at(n->input(1)).IValue())));
+ break;
+ }
+ case 3: {
+ tuple = c10::ivalue::Tuple::create(
+ std::move(*(args.at(n->input(0)).IValue())),
+ std::move(*(args.at(n->input(1)).IValue())),
+ std::move(*(args.at(n->input(2)).IValue())));
+ break;
+ }
+ default: {
+ std::vector<c10::IValue> elems;
+ for (size_t i = 0; i < num_inputs; i++) {
+ elems.push_back(*(args.at(n->input(i)).IValue()));
+ }
+ tuple = c10::ivalue::Tuple::create(std::move(elems));
+ break;
}
- tuple = c10::ivalue::Tuple::create(std::move(elems));
- break;
}
- }
- return c10::optional<torch::jit::IValue>(std::move(tuple));
- }})
+ return c10::optional<torch::jit::IValue>(std::move(tuple));
+ }})
.evaluator({torch::jit::prim::TupleIndex,
[](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
// Outputs is an IValue which has list of tensors which can be found in ctx->evaluated_value_map
diff --git a/workspace/core/conversion/converters/impl/unary.cpp b/tmp/changes.txt
index a1d03a3..6b0ee2b 100644
--- a/workspace/core/conversion/converters/impl/unary.cpp
+++ b/tmp/changes.txt
@@ -10,45 +10,41 @@ namespace converters {
namespace impl {
namespace {
-
auto abs_registration TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern(
- {"aten::abs (Tensor self) -> Tensor",
- [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
- auto in = args[0].ITensor();
- bool unary_supported_input = in->getType() == nvinfer1::DataType::kFLOAT
- || in->getType() == nvinfer1::DataType::kHALF
- || in->getType() == nvinfer1::DataType::kINT8;
- if(unary_supported_input){
- auto unary_layer = ctx->net->addUnary(*in, nvinfer1::UnaryOperation::kABS);
- TORCHTRT_CHECK(unary_layer, "Unable to create abs layer from node: " << *n);
- unary_layer->setName(util::node_info(n).c_str());
- auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], unary_layer->getOutput(0));
- LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
- return true;
- }
- else{
- //For types not supported by kABS, use an elementwise implementation abs(x) = max(x, -1 * x)
- at::Tensor neg_one = torch::full({1}, -1).to(util::TRTDataTypeToScalarType(in->getType()));
- auto neg_one_const = tensor_to_const(ctx, neg_one);
- auto neg_layer = add_elementwise(
+ {"aten::abs (Tensor self) -> Tensor", [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+ auto in = args[0].ITensor();
+ bool unary_supported_input = in->getType() == nvinfer1::DataType::kFLOAT ||
+ in->getType() == nvinfer1::DataType::kHALF || in->getType() == nvinfer1::DataType::kINT8;
+ if (unary_supported_input) {
+ auto unary_layer = ctx->net->addUnary(*in, nvinfer1::UnaryOperation::kABS);
+ TORCHTRT_CHECK(unary_layer, "Unable to create abs layer from node: " << *n);
+ unary_layer->setName(util::node_info(n).c_str());
+ auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], unary_layer->getOutput(0));
+ LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
+ return true;
+ } else {
+ // For types not supported by kABS, use an elementwise implementation abs(x) = max(x, -1 * x)
+ at::Tensor neg_one = torch::full({1}, -1).to(util::TRTDataTypeToScalarType(in->getType()));
+ auto neg_one_const = tensor_to_const(ctx, neg_one);
+ auto neg_layer = add_elementwise(
ctx,
nvinfer1::ElementWiseOperation::kPROD,
in,
neg_one_const,
util::node_info(n) + std::string("_Negation"));
- TORCHTRT_CHECK(neg_layer, "Unable to create prod layer from node: " << *n);
- auto max_layer = add_elementwise(
+ TORCHTRT_CHECK(neg_layer, "Unable to create prod layer from node: " << *n);
+ auto max_layer = add_elementwise(
ctx,
nvinfer1::ElementWiseOperation::kMAX,
in,
neg_layer->getOutput(0),
util::node_info(n) + std::string("_Max"));
- TORCHTRT_CHECK(max_layer, "Unable to create max layer from node: " << *n);
- auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], max_layer->getOutput(0));
- LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
- return true;
- }
- }});
+ TORCHTRT_CHECK(max_layer, "Unable to create max layer from node: " << *n);
+ auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], max_layer->getOutput(0));
+ LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
+ return true;
+ }
+ }});
#define convert(unary, trt_type) \
auto unary##_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern( \
diff --git a/workspace/tests/core/conversion/converters/test_element_wise.cpp b/tmp/changes.txt
index 939c9b7..db60896 100644
--- a/workspace/tests/core/conversion/converters/test_element_wise.cpp
+++ b/tmp/changes.txt
@@ -30,14 +30,15 @@ void pointwise_test_helper(
torch_inputs.push_back(at::randint(1, 5, shape2, {at::kCUDA}));
}
- TORCHTRT_CHECK(!((int_tensors && (float_int_tensors || int_float_tensors)) || (float_int_tensors && int_float_tensors)),
- "Invalid test configuration, only one of int_tensors, float_int_tensors, int_float_tensors can be true");
+ TORCHTRT_CHECK(
+ !((int_tensors && (float_int_tensors || int_float_tensors)) || (float_int_tensors && int_float_tensors)),
+ "Invalid test configuration, only one of int_tensors, float_int_tensors, int_float_tensors can be true");
- if(int_tensors){
- for(size_t i = 0UL; i < torch_inputs.size(); ++i){
+ if (int_tensors) {
+ for (size_t i = 0UL; i < torch_inputs.size(); ++i) {
torch_inputs[i] = torch_inputs[i].to(at::kInt);
}
- } else if(float_int_tensors) {
+ } else if (float_int_tensors) {
TORCHTRT_CHECK(!singleInput, "float_int_tensors tests require two inputs");
torch_inputs[0] = torch_inputs[0].to(at::kFloat);
torch_inputs[1] = torch_inputs[1].to(at::kInt);
diff --git a/workspace/tests/core/conversion/converters/test_unary.cpp b/tmp/changes.txt
index a7ab3bb..1d40c3c 100644
--- a/workspace/tests/core/conversion/converters/test_unary.cpp
+++ b/tmp/changes.txt
@@ -1,9 +1,9 @@
#include <string>
-#include "torch/torch.h"
#include "core/compiler.h"
#include "gtest/gtest.h"
#include "tests/util/util.h"
#include "torch/csrc/jit/ir/irparser.h"
+#include "torch/torch.h"
namespace {
std::string gen_test_graph(const std::string& unary) {
@@ -22,7 +22,7 @@ TEST(Converters, ATenAbsIntConvertsCorrectly) {
auto in = at::tensor({-1, 1, -2, 2, -3, 3}, {at::kCUDA}).to(torch::kInt32);
auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
- auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+ auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
in = at::clone(in);
params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
diff --git a/workspace/tests/core/conversion/converters/test_select.cpp b/tmp/changes.txt
index 03b6bda..67b760a 100644
--- a/workspace/tests/core/conversion/converters/test_select.cpp
+++ b/tmp/changes.txt
@@ -376,7 +376,7 @@ TEST(Converters, ATenSliceListConvertsCorrectly) {
%slice : Tensor[] = aten::slice(%list, %1, %2, %3)
%out.1 : Tensor, %out.2 : Tensor = prim::ListUnpack(%slice)
return (%out.1, %out.2))IR";
-
+
auto g = std::make_shared<torch::jit::Graph>();
torch::jit::parseIR(graph, g.get());
diff --git a/workspace/tests/cpp/test_collections.cpp b/tmp/changes.txt
index 31495a4..d01665a 100644
--- a/workspace/tests/cpp/test_collections.cpp
+++ b/tmp/changes.txt
@@ -135,7 +135,7 @@ TEST(CppAPITests, TestCollectionListInput) {
auto compile_settings = torch_tensorrt::ts::CompileSpec(complex_input_shape2);
compile_settings.min_block_size = 1;
- //compile_settings.torch_executed_ops.push_back("aten::__getitem__");
+ // compile_settings.torch_executed_ops.push_back("aten::__getitem__");
// // FP16 execution
compile_settings.enabled_precisions = {torch::kHalf};
diff --git a/workspace/cpp/src/compile_spec.cpp b/tmp/changes.txt
index 8c7bb8b..cfbc228 100644
--- a/workspace/cpp/src/compile_spec.cpp
+++ b/tmp/changes.txt
@@ -69,22 +69,24 @@ torchtrt::core::CompileSpec init_compile_spec(CompileSpec& external) {
return internal;
} else {
torch::jit::IValue converted_input_signature;
- LOG_WARNING( "Input signature parsing is an experimental feature, behavior and APIs may change");
+ LOG_WARNING("Input signature parsing is an experimental feature, behavior and APIs may change");
to_internal_input_signature(external.graph_inputs.input_signature, converted_input_signature);
torchtrt::core::CompileSpec internal(converted_input_signature);
- TORCHTRT_CHECK(!external.require_full_compilation, \
- "Grouped inputs currently requires partial compilation to be enabled, \
+ TORCHTRT_CHECK(
+ !external.require_full_compilation,
+ "Grouped inputs currently requires partial compilation to be enabled, \
this restriction will be relaxed in a future release");
LOG_DEBUG("Grouped inputs currently requires additional settings to enable the feature");
- LOG_DEBUG("Adding the following ops to torch_executed_ops:" \
- << std::endl << " - aten::__getitem__" \
- << std::endl << " - prim::ListConstruct" \
- << std::endl << " - prim::ListUnpack" \
- << std::endl << " - prim::TupleIndex" \
- << std::endl << " - prim::TupleConstruct" \
- << std::endl << " - prim::TupleUnpack");
+ LOG_DEBUG(
+ "Adding the following ops to torch_executed_ops:" << std::endl
+ << " - aten::__getitem__" << std::endl
+ << " - prim::ListConstruct" << std::endl
+ << " - prim::ListUnpack" << std::endl
+ << " - prim::TupleIndex" << std::endl
+ << " - prim::TupleConstruct" << std::endl
+ << " - prim::TupleUnpack");
external.torch_executed_ops.push_back("aten::__getitem__");
external.torch_executed_ops.push_back("prim::ListConstruct");
external.torch_executed_ops.push_back("prim::ListUnpack");
ERROR: Some files do not conform to style guidelines
There was a problem hiding this comment.
There are some changes that do not conform to C++ style guidelines:
diff --git a/workspace/py/torch_tensorrt/csrc/register_tensorrt_classes.cpp b/tmp/changes.txt
index 9db567c..ba07250 100644
--- a/workspace/py/torch_tensorrt/csrc/register_tensorrt_classes.cpp
+++ b/tmp/changes.txt
@@ -26,11 +26,11 @@ void RegisterTRTCompileSpec() {
static auto TORCHTRT_UNUSED TRTInputSignatureTSRegistration =
torch::class_<torch_tensorrt::pyapi::InputSignature>("tensorrt", "_InputSignature")
.def(torch::init<>())
- .def("_set_signature_ivalue_torchbind",
- [](const c10::intrusive_ptr<torch_tensorrt::pyapi::InputSignature>& self,
- torch::jit::IValue ival) {
- self->signature_ivalue = ival;
- })
+ .def(
+ "_set_signature_ivalue_torchbind",
+ [](const c10::intrusive_ptr<torch_tensorrt::pyapi::InputSignature>& self, torch::jit::IValue ival) {
+ self->signature_ivalue = ival;
+ })
.def("__str__", &torch_tensorrt::pyapi::InputSignature::to_str);
ADD_FIELD_GET_SET_REGISTRATION(
diff --git a/workspace/core/conversion/evaluators/aten.cpp b/tmp/changes.txt
index ca9ff4d..aa58dc7 100644
--- a/workspace/core/conversion/evaluators/aten.cpp
+++ b/tmp/changes.txt
@@ -176,7 +176,7 @@ auto aten_registrations TORCHTRT_UNUSED =
int64_t start = 0;
auto startIVal = args.at(n->input(1)).IValue();
- if(!startIVal->isNone()){
+ if (!startIVal->isNone()) {
start = args.at(n->input(1)).unwrapToInt();
}
int64_t end = args.at(n->input(2)).unwrapToInt();
diff --git a/workspace/core/conversion/evaluators/eval_util.cpp b/tmp/changes.txt
index 742a4f4..c14f9a6 100644
--- a/workspace/core/conversion/evaluators/eval_util.cpp
+++ b/tmp/changes.txt
@@ -20,7 +20,6 @@ int64_t normalizeIndex(int64_t idx, int64_t list_size) {
return idx;
}
-
// TODO: Switch back to PyTorch canonical implimentation
c10::optional<torch::jit::IValue> toIValue(const torch::jit::Value* v) {
if (v->node()->kind() != torch::jit::prim::Constant || v->type()->cast<c10::FunctionType>()) {
diff --git a/workspace/core/conversion/evaluators/prim.cpp b/tmp/changes.txt
index 338c427..244c3e8 100644
--- a/workspace/core/conversion/evaluators/prim.cpp
+++ b/tmp/changes.txt
@@ -260,40 +260,39 @@ auto prim_registrations =
},
EvalOptions().validSchemas({"prim::shape(Tensor a) -> (int[])"})})
.evaluator({torch::jit::prim::TupleConstruct,
- [](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
- auto num_inputs = n->inputs().size();
- c10::IValue tuple = c10::ivalue::Tuple::create();
- switch (num_inputs) {
- case 0:
- tuple = c10::ivalue::Tuple::create();
- break;
- case 1:
- tuple = c10::ivalue::Tuple::create(std::move((*args.at(n->input(0)).IValue())));
- break;
- case 2: {
- tuple = c10::ivalue::Tuple::create(
- std::move(*(args.at(n->input(0)).IValue())),
- std::move(*(args.at(n->input(1)).IValue())));
- break;
- }
- case 3: {
- tuple = c10::ivalue::Tuple::create(
- std::move(*(args.at(n->input(0)).IValue())),
- std::move(*(args.at(n->input(1)).IValue())),
- std::move(*(args.at(n->input(2)).IValue())));
- break;
- }
- default: {
- std::vector<c10::IValue> elems;
- for (size_t i = 0; i < num_inputs; i++) {
- elems.push_back(*(args.at(n->input(i)).IValue()));
+ [](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
+ auto num_inputs = n->inputs().size();
+ c10::IValue tuple = c10::ivalue::Tuple::create();
+ switch (num_inputs) {
+ case 0:
+ tuple = c10::ivalue::Tuple::create();
+ break;
+ case 1:
+ tuple = c10::ivalue::Tuple::create(std::move((*args.at(n->input(0)).IValue())));
+ break;
+ case 2: {
+ tuple = c10::ivalue::Tuple::create(
+ std::move(*(args.at(n->input(0)).IValue())), std::move(*(args.at(n->input(1)).IValue())));
+ break;
+ }
+ case 3: {
+ tuple = c10::ivalue::Tuple::create(
+ std::move(*(args.at(n->input(0)).IValue())),
+ std::move(*(args.at(n->input(1)).IValue())),
+ std::move(*(args.at(n->input(2)).IValue())));
+ break;
+ }
+ default: {
+ std::vector<c10::IValue> elems;
+ for (size_t i = 0; i < num_inputs; i++) {
+ elems.push_back(*(args.at(n->input(i)).IValue()));
+ }
+ tuple = c10::ivalue::Tuple::create(std::move(elems));
+ break;
}
- tuple = c10::ivalue::Tuple::create(std::move(elems));
- break;
}
- }
- return c10::optional<torch::jit::IValue>(std::move(tuple));
- }})
+ return c10::optional<torch::jit::IValue>(std::move(tuple));
+ }})
.evaluator({torch::jit::prim::TupleIndex,
[](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
// Outputs is an IValue which has list of tensors which can be found in ctx->evaluated_value_map
diff --git a/workspace/core/conversion/converters/impl/unary.cpp b/tmp/changes.txt
index a1d03a3..6b0ee2b 100644
--- a/workspace/core/conversion/converters/impl/unary.cpp
+++ b/tmp/changes.txt
@@ -10,45 +10,41 @@ namespace converters {
namespace impl {
namespace {
-
auto abs_registration TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern(
- {"aten::abs (Tensor self) -> Tensor",
- [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
- auto in = args[0].ITensor();
- bool unary_supported_input = in->getType() == nvinfer1::DataType::kFLOAT
- || in->getType() == nvinfer1::DataType::kHALF
- || in->getType() == nvinfer1::DataType::kINT8;
- if(unary_supported_input){
- auto unary_layer = ctx->net->addUnary(*in, nvinfer1::UnaryOperation::kABS);
- TORCHTRT_CHECK(unary_layer, "Unable to create abs layer from node: " << *n);
- unary_layer->setName(util::node_info(n).c_str());
- auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], unary_layer->getOutput(0));
- LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
- return true;
- }
- else{
- //For types not supported by kABS, use an elementwise implementation abs(x) = max(x, -1 * x)
- at::Tensor neg_one = torch::full({1}, -1).to(util::TRTDataTypeToScalarType(in->getType()));
- auto neg_one_const = tensor_to_const(ctx, neg_one);
- auto neg_layer = add_elementwise(
+ {"aten::abs (Tensor self) -> Tensor", [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+ auto in = args[0].ITensor();
+ bool unary_supported_input = in->getType() == nvinfer1::DataType::kFLOAT ||
+ in->getType() == nvinfer1::DataType::kHALF || in->getType() == nvinfer1::DataType::kINT8;
+ if (unary_supported_input) {
+ auto unary_layer = ctx->net->addUnary(*in, nvinfer1::UnaryOperation::kABS);
+ TORCHTRT_CHECK(unary_layer, "Unable to create abs layer from node: " << *n);
+ unary_layer->setName(util::node_info(n).c_str());
+ auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], unary_layer->getOutput(0));
+ LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
+ return true;
+ } else {
+ // For types not supported by kABS, use an elementwise implementation abs(x) = max(x, -1 * x)
+ at::Tensor neg_one = torch::full({1}, -1).to(util::TRTDataTypeToScalarType(in->getType()));
+ auto neg_one_const = tensor_to_const(ctx, neg_one);
+ auto neg_layer = add_elementwise(
ctx,
nvinfer1::ElementWiseOperation::kPROD,
in,
neg_one_const,
util::node_info(n) + std::string("_Negation"));
- TORCHTRT_CHECK(neg_layer, "Unable to create prod layer from node: " << *n);
- auto max_layer = add_elementwise(
+ TORCHTRT_CHECK(neg_layer, "Unable to create prod layer from node: " << *n);
+ auto max_layer = add_elementwise(
ctx,
nvinfer1::ElementWiseOperation::kMAX,
in,
neg_layer->getOutput(0),
util::node_info(n) + std::string("_Max"));
- TORCHTRT_CHECK(max_layer, "Unable to create max layer from node: " << *n);
- auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], max_layer->getOutput(0));
- LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
- return true;
- }
- }});
+ TORCHTRT_CHECK(max_layer, "Unable to create max layer from node: " << *n);
+ auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], max_layer->getOutput(0));
+ LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
+ return true;
+ }
+ }});
#define convert(unary, trt_type) \
auto unary##_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern( \
diff --git a/workspace/tests/core/conversion/converters/test_element_wise.cpp b/tmp/changes.txt
index 939c9b7..db60896 100644
--- a/workspace/tests/core/conversion/converters/test_element_wise.cpp
+++ b/tmp/changes.txt
@@ -30,14 +30,15 @@ void pointwise_test_helper(
torch_inputs.push_back(at::randint(1, 5, shape2, {at::kCUDA}));
}
- TORCHTRT_CHECK(!((int_tensors && (float_int_tensors || int_float_tensors)) || (float_int_tensors && int_float_tensors)),
- "Invalid test configuration, only one of int_tensors, float_int_tensors, int_float_tensors can be true");
+ TORCHTRT_CHECK(
+ !((int_tensors && (float_int_tensors || int_float_tensors)) || (float_int_tensors && int_float_tensors)),
+ "Invalid test configuration, only one of int_tensors, float_int_tensors, int_float_tensors can be true");
- if(int_tensors){
- for(size_t i = 0UL; i < torch_inputs.size(); ++i){
+ if (int_tensors) {
+ for (size_t i = 0UL; i < torch_inputs.size(); ++i) {
torch_inputs[i] = torch_inputs[i].to(at::kInt);
}
- } else if(float_int_tensors) {
+ } else if (float_int_tensors) {
TORCHTRT_CHECK(!singleInput, "float_int_tensors tests require two inputs");
torch_inputs[0] = torch_inputs[0].to(at::kFloat);
torch_inputs[1] = torch_inputs[1].to(at::kInt);
diff --git a/workspace/tests/core/conversion/converters/test_unary.cpp b/tmp/changes.txt
index a7ab3bb..1d40c3c 100644
--- a/workspace/tests/core/conversion/converters/test_unary.cpp
+++ b/tmp/changes.txt
@@ -1,9 +1,9 @@
#include <string>
-#include "torch/torch.h"
#include "core/compiler.h"
#include "gtest/gtest.h"
#include "tests/util/util.h"
#include "torch/csrc/jit/ir/irparser.h"
+#include "torch/torch.h"
namespace {
std::string gen_test_graph(const std::string& unary) {
@@ -22,7 +22,7 @@ TEST(Converters, ATenAbsIntConvertsCorrectly) {
auto in = at::tensor({-1, 1, -2, 2, -3, 3}, {at::kCUDA}).to(torch::kInt32);
auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
- auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+ auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
in = at::clone(in);
params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
diff --git a/workspace/tests/core/conversion/converters/test_select.cpp b/tmp/changes.txt
index 03b6bda..67b760a 100644
--- a/workspace/tests/core/conversion/converters/test_select.cpp
+++ b/tmp/changes.txt
@@ -376,7 +376,7 @@ TEST(Converters, ATenSliceListConvertsCorrectly) {
%slice : Tensor[] = aten::slice(%list, %1, %2, %3)
%out.1 : Tensor, %out.2 : Tensor = prim::ListUnpack(%slice)
return (%out.1, %out.2))IR";
-
+
auto g = std::make_shared<torch::jit::Graph>();
torch::jit::parseIR(graph, g.get());
diff --git a/workspace/tests/cpp/test_collections.cpp b/tmp/changes.txt
index 31495a4..d01665a 100644
--- a/workspace/tests/cpp/test_collections.cpp
+++ b/tmp/changes.txt
@@ -135,7 +135,7 @@ TEST(CppAPITests, TestCollectionListInput) {
auto compile_settings = torch_tensorrt::ts::CompileSpec(complex_input_shape2);
compile_settings.min_block_size = 1;
- //compile_settings.torch_executed_ops.push_back("aten::__getitem__");
+ // compile_settings.torch_executed_ops.push_back("aten::__getitem__");
// // FP16 execution
compile_settings.enabled_precisions = {torch::kHalf};
diff --git a/workspace/cpp/src/compile_spec.cpp b/tmp/changes.txt
index 8c7bb8b..cfbc228 100644
--- a/workspace/cpp/src/compile_spec.cpp
+++ b/tmp/changes.txt
@@ -69,22 +69,24 @@ torchtrt::core::CompileSpec init_compile_spec(CompileSpec& external) {
return internal;
} else {
torch::jit::IValue converted_input_signature;
- LOG_WARNING( "Input signature parsing is an experimental feature, behavior and APIs may change");
+ LOG_WARNING("Input signature parsing is an experimental feature, behavior and APIs may change");
to_internal_input_signature(external.graph_inputs.input_signature, converted_input_signature);
torchtrt::core::CompileSpec internal(converted_input_signature);
- TORCHTRT_CHECK(!external.require_full_compilation, \
- "Grouped inputs currently requires partial compilation to be enabled, \
+ TORCHTRT_CHECK(
+ !external.require_full_compilation,
+ "Grouped inputs currently requires partial compilation to be enabled, \
this restriction will be relaxed in a future release");
LOG_DEBUG("Grouped inputs currently requires additional settings to enable the feature");
- LOG_DEBUG("Adding the following ops to torch_executed_ops:" \
- << std::endl << " - aten::__getitem__" \
- << std::endl << " - prim::ListConstruct" \
- << std::endl << " - prim::ListUnpack" \
- << std::endl << " - prim::TupleIndex" \
- << std::endl << " - prim::TupleConstruct" \
- << std::endl << " - prim::TupleUnpack");
+ LOG_DEBUG(
+ "Adding the following ops to torch_executed_ops:" << std::endl
+ << " - aten::__getitem__" << std::endl
+ << " - prim::ListConstruct" << std::endl
+ << " - prim::ListUnpack" << std::endl
+ << " - prim::TupleIndex" << std::endl
+ << " - prim::TupleConstruct" << std::endl
+ << " - prim::TupleUnpack");
external.torch_executed_ops.push_back("aten::__getitem__");
external.torch_executed_ops.push_back("prim::ListConstruct");
external.torch_executed_ops.push_back("prim::ListUnpack");
ERROR: Some files do not conform to style guidelines
There was a problem hiding this comment.
There are some changes that do not conform to C++ style guidelines:
diff --git a/workspace/py/torch_tensorrt/csrc/register_tensorrt_classes.cpp b/tmp/changes.txt
index 9db567c..ba07250 100644
--- a/workspace/py/torch_tensorrt/csrc/register_tensorrt_classes.cpp
+++ b/tmp/changes.txt
@@ -26,11 +26,11 @@ void RegisterTRTCompileSpec() {
static auto TORCHTRT_UNUSED TRTInputSignatureTSRegistration =
torch::class_<torch_tensorrt::pyapi::InputSignature>("tensorrt", "_InputSignature")
.def(torch::init<>())
- .def("_set_signature_ivalue_torchbind",
- [](const c10::intrusive_ptr<torch_tensorrt::pyapi::InputSignature>& self,
- torch::jit::IValue ival) {
- self->signature_ivalue = ival;
- })
+ .def(
+ "_set_signature_ivalue_torchbind",
+ [](const c10::intrusive_ptr<torch_tensorrt::pyapi::InputSignature>& self, torch::jit::IValue ival) {
+ self->signature_ivalue = ival;
+ })
.def("__str__", &torch_tensorrt::pyapi::InputSignature::to_str);
ADD_FIELD_GET_SET_REGISTRATION(
diff --git a/workspace/core/conversion/evaluators/aten.cpp b/tmp/changes.txt
index ca9ff4d..aa58dc7 100644
--- a/workspace/core/conversion/evaluators/aten.cpp
+++ b/tmp/changes.txt
@@ -176,7 +176,7 @@ auto aten_registrations TORCHTRT_UNUSED =
int64_t start = 0;
auto startIVal = args.at(n->input(1)).IValue();
- if(!startIVal->isNone()){
+ if (!startIVal->isNone()) {
start = args.at(n->input(1)).unwrapToInt();
}
int64_t end = args.at(n->input(2)).unwrapToInt();
diff --git a/workspace/core/conversion/evaluators/eval_util.cpp b/tmp/changes.txt
index 742a4f4..c14f9a6 100644
--- a/workspace/core/conversion/evaluators/eval_util.cpp
+++ b/tmp/changes.txt
@@ -20,7 +20,6 @@ int64_t normalizeIndex(int64_t idx, int64_t list_size) {
return idx;
}
-
// TODO: Switch back to PyTorch canonical implimentation
c10::optional<torch::jit::IValue> toIValue(const torch::jit::Value* v) {
if (v->node()->kind() != torch::jit::prim::Constant || v->type()->cast<c10::FunctionType>()) {
diff --git a/workspace/core/conversion/evaluators/prim.cpp b/tmp/changes.txt
index 338c427..244c3e8 100644
--- a/workspace/core/conversion/evaluators/prim.cpp
+++ b/tmp/changes.txt
@@ -260,40 +260,39 @@ auto prim_registrations =
},
EvalOptions().validSchemas({"prim::shape(Tensor a) -> (int[])"})})
.evaluator({torch::jit::prim::TupleConstruct,
- [](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
- auto num_inputs = n->inputs().size();
- c10::IValue tuple = c10::ivalue::Tuple::create();
- switch (num_inputs) {
- case 0:
- tuple = c10::ivalue::Tuple::create();
- break;
- case 1:
- tuple = c10::ivalue::Tuple::create(std::move((*args.at(n->input(0)).IValue())));
- break;
- case 2: {
- tuple = c10::ivalue::Tuple::create(
- std::move(*(args.at(n->input(0)).IValue())),
- std::move(*(args.at(n->input(1)).IValue())));
- break;
- }
- case 3: {
- tuple = c10::ivalue::Tuple::create(
- std::move(*(args.at(n->input(0)).IValue())),
- std::move(*(args.at(n->input(1)).IValue())),
- std::move(*(args.at(n->input(2)).IValue())));
- break;
- }
- default: {
- std::vector<c10::IValue> elems;
- for (size_t i = 0; i < num_inputs; i++) {
- elems.push_back(*(args.at(n->input(i)).IValue()));
+ [](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
+ auto num_inputs = n->inputs().size();
+ c10::IValue tuple = c10::ivalue::Tuple::create();
+ switch (num_inputs) {
+ case 0:
+ tuple = c10::ivalue::Tuple::create();
+ break;
+ case 1:
+ tuple = c10::ivalue::Tuple::create(std::move((*args.at(n->input(0)).IValue())));
+ break;
+ case 2: {
+ tuple = c10::ivalue::Tuple::create(
+ std::move(*(args.at(n->input(0)).IValue())), std::move(*(args.at(n->input(1)).IValue())));
+ break;
+ }
+ case 3: {
+ tuple = c10::ivalue::Tuple::create(
+ std::move(*(args.at(n->input(0)).IValue())),
+ std::move(*(args.at(n->input(1)).IValue())),
+ std::move(*(args.at(n->input(2)).IValue())));
+ break;
+ }
+ default: {
+ std::vector<c10::IValue> elems;
+ for (size_t i = 0; i < num_inputs; i++) {
+ elems.push_back(*(args.at(n->input(i)).IValue()));
+ }
+ tuple = c10::ivalue::Tuple::create(std::move(elems));
+ break;
}
- tuple = c10::ivalue::Tuple::create(std::move(elems));
- break;
}
- }
- return c10::optional<torch::jit::IValue>(std::move(tuple));
- }})
+ return c10::optional<torch::jit::IValue>(std::move(tuple));
+ }})
.evaluator({torch::jit::prim::TupleIndex,
[](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
// Outputs is an IValue which has list of tensors which can be found in ctx->evaluated_value_map
diff --git a/workspace/core/conversion/converters/impl/unary.cpp b/tmp/changes.txt
index a1d03a3..6b0ee2b 100644
--- a/workspace/core/conversion/converters/impl/unary.cpp
+++ b/tmp/changes.txt
@@ -10,45 +10,41 @@ namespace converters {
namespace impl {
namespace {
-
auto abs_registration TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern(
- {"aten::abs (Tensor self) -> Tensor",
- [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
- auto in = args[0].ITensor();
- bool unary_supported_input = in->getType() == nvinfer1::DataType::kFLOAT
- || in->getType() == nvinfer1::DataType::kHALF
- || in->getType() == nvinfer1::DataType::kINT8;
- if(unary_supported_input){
- auto unary_layer = ctx->net->addUnary(*in, nvinfer1::UnaryOperation::kABS);
- TORCHTRT_CHECK(unary_layer, "Unable to create abs layer from node: " << *n);
- unary_layer->setName(util::node_info(n).c_str());
- auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], unary_layer->getOutput(0));
- LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
- return true;
- }
- else{
- //For types not supported by kABS, use an elementwise implementation abs(x) = max(x, -1 * x)
- at::Tensor neg_one = torch::full({1}, -1).to(util::TRTDataTypeToScalarType(in->getType()));
- auto neg_one_const = tensor_to_const(ctx, neg_one);
- auto neg_layer = add_elementwise(
+ {"aten::abs (Tensor self) -> Tensor", [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+ auto in = args[0].ITensor();
+ bool unary_supported_input = in->getType() == nvinfer1::DataType::kFLOAT ||
+ in->getType() == nvinfer1::DataType::kHALF || in->getType() == nvinfer1::DataType::kINT8;
+ if (unary_supported_input) {
+ auto unary_layer = ctx->net->addUnary(*in, nvinfer1::UnaryOperation::kABS);
+ TORCHTRT_CHECK(unary_layer, "Unable to create abs layer from node: " << *n);
+ unary_layer->setName(util::node_info(n).c_str());
+ auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], unary_layer->getOutput(0));
+ LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
+ return true;
+ } else {
+ // For types not supported by kABS, use an elementwise implementation abs(x) = max(x, -1 * x)
+ at::Tensor neg_one = torch::full({1}, -1).to(util::TRTDataTypeToScalarType(in->getType()));
+ auto neg_one_const = tensor_to_const(ctx, neg_one);
+ auto neg_layer = add_elementwise(
ctx,
nvinfer1::ElementWiseOperation::kPROD,
in,
neg_one_const,
util::node_info(n) + std::string("_Negation"));
- TORCHTRT_CHECK(neg_layer, "Unable to create prod layer from node: " << *n);
- auto max_layer = add_elementwise(
+ TORCHTRT_CHECK(neg_layer, "Unable to create prod layer from node: " << *n);
+ auto max_layer = add_elementwise(
ctx,
nvinfer1::ElementWiseOperation::kMAX,
in,
neg_layer->getOutput(0),
util::node_info(n) + std::string("_Max"));
- TORCHTRT_CHECK(max_layer, "Unable to create max layer from node: " << *n);
- auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], max_layer->getOutput(0));
- LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
- return true;
- }
- }});
+ TORCHTRT_CHECK(max_layer, "Unable to create max layer from node: " << *n);
+ auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], max_layer->getOutput(0));
+ LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
+ return true;
+ }
+ }});
#define convert(unary, trt_type) \
auto unary##_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern( \
diff --git a/workspace/tests/core/conversion/converters/test_element_wise.cpp b/tmp/changes.txt
index 939c9b7..db60896 100644
--- a/workspace/tests/core/conversion/converters/test_element_wise.cpp
+++ b/tmp/changes.txt
@@ -30,14 +30,15 @@ void pointwise_test_helper(
torch_inputs.push_back(at::randint(1, 5, shape2, {at::kCUDA}));
}
- TORCHTRT_CHECK(!((int_tensors && (float_int_tensors || int_float_tensors)) || (float_int_tensors && int_float_tensors)),
- "Invalid test configuration, only one of int_tensors, float_int_tensors, int_float_tensors can be true");
+ TORCHTRT_CHECK(
+ !((int_tensors && (float_int_tensors || int_float_tensors)) || (float_int_tensors && int_float_tensors)),
+ "Invalid test configuration, only one of int_tensors, float_int_tensors, int_float_tensors can be true");
- if(int_tensors){
- for(size_t i = 0UL; i < torch_inputs.size(); ++i){
+ if (int_tensors) {
+ for (size_t i = 0UL; i < torch_inputs.size(); ++i) {
torch_inputs[i] = torch_inputs[i].to(at::kInt);
}
- } else if(float_int_tensors) {
+ } else if (float_int_tensors) {
TORCHTRT_CHECK(!singleInput, "float_int_tensors tests require two inputs");
torch_inputs[0] = torch_inputs[0].to(at::kFloat);
torch_inputs[1] = torch_inputs[1].to(at::kInt);
diff --git a/workspace/tests/core/conversion/converters/test_unary.cpp b/tmp/changes.txt
index a7ab3bb..1d40c3c 100644
--- a/workspace/tests/core/conversion/converters/test_unary.cpp
+++ b/tmp/changes.txt
@@ -1,9 +1,9 @@
#include <string>
-#include "torch/torch.h"
#include "core/compiler.h"
#include "gtest/gtest.h"
#include "tests/util/util.h"
#include "torch/csrc/jit/ir/irparser.h"
+#include "torch/torch.h"
namespace {
std::string gen_test_graph(const std::string& unary) {
@@ -22,7 +22,7 @@ TEST(Converters, ATenAbsIntConvertsCorrectly) {
auto in = at::tensor({-1, 1, -2, 2, -3, 3}, {at::kCUDA}).to(torch::kInt32);
auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
- auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+ auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
in = at::clone(in);
params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
diff --git a/workspace/tests/core/conversion/converters/test_select.cpp b/tmp/changes.txt
index 03b6bda..67b760a 100644
--- a/workspace/tests/core/conversion/converters/test_select.cpp
+++ b/tmp/changes.txt
@@ -376,7 +376,7 @@ TEST(Converters, ATenSliceListConvertsCorrectly) {
%slice : Tensor[] = aten::slice(%list, %1, %2, %3)
%out.1 : Tensor, %out.2 : Tensor = prim::ListUnpack(%slice)
return (%out.1, %out.2))IR";
-
+
auto g = std::make_shared<torch::jit::Graph>();
torch::jit::parseIR(graph, g.get());
diff --git a/workspace/tests/cpp/test_collections.cpp b/tmp/changes.txt
index 31495a4..d01665a 100644
--- a/workspace/tests/cpp/test_collections.cpp
+++ b/tmp/changes.txt
@@ -135,7 +135,7 @@ TEST(CppAPITests, TestCollectionListInput) {
auto compile_settings = torch_tensorrt::ts::CompileSpec(complex_input_shape2);
compile_settings.min_block_size = 1;
- //compile_settings.torch_executed_ops.push_back("aten::__getitem__");
+ // compile_settings.torch_executed_ops.push_back("aten::__getitem__");
// // FP16 execution
compile_settings.enabled_precisions = {torch::kHalf};
diff --git a/workspace/cpp/src/compile_spec.cpp b/tmp/changes.txt
index 8c7bb8b..cfbc228 100644
--- a/workspace/cpp/src/compile_spec.cpp
+++ b/tmp/changes.txt
@@ -69,22 +69,24 @@ torchtrt::core::CompileSpec init_compile_spec(CompileSpec& external) {
return internal;
} else {
torch::jit::IValue converted_input_signature;
- LOG_WARNING( "Input signature parsing is an experimental feature, behavior and APIs may change");
+ LOG_WARNING("Input signature parsing is an experimental feature, behavior and APIs may change");
to_internal_input_signature(external.graph_inputs.input_signature, converted_input_signature);
torchtrt::core::CompileSpec internal(converted_input_signature);
- TORCHTRT_CHECK(!external.require_full_compilation, \
- "Grouped inputs currently requires partial compilation to be enabled, \
+ TORCHTRT_CHECK(
+ !external.require_full_compilation,
+ "Grouped inputs currently requires partial compilation to be enabled, \
this restriction will be relaxed in a future release");
LOG_DEBUG("Grouped inputs currently requires additional settings to enable the feature");
- LOG_DEBUG("Adding the following ops to torch_executed_ops:" \
- << std::endl << " - aten::__getitem__" \
- << std::endl << " - prim::ListConstruct" \
- << std::endl << " - prim::ListUnpack" \
- << std::endl << " - prim::TupleIndex" \
- << std::endl << " - prim::TupleConstruct" \
- << std::endl << " - prim::TupleUnpack");
+ LOG_DEBUG(
+ "Adding the following ops to torch_executed_ops:" << std::endl
+ << " - aten::__getitem__" << std::endl
+ << " - prim::ListConstruct" << std::endl
+ << " - prim::ListUnpack" << std::endl
+ << " - prim::TupleIndex" << std::endl
+ << " - prim::TupleConstruct" << std::endl
+ << " - prim::TupleUnpack");
external.torch_executed_ops.push_back("aten::__getitem__");
external.torch_executed_ops.push_back("prim::ListConstruct");
external.torch_executed_ops.push_back("prim::ListUnpack");
ERROR: Some files do not conform to style guidelines
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There was a problem hiding this comment.
There are some changes that do not conform to C++ style guidelines:
diff --git a/workspace/py/torch_tensorrt/csrc/register_tensorrt_classes.cpp b/tmp/changes.txt
index 9db567c..ba07250 100644
--- a/workspace/py/torch_tensorrt/csrc/register_tensorrt_classes.cpp
+++ b/tmp/changes.txt
@@ -26,11 +26,11 @@ void RegisterTRTCompileSpec() {
static auto TORCHTRT_UNUSED TRTInputSignatureTSRegistration =
torch::class_<torch_tensorrt::pyapi::InputSignature>("tensorrt", "_InputSignature")
.def(torch::init<>())
- .def("_set_signature_ivalue_torchbind",
- [](const c10::intrusive_ptr<torch_tensorrt::pyapi::InputSignature>& self,
- torch::jit::IValue ival) {
- self->signature_ivalue = ival;
- })
+ .def(
+ "_set_signature_ivalue_torchbind",
+ [](const c10::intrusive_ptr<torch_tensorrt::pyapi::InputSignature>& self, torch::jit::IValue ival) {
+ self->signature_ivalue = ival;
+ })
.def("__str__", &torch_tensorrt::pyapi::InputSignature::to_str);
ADD_FIELD_GET_SET_REGISTRATION(
diff --git a/workspace/core/conversion/evaluators/aten.cpp b/tmp/changes.txt
index ca9ff4d..aa58dc7 100644
--- a/workspace/core/conversion/evaluators/aten.cpp
+++ b/tmp/changes.txt
@@ -176,7 +176,7 @@ auto aten_registrations TORCHTRT_UNUSED =
int64_t start = 0;
auto startIVal = args.at(n->input(1)).IValue();
- if(!startIVal->isNone()){
+ if (!startIVal->isNone()) {
start = args.at(n->input(1)).unwrapToInt();
}
int64_t end = args.at(n->input(2)).unwrapToInt();
diff --git a/workspace/core/conversion/evaluators/eval_util.cpp b/tmp/changes.txt
index 742a4f4..c14f9a6 100644
--- a/workspace/core/conversion/evaluators/eval_util.cpp
+++ b/tmp/changes.txt
@@ -20,7 +20,6 @@ int64_t normalizeIndex(int64_t idx, int64_t list_size) {
return idx;
}
-
// TODO: Switch back to PyTorch canonical implimentation
c10::optional<torch::jit::IValue> toIValue(const torch::jit::Value* v) {
if (v->node()->kind() != torch::jit::prim::Constant || v->type()->cast<c10::FunctionType>()) {
diff --git a/workspace/core/conversion/evaluators/prim.cpp b/tmp/changes.txt
index 338c427..244c3e8 100644
--- a/workspace/core/conversion/evaluators/prim.cpp
+++ b/tmp/changes.txt
@@ -260,40 +260,39 @@ auto prim_registrations =
},
EvalOptions().validSchemas({"prim::shape(Tensor a) -> (int[])"})})
.evaluator({torch::jit::prim::TupleConstruct,
- [](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
- auto num_inputs = n->inputs().size();
- c10::IValue tuple = c10::ivalue::Tuple::create();
- switch (num_inputs) {
- case 0:
- tuple = c10::ivalue::Tuple::create();
- break;
- case 1:
- tuple = c10::ivalue::Tuple::create(std::move((*args.at(n->input(0)).IValue())));
- break;
- case 2: {
- tuple = c10::ivalue::Tuple::create(
- std::move(*(args.at(n->input(0)).IValue())),
- std::move(*(args.at(n->input(1)).IValue())));
- break;
- }
- case 3: {
- tuple = c10::ivalue::Tuple::create(
- std::move(*(args.at(n->input(0)).IValue())),
- std::move(*(args.at(n->input(1)).IValue())),
- std::move(*(args.at(n->input(2)).IValue())));
- break;
- }
- default: {
- std::vector<c10::IValue> elems;
- for (size_t i = 0; i < num_inputs; i++) {
- elems.push_back(*(args.at(n->input(i)).IValue()));
+ [](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
+ auto num_inputs = n->inputs().size();
+ c10::IValue tuple = c10::ivalue::Tuple::create();
+ switch (num_inputs) {
+ case 0:
+ tuple = c10::ivalue::Tuple::create();
+ break;
+ case 1:
+ tuple = c10::ivalue::Tuple::create(std::move((*args.at(n->input(0)).IValue())));
+ break;
+ case 2: {
+ tuple = c10::ivalue::Tuple::create(
+ std::move(*(args.at(n->input(0)).IValue())), std::move(*(args.at(n->input(1)).IValue())));
+ break;
+ }
+ case 3: {
+ tuple = c10::ivalue::Tuple::create(
+ std::move(*(args.at(n->input(0)).IValue())),
+ std::move(*(args.at(n->input(1)).IValue())),
+ std::move(*(args.at(n->input(2)).IValue())));
+ break;
+ }
+ default: {
+ std::vector<c10::IValue> elems;
+ for (size_t i = 0; i < num_inputs; i++) {
+ elems.push_back(*(args.at(n->input(i)).IValue()));
+ }
+ tuple = c10::ivalue::Tuple::create(std::move(elems));
+ break;
}
- tuple = c10::ivalue::Tuple::create(std::move(elems));
- break;
}
- }
- return c10::optional<torch::jit::IValue>(std::move(tuple));
- }})
+ return c10::optional<torch::jit::IValue>(std::move(tuple));
+ }})
.evaluator({torch::jit::prim::TupleIndex,
[](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
// Outputs is an IValue which has list of tensors which can be found in ctx->evaluated_value_map
diff --git a/workspace/core/conversion/converters/impl/unary.cpp b/tmp/changes.txt
index a1d03a3..6b0ee2b 100644
--- a/workspace/core/conversion/converters/impl/unary.cpp
+++ b/tmp/changes.txt
@@ -10,45 +10,41 @@ namespace converters {
namespace impl {
namespace {
-
auto abs_registration TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern(
- {"aten::abs (Tensor self) -> Tensor",
- [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
- auto in = args[0].ITensor();
- bool unary_supported_input = in->getType() == nvinfer1::DataType::kFLOAT
- || in->getType() == nvinfer1::DataType::kHALF
- || in->getType() == nvinfer1::DataType::kINT8;
- if(unary_supported_input){
- auto unary_layer = ctx->net->addUnary(*in, nvinfer1::UnaryOperation::kABS);
- TORCHTRT_CHECK(unary_layer, "Unable to create abs layer from node: " << *n);
- unary_layer->setName(util::node_info(n).c_str());
- auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], unary_layer->getOutput(0));
- LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
- return true;
- }
- else{
- //For types not supported by kABS, use an elementwise implementation abs(x) = max(x, -1 * x)
- at::Tensor neg_one = torch::full({1}, -1).to(util::TRTDataTypeToScalarType(in->getType()));
- auto neg_one_const = tensor_to_const(ctx, neg_one);
- auto neg_layer = add_elementwise(
+ {"aten::abs (Tensor self) -> Tensor", [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+ auto in = args[0].ITensor();
+ bool unary_supported_input = in->getType() == nvinfer1::DataType::kFLOAT ||
+ in->getType() == nvinfer1::DataType::kHALF || in->getType() == nvinfer1::DataType::kINT8;
+ if (unary_supported_input) {
+ auto unary_layer = ctx->net->addUnary(*in, nvinfer1::UnaryOperation::kABS);
+ TORCHTRT_CHECK(unary_layer, "Unable to create abs layer from node: " << *n);
+ unary_layer->setName(util::node_info(n).c_str());
+ auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], unary_layer->getOutput(0));
+ LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
+ return true;
+ } else {
+ // For types not supported by kABS, use an elementwise implementation abs(x) = max(x, -1 * x)
+ at::Tensor neg_one = torch::full({1}, -1).to(util::TRTDataTypeToScalarType(in->getType()));
+ auto neg_one_const = tensor_to_const(ctx, neg_one);
+ auto neg_layer = add_elementwise(
ctx,
nvinfer1::ElementWiseOperation::kPROD,
in,
neg_one_const,
util::node_info(n) + std::string("_Negation"));
- TORCHTRT_CHECK(neg_layer, "Unable to create prod layer from node: " << *n);
- auto max_layer = add_elementwise(
+ TORCHTRT_CHECK(neg_layer, "Unable to create prod layer from node: " << *n);
+ auto max_layer = add_elementwise(
ctx,
nvinfer1::ElementWiseOperation::kMAX,
in,
neg_layer->getOutput(0),
util::node_info(n) + std::string("_Max"));
- TORCHTRT_CHECK(max_layer, "Unable to create max layer from node: " << *n);
- auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], max_layer->getOutput(0));
- LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
- return true;
- }
- }});
+ TORCHTRT_CHECK(max_layer, "Unable to create max layer from node: " << *n);
+ auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], max_layer->getOutput(0));
+ LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
+ return true;
+ }
+ }});
#define convert(unary, trt_type) \
auto unary##_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern( \
diff --git a/workspace/tests/core/conversion/converters/test_element_wise.cpp b/tmp/changes.txt
index 939c9b7..db60896 100644
--- a/workspace/tests/core/conversion/converters/test_element_wise.cpp
+++ b/tmp/changes.txt
@@ -30,14 +30,15 @@ void pointwise_test_helper(
torch_inputs.push_back(at::randint(1, 5, shape2, {at::kCUDA}));
}
- TORCHTRT_CHECK(!((int_tensors && (float_int_tensors || int_float_tensors)) || (float_int_tensors && int_float_tensors)),
- "Invalid test configuration, only one of int_tensors, float_int_tensors, int_float_tensors can be true");
+ TORCHTRT_CHECK(
+ !((int_tensors && (float_int_tensors || int_float_tensors)) || (float_int_tensors && int_float_tensors)),
+ "Invalid test configuration, only one of int_tensors, float_int_tensors, int_float_tensors can be true");
- if(int_tensors){
- for(size_t i = 0UL; i < torch_inputs.size(); ++i){
+ if (int_tensors) {
+ for (size_t i = 0UL; i < torch_inputs.size(); ++i) {
torch_inputs[i] = torch_inputs[i].to(at::kInt);
}
- } else if(float_int_tensors) {
+ } else if (float_int_tensors) {
TORCHTRT_CHECK(!singleInput, "float_int_tensors tests require two inputs");
torch_inputs[0] = torch_inputs[0].to(at::kFloat);
torch_inputs[1] = torch_inputs[1].to(at::kInt);
diff --git a/workspace/tests/core/conversion/converters/test_unary.cpp b/tmp/changes.txt
index a7ab3bb..1d40c3c 100644
--- a/workspace/tests/core/conversion/converters/test_unary.cpp
+++ b/tmp/changes.txt
@@ -1,9 +1,9 @@
#include <string>
-#include "torch/torch.h"
#include "core/compiler.h"
#include "gtest/gtest.h"
#include "tests/util/util.h"
#include "torch/csrc/jit/ir/irparser.h"
+#include "torch/torch.h"
namespace {
std::string gen_test_graph(const std::string& unary) {
@@ -22,7 +22,7 @@ TEST(Converters, ATenAbsIntConvertsCorrectly) {
auto in = at::tensor({-1, 1, -2, 2, -3, 3}, {at::kCUDA}).to(torch::kInt32);
auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
- auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+ auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
in = at::clone(in);
params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
diff --git a/workspace/tests/core/conversion/converters/test_select.cpp b/tmp/changes.txt
index 03b6bda..67b760a 100644
--- a/workspace/tests/core/conversion/converters/test_select.cpp
+++ b/tmp/changes.txt
@@ -376,7 +376,7 @@ TEST(Converters, ATenSliceListConvertsCorrectly) {
%slice : Tensor[] = aten::slice(%list, %1, %2, %3)
%out.1 : Tensor, %out.2 : Tensor = prim::ListUnpack(%slice)
return (%out.1, %out.2))IR";
-
+
auto g = std::make_shared<torch::jit::Graph>();
torch::jit::parseIR(graph, g.get());
diff --git a/workspace/tests/cpp/test_collections.cpp b/tmp/changes.txt
index 31495a4..d01665a 100644
--- a/workspace/tests/cpp/test_collections.cpp
+++ b/tmp/changes.txt
@@ -135,7 +135,7 @@ TEST(CppAPITests, TestCollectionListInput) {
auto compile_settings = torch_tensorrt::ts::CompileSpec(complex_input_shape2);
compile_settings.min_block_size = 1;
- //compile_settings.torch_executed_ops.push_back("aten::__getitem__");
+ // compile_settings.torch_executed_ops.push_back("aten::__getitem__");
// // FP16 execution
compile_settings.enabled_precisions = {torch::kHalf};
diff --git a/workspace/cpp/src/compile_spec.cpp b/tmp/changes.txt
index 8c7bb8b..cfbc228 100644
--- a/workspace/cpp/src/compile_spec.cpp
+++ b/tmp/changes.txt
@@ -69,22 +69,24 @@ torchtrt::core::CompileSpec init_compile_spec(CompileSpec& external) {
return internal;
} else {
torch::jit::IValue converted_input_signature;
- LOG_WARNING( "Input signature parsing is an experimental feature, behavior and APIs may change");
+ LOG_WARNING("Input signature parsing is an experimental feature, behavior and APIs may change");
to_internal_input_signature(external.graph_inputs.input_signature, converted_input_signature);
torchtrt::core::CompileSpec internal(converted_input_signature);
- TORCHTRT_CHECK(!external.require_full_compilation, \
- "Grouped inputs currently requires partial compilation to be enabled, \
+ TORCHTRT_CHECK(
+ !external.require_full_compilation,
+ "Grouped inputs currently requires partial compilation to be enabled, \
this restriction will be relaxed in a future release");
LOG_DEBUG("Grouped inputs currently requires additional settings to enable the feature");
- LOG_DEBUG("Adding the following ops to torch_executed_ops:" \
- << std::endl << " - aten::__getitem__" \
- << std::endl << " - prim::ListConstruct" \
- << std::endl << " - prim::ListUnpack" \
- << std::endl << " - prim::TupleIndex" \
- << std::endl << " - prim::TupleConstruct" \
- << std::endl << " - prim::TupleUnpack");
+ LOG_DEBUG(
+ "Adding the following ops to torch_executed_ops:" << std::endl
+ << " - aten::__getitem__" << std::endl
+ << " - prim::ListConstruct" << std::endl
+ << " - prim::ListUnpack" << std::endl
+ << " - prim::TupleIndex" << std::endl
+ << " - prim::TupleConstruct" << std::endl
+ << " - prim::TupleUnpack");
external.torch_executed_ops.push_back("aten::__getitem__");
external.torch_executed_ops.push_back("prim::ListConstruct");
external.torch_executed_ops.push_back("prim::ListUnpack");
ERROR: Some files do not conform to style guidelines
Signed-off-by: Anurag Dixit <a.dixit91@gmail.com>
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Signed-off-by: Anurag Dixit a.dixit91@gmail.com
Description
Currently, the code base for torch.fx.passes.pass_manager does not work with Jetson AGX platform. Hence, causing failure to import torch_tensorrt on Jetson AGX with Build artifact for Jetson AGX.
Masking the FX path to workaround this issue.
Fixes # (issue)
Type of change
Please delete options that are not relevant and/or add your own.
Checklist: