Skip to content

fix: Add aten::full_like evaluator #1584

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jan 17, 2023
Merged

fix: Add aten::full_like evaluator #1584

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
49 changes: 48 additions & 1 deletion core/conversion/evaluators/aten.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -172,6 +172,53 @@ auto aten_registrations TORCHTRT_UNUSED =
auto out_tensor = torch::full(args.at(n->input(0)).unwrapToIntList().vec(), scalar_value, options);
return out_tensor;
}})
.evaluator(
{c10::Symbol::fromQualString("aten::full_like"),
// aten::full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None,
// Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> (Tensor)
[](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
// Override options related to layout and device for TensorRT
auto options = torch::TensorOptions().layout(torch::kStrided).device(torch::kCUDA);
auto input_tensor_var = args.at(n->input(0));

std::vector<int64_t> input_shape;
c10::ScalarType input_dtype;

// Extract data type and shape of input tensor
if (input_tensor_var.isITensor()) {
auto tensor = input_tensor_var.ITensor();
input_shape = util::toVec(tensor->getDimensions());
input_dtype = util::TRTDataTypeToScalarType(tensor->getType());
} else if (input_tensor_var.IValue()->isTensor()) {
auto tensor = input_tensor_var.unwrapToTensor();
input_shape = tensor.sizes().vec();
input_dtype = tensor.scalar_type();
} else if (input_tensor_var.IValue()->isCustomClass()) {
auto tensor = input_tensor_var.IValue()->toCustomClass<TensorContainer>()->tensor();
input_shape = util::toVec(tensor->getDimensions());
input_dtype = util::TRTDataTypeToScalarType(tensor->getType());
} else {
TORCHTRT_THROW_ERROR(
"Invalid IValue type. IValue is not some class of torch::Tensor or nvinfer1::ITensor. Found: "
<< input_tensor_var.IValue()->type());
}

// If specified, use third input arg to determine data type, otherwise default to input tensor data type
if (!args.at(n->input(2)).isNone() && !args.at(n->input(2)).IValue()->isNone()) {
options = options.dtype(c10::ScalarType(args.at(n->input(2)).unwrapToInt()));
} else {
options = options.dtype(input_dtype);
}

// Generate full tensor with specified input options
auto scalar_value = args.at(n->input(1)).unwrapToScalar();
auto out_tensor = torch::full(input_shape, scalar_value, options);
return out_tensor;
},
EvalOptions().validSchemas(
{R"SIG(aten::full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None,
Layout? layout=None, Device? device=None, bool? pin_memory=None,
MemoryFormat? memory_format=None) -> (Tensor))SIG"})})
.evaluator(
{c10::Symbol::fromQualString("aten::slice"),
[](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
Expand Down Expand Up @@ -821,4 +868,4 @@ auto aten_registrations TORCHTRT_UNUSED =
} // namespace evaluators
} // namespace conversion
} // namespace core
} // namespace torch_tensorrt
} // namespace torch_tensorrt
65 changes: 65 additions & 0 deletions tests/core/conversion/evaluators/test_aten_evaluators.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -83,6 +83,71 @@ TEST(Evaluators, FullEvaluatesCorrectly) {
ASSERT_TRUE(at::equal(jit_results[0].toTensor().to(at::kCUDA), trt_results[0].toTensor()));
}

TEST(Evaluators, FullLikeEvaluatesCorrectly) {
const auto graph = R"IR(
graph(%x.1 : Tensor):
%9 : None = prim::Constant()
%13 : float = prim::Constant[value=1.3]()
%14 : int = prim::Constant[value=4]()
%35 : Device = prim::Constant[value="cuda:0"]()
%19 : Tensor = aten::full_like(%x.1, %13, %14, %9, %35, %9, %9)
return (%19))IR";

auto in = at::randint(1, 10, {1, 2, 3, 5}, {at::kCUDA});

auto g = std::make_shared<torch::jit::Graph>();
torch::jit::parseIR(graph, g.get());

auto jit_results = torch_tensorrt::tests::util::EvaluateGraphJIT(g, {in});
auto trt_results = torch_tensorrt::tests::util::EvaluateGraph(g->block(), {in});

ASSERT_TRUE(at::equal(jit_results[0].toTensor().to(at::kCUDA), trt_results[0].toTensor()));
ASSERT_TRUE(jit_results[0].toTensor().dtype() == trt_results[0].toTensor().dtype());
}

TEST(Evaluators, FullLikeNewDtypeEvaluatesCorrectly) {
const auto graph = R"IR(
graph(%x.1 : Tensor):
%9 : None = prim::Constant()
%13 : Scalar = prim::Constant[value=1]()
%14 : int = prim::Constant[value=11]()
%35 : Device = prim::Constant[value="cuda:0"]()
%19 : Tensor = aten::full_like(%x.1, %13, %14, %9, %35, %9, %9)
return (%19))IR";

auto in = at::randint(1, 10, {1, 2, 3, 5}, {at::kCUDA});

auto g = std::make_shared<torch::jit::Graph>();
torch::jit::parseIR(graph, g.get());

auto jit_results = torch_tensorrt::tests::util::EvaluateGraphJIT(g, {in});
auto trt_results = torch_tensorrt::tests::util::EvaluateGraph(g->block(), {in});

ASSERT_TRUE(at::equal(jit_results[0].toTensor().to(at::kCUDA), trt_results[0].toTensor()));
ASSERT_TRUE(jit_results[0].toTensor().dtype() == trt_results[0].toTensor().dtype());
}

TEST(Evaluators, FullLikeOldDtypeEvaluatesCorrectly) {
const auto graph = R"IR(
graph(%x.1 : Tensor):
%9 : None = prim::Constant()
%13 : Scalar = prim::Constant[value=1.5]()
%35 : Device = prim::Constant[value="cuda:0"]()
%19 : Tensor = aten::full_like(%x.1, %13, %9, %9, %35, %9, %9)
return (%19))IR";

auto in = at::randint(1, 10, {1, 2, 3, 5}, {at::kCUDA}).to(torch::kInt32);

auto g = std::make_shared<torch::jit::Graph>();
torch::jit::parseIR(graph, g.get());

auto jit_results = torch_tensorrt::tests::util::EvaluateGraphJIT(g, {in});
auto trt_results = torch_tensorrt::tests::util::EvaluateGraph(g->block(), {in});

ASSERT_TRUE(at::equal(jit_results[0].toTensor().to(at::kCUDA), trt_results[0].toTensor()));
ASSERT_TRUE(jit_results[0].toTensor().dtype() == trt_results[0].toTensor().dtype());
}

TEST(Evaluators, OnesDataTypeEvaluatesCorrectly) {
const auto graph = R"IR(
graph(%x.1 : Tensor):
Expand Down