Skip to content

fix deconv with groups > 1 #238

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 2 commits into from
Dec 8, 2020
Merged

fix deconv with groups > 1 #238

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
e994354
fix deconv with groups > 1
uni19 Nov 24, 2020
b696497
add comments
uni19 Dec 7, 2020
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
28 changes: 18 additions & 10 deletions core/conversion/converters/impl/conv_deconv.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,6 @@ auto conv_registrations TRTORCH_UNUSED = RegisterNodeConversionPatterns().patter
bool deterministic, bool cudnn_enabled) -> (Tensor))SIG",
[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
auto in = args[0].ITensor(); // assumes non-static input Tensor

auto w = Weights(ctx, args[1].unwrapToTensor());
auto stride = util::toDims(args[3].unwrapToIntList());
LOG_DEBUG("stride: " << stride);
Expand All @@ -29,36 +28,45 @@ auto conv_registrations TRTORCH_UNUSED = RegisterNodeConversionPatterns().patter
auto out_padding = util::toDims(args[7].unwrapToIntList());
LOG_DEBUG("out_padding: " << out_padding);
int64_t groups = args[8].unwrapToInt();
LOG_DEBUG("groups: " << groups);

nvinfer1::ILayer* new_layer;
if (transposed) {
nvinfer1::IDeconvolutionLayer* deconv;
Weights bias;
if (args[2].IValue()->isTensor()) {
Weights b(ctx, args[2].IValue()->toTensor());
deconv = ctx->net->addDeconvolutionNd(*in, w.num_input_maps, w.kernel_shape, w.data, b.data);
bias = Weights(ctx, args[2].unwrapToTensor());
} else {
deconv = ctx->net->addDeconvolutionNd(*in, w.num_input_maps, w.kernel_shape, w.data, {});
bias = Weights(ctx, torch::zeros(args[1].unwrapToTensor().sizes()[1] * groups));
}

// shape of deconvolution's weight: [in, out/groups, ...]
auto deconv = ctx->net->addDeconvolutionNd(
*in, args[1].unwrapToTensor().sizes()[1] * groups, w.kernel_shape, w.data, bias.data);
TRTORCH_CHECK(deconv, "Unable to create deconvolution layer from node: " << *n);

deconv->setStrideNd(stride);
deconv->setPaddingNd(padding);
#if NV_TENSORRT_MAJOR > 7 || (NV_TENSORRT_MAJOR == 7 && NV_TENSORRT_MINOR == 1)
deconv->setDilationNd(dilation);
deconv->setNbGroups(groups);
#else
TRTORCH_CHECK(groups == 1, "for deconv with groups > 1, require TensorRT version >= 7.1");
for (auto it = dilation.begin(); it != dilation.end(); ++it) {
TRTORCH_CHECK(*it == 1, "for deconv with dilation > 1, require TensorRT version >= 7.1");
}
#endif
new_layer = deconv;
} else {
nvinfer1::IConvolutionLayer* conv;
Weights bias;
if (args[2].IValue()->isTensor()) {
Weights b(ctx, args[2].unwrapToTensor());
conv = ctx->net->addConvolutionNd(*in, w.num_output_maps, w.kernel_shape, w.data, b.data);
bias = Weights(ctx, args[2].unwrapToTensor());
} else {
Weights b(ctx, torch::zeros(args[1].unwrapToTensor().sizes()[0]));
conv = ctx->net->addConvolutionNd(*in, w.num_output_maps, w.kernel_shape, w.data, b.data);
bias = Weights(ctx, torch::zeros(args[1].unwrapToTensor().sizes()[0]));
}

// shape of convolution's weight: [out, in/groups, ...]
auto conv =
ctx->net->addConvolutionNd(*in, args[1].unwrapToTensor().sizes()[0], w.kernel_shape, w.data, bias.data);
TRTORCH_CHECK(conv, "Unable to create convolution layer from node: " << *n);

conv->setStrideNd(stride);
Expand Down
147 changes: 85 additions & 62 deletions tests/core/converters/test_conv_deconv.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -532,65 +532,88 @@ TEST(Converters, ATenConvTransposeWithPaddingConvertsCorrectly) {
ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt, 2e-6));
}

// TEST(Converters, ATenConvolutionWithDialationConvertsCorrectly) {
// const auto graph = R"IR(
// graph(%0 : Tensor,
// %1 : Float(8, 3, 5, 5),
// %2 : Float(8)):
// %3 : int = prim::Constant[value=1]()
// %4 : int = prim::Constant[value=0]()
// %5 : int = prim::Constant[value=2]()
// %6 : int = prim::Constant[value=0]()
// %7 : bool = prim::Constant[value=0]()
// %8 : int[] = prim::ListConstruct(%3, %3)
// %9 : int[] = prim::ListConstruct(%4, %4)
// %10 : int[] = prim::ListConstruct(%5, %5)
// %11 : int[] = prim::ListConstruct(%6, %6)
// %12 : int = prim::Constant[value=1]()
// %13 : Tensor = aten::_convolution(%0, %1, %2, %8, %9, %10, %7, %11,
// %12, %7, %7, %7) return (%13))IR";

// conv_test_helper(graph);
// }

// TEST(Converters, ATenConvolutionWithPostPaddingConvertsCorrectly) {
// const auto graph = R"IR(
// graph(%0 : Tensor,
// %1 : Float(8, 3, 5, 5),
// %2 : Float(8)):
// %3 : int = prim::Constant[value=1]()
// %4 : int = prim::Constant[value=0]()
// %5 : int = prim::Constant[value=1]()
// %6 : int = prim::Constant[value=2]()
// %7 : bool = prim::Constant[value=0]()
// %8 : int[] = prim::ListConstruct(%3, %3)
// %9 : int[] = prim::ListConstruct(%4, %4)
// %10 : int[] = prim::ListConstruct(%5, %5)
// %11 : int[] = prim::ListConstruct(%6, %6)
// %12 : int = prim::Constant[value=1]()
// %13 : Tensor = aten::_convolution(%0, %1, %2, %8, %9, %10, %7, %11,
// %12, %7, %7, %7) return (%13))IR";

// conv_test_helper(graph);
// }

// TEST(Converters, ATenConvolutionWithGroupConvertsCorrectly) {
// const auto graph = R"IR(
// graph(%0 : Tensor,
// %1 : Float(8, 3, 5, 5),
// %2 : Float(8)):
// %3 : int = prim::Constant[value=1]()
// %4 : int = prim::Constant[value=0]()
// %5 : int = prim::Constant[value=1]()
// %6 : int = prim::Constant[value=0]()
// %7 : bool = prim::Constant[value=0]()
// %8 : int[] = prim::ListConstruct(%3, %3)
// %9 : int[] = prim::ListConstruct(%4, %4)
// %10 : int[] = prim::ListConstruct(%5, %5)
// %11 : int[] = prim::ListConstruct(%6, %6)
// %12 : int = prim::Constant[value=2]()
// %13 : Tensor = aten::_convolution(%0, %1, %2, %8, %9, %10, %7, %11,
// %12, %7, %7, %7) return (%13))IR";

// conv_test_helper(graph);
// }
TEST(Converters, ATenConvolutionWithGroupConvertsCorrectly) {
const auto graph = R"IR(
graph(%0 : Tensor,
%1 : Float(8:48, 1:16, 2:4, 2:1),
%2 : Float(8:1)):
%3 : int = prim::Constant[value=1]()
%4 : int = prim::Constant[value=2]()
%5 : int = prim::Constant[value=1]()
%6 : int = prim::Constant[value=0]()
%7 : bool = prim::Constant[value=0]()
%8 : int[] = prim::ListConstruct(%3, %3)
%9 : int[] = prim::ListConstruct(%4, %4)
%10 : int[] = prim::ListConstruct(%5, %5)
%11 : int[] = prim::ListConstruct(%6, %6)
%12 : int = prim::Constant[value=4]()
%13 : Tensor = aten::_convolution(%0, %1, %2, %8, %9, %10, %7, %11, %12, %7, %7, %7)
return (%13))IR";

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

auto in = at::randint(1, 10, {1, 4, 4, 4}, {at::kCUDA});
auto w = at::randint(1, 10, {8, 1, 2, 2}, {at::kCUDA});
auto b = at::randint(1, 10, {8}, {at::kCUDA});

auto jit_in = at::clone(in);
auto jit_w = at::clone(w);
auto jit_b = at::clone(b);

auto params = trtorch::core::conversion::get_named_params(g->inputs(), {jit_w, jit_b});
auto jit_results = trtorch::tests::util::RunGraph(g, params, {jit_in});

auto trt_in = at::clone(in);
auto trt_w = at::clone(w);
auto trt_b = at::clone(b);
params = trtorch::core::conversion::get_named_params(g->inputs(), {trt_w, trt_b});
auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {trt_in});

auto trt = trt_results[0].reshape(jit_results[0].sizes());

ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt, 2e-6));
}

TEST(Converters, ATenConvTransposeWithGroupConvertsCorrectly) {
const auto graph = R"IR(
graph(%0 : Tensor,
%1 : Float(8:56, 4:16, 3:3, 3:1),
%2 : Float(16:1)):
%3 : int = prim::Constant[value=1]()
%4 : int = prim::Constant[value=1]()
%5 : int = prim::Constant[value=1]()
%6 : int = prim::Constant[value=0]()
%7 : bool = prim::Constant[value=1]()
%8 : int[] = prim::ListConstruct(%3, %3)
%9 : int[] = prim::ListConstruct(%4, %4)
%10 : int[] = prim::ListConstruct(%5, %5)
%11 : int[] = prim::ListConstruct(%6, %6)
%12 : int = prim::Constant[value=4]()
%13 : Tensor = aten::_convolution(%0, %1, %2, %8, %9, %10, %7, %11, %12, %7, %7, %7)
return (%13))IR";

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

auto in = at::randint(1, 10, {1, 8, 5, 5}, {at::kCUDA});
auto w = at::randint(1, 10, {8, 4, 3, 3}, {at::kCUDA});
auto b = at::randint(1, 10, {16}, {at::kCUDA});

auto jit_in = at::clone(in);
auto jit_w = at::clone(w);
auto jit_b = at::clone(b);

auto params = trtorch::core::conversion::get_named_params(g->inputs(), {jit_w, jit_b});
auto jit_results = trtorch::tests::util::RunGraph(g, params, {jit_in});

auto trt_in = at::clone(in);
auto trt_w = at::clone(w);
auto trt_b = at::clone(b);
params = trtorch::core::conversion::get_named_params(g->inputs(), {trt_w, trt_b});
auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {trt_in});

auto trt = trt_results[0].reshape(jit_results[0].sizes());

ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt, 2e-6));
}