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fix(aten::flatten): Fixes dynamic shape for flatten #120

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Merged
merged 1 commit into from
Jul 1, 2020
Merged

fix(aten::flatten): Fixes dynamic shape for flatten #120

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14 changes: 12 additions & 2 deletions core/conversion/converters/impl/shuffle.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,12 @@ static auto shuffle_registrations TRTORCH_UNUSED = RegisterNodeConversionPattern
auto start_dim = args[1].unwrapToInt();
auto end_dim = args[2].unwrapToInt();
auto in_shape = util::toVec(in->getDimensions());
auto out_shape = torch::flatten(torch::rand(in_shape), start_dim, end_dim).sizes();
std::vector<int64_t> out_shape;
if (ctx->input_is_dynamic) {
out_shape = std::vector<int64_t>({in_shape[0], -1});
} else {
out_shape = torch::flatten(torch::rand(in_shape), start_dim, end_dim).sizes().vec();
}

auto shuffle = ctx->net->addShuffle(*in);
TRTORCH_CHECK(shuffle, "Unable to create shuffle layer from node: " << *n);
Expand All @@ -33,7 +38,12 @@ static auto shuffle_registrations TRTORCH_UNUSED = RegisterNodeConversionPattern
[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
auto in = args[0].ITensor();
auto in_shape = util::toVec(in->getDimensions());
auto new_shape = torch::reshape(torch::rand(in_shape), args[1].unwrapToIntList().vec()).sizes();
std::vector<int64_t> new_shape;
if (ctx->input_is_dynamic) {
TRTORCH_THROW_ERROR("Resize is currently not support in dynamic input shape compilation");
} else {
new_shape = torch::reshape(torch::rand(in_shape), args[1].unwrapToIntList().vec()).sizes().vec();
}

auto shuffle = ctx->net->addShuffle(*in);
TRTORCH_CHECK(shuffle, "Unable to create shuffle layer from node: " << *n);
Expand Down
59 changes: 41 additions & 18 deletions tests/core/converters/test_shuffle.cpp
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Expand Up @@ -6,26 +6,26 @@

// TODO: IR Parser doesnt work well with neg numbers
TEST(Converters, ATenFlattenConvertsCorrectly) {
const auto graph = R"IR(
graph(%0 : Tensor):
%1 : int = prim::Constant[value=0]()
%2 : int = prim::Constant[value=1]()
%3 : Tensor = aten::flatten(%0, %1, %2)
return (%3))IR";

auto g = std::make_shared<torch::jit::Graph>();
torch::jit::parseIR(graph, &*g);
const auto graph = R"IR(
graph(%0 : Tensor):
%1 : int = prim::Constant[value=0]()
%2 : int = prim::Constant[value=1]()
%3 : Tensor = aten::flatten(%0, %1, %2)
return (%3))IR";

auto in = at::randint(0, 5, {2, 3}, {at::kCUDA});
auto params = trtorch::core::conversion::get_named_params(g->inputs(), {});
auto jit_results = trtorch::tests::util::RunGraph(g, params, {in});
auto g = std::make_shared<torch::jit::Graph>();
torch::jit::parseIR(graph, &*g);

in = at::clone(in);
params = trtorch::core::conversion::get_named_params(g->inputs(), {});
auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {in});
auto trt = trt_results[0].reshape_as(jit_results[0]);
auto in = at::randint(0, 5, {2, 3}, {at::kCUDA});
auto params = trtorch::core::conversion::get_named_params(g->inputs(), {});
auto jit_results = trtorch::tests::util::RunGraph(g, params, {in});

ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt, 2e-6));
in = at::clone(in);
params = trtorch::core::conversion::get_named_params(g->inputs(), {});
auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {in});
auto trt = trt_results[0].reshape_as(jit_results[0]);

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

// TODO: IR Parser doesnt work well with neg numbers
Expand Down Expand Up @@ -164,4 +164,27 @@ TEST(Converters, ATenPermute5DConvertsCorrectly) {
auto trt = trt_results[0].reshape_as(jit_results[0]);

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

TEST(Converters, ATenFlattenConvertsCorrectlyWithDynamicInput) {
const auto graph = R"IR(
graph(%0 : Tensor):
%1 : int = prim::Constant[value=0]()
%2 : int = prim::Constant[value=1]()
%3 : Tensor = aten::flatten(%0, %1, %2)
return (%3))IR";

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

auto in = at::randint(0, 5, {2, 3}, {at::kCUDA});
auto params = trtorch::core::conversion::get_named_params(g->inputs(), {});
auto jit_results = trtorch::tests::util::RunGraph(g, params, {in});

in = at::clone(in);
params = trtorch::core::conversion::get_named_params(g->inputs(), {});
auto trt_results = trtorch::tests::util::RunGraphEngineDynamic(g, params, {in});
auto trt = trt_results[0].reshape_as(jit_results[0]);

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