[FX] Changes done internally at Facebook #1194
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6703b98dff0695d91026f057b951dba1355825fa Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for acc_ops.prod c822345d6d673e1653c2208435e34ab400bada3d Jason Park <jasonjk@fb.com> Add support for generic torch ops to be used in training. e5758602a0592d6c2b71d6d66a0398c4dd9b5e20 Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for repeat interleave c13c633f04df162500eed477c0569eb2b81eb070 Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for reduce ops 863476cf43b210922b88585b8f196dd84fbebb56 Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for acc_op.convolution 68dff39793e5c30c20010919a855bb3d984015d7 Ruichao Xiao <xiaoruichao@fb.com> [fbcode][GPU][DHEN]fuse split squeeze cat as reshape f8b920769507ebd2ff02419b4aece25451298a95 Ruichao Xiao <xiaoruichao@fb.com> [fbcode][DHEN][GPU] reorder and merge cats whose input is a sublist of another cat 5b6a8d2d6be979983a52ac96225fefb510c3817c Andrew Or <andrewor@fb.com> [Quant][fx] Rename convert_to_reference to convert_to_reference_fx 996a0e080b8a8bc0b292a7c2ac92f41f6db33a2e Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for acc_op.expand 084631fe74b304fbb9481ca15fd452a3714fb1b8 Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for acc_op.to_dtype b3195e76329ccddbb5c4640cfa884d0e457d2d34 Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for std a5d964e62bdf769cf8c2e67321138b33e1f524a7 Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for acc_op.tile 3d33d45b2fc7f10f25c22946ba474b227e4b6529 Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for squeeze 09085abf63d7e7732e2cd66e600e8afc6d58964f Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for acc_op.topk 65edc7ea12899e9bd2af42c890a64de853d9b7fe Huamin Li <huaminli@fb.com> temporarily skip gelu tests d11e521f9b90554ca86912a49920afa4406bb40d Shirong Wu <shirong@fb.com> Suppress accuracy check for remove_reshape_with_batch_size_change 6d948298b2327d229e010a34f1c221b11d2eb504 Ankur Singla <ankursingla@fb.com> [GPULowering] Suppress accuracy check for fuse_unsqueeze_cat_sum e780b647fc9571b77d9f41c963041a6ac3d66f33 Janet Yang <qxy11@fb.com> Lower xrayvideo2022 to fx2trt 433c7207fef16b1fdff985546ea969c39fa83e7c generatedunixname89002005287564 <generatedunixname89002005287564@fb.com> [Codemod][Remove @noautodeps and @autodeps-skip tags] deeplearning/trt 1/2 66fdb65cffa925660c77b4758388399db3cbfe48 Scott Wolchok <swolchok@fb.com> [fx2ait] Minor Python cleanup in acc_ops_getitem 188132ecb2c19bcbf83cb2dc381f6e3798629f87 generatedunixname89002005324833 <generatedunixname89002005324833@fb.com> [AutoAccept][Codemod][FBSourceBuckFormatLinter] Daily `arc lint --take BUCKFORMAT` 4536bae4686dd01f2149541ea7fb330e178a4969 Wei Wei <wwei6@fb.com> [fx2trt] support sub 064602e666f86c110d931cd90a8536112a19b4ad Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for acc_ops.interpolate 9dfd0ee0cecb1975e3f53c44de237d67ca443ec5 Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for unary_ops 39b9efad8d5d82463a2016d135c0cf277de1c3c6 Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for unsqueeze 2bb17667d1dabc95391950426fc1f921eb3d0959 Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for acc_ops.split 64dfb7b096686cb2fd33197340dc72f30d525456 Shirong Wu <shirong@fb.com> Group LN trt plugin 438f670e28df59b0734baa092a514fba3d75eb4f Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for acc_ops.avgpool df0fe32dae4343827bd9b37b72daae761b02f228 Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for acc_ops masked fill 44fe735d3493ea2d05a56b49093e4a23dd63a98e Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shaope support for acc_ops.pad 4f931acca706d8ce79045ceafef2ea0486609149 Wei Wei <wwei6@fb.com> [fx2trt] torch.max dynamic shape test bf6f6cbe217d26a95ca9122574adf7de3966db9e Shreyansh Prajapati <shreyanshp@fb.com> Change the name of the test from full_reduce to dim_reduce 1c5680ed107d9206f3514eff4069a3f6c870ba8c Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for acc_ops.type_as 33e4c175a4f5fec78ac0b1c8eb262ca777c7aaba Shreyansh Prajapati <shreyanshp@fb.com> Test dynamic shape support for acc_ops.min f37be34bcef9716080b8bafbd1f4ad72e412c44c Wei Wei <wwei6@fb.com> [fx2trt] plugin for grid_sample 57b5cc6a0f4839686ae360361a3a13b424794ee7 generatedunixname89002005367269 <generatedunixname89002005367269@fb.com> [AutoAccept][Codemod][FBSourceBlackLinter] Daily `arc lint --take BLACK` eb741cc5e5a7babdc94e72d411670905f54da3e0 Shreyansh Prajapati <shreyanshp@fb.com> Updated the dynamic shape support for narrow op 521c36b96a14741ae89d7af6cbb658120bcec2ea Shreyansh Prajapati <shreyanshp@fb.com> Removing the comment for 4 dims dynamic shape support after analysis e947343375967fe9efb0a16fdb9f63bff1449328 Shreyansh Prajapati <shreyanshp@fb.com> Updated the pad test for dynamic batch for analysis 3d64087014e91bc301a315eae43683b1aa2b66bc Oleg Khabinov <khabinov@fb.com> [trt_bc] Some improvements dfd937a56fa01aca88a89b46176befdac4c202c4 Shreyansh Prajapati <shreyanshp@fb.com> Updated the test for as_strided op for analysis 11d76d0420dcaa4bb8890dcdeb86b6e534af831c Bangsheng Tang <bangsheng@fb.com> [gpu][infer] replace fx2trt_layer_norm with fbgemm layer_norm 932046ff6ea6dead114c0222b23ca3854690cffa Wei Wei <wwei6@fb.com> [fx2trt] bridge the dynamic batch and fixed shape
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There are some changes that do not conform to C++ style guidelines:
diff --git a/workspace/core/conversion/converters/converter_util.cpp b/tmp/changes.txt
index 1346f7e..3ef738a 100644
--- a/workspace/core/conversion/converters/converter_util.cpp
+++ b/tmp/changes.txt
@@ -206,13 +206,13 @@ nvinfer1::ITensor* clamp(
nvinfer1::ITensor* lower_bound,
nvinfer1::ITensor* upper_bound,
std::string const& name) {
-
auto max_layer = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kMAX, x, lower_bound, "max layer for " + name);
TORCHTRT_CHECK(max_layer, "Unable to create max layer for clamp");
LOG_DEBUG(ctx->logger, "Create " << max_layer->getName() << " for clamp");
auto max_itensor = max_layer->getOutput(0);
- auto min_layer = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kMIN, max_itensor, upper_bound, "min layer for " + name);
+ auto min_layer =
+ add_elementwise(ctx, nvinfer1::ElementWiseOperation::kMIN, max_itensor, upper_bound, "min layer for " + name);
TORCHTRT_CHECK(min_layer, "Unable to create min layer for clamp");
LOG_DEBUG(ctx->logger, "Create " << min_layer->getName() << " for clamp");
auto min_itensor = min_layer->getOutput(0);
@@ -226,13 +226,13 @@ nvinfer1::ITensor* clamp_to_input_dim(
nvinfer1::ITensor* input_dim,
int nbdims,
std::string const& name) {
-
auto zero = torch::zeros({nbdims}).to(torch::kI32);
auto zero_itensor = tensor_to_const(ctx, zero);
auto one = torch::ones({nbdims}).to(torch::kI32);
auto one_itensor = tensor_to_const(ctx, one);
- auto upper_bound_layer = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUB, input_dim, one_itensor, "sub layer for " + name);
+ auto upper_bound_layer =
+ add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUB, input_dim, one_itensor, "sub layer for " + name);
TORCHTRT_CHECK(upper_bound_layer, "Unable to create sub layer for clamp to inputDim");
LOG_DEBUG(ctx->logger, "Create " << upper_bound_layer->getName() << " for clamp to inputDim");
auto upper_bound = upper_bound_layer->getOutput(0);
@@ -242,7 +242,8 @@ nvinfer1::ITensor* clamp_to_input_dim(
LOG_DEBUG(ctx->logger, "Create " << max_layer->getName() << " for clamp to inputDim");
auto max_itensor = max_layer->getOutput(0);
- auto min_layer = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kMIN, max_itensor, upper_bound, "min layer for " + name);
+ auto min_layer =
+ add_elementwise(ctx, nvinfer1::ElementWiseOperation::kMIN, max_itensor, upper_bound, "min layer for " + name);
TORCHTRT_CHECK(min_layer, "Unable to create min_layer for clamp to inputDim");
LOG_DEBUG(ctx->logger, "Create " << min_layer->getName() << " for clamp to inputDim");
auto min_itensor = min_layer->getOutput(0);
@@ -256,7 +257,6 @@ nvinfer1::ITensor* normalize_indices(
nvinfer1::ITensor* indices,
int nbdims,
std::string const& name) {
-
auto zero = torch::zeros({nbdims}).to(torch::kI32);
auto neg = -torch::ones({nbdims}).to(torch::kI32);
auto zero_itensor = tensor_to_const(ctx, zero);
@@ -306,17 +306,20 @@ nvinfer1::ITensor* get_slice_size(
at::Tensor one_tensor = torch::ones({nbdims}).to(torch::kI32);
auto one_itensor = tensor_to_const(ctx, one_tensor);
- auto sub_layer = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUB, end, start, "get_slice_size sub layer for " + name);
+ auto sub_layer =
+ add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUB, end, start, "get_slice_size sub layer for " + name);
TORCHTRT_CHECK(sub_layer, "Unable to create sub layer in calculate_output_size");
LOG_DEBUG(ctx->logger, "Create " << sub_layer->getName() << " for calculate_output_size");
auto sub_itensor = sub_layer->getOutput(0);
- auto div_layer = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kDIV, sub_itensor, stride, "get_slice_size div layer for " + name);
+ auto div_layer = add_elementwise(
+ ctx, nvinfer1::ElementWiseOperation::kDIV, sub_itensor, stride, "get_slice_size div layer for " + name);
TORCHTRT_CHECK(div_layer, "Unable to create div layer in calculate_output_size");
LOG_DEBUG(ctx->logger, "Create " << div_layer->getName() << " for calculate_output_size");
auto div_itensor = div_layer->getOutput(0);
- auto add_layer = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUM, div_itensor, one_itensor, "get_slice_size sum layer for " + name);
+ auto add_layer = add_elementwise(
+ ctx, nvinfer1::ElementWiseOperation::kSUM, div_itensor, one_itensor, "get_slice_size sum layer for " + name);
TORCHTRT_CHECK(add_layer, "Unable to create add layer in calculate_output_size");
LOG_DEBUG(ctx->logger, "Create " << add_layer->getName() << " for calculate_output_size");
auto size_itensor = add_layer->getOutput(0);
diff --git a/workspace/core/conversion/converters/impl/select.cpp b/tmp/changes.txt
index 3599ab9..d33f09a 100644
--- a/workspace/core/conversion/converters/impl/select.cpp
+++ b/tmp/changes.txt
@@ -103,121 +103,118 @@ nvinfer1::ITensor* roll(
auto select_registrations TORCHTRT_UNUSED =
RegisterNodeConversionPatterns()
- .pattern(
- {"aten::select.int(Tensor(a) self, int dim, int index) -> (Tensor(a))",
- [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
- auto in = args[0].ITensorOrFreeze(ctx);
- auto maxDim = static_cast<int64_t>(in->getDimensions().nbDims);
- auto dim = args[1].unwrapToInt();
- // Handle negative axis by refering to nbDims of input Tensor
- dim = dim < 0 ? dim + maxDim : dim;
- auto ind = (int32_t)args[2].unwrapToInt();
- // Along the specified dimension, handle negative index by subtracting along length of dimension.
- ind = ind < 0 ? ind + in->getDimensions().d[dim] : ind;
- LOG_DEBUG("Gather input dimensions: " << in->getDimensions());
- LOG_DEBUG("Dimension to select: " << dim);
- LOG_DEBUG("Index: " << ind);
-
- // index to access needs to be an at::Tensor
- at::Tensor indices = torch::tensor({ind}).to(torch::kI32);
- auto const_out = tensor_to_const(ctx, indices);
-
- // IGatherLayer takes in input tensor, the indices, and the axis
- // of input tensor to take indices from
- auto gather_layer = ctx->net->addGather(*in, *const_out, dim);
- TORCHTRT_CHECK(gather_layer, "Unable to create gather layer from node: " << *n);
- auto out = gather_layer->getOutput(0);
+ .pattern({"aten::select.int(Tensor(a) self, int dim, int index) -> (Tensor(a))",
+ [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+ auto in = args[0].ITensorOrFreeze(ctx);
+ auto maxDim = static_cast<int64_t>(in->getDimensions().nbDims);
+ auto dim = args[1].unwrapToInt();
+ // Handle negative axis by refering to nbDims of input Tensor
+ dim = dim < 0 ? dim + maxDim : dim;
+ auto ind = (int32_t)args[2].unwrapToInt();
+ // Along the specified dimension, handle negative index by subtracting along length of dimension.
+ ind = ind < 0 ? ind + in->getDimensions().d[dim] : ind;
+ LOG_DEBUG("Gather input dimensions: " << in->getDimensions());
+ LOG_DEBUG("Dimension to select: " << dim);
+ LOG_DEBUG("Index: " << ind);
+
+ // index to access needs to be an at::Tensor
+ at::Tensor indices = torch::tensor({ind}).to(torch::kI32);
+ auto const_out = tensor_to_const(ctx, indices);
+
+ // IGatherLayer takes in input tensor, the indices, and the axis
+ // of input tensor to take indices from
+ auto gather_layer = ctx->net->addGather(*in, *const_out, dim);
+ TORCHTRT_CHECK(gather_layer, "Unable to create gather layer from node: " << *n);
+ auto out = gather_layer->getOutput(0);
+
+ LOG_DEBUG("Gather tensor shape: " << out->getDimensions());
+
+ if (out->getDimensions().nbDims != 1) {
+ // IShuffleLayer removes redundant dimensions
+ auto shuffle_layer = ctx->net->addShuffle(*out);
+ TORCHTRT_CHECK(shuffle_layer, "Unable to create shuffle layer from node: " << *n);
+ shuffle_layer->setReshapeDimensions(util::squeezeDims(out->getDimensions(), dim));
+ shuffle_layer->setName(util::node_info(n).c_str());
+ out = shuffle_layer->getOutput(0);
+ }
+
+ out = ctx->AssociateValueAndTensor(n->outputs()[0], out);
+
+ LOG_DEBUG("Output tensor shape: " << out->getDimensions());
- LOG_DEBUG("Gather tensor shape: " << out->getDimensions());
+ return true;
+ }})
+ .pattern({"aten::narrow(Tensor(a) self, int dim, int start, int length) -> Tensor(a)",
+ [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+ auto in = args[0].ITensor();
+ auto axis = args[1].unwrapToInt();
+ auto start = (int32_t)args[2].unwrapToInt();
+ auto length = (int32_t)args[3].unwrapToInt();
- if (out->getDimensions().nbDims != 1) {
- // IShuffleLayer removes redundant dimensions
- auto shuffle_layer = ctx->net->addShuffle(*out);
- TORCHTRT_CHECK(shuffle_layer, "Unable to create shuffle layer from node: " << *n);
- shuffle_layer->setReshapeDimensions(util::squeezeDims(out->getDimensions(), dim));
- shuffle_layer->setName(util::node_info(n).c_str());
- out = shuffle_layer->getOutput(0);
- }
+ // index to access needs to be an at::Tensor
+ at::Tensor indices = torch::arange(start, start + length, 1).to(torch::kI32);
+ auto weights = Weights(ctx, indices);
- out = ctx->AssociateValueAndTensor(n->outputs()[0], out);
+ // IConstantLayer to convert indices from Weights to ITensor
+ auto const_layer = ctx->net->addConstant(weights.shape, weights.data);
+ TORCHTRT_CHECK(const_layer, "Unable to create constant layer from node: " << *n);
+ auto const_out = const_layer->getOutput(0);
- LOG_DEBUG("Output tensor shape: " << out->getDimensions());
+ // IGatherLayer takes in input tensor, the indices, and the axis
+ // of input tensor to take indices from
+ auto gather_layer = ctx->net->addGather(*in, *const_out, axis);
+ TORCHTRT_CHECK(gather_layer, "Unable to create gather layer from node: " << *n);
+ auto gather_out = gather_layer->getOutput(0);
- return true;
- }})
- .pattern(
- {"aten::narrow(Tensor(a) self, int dim, int start, int length) -> Tensor(a)",
- [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
- auto in = args[0].ITensor();
- auto axis = args[1].unwrapToInt();
- auto start = (int32_t)args[2].unwrapToInt();
- auto length = (int32_t)args[3].unwrapToInt();
-
- // index to access needs to be an at::Tensor
- at::Tensor indices = torch::arange(start, start + length, 1).to(torch::kI32);
- auto weights = Weights(ctx, indices);
-
- // IConstantLayer to convert indices from Weights to ITensor
- auto const_layer = ctx->net->addConstant(weights.shape, weights.data);
- TORCHTRT_CHECK(const_layer, "Unable to create constant layer from node: " << *n);
- auto const_out = const_layer->getOutput(0);
-
- // IGatherLayer takes in input tensor, the indices, and the axis
- // of input tensor to take indices from
- auto gather_layer = ctx->net->addGather(*in, *const_out, axis);
- TORCHTRT_CHECK(gather_layer, "Unable to create gather layer from node: " << *n);
- auto gather_out = gather_layer->getOutput(0);
-
- // IShuffleLayer removes redundant dimensions
- auto shuffle_layer = ctx->net->addShuffle(*gather_out);
- TORCHTRT_CHECK(shuffle_layer, "Unable to create shuffle layer from node: " << *n);
- shuffle_layer->setReshapeDimensions(util::unpadDims(gather_out->getDimensions()));
- shuffle_layer->setName(util::node_info(n).c_str());
- auto shuffle_out = shuffle_layer->getOutput(0);
+ // IShuffleLayer removes redundant dimensions
+ auto shuffle_layer = ctx->net->addShuffle(*gather_out);
+ TORCHTRT_CHECK(shuffle_layer, "Unable to create shuffle layer from node: " << *n);
+ shuffle_layer->setReshapeDimensions(util::unpadDims(gather_out->getDimensions()));
+ shuffle_layer->setName(util::node_info(n).c_str());
+ auto shuffle_out = shuffle_layer->getOutput(0);
- auto out = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle_out);
+ auto out = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle_out);
- LOG_DEBUG("Output tensor shape: " << out->getDimensions());
+ LOG_DEBUG("Output tensor shape: " << out->getDimensions());
- return true;
- }})
- .pattern(
- {"aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, int length) -> Tensor(a)",
- [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
- auto in = args[0].ITensor();
- auto axis = args[1].unwrapToInt();
- torch::Tensor start = args[2].IValue()->toTensor().to(torch::kI32);
- int32_t startIdx = start.item().to<int32_t>();
- auto length = (int32_t)args[3].unwrapToInt();
-
- // index to access needs to be an at::Tensor
- at::Tensor indices = torch::arange(startIdx, startIdx + length, 1).to(torch::kI32);
- auto weights = Weights(ctx, indices);
-
- // IConstantLayer to convert indices from Weights to ITensor
- auto const_layer = ctx->net->addConstant(weights.shape, weights.data);
- TORCHTRT_CHECK(const_layer, "Unable to create constant layer from node: " << *n);
- auto const_out = const_layer->getOutput(0);
-
- // IGatherLayer takes in input tensor, the indices, and the axis
- // of input tensor to take indices from
- auto gather_layer = ctx->net->addGather(*in, *const_out, axis);
- TORCHTRT_CHECK(gather_layer, "Unable to create gather layer from node: " << *n);
- auto gather_out = gather_layer->getOutput(0);
-
- // IShuffleLayer removes redundant dimensions
- auto shuffle_layer = ctx->net->addShuffle(*gather_out);
- TORCHTRT_CHECK(shuffle_layer, "Unable to create shuffle layer from node: " << *n);
- shuffle_layer->setReshapeDimensions(util::unpadDims(gather_out->getDimensions()));
- shuffle_layer->setName(util::node_info(n).c_str());
- auto shuffle_out = shuffle_layer->getOutput(0);
-
- auto out = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle_out);
-
- LOG_DEBUG("Output tensor shape: " << out->getDimensions());
+ return true;
+ }})
+ .pattern({"aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, int length) -> Tensor(a)",
+ [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+ auto in = args[0].ITensor();
+ auto axis = args[1].unwrapToInt();
+ torch::Tensor start = args[2].IValue()->toTensor().to(torch::kI32);
+ int32_t startIdx = start.item().to<int32_t>();
+ auto length = (int32_t)args[3].unwrapToInt();
+
+ // index to access needs to be an at::Tensor
+ at::Tensor indices = torch::arange(startIdx, startIdx + length, 1).to(torch::kI32);
+ auto weights = Weights(ctx, indices);
+
+ // IConstantLayer to convert indices from Weights to ITensor
+ auto const_layer = ctx->net->addConstant(weights.shape, weights.data);
+ TORCHTRT_CHECK(const_layer, "Unable to create constant layer from node: " << *n);
+ auto const_out = const_layer->getOutput(0);
+
+ // IGatherLayer takes in input tensor, the indices, and the axis
+ // of input tensor to take indices from
+ auto gather_layer = ctx->net->addGather(*in, *const_out, axis);
+ TORCHTRT_CHECK(gather_layer, "Unable to create gather layer from node: " << *n);
+ auto gather_out = gather_layer->getOutput(0);
+
+ // IShuffleLayer removes redundant dimensions
+ auto shuffle_layer = ctx->net->addShuffle(*gather_out);
+ TORCHTRT_CHECK(shuffle_layer, "Unable to create shuffle layer from node: " << *n);
+ shuffle_layer->setReshapeDimensions(util::unpadDims(gather_out->getDimensions()));
+ shuffle_layer->setName(util::node_info(n).c_str());
+ auto shuffle_out = shuffle_layer->getOutput(0);
+
+ auto out = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle_out);
+
+ LOG_DEBUG("Output tensor shape: " << out->getDimensions());
- return true;
- }})
+ return true;
+ }})
.pattern(
{"aten::embedding(Tensor weight, Tensor indices, int padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> (Tensor)",
[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
@@ -239,30 +236,29 @@ auto select_registrations TORCHTRT_UNUSED =
return true;
}})
- .pattern(
- {"aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -> (Tensor)",
- [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
- auto in = args[0].ITensor();
- auto shifts = args[1].unwrapToIntList().vec();
- auto dims = args[2].unwrapToIntList().vec();
-
- TORCHTRT_CHECK(dims.size() == shifts.size(), "dims.size() should be equal to shifts.size()");
- if (ctx->input_is_dynamic) {
- TORCHTRT_THROW_ERROR("aten::roll is currently not support in dynamic input shape compilation");
- } else {
- auto in_shape = util::toVec(in->getDimensions());
- for (size_t i = 0; i < dims.size(); i++) {
- auto dim = dims[i] < 0 ? (in_shape.size() + dims[i]) : dims[i];
- TORCHTRT_CHECK(dim < in_shape.size(), "Dimension out of range");
- in = roll(ctx, in, shifts[i], dim, in_shape);
- }
- auto out = ctx->AssociateValueAndTensor(n->outputs()[0], in);
-
- LOG_DEBUG("Output tensor shape: " << out->getDimensions());
-
- return true;
- }
- }})
+ .pattern({"aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -> (Tensor)",
+ [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+ auto in = args[0].ITensor();
+ auto shifts = args[1].unwrapToIntList().vec();
+ auto dims = args[2].unwrapToIntList().vec();
+
+ TORCHTRT_CHECK(dims.size() == shifts.size(), "dims.size() should be equal to shifts.size()");
+ if (ctx->input_is_dynamic) {
+ TORCHTRT_THROW_ERROR("aten::roll is currently not support in dynamic input shape compilation");
+ } else {
+ auto in_shape = util::toVec(in->getDimensions());
+ for (size_t i = 0; i < dims.size(); i++) {
+ auto dim = dims[i] < 0 ? (in_shape.size() + dims[i]) : dims[i];
+ TORCHTRT_CHECK(dim < in_shape.size(), "Dimension out of range");
+ in = roll(ctx, in, shifts[i], dim, in_shape);
+ }
+ auto out = ctx->AssociateValueAndTensor(n->outputs()[0], in);
+
+ LOG_DEBUG("Output tensor shape: " << out->getDimensions());
+
+ return true;
+ }
+ }})
.pattern(
{"aten::index.Tensor(Tensor self, Tensor?[] indices) -> (Tensor)",
[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
@@ -319,7 +315,8 @@ auto select_registrations TORCHTRT_UNUSED =
int startIdx = 0;
auto startIdxIVal = args[2].IValue();
if (!startIdxIVal->isNone()) {
- startIdx = startIdxIVal->toInt() > std::numeric_limits<int32_t>::max() ? maxDim : startIdxIVal->toInt();
+ startIdx =
+ startIdxIVal->toInt() > std::numeric_limits<int32_t>::max() ? maxDim : startIdxIVal->toInt();
startIdx = maxDim == -1 ? startIdx : std::min(startIdx, maxDim);
}
// Handle case when given tensor index is negative
@@ -331,7 +328,8 @@ auto select_registrations TORCHTRT_UNUSED =
int endIdx = maxDim; // -1 for dynamic shape
auto endIdxIVal = args[3].IValue();
if (!endIdxIVal->isNone()) {
- int truncate_value = endIdxIVal->toInt() > std::numeric_limits<int32_t>::max() ? maxDim : endIdxIVal->toInt();
+ int truncate_value =
+ endIdxIVal->toInt() > std::numeric_limits<int32_t>::max() ? maxDim : endIdxIVal->toInt();
endIdx = maxDim == -1 ? truncate_value : std::min(truncate_value, maxDim);
}
if (maxDim > 0) {
@@ -385,7 +383,8 @@ auto select_registrations TORCHTRT_UNUSED =
// update start and end
nvinfer1::ITensor* out_start;
nvinfer1::ITensor* out_end;
- auto start_end = normalize_start_and_end(ctx, ishape_tensor, start_itensor, end_itensor, nbdims, node_name);
+ auto start_end =
+ normalize_start_and_end(ctx, ishape_tensor, start_itensor, end_itensor, nbdims, node_name);
out_start = start_end[0];
out_end = start_end[1];
@@ -397,7 +396,7 @@ auto select_registrations TORCHTRT_UNUSED =
slice_layer->setInput(2, *size_itensor); // size, must be set if input is dynamic
}
auto slice_out = slice_layer->getOutput(0);
-
+
auto out = ctx->AssociateValueAndTensor(n->outputs()[0], slice_out);
LOG_DEBUG("Slice layer output shape: " << out->getDimensions());
diff --git a/workspace/core/conversion/converters/converter_util.h b/tmp/changes.txt
index cdf2ee5..b155499 100644
--- a/workspace/core/conversion/converters/converter_util.h
+++ b/tmp/changes.txt
@@ -1,8 +1,8 @@
#pragma once
+#include <limits>
#include <map>
#include <string>
-#include <limits>
#include "core/conversion/conversionctx/ConversionCtx.h"
#include "core/conversion/converters/Weights.h"
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/core/conversion/converters/converter_util.cpp b/tmp/changes.txt
index 1346f7e..3ef738a 100644
--- a/workspace/core/conversion/converters/converter_util.cpp
+++ b/tmp/changes.txt
@@ -206,13 +206,13 @@ nvinfer1::ITensor* clamp(
nvinfer1::ITensor* lower_bound,
nvinfer1::ITensor* upper_bound,
std::string const& name) {
-
auto max_layer = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kMAX, x, lower_bound, "max layer for " + name);
TORCHTRT_CHECK(max_layer, "Unable to create max layer for clamp");
LOG_DEBUG(ctx->logger, "Create " << max_layer->getName() << " for clamp");
auto max_itensor = max_layer->getOutput(0);
- auto min_layer = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kMIN, max_itensor, upper_bound, "min layer for " + name);
+ auto min_layer =
+ add_elementwise(ctx, nvinfer1::ElementWiseOperation::kMIN, max_itensor, upper_bound, "min layer for " + name);
TORCHTRT_CHECK(min_layer, "Unable to create min layer for clamp");
LOG_DEBUG(ctx->logger, "Create " << min_layer->getName() << " for clamp");
auto min_itensor = min_layer->getOutput(0);
@@ -226,13 +226,13 @@ nvinfer1::ITensor* clamp_to_input_dim(
nvinfer1::ITensor* input_dim,
int nbdims,
std::string const& name) {
-
auto zero = torch::zeros({nbdims}).to(torch::kI32);
auto zero_itensor = tensor_to_const(ctx, zero);
auto one = torch::ones({nbdims}).to(torch::kI32);
auto one_itensor = tensor_to_const(ctx, one);
- auto upper_bound_layer = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUB, input_dim, one_itensor, "sub layer for " + name);
+ auto upper_bound_layer =
+ add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUB, input_dim, one_itensor, "sub layer for " + name);
TORCHTRT_CHECK(upper_bound_layer, "Unable to create sub layer for clamp to inputDim");
LOG_DEBUG(ctx->logger, "Create " << upper_bound_layer->getName() << " for clamp to inputDim");
auto upper_bound = upper_bound_layer->getOutput(0);
@@ -242,7 +242,8 @@ nvinfer1::ITensor* clamp_to_input_dim(
LOG_DEBUG(ctx->logger, "Create " << max_layer->getName() << " for clamp to inputDim");
auto max_itensor = max_layer->getOutput(0);
- auto min_layer = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kMIN, max_itensor, upper_bound, "min layer for " + name);
+ auto min_layer =
+ add_elementwise(ctx, nvinfer1::ElementWiseOperation::kMIN, max_itensor, upper_bound, "min layer for " + name);
TORCHTRT_CHECK(min_layer, "Unable to create min_layer for clamp to inputDim");
LOG_DEBUG(ctx->logger, "Create " << min_layer->getName() << " for clamp to inputDim");
auto min_itensor = min_layer->getOutput(0);
@@ -256,7 +257,6 @@ nvinfer1::ITensor* normalize_indices(
nvinfer1::ITensor* indices,
int nbdims,
std::string const& name) {
-
auto zero = torch::zeros({nbdims}).to(torch::kI32);
auto neg = -torch::ones({nbdims}).to(torch::kI32);
auto zero_itensor = tensor_to_const(ctx, zero);
@@ -306,17 +306,20 @@ nvinfer1::ITensor* get_slice_size(
at::Tensor one_tensor = torch::ones({nbdims}).to(torch::kI32);
auto one_itensor = tensor_to_const(ctx, one_tensor);
- auto sub_layer = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUB, end, start, "get_slice_size sub layer for " + name);
+ auto sub_layer =
+ add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUB, end, start, "get_slice_size sub layer for " + name);
TORCHTRT_CHECK(sub_layer, "Unable to create sub layer in calculate_output_size");
LOG_DEBUG(ctx->logger, "Create " << sub_layer->getName() << " for calculate_output_size");
auto sub_itensor = sub_layer->getOutput(0);
- auto div_layer = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kDIV, sub_itensor, stride, "get_slice_size div layer for " + name);
+ auto div_layer = add_elementwise(
+ ctx, nvinfer1::ElementWiseOperation::kDIV, sub_itensor, stride, "get_slice_size div layer for " + name);
TORCHTRT_CHECK(div_layer, "Unable to create div layer in calculate_output_size");
LOG_DEBUG(ctx->logger, "Create " << div_layer->getName() << " for calculate_output_size");
auto div_itensor = div_layer->getOutput(0);
- auto add_layer = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUM, div_itensor, one_itensor, "get_slice_size sum layer for " + name);
+ auto add_layer = add_elementwise(
+ ctx, nvinfer1::ElementWiseOperation::kSUM, div_itensor, one_itensor, "get_slice_size sum layer for " + name);
TORCHTRT_CHECK(add_layer, "Unable to create add layer in calculate_output_size");
LOG_DEBUG(ctx->logger, "Create " << add_layer->getName() << " for calculate_output_size");
auto size_itensor = add_layer->getOutput(0);
diff --git a/workspace/core/conversion/converters/impl/select.cpp b/tmp/changes.txt
index 3599ab9..d33f09a 100644
--- a/workspace/core/conversion/converters/impl/select.cpp
+++ b/tmp/changes.txt
@@ -103,121 +103,118 @@ nvinfer1::ITensor* roll(
auto select_registrations TORCHTRT_UNUSED =
RegisterNodeConversionPatterns()
- .pattern(
- {"aten::select.int(Tensor(a) self, int dim, int index) -> (Tensor(a))",
- [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
- auto in = args[0].ITensorOrFreeze(ctx);
- auto maxDim = static_cast<int64_t>(in->getDimensions().nbDims);
- auto dim = args[1].unwrapToInt();
- // Handle negative axis by refering to nbDims of input Tensor
- dim = dim < 0 ? dim + maxDim : dim;
- auto ind = (int32_t)args[2].unwrapToInt();
- // Along the specified dimension, handle negative index by subtracting along length of dimension.
- ind = ind < 0 ? ind + in->getDimensions().d[dim] : ind;
- LOG_DEBUG("Gather input dimensions: " << in->getDimensions());
- LOG_DEBUG("Dimension to select: " << dim);
- LOG_DEBUG("Index: " << ind);
-
- // index to access needs to be an at::Tensor
- at::Tensor indices = torch::tensor({ind}).to(torch::kI32);
- auto const_out = tensor_to_const(ctx, indices);
-
- // IGatherLayer takes in input tensor, the indices, and the axis
- // of input tensor to take indices from
- auto gather_layer = ctx->net->addGather(*in, *const_out, dim);
- TORCHTRT_CHECK(gather_layer, "Unable to create gather layer from node: " << *n);
- auto out = gather_layer->getOutput(0);
+ .pattern({"aten::select.int(Tensor(a) self, int dim, int index) -> (Tensor(a))",
+ [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+ auto in = args[0].ITensorOrFreeze(ctx);
+ auto maxDim = static_cast<int64_t>(in->getDimensions().nbDims);
+ auto dim = args[1].unwrapToInt();
+ // Handle negative axis by refering to nbDims of input Tensor
+ dim = dim < 0 ? dim + maxDim : dim;
+ auto ind = (int32_t)args[2].unwrapToInt();
+ // Along the specified dimension, handle negative index by subtracting along length of dimension.
+ ind = ind < 0 ? ind + in->getDimensions().d[dim] : ind;
+ LOG_DEBUG("Gather input dimensions: " << in->getDimensions());
+ LOG_DEBUG("Dimension to select: " << dim);
+ LOG_DEBUG("Index: " << ind);
+
+ // index to access needs to be an at::Tensor
+ at::Tensor indices = torch::tensor({ind}).to(torch::kI32);
+ auto const_out = tensor_to_const(ctx, indices);
+
+ // IGatherLayer takes in input tensor, the indices, and the axis
+ // of input tensor to take indices from
+ auto gather_layer = ctx->net->addGather(*in, *const_out, dim);
+ TORCHTRT_CHECK(gather_layer, "Unable to create gather layer from node: " << *n);
+ auto out = gather_layer->getOutput(0);
+
+ LOG_DEBUG("Gather tensor shape: " << out->getDimensions());
+
+ if (out->getDimensions().nbDims != 1) {
+ // IShuffleLayer removes redundant dimensions
+ auto shuffle_layer = ctx->net->addShuffle(*out);
+ TORCHTRT_CHECK(shuffle_layer, "Unable to create shuffle layer from node: " << *n);
+ shuffle_layer->setReshapeDimensions(util::squeezeDims(out->getDimensions(), dim));
+ shuffle_layer->setName(util::node_info(n).c_str());
+ out = shuffle_layer->getOutput(0);
+ }
+
+ out = ctx->AssociateValueAndTensor(n->outputs()[0], out);
+
+ LOG_DEBUG("Output tensor shape: " << out->getDimensions());
- LOG_DEBUG("Gather tensor shape: " << out->getDimensions());
+ return true;
+ }})
+ .pattern({"aten::narrow(Tensor(a) self, int dim, int start, int length) -> Tensor(a)",
+ [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+ auto in = args[0].ITensor();
+ auto axis = args[1].unwrapToInt();
+ auto start = (int32_t)args[2].unwrapToInt();
+ auto length = (int32_t)args[3].unwrapToInt();
- if (out->getDimensions().nbDims != 1) {
- // IShuffleLayer removes redundant dimensions
- auto shuffle_layer = ctx->net->addShuffle(*out);
- TORCHTRT_CHECK(shuffle_layer, "Unable to create shuffle layer from node: " << *n);
- shuffle_layer->setReshapeDimensions(util::squeezeDims(out->getDimensions(), dim));
- shuffle_layer->setName(util::node_info(n).c_str());
- out = shuffle_layer->getOutput(0);
- }
+ // index to access needs to be an at::Tensor
+ at::Tensor indices = torch::arange(start, start + length, 1).to(torch::kI32);
+ auto weights = Weights(ctx, indices);
- out = ctx->AssociateValueAndTensor(n->outputs()[0], out);
+ // IConstantLayer to convert indices from Weights to ITensor
+ auto const_layer = ctx->net->addConstant(weights.shape, weights.data);
+ TORCHTRT_CHECK(const_layer, "Unable to create constant layer from node: " << *n);
+ auto const_out = const_layer->getOutput(0);
- LOG_DEBUG("Output tensor shape: " << out->getDimensions());
+ // IGatherLayer takes in input tensor, the indices, and the axis
+ // of input tensor to take indices from
+ auto gather_layer = ctx->net->addGather(*in, *const_out, axis);
+ TORCHTRT_CHECK(gather_layer, "Unable to create gather layer from node: " << *n);
+ auto gather_out = gather_layer->getOutput(0);
- return true;
- }})
- .pattern(
- {"aten::narrow(Tensor(a) self, int dim, int start, int length) -> Tensor(a)",
- [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
- auto in = args[0].ITensor();
- auto axis = args[1].unwrapToInt();
- auto start = (int32_t)args[2].unwrapToInt();
- auto length = (int32_t)args[3].unwrapToInt();
-
- // index to access needs to be an at::Tensor
- at::Tensor indices = torch::arange(start, start + length, 1).to(torch::kI32);
- auto weights = Weights(ctx, indices);
-
- // IConstantLayer to convert indices from Weights to ITensor
- auto const_layer = ctx->net->addConstant(weights.shape, weights.data);
- TORCHTRT_CHECK(const_layer, "Unable to create constant layer from node: " << *n);
- auto const_out = const_layer->getOutput(0);
-
- // IGatherLayer takes in input tensor, the indices, and the axis
- // of input tensor to take indices from
- auto gather_layer = ctx->net->addGather(*in, *const_out, axis);
- TORCHTRT_CHECK(gather_layer, "Unable to create gather layer from node: " << *n);
- auto gather_out = gather_layer->getOutput(0);
-
- // IShuffleLayer removes redundant dimensions
- auto shuffle_layer = ctx->net->addShuffle(*gather_out);
- TORCHTRT_CHECK(shuffle_layer, "Unable to create shuffle layer from node: " << *n);
- shuffle_layer->setReshapeDimensions(util::unpadDims(gather_out->getDimensions()));
- shuffle_layer->setName(util::node_info(n).c_str());
- auto shuffle_out = shuffle_layer->getOutput(0);
+ // IShuffleLayer removes redundant dimensions
+ auto shuffle_layer = ctx->net->addShuffle(*gather_out);
+ TORCHTRT_CHECK(shuffle_layer, "Unable to create shuffle layer from node: " << *n);
+ shuffle_layer->setReshapeDimensions(util::unpadDims(gather_out->getDimensions()));
+ shuffle_layer->setName(util::node_info(n).c_str());
+ auto shuffle_out = shuffle_layer->getOutput(0);
- auto out = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle_out);
+ auto out = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle_out);
- LOG_DEBUG("Output tensor shape: " << out->getDimensions());
+ LOG_DEBUG("Output tensor shape: " << out->getDimensions());
- return true;
- }})
- .pattern(
- {"aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, int length) -> Tensor(a)",
- [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
- auto in = args[0].ITensor();
- auto axis = args[1].unwrapToInt();
- torch::Tensor start = args[2].IValue()->toTensor().to(torch::kI32);
- int32_t startIdx = start.item().to<int32_t>();
- auto length = (int32_t)args[3].unwrapToInt();
-
- // index to access needs to be an at::Tensor
- at::Tensor indices = torch::arange(startIdx, startIdx + length, 1).to(torch::kI32);
- auto weights = Weights(ctx, indices);
-
- // IConstantLayer to convert indices from Weights to ITensor
- auto const_layer = ctx->net->addConstant(weights.shape, weights.data);
- TORCHTRT_CHECK(const_layer, "Unable to create constant layer from node: " << *n);
- auto const_out = const_layer->getOutput(0);
-
- // IGatherLayer takes in input tensor, the indices, and the axis
- // of input tensor to take indices from
- auto gather_layer = ctx->net->addGather(*in, *const_out, axis);
- TORCHTRT_CHECK(gather_layer, "Unable to create gather layer from node: " << *n);
- auto gather_out = gather_layer->getOutput(0);
-
- // IShuffleLayer removes redundant dimensions
- auto shuffle_layer = ctx->net->addShuffle(*gather_out);
- TORCHTRT_CHECK(shuffle_layer, "Unable to create shuffle layer from node: " << *n);
- shuffle_layer->setReshapeDimensions(util::unpadDims(gather_out->getDimensions()));
- shuffle_layer->setName(util::node_info(n).c_str());
- auto shuffle_out = shuffle_layer->getOutput(0);
-
- auto out = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle_out);
-
- LOG_DEBUG("Output tensor shape: " << out->getDimensions());
+ return true;
+ }})
+ .pattern({"aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, int length) -> Tensor(a)",
+ [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+ auto in = args[0].ITensor();
+ auto axis = args[1].unwrapToInt();
+ torch::Tensor start = args[2].IValue()->toTensor().to(torch::kI32);
+ int32_t startIdx = start.item().to<int32_t>();
+ auto length = (int32_t)args[3].unwrapToInt();
+
+ // index to access needs to be an at::Tensor
+ at::Tensor indices = torch::arange(startIdx, startIdx + length, 1).to(torch::kI32);
+ auto weights = Weights(ctx, indices);
+
+ // IConstantLayer to convert indices from Weights to ITensor
+ auto const_layer = ctx->net->addConstant(weights.shape, weights.data);
+ TORCHTRT_CHECK(const_layer, "Unable to create constant layer from node: " << *n);
+ auto const_out = const_layer->getOutput(0);
+
+ // IGatherLayer takes in input tensor, the indices, and the axis
+ // of input tensor to take indices from
+ auto gather_layer = ctx->net->addGather(*in, *const_out, axis);
+ TORCHTRT_CHECK(gather_layer, "Unable to create gather layer from node: " << *n);
+ auto gather_out = gather_layer->getOutput(0);
+
+ // IShuffleLayer removes redundant dimensions
+ auto shuffle_layer = ctx->net->addShuffle(*gather_out);
+ TORCHTRT_CHECK(shuffle_layer, "Unable to create shuffle layer from node: " << *n);
+ shuffle_layer->setReshapeDimensions(util::unpadDims(gather_out->getDimensions()));
+ shuffle_layer->setName(util::node_info(n).c_str());
+ auto shuffle_out = shuffle_layer->getOutput(0);
+
+ auto out = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle_out);
+
+ LOG_DEBUG("Output tensor shape: " << out->getDimensions());
- return true;
- }})
+ return true;
+ }})
.pattern(
{"aten::embedding(Tensor weight, Tensor indices, int padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> (Tensor)",
[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
@@ -239,30 +236,29 @@ auto select_registrations TORCHTRT_UNUSED =
return true;
}})
- .pattern(
- {"aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -> (Tensor)",
- [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
- auto in = args[0].ITensor();
- auto shifts = args[1].unwrapToIntList().vec();
- auto dims = args[2].unwrapToIntList().vec();
-
- TORCHTRT_CHECK(dims.size() == shifts.size(), "dims.size() should be equal to shifts.size()");
- if (ctx->input_is_dynamic) {
- TORCHTRT_THROW_ERROR("aten::roll is currently not support in dynamic input shape compilation");
- } else {
- auto in_shape = util::toVec(in->getDimensions());
- for (size_t i = 0; i < dims.size(); i++) {
- auto dim = dims[i] < 0 ? (in_shape.size() + dims[i]) : dims[i];
- TORCHTRT_CHECK(dim < in_shape.size(), "Dimension out of range");
- in = roll(ctx, in, shifts[i], dim, in_shape);
- }
- auto out = ctx->AssociateValueAndTensor(n->outputs()[0], in);
-
- LOG_DEBUG("Output tensor shape: " << out->getDimensions());
-
- return true;
- }
- }})
+ .pattern({"aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -> (Tensor)",
+ [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+ auto in = args[0].ITensor();
+ auto shifts = args[1].unwrapToIntList().vec();
+ auto dims = args[2].unwrapToIntList().vec();
+
+ TORCHTRT_CHECK(dims.size() == shifts.size(), "dims.size() should be equal to shifts.size()");
+ if (ctx->input_is_dynamic) {
+ TORCHTRT_THROW_ERROR("aten::roll is currently not support in dynamic input shape compilation");
+ } else {
+ auto in_shape = util::toVec(in->getDimensions());
+ for (size_t i = 0; i < dims.size(); i++) {
+ auto dim = dims[i] < 0 ? (in_shape.size() + dims[i]) : dims[i];
+ TORCHTRT_CHECK(dim < in_shape.size(), "Dimension out of range");
+ in = roll(ctx, in, shifts[i], dim, in_shape);
+ }
+ auto out = ctx->AssociateValueAndTensor(n->outputs()[0], in);
+
+ LOG_DEBUG("Output tensor shape: " << out->getDimensions());
+
+ return true;
+ }
+ }})
.pattern(
{"aten::index.Tensor(Tensor self, Tensor?[] indices) -> (Tensor)",
[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
@@ -319,7 +315,8 @@ auto select_registrations TORCHTRT_UNUSED =
int startIdx = 0;
auto startIdxIVal = args[2].IValue();
if (!startIdxIVal->isNone()) {
- startIdx = startIdxIVal->toInt() > std::numeric_limits<int32_t>::max() ? maxDim : startIdxIVal->toInt();
+ startIdx =
+ startIdxIVal->toInt() > std::numeric_limits<int32_t>::max() ? maxDim : startIdxIVal->toInt();
startIdx = maxDim == -1 ? startIdx : std::min(startIdx, maxDim);
}
// Handle case when given tensor index is negative
@@ -331,7 +328,8 @@ auto select_registrations TORCHTRT_UNUSED =
int endIdx = maxDim; // -1 for dynamic shape
auto endIdxIVal = args[3].IValue();
if (!endIdxIVal->isNone()) {
- int truncate_value = endIdxIVal->toInt() > std::numeric_limits<int32_t>::max() ? maxDim : endIdxIVal->toInt();
+ int truncate_value =
+ endIdxIVal->toInt() > std::numeric_limits<int32_t>::max() ? maxDim : endIdxIVal->toInt();
endIdx = maxDim == -1 ? truncate_value : std::min(truncate_value, maxDim);
}
if (maxDim > 0) {
@@ -385,7 +383,8 @@ auto select_registrations TORCHTRT_UNUSED =
// update start and end
nvinfer1::ITensor* out_start;
nvinfer1::ITensor* out_end;
- auto start_end = normalize_start_and_end(ctx, ishape_tensor, start_itensor, end_itensor, nbdims, node_name);
+ auto start_end =
+ normalize_start_and_end(ctx, ishape_tensor, start_itensor, end_itensor, nbdims, node_name);
out_start = start_end[0];
out_end = start_end[1];
@@ -397,7 +396,7 @@ auto select_registrations TORCHTRT_UNUSED =
slice_layer->setInput(2, *size_itensor); // size, must be set if input is dynamic
}
auto slice_out = slice_layer->getOutput(0);
-
+
auto out = ctx->AssociateValueAndTensor(n->outputs()[0], slice_out);
LOG_DEBUG("Slice layer output shape: " << out->getDimensions());
diff --git a/workspace/core/conversion/converters/converter_util.h b/tmp/changes.txt
index cdf2ee5..b155499 100644
--- a/workspace/core/conversion/converters/converter_util.h
+++ b/tmp/changes.txt
@@ -1,8 +1,8 @@
#pragma once
+#include <limits>
#include <map>
#include <string>
-#include <limits>
#include "core/conversion/conversionctx/ConversionCtx.h"
#include "core/conversion/converters/Weights.h"
ERROR: Some files do not conform to style guidelines
yinghai
approved these changes
Jul 21, 2022
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6703b98dff0695d91026f057b951dba1355825fa Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for acc_ops.prod
c822345d6d673e1653c2208435e34ab400bada3d Jason Park jasonjk@fb.com Add support for generic torch ops to be used in training.
e5758602a0592d6c2b71d6d66a0398c4dd9b5e20 Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for repeat interleave
c13c633f04df162500eed477c0569eb2b81eb070 Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for reduce ops
863476cf43b210922b88585b8f196dd84fbebb56 Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for acc_op.convolution
5b6a8d2d6be979983a52ac96225fefb510c3817c Andrew Or andrewor@fb.com [Quant][fx] Rename convert_to_reference to convert_to_reference_fx
996a0e080b8a8bc0b292a7c2ac92f41f6db33a2e Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for acc_op.expand
084631fe74b304fbb9481ca15fd452a3714fb1b8 Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for acc_op.to_dtype
b3195e76329ccddbb5c4640cfa884d0e457d2d34 Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for std
a5d964e62bdf769cf8c2e67321138b33e1f524a7 Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for acc_op.tile
3d33d45b2fc7f10f25c22946ba474b227e4b6529 Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for squeeze
09085abf63d7e7732e2cd66e600e8afc6d58964f Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for acc_op.topk
65edc7ea12899e9bd2af42c890a64de853d9b7fe Huamin Li huaminli@fb.com temporarily skip gelu tests
d11e521f9b90554ca86912a49920afa4406bb40d Shirong Wu shirong@fb.com Suppress accuracy check for remove_reshape_with_batch_size_change
6d948298b2327d229e010a34f1c221b11d2eb504 Ankur Singla ankursingla@fb.com [GPULowering] Suppress accuracy check for fuse_unsqueeze_cat_sum
e780b647fc9571b77d9f41c963041a6ac3d66f33 Janet Yang qxy11@fb.com Lower xrayvideo2022 to fx2trt
433c7207fef16b1fdff985546ea969c39fa83e7c generatedunixname89002005287564 generatedunixname89002005287564@fb.com [Codemod][Remove @noautodeps and @autodeps-skip tags] deeplearning/trt 1/2
66fdb65cffa925660c77b4758388399db3cbfe48 Scott Wolchok swolchok@fb.com [fx2ait] Minor Python cleanup in acc_ops_getitem
188132ecb2c19bcbf83cb2dc381f6e3798629f87 generatedunixname89002005324833 generatedunixname89002005324833@fb.com [AutoAccept][Codemod][FBSourceBuckFormatLinter] Daily
arc lint --take BUCKFORMAT4536bae4686dd01f2149541ea7fb330e178a4969 Wei Wei wwei6@fb.com [fx2trt] support sub
064602e666f86c110d931cd90a8536112a19b4ad Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for acc_ops.interpolate
9dfd0ee0cecb1975e3f53c44de237d67ca443ec5 Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for unary_ops
39b9efad8d5d82463a2016d135c0cf277de1c3c6 Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for unsqueeze
2bb17667d1dabc95391950426fc1f921eb3d0959 Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for acc_ops.split
64dfb7b096686cb2fd33197340dc72f30d525456 Shirong Wu shirong@fb.com Group LN trt plugin
438f670e28df59b0734baa092a514fba3d75eb4f Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for acc_ops.avgpool
df0fe32dae4343827bd9b37b72daae761b02f228 Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for acc_ops masked fill
44fe735d3493ea2d05a56b49093e4a23dd63a98e Shreyansh Prajapati shreyanshp@fb.com Test dynamic shaope support for acc_ops.pad
4f931acca706d8ce79045ceafef2ea0486609149 Wei Wei wwei6@fb.com [fx2trt] torch.max dynamic shape test
bf6f6cbe217d26a95ca9122574adf7de3966db9e Shreyansh Prajapati shreyanshp@fb.com Change the name of the test from full_reduce to dim_reduce
1c5680ed107d9206f3514eff4069a3f6c870ba8c Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for acc_ops.type_as
33e4c175a4f5fec78ac0b1c8eb262ca777c7aaba Shreyansh Prajapati shreyanshp@fb.com Test dynamic shape support for acc_ops.min
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57b5cc6a0f4839686ae360361a3a13b424794ee7 generatedunixname89002005367269 generatedunixname89002005367269@fb.com [AutoAccept][Codemod][FBSourceBlackLinter] Daily
arc lint --take BLACKeb741cc5e5a7babdc94e72d411670905f54da3e0 Shreyansh Prajapati shreyanshp@fb.com Updated the dynamic shape support for narrow op
521c36b96a14741ae89d7af6cbb658120bcec2ea Shreyansh Prajapati shreyanshp@fb.com Removing the comment for 4 dims dynamic shape support after analysis
e947343375967fe9efb0a16fdb9f63bff1449328 Shreyansh Prajapati shreyanshp@fb.com Updated the pad test for dynamic batch for analysis
3d64087014e91bc301a315eae43683b1aa2b66bc Oleg Khabinov khabinov@fb.com [trt_bc] Some improvements
dfd937a56fa01aca88a89b46176befdac4c202c4 Shreyansh Prajapati shreyanshp@fb.com Updated the test for as_strided op for analysis
932046ff6ea6dead114c0222b23ca3854690cffa Wei Wei wwei6@fb.com [fx2trt] bridge the dynamic batch and fixed shape
Description
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