refactor: remove the code that covers the no output segmented block #1174
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…ases Signed-off-by: Bo Wang <[email protected]>
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There are some changes that do not conform to C++ style guidelines:
diff --git a/workspace/py/torch_tensorrt/csrc/tensorrt_classes.cpp b/tmp/changes.txt
index 5aeac3b..775c71d 100644
--- a/workspace/py/torch_tensorrt/csrc/tensorrt_classes.cpp
+++ b/tmp/changes.txt
@@ -225,11 +225,17 @@ core::CompileSpec CompileSpec::toInternalCompileSpec() {
info.convert_info.engine_settings.num_avg_timing_iters = num_avg_timing_iters;
TORCHTRT_CHECK(workspace_size >= 0, "workspace_size must be 0 or greater");
info.convert_info.engine_settings.workspace_size = workspace_size;
- TORCHTRT_CHECK(dla_sram_size >= 4096, "DLA managed SRAM size must be at least 4 KiB and must be a power of 2. This defaults to 1 MiB");
+ TORCHTRT_CHECK(
+ dla_sram_size >= 4096,
+ "DLA managed SRAM size must be at least 4 KiB and must be a power of 2. This defaults to 1 MiB");
info.convert_info.engine_settings.dla_sram_size = dla_sram_size;
- TORCHTRT_CHECK(dla_local_dram_size >= 4096, "DLA Local DRAM size must be at least 4 KiB and must be a power of 2. This defaults to 1 GiB");
+ TORCHTRT_CHECK(
+ dla_local_dram_size >= 4096,
+ "DLA Local DRAM size must be at least 4 KiB and must be a power of 2. This defaults to 1 GiB");
info.convert_info.engine_settings.dla_local_dram_size = dla_local_dram_size;
- TORCHTRT_CHECK(dla_global_dram_size >= 4096, "DLA Global DRAM size must be at least 4 KiB and must be a power of 2. This defaults to 512 MiB");
+ TORCHTRT_CHECK(
+ dla_global_dram_size >= 4096,
+ "DLA Global DRAM size must be at least 4 KiB and must be a power of 2. This defaults to 512 MiB");
info.convert_info.engine_settings.dla_global_dram_size = dla_global_dram_size;
return info;
}
diff --git a/workspace/py/torch_tensorrt/csrc/register_tensorrt_classes.cpp b/tmp/changes.txt
index 9165b21..ba2e168 100644
--- a/workspace/py/torch_tensorrt/csrc/register_tensorrt_classes.cpp
+++ b/tmp/changes.txt
@@ -65,7 +65,8 @@ void RegisterTRTCompileSpec() {
ADD_FIELD_GET_SET_REGISTRATION(TRTCompileSpecTSRegistration, torch_tensorrt::pyapi::CompileSpec, workspace_size);
ADD_FIELD_GET_SET_REGISTRATION(TRTCompileSpecTSRegistration, torch_tensorrt::pyapi::CompileSpec, dla_sram_size);
ADD_FIELD_GET_SET_REGISTRATION(TRTCompileSpecTSRegistration, torch_tensorrt::pyapi::CompileSpec, dla_local_dram_size);
- ADD_FIELD_GET_SET_REGISTRATION(TRTCompileSpecTSRegistration, torch_tensorrt::pyapi::CompileSpec, dla_global_dram_size);
+ ADD_FIELD_GET_SET_REGISTRATION(
+ TRTCompileSpecTSRegistration, torch_tensorrt::pyapi::CompileSpec, dla_global_dram_size);
ADD_FIELD_GET_SET_REGISTRATION(
TRTCompileSpecTSRegistration, torch_tensorrt::pyapi::CompileSpec, truncate_long_and_double);
}
diff --git a/workspace/core/conversion/conversionctx/ConversionCtx.cpp b/tmp/changes.txt
index a24a159..71159eb 100644
--- a/workspace/core/conversion/conversionctx/ConversionCtx.cpp
+++ b/tmp/changes.txt
@@ -107,7 +107,7 @@ ConversionCtx::ConversionCtx(BuilderSettings build_settings)
}
cfg->setAvgTimingIterations(settings.num_avg_timing_iters);
- if (settings.workspace_size != 0){
+ if (settings.workspace_size != 0) {
cfg->setMemoryPoolLimit(nvinfer1::MemoryPoolType::kWORKSPACE, settings.workspace_size);
}
@@ -124,13 +124,13 @@ ConversionCtx::ConversionCtx(BuilderSettings build_settings)
settings.enabled_precisions.find(nvinfer1::DataType::kFLOAT) == settings.enabled_precisions.end(),
"DLA supports only fp16 or int8 precision");
cfg->setDLACore(settings.device.dla_core);
- if (settings.dla_sram_size != 1048576){
+ if (settings.dla_sram_size != 1048576) {
cfg->setMemoryPoolLimit(nvinfer1::MemoryPoolType::kDLA_MANAGED_SRAM, settings.dla_sram_size);
}
- if (settings.dla_local_dram_size != 1073741824){
+ if (settings.dla_local_dram_size != 1073741824) {
cfg->setMemoryPoolLimit(nvinfer1::MemoryPoolType::kDLA_LOCAL_DRAM, settings.dla_local_dram_size);
}
- if (settings.dla_global_dram_size != 536870912){
+ if (settings.dla_global_dram_size != 536870912) {
cfg->setMemoryPoolLimit(nvinfer1::MemoryPoolType::kDLA_GLOBAL_DRAM, settings.dla_global_dram_size);
}
}
diff --git a/workspace/core/conversion/converters/converter_util.cpp b/tmp/changes.txt
index a6a2bbd..7452615 100644
--- a/workspace/core/conversion/converters/converter_util.cpp
+++ b/tmp/changes.txt
@@ -207,13 +207,13 @@ 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);
@@ -227,13 +227,13 @@ 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);
@@ -243,7 +243,8 @@ 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);
@@ -257,7 +258,6 @@ 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);
@@ -307,17 +307,20 @@ 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/partitioning/partitioning.cpp b/tmp/changes.txt
index 70175f0..e9f7325 100644
--- a/workspace/core/partitioning/partitioning.cpp
+++ b/tmp/changes.txt
@@ -124,7 +124,8 @@ void find_all_fallback_nodes(
if (!isTensor(output)) {
for (auto use : output->uses()) {
auto node = use.user;
- if (node->kind() != torch::jit::prim::Constant && global_fallback_nodes.insert({node, FallbackNodeType::kNON_TENSOR}).second) {
+ if (node->kind() != torch::jit::prim::Constant &&
+ global_fallback_nodes.insert({node, FallbackNodeType::kNON_TENSOR}).second) {
q.push(node);
}
}
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"
diff --git a/workspace/tests/core/conversion/converters/test_cast.cpp b/tmp/changes.txt
index 092cdb3..d26c7a0 100644
--- a/workspace/tests/core/conversion/converters/test_cast.cpp
+++ b/tmp/changes.txt
@@ -135,7 +135,6 @@ TEST(Converters, ATenBoolToINT32TensorConvertsCorrectly) {
ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
}
-
TEST(Converters, ATenToSingleConvertsCorrectly) {
const auto graph = R"IR(
graph(%y.1 : Tensor):
@@ -164,7 +163,6 @@ TEST(Converters, ATenToSingleConvertsCorrectly) {
ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
}
-
TEST(Converters, ATenTypeAsConvertsCorrectly) {
const auto graph = R"IR(
graph(%0 : Tensor,
diff --git a/workspace/cpp/bin/torchtrtc/main.cpp b/tmp/changes.txt
index 6c207d7..51ec2c5 100644
--- a/workspace/cpp/bin/torchtrtc/main.cpp
+++ b/tmp/changes.txt
@@ -117,8 +117,7 @@ int main(int argc, char** argv) {
parser, "num_iters", "Number of averaging timing iterations used to select kernels", {"num-avg-timing-iters"});
args::ValueFlag<uint64_t> workspace_size(
parser, "workspace_size", "Maximum size of workspace given to TensorRT", {"workspace-size"});
- args::ValueFlag<uint64_t> dla_sram_size(
- parser, "dla_sram_size", "DLA managed SRAM size", {"dla-sram-size"});
+ args::ValueFlag<uint64_t> dla_sram_size(parser, "dla_sram_size", "DLA managed SRAM size", {"dla-sram-size"});
args::ValueFlag<uint64_t> dla_local_dram_size(
parser, "dla_local_dram_size", "DLA Local DRAM size", {"dla-local-dram-size"});
args::ValueFlag<uint64_t> dla_global_dram_size(
ERROR: Some files do not conform to style guidelines
narendasan
requested changes
Jul 28, 2022
|
This PR has not seen activity for 90 days, Remove stale label or comment or this will be closed in 10 days |
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Signed-off-by: Bo Wang [email protected]
Description
Before this feature #1031 is supported, there might be some cases that TensorRT segmented block has NonTensor input, so we go through the nodes' output one by one to determine the output for this block if there is no output for this block.
However, this this PR merged #1140, we can ensure that every TensorRT segmented block has Tensor input, which means there will never be no output for segmented block.
Therefore, we can erase these lines.
support #1121
Checklist: