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Merge pull request pytorch#425 from NVIDIA/plugins
feat(core/plugins): Plugins redesign
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,79 @@ | ||
| #include "NvInfer.h" | ||
| #include "NvInferRuntimeCommon.h" | ||
| #include "core/conversion/converters/converters.h" | ||
| #include "core/util/prelude.h" | ||
| #include "torch/torch.h" | ||
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|
||
| namespace trtorch { | ||
| namespace core { | ||
| namespace conversion { | ||
| namespace converters { | ||
| namespace impl { | ||
| namespace { | ||
|
|
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| /* | ||
| * Helper functions | ||
| */ | ||
| void create_plugin( | ||
| ConversionCtx* ctx, | ||
| const torch::jit::Node* n, | ||
| nvinfer1::ITensor* in, | ||
| int64_t order, | ||
| std::vector<int32_t> axes, | ||
| bool keep_dims, | ||
| const char* name) { | ||
| LOG_WARNING("Normalize layer will be run through ATen, not TensorRT. Performance may be lower than expected"); | ||
| nvinfer1::PluginFieldCollection fc; | ||
| std::vector<nvinfer1::PluginField> f; | ||
| f.emplace_back(nvinfer1::PluginField("order", &order, nvinfer1::PluginFieldType::kINT32, 1)); | ||
| f.emplace_back(nvinfer1::PluginField("axes", axes.data(), nvinfer1::PluginFieldType::kINT32, axes.size())); | ||
| f.emplace_back(nvinfer1::PluginField("keep_dims", &keep_dims, nvinfer1::PluginFieldType::kINT32, 1)); | ||
| fc.nbFields = f.size(); | ||
| fc.fields = f.data(); | ||
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| auto inputnbDims = in->getDimensions().nbDims; | ||
| for (int64_t i = 0; i < (int64_t)axes.size(); i++) { | ||
| if (axes[i] < 0) { | ||
| axes[i] += inputnbDims; | ||
| } | ||
| if (axes[i] > inputnbDims - 1) { | ||
| TRTORCH_THROW_ERROR("Axis of normalization layer cannot exceed input rank"); | ||
| } | ||
| } | ||
|
|
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| auto creator = getPluginRegistry()->getPluginCreator("NormalizePlugin", "1", "trtorch"); | ||
| auto plugin = creator->createPlugin(name, &fc); | ||
| auto normalize_layer = ctx->net->addPluginV2(reinterpret_cast<nvinfer1::ITensor* const*>(&in), 1, *plugin); | ||
| TRTORCH_CHECK(normalize_layer, "Unable to create normalization plugin from node" << *n); | ||
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| normalize_layer->setName(util::node_info(n).c_str()); | ||
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| auto layer_output = ctx->AssociateValueAndTensor(n->outputs()[0], normalize_layer->getOutput(0)); | ||
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| LOG_DEBUG("Normalize layer output tensor shape: " << layer_output->getDimensions()); | ||
| } | ||
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| auto normalize_registrations TRTORCH_UNUSED = RegisterNodeConversionPatterns().pattern( | ||
| {"aten::norm.ScalarOpt_dim(Tensor self, Scalar? p, int[1] dim, bool keepdim=False) -> (Tensor)", | ||
| [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool { | ||
| auto in = args[0].ITensor(); | ||
| auto in_shape = util::toVec(in->getDimensions()); | ||
| auto order = args[1].unwrapToScalar().to<int32_t>(); | ||
| auto axes_values = args[2].unwrapToIntList().vec(); | ||
| std::vector<int32_t> axes(axes_values.begin(), axes_values.end()); | ||
| auto keep_dims = (int32_t)args[3].unwrapToBool(); | ||
| LOG_DEBUG("Order of normalize_plugin: " << order); | ||
| LOG_DEBUG("Axis: " << axes); | ||
| LOG_DEBUG("keep_dims: " << keep_dims); | ||
| create_plugin(ctx, n, in, order, axes, keep_dims, "NormalizePlugintrtorch"); | ||
| return true; | ||
| } | ||
|
|
||
| }); | ||
|
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| } // namespace | ||
| } // namespace impl | ||
| } // namespace converters | ||
| } // namespace conversion | ||
| } // namespace core | ||
| } // namespace trtorch |
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