refactor(//core)!: Introducing a binding convention that will address

determinism issues with TensorRT The binding convention now looks for bindings by name and reorders outputs to match the order expected by PyTorch. BREAKING CHANGE: This changes the "ABI" of compiled TRTorch programs and the runtime and breaks backwards compatability between the runtime in 0.1.0+ and programs compiled pre-0.1.0 Signed-off-by: Naren Dasan <[email protected]> Signed-off-by: Naren Dasan <[email protected]>
pytorch · Aug 28, 2020 · 5a105c6 · 5a105c6
1 parent 7cfcca4
commit 5a105c6
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Showing 6 changed files with 48 additions and 71 deletions.
diff --git a/core/conversion/conversion.cpp b/core/conversion/conversion.cpp
@@ -143,11 +143,12 @@ void AddInputs(ConversionCtx* ctx,
     for (size_t i = 0; i < input_tensors.size(); i++) {
         auto in = input_tensors[i];
         auto dims = input_dims[i];
+        std::string name = std::string("input_") + std::to_string(ctx->num_inputs);
         LOG_INFO(ctx->logger,
                  "Adding Input " << in->debugName()  \
-                 << " (conversion.AddInputs)");
+                 << " named " << name << " in engine (conversion.AddInputs)");
         LOG_DEBUG(ctx->logger, "Input shape set to " << dims.input_shape);
-        auto trt_in = ctx->net->addInput(in->debugName().c_str(),
+        auto trt_in = ctx->net->addInput(name.c_str(),
                                          ctx->input_type, dims.input_shape);
         TRTORCH_CHECK(trt_in, "Failed to add input node: " << in->debugName() << " (conversion.AddInputs)");
 
@@ -160,6 +161,7 @@ void AddInputs(ConversionCtx* ctx,
         }
 
         ctx->value_tensor_map[in] = trt_in;
+        ctx->num_inputs += 1;
     }
 
     TRTORCH_CHECK(profile->isValid(), "Optimization profile is invalid, please check the input range provided (conversion.AddInputs)");
@@ -174,14 +176,17 @@ void AddInputs(ConversionCtx* ctx,
 
 void MarkOutputs(ConversionCtx* ctx, at::ArrayRef<const torch::jit::Value*> outputs) {
     for (auto out : outputs) {
+        std::string name = std::string("output_") + std::to_string(ctx->num_outputs);
         auto it = ctx->value_tensor_map.find(out);
         // Leaves the potential for unused outputs to be populated with nullptr "safely"
         TRTORCH_CHECK(it != ctx->value_tensor_map.end() && it->second,
                       "No corresponding output TRT Tensor found for TorchScript output: " << out->debugName());
         auto out_tensor = it->second;
+        out_tensor->setName(name.c_str());
         ctx->net->markOutput(*out_tensor);
         LOG_INFO(ctx->logger,
-                 "Marking Output " << out->debugName() << " (ctx.MarkOutput)");
+                 "Marking Output " << out->debugName() << " named " << name << " in engine (ctx.MarkOutput)");
+        ctx->num_outputs += 1;
     }
 }
 

diff --git a/core/conversion/conversionctx/ConversionCtx.h b/core/conversion/conversionctx/ConversionCtx.h
@@ -42,6 +42,8 @@ struct ConversionCtx {
 
     ~ConversionCtx();
 
+    uint64_t num_inputs = 0;
+    uint64_t num_outputs = 0;
     bool input_is_dynamic = false;
     nvinfer1::IBuilder* builder;
     nvinfer1::INetworkDefinition* net;

diff --git a/core/execution/TRTEngine.cpp b/core/execution/TRTEngine.cpp
@@ -42,13 +42,20 @@ TRTEngine::TRTEngine(std::string mod_name, std::string serialized_engine)
     uint64_t outputs = 0;
 
     for (int64_t x = 0; x < cuda_engine->getNbBindings(); x++) {
+        std::string name = cuda_engine->getBindingName(x);
+        std::string idx_s = name.substr(name.find("_") + 1);
+        uint64_t idx = static_cast<uint64_t>(std::stoi(idx_s));
+
         if(cuda_engine->bindingIsInput(x)) {
             inputs++;
+            in_binding_map[x] = idx;
         } else {
             outputs++;
+            out_binding_map[x] = idx;
         }
     }
     num_io = std::make_pair(inputs, outputs);
+
 }
 
 TRTEngine& TRTEngine::operator=(const TRTEngine& other) {

diff --git a/core/execution/execution.h b/core/execution/execution.h
@@ -22,6 +22,9 @@ struct TRTEngine : torch::CustomClassHolder {
     std::string name;
     util::logging::TRTorchLogger logger;
 
+    std::unordered_map<uint64_t, uint64_t> in_binding_map;
+    std::unordered_map<uint64_t, uint64_t> out_binding_map;
+
     ~TRTEngine();
     TRTEngine(std::string serialized_engine);
     TRTEngine(std::string mod_name, std::string serialized_engine);
@@ -30,7 +33,7 @@ struct TRTEngine : torch::CustomClassHolder {
     //c10::List<at::Tensor> Run(c10::List<at::Tensor> inputs);
 };
 
-std::vector<at::Tensor> RunCudaEngine(nvinfer1::IExecutionContext* ctx, std::pair<uint64_t, uint64_t> io, std::vector<at::Tensor>& inputs);
+std::vector<at::Tensor> execute_engine(std::vector<at::Tensor> inputs, c10::intrusive_ptr<TRTEngine> compiled_engine);
 
 } // namespace execution
 } // namespace core

diff --git a/core/execution/register_trt_op.cpp b/core/execution/register_trt_op.cpp
@@ -9,50 +9,43 @@
 namespace trtorch {
 namespace core {
 namespace execution {
-std::vector<at::Tensor> RunCudaEngine(nvinfer1::IExecutionContext* ctx, std::pair<uint64_t, uint64_t> io, std::vector<at::Tensor>& inputs) {
+
+std::vector<at::Tensor> execute_engine(std::vector<at::Tensor> inputs, c10::intrusive_ptr<TRTEngine> compiled_engine) {
+    LOG_DEBUG("Attempting to run engine (ID: " << compiled_engine->name << ")");
     std::vector<void*> gpu_handles;
 
     std::vector<at::Tensor> contig_inputs{};
     contig_inputs.reserve(inputs.size());
+
     for (size_t i = 0; i < inputs.size(); i++) {
-        TRTORCH_CHECK(inputs[i].is_cuda(), "Expected input tensors to have device cuda, found device " << inputs[i].device());
-        auto expected_type = util::toATenDType(ctx->getEngine().getBindingDataType(i));
-        TRTORCH_CHECK(inputs[i].dtype() == expected_type, "Expected input tensors to have type " << expected_type << ", found type " << inputs[i].dtype());
-        auto dims = core::util::toDimsPad(inputs[i].sizes(), 1);
+        uint64_t pyt_idx = compiled_engine->in_binding_map[i];
+        TRTORCH_CHECK(inputs[pyt_idx].is_cuda(), "Expected input tensors to have device cuda, found device " << inputs[pyt_idx].device());
+        auto expected_type = util::toATenDType(compiled_engine->exec_ctx->getEngine().getBindingDataType(i));
+        TRTORCH_CHECK(inputs[pyt_idx].dtype() == expected_type, "Expected input tensors to have type " << expected_type << ", found type " << inputs[pyt_idx].dtype());
+        auto dims = core::util::toDimsPad(inputs[pyt_idx].sizes(), 1);
         auto shape = core::util::toVec(dims);
-        contig_inputs.push_back(inputs[i].view(shape).contiguous());
+        contig_inputs.push_back(inputs[pyt_idx].view(shape).contiguous());
         LOG_DEBUG("Input shape: " << dims);
-        ctx->setBindingDimensions(i, dims);
+        compiled_engine->exec_ctx->setBindingDimensions(i, dims);
         gpu_handles.push_back(contig_inputs.back().data_ptr());
     }
 
-    TRTORCH_CHECK(ctx->allInputDimensionsSpecified(), "Not enough inputs provided (execution.RunCudaEngine)");
+    TRTORCH_CHECK(compiled_engine->exec_ctx->allInputDimensionsSpecified(), "Not enough inputs provided (execution.RunCudaEngine)");
 
-    std::vector<at::Tensor> outputs;
-    for (uint64_t o = inputs.size(); o < (io.first + io.second); o++) {
-        auto out_shape = ctx->getBindingDimensions(o);
+    std::vector<at::Tensor> outputs(compiled_engine->num_io.second);
+    for (size_t o = inputs.size(); o < (compiled_engine->num_io.first + compiled_engine->num_io.second); o++) {
+        uint64_t pyt_idx = compiled_engine->out_binding_map[o];
+        auto out_shape = compiled_engine->exec_ctx->getBindingDimensions(o);
         LOG_DEBUG("Output shape: " << out_shape);
         auto dims = core::util::toVec(out_shape);
-        auto type = util::toATenDType(ctx->getEngine().getBindingDataType(o));
-        outputs.push_back(at::empty(dims, {at::kCUDA}).to(type).contiguous());
-        gpu_handles.push_back(outputs[outputs.size() - 1].data_ptr());
+        auto type = util::toATenDType(compiled_engine->exec_ctx->getEngine().getBindingDataType(o));
+        std::cout << pyt_idx << std::endl;
+        outputs[pyt_idx] = std::move(at::empty(dims, {at::kCUDA}).to(type).contiguous());
+        gpu_handles.push_back(outputs[pyt_idx].data_ptr());
     }
 
-    // Is this the right stream?
     c10::cuda::CUDAStream stream = c10::cuda::getCurrentCUDAStream(inputs[0].device().index());
-
-    ctx->enqueueV2(gpu_handles.data(), stream, nullptr);
-
-    return outputs;
-}
-
-std::vector<at::Tensor> execute_engine(std::vector<at::Tensor> inputs, c10::intrusive_ptr<TRTEngine> engine) {
-    // Verify calling convention (right to left or left to right)
-    LOG_DEBUG("Attempting to run engine (ID: " << std::hex << engine->name << ")");
-
-    auto io = engine->num_io;
-    auto ctx = engine->exec_ctx;
-    auto outputs = RunCudaEngine(ctx, io, inputs);
+    compiled_engine->exec_ctx->enqueueV2(gpu_handles.data(), stream, nullptr);
 
     return outputs;
 }

diff --git a/tests/util/run_graph_engine.cpp b/tests/util/run_graph_engine.cpp
@@ -3,7 +3,9 @@
 #include "c10/cuda/CUDAStream.h"
 #include "torch/csrc/jit/ir/ir.h"
 #include "torch/csrc/jit/ir/irparser.h"
+#include "torch/custom_class.h"
 #include "core/conversion/conversion.h"
+#include "core/execution/execution.h"
 #include "cuda_runtime_api.h"
 
 #include <vector>
@@ -28,7 +30,7 @@ std::vector<core::conversion::InputRange> toInputRangesDynamic(std::vector<at::T
         auto opt = core::util::toVec(i.sizes());
 
         std::vector<int64_t> min_range(opt);
-        std::vector<int64_t> max_range(opt); 
+        std::vector<int64_t> max_range(opt);
 
         min_range[1] = ceil(opt[1]/2.0);
         max_range[1] = 2*opt[1];
@@ -40,44 +42,9 @@ std::vector<core::conversion::InputRange> toInputRangesDynamic(std::vector<at::T
 }
 
 std::vector<at::Tensor> RunEngine(std::string& eng, std::vector<at::Tensor> inputs) {
-    auto rt = nvinfer1::createInferRuntime(core::util::logging::get_logger());
-    auto engine = rt->deserializeCudaEngine(eng.c_str(), eng.size());
-    auto ctx = engine->createExecutionContext();
-
-    std::vector<void*> gpu_handles;
-
-    std::vector<at::Tensor> contig_inputs{};
-    contig_inputs.reserve(inputs.size());
-    for (size_t i = 0; i < inputs.size(); i++) {
-        TRTORCH_CHECK(inputs[i].is_cuda(), "Expected input tensors to have device cuda, found device " << inputs[i].device());
-        auto expected_type = core::util::toATenDType(ctx->getEngine().getBindingDataType(i));
-        TRTORCH_CHECK(inputs[i].dtype() == expected_type, "Expected input tensors to have type " << expected_type << ", found type " << inputs[i].dtype());
-        auto dims = core::util::toDimsPad(inputs[i].sizes(), 1);
-        auto shape = core::util::toVec(dims);
-        contig_inputs.push_back(inputs[i].view(shape).contiguous());
-        LOG_DEBUG("In shape:" << shape);
-        ctx->setBindingDimensions(i, dims);
-        gpu_handles.push_back(contig_inputs.back().data_ptr());
-    }
-
-    TRTORCH_CHECK(ctx->allInputDimensionsSpecified(), "Not enough inputs provided (execution.RunCudaEngine)");
-
-    std::vector<at::Tensor> outputs;
-    for (int64_t o = inputs.size(); o < engine->getNbBindings(); o++) {
-        auto out_shape = ctx->getBindingDimensions(o);
-        LOG_DEBUG("Output: " << engine->getBindingName(o) << " out shape: " << out_shape);
-        auto dims = core::util::toVec(out_shape);
-        auto type = core::util::toATenDType(ctx->getEngine().getBindingDataType(o));
-        outputs.push_back(at::empty(dims, {at::kCUDA}).to(type).contiguous());
-        gpu_handles.push_back(outputs[outputs.size() - 1].data_ptr());
-    }
-
-    // Is this the right stream?
-    c10::cuda::CUDAStream stream = c10::cuda::getCurrentCUDAStream(inputs[0].device().index());
-
-    ctx->enqueueV2(gpu_handles.data(), stream, nullptr);
-
-    stream.synchronize();
+    LOG_DEBUG("Running TRT version");
+    auto engine_ptr = c10::make_intrusive<trtorch::core::execution::TRTEngine>("test_engine", eng);
+    auto outputs = trtorch::core::execution::execute_engine(inputs, engine_ptr);
     return outputs;
 }