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‎core/runtime/TRTEngine.cpp

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@@ -89,6 +89,12 @@ TRTEngine::TRTEngine(
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cuda_engine = make_trt(rt->deserializeCudaEngine(serialized_engine.c_str(), serialized_engine.size()));
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TORCHTRT_CHECK((cuda_engine.get() != nullptr), "Unable to deserialize the TensorRT engine");
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if (get_streamable_device_memory_budget() > 0) {
93+
int64_t budget_bytes = get_automatic_device_memory_budget();
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LOG_DEBUG("Weight streaming budget set to " << budget_bytes << "B");
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cuda_engine->setWeightStreamingBudgetV2(budget_bytes);
96+
}
97+
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exec_ctx = make_trt(cuda_engine->createExecutionContext());
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TORCHTRT_CHECK((exec_ctx.get() != nullptr), "Unable to create TensorRT execution context");
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@@ -258,6 +264,38 @@ void TRTEngine::set_profiling_paths() {
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cuda_graph_debug_path = std::filesystem::path{profile_path_prefix + "/" + name + "_cudagraph.dot"}.string();
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}
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267+
int64_t TRTEngine::get_device_memory_budget() {
268+
return cuda_engine->getWeightStreamingBudgetV2();
269+
}
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bool TRTEngine::set_device_memory_budget(int64_t budget) {
272+
// Recreating the context because weight streaming budget cannot be modified while there are active context.
273+
if (exec_ctx.get() != nullptr) {
274+
exec_ctx.reset();
275+
}
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if (profile_execution) {
277+
trt_engine_profiler.reset();
278+
}
279+
bool result = cuda_engine->setWeightStreamingBudgetV2(budget);
280+
exec_ctx = make_trt(cuda_engine->createExecutionContext());
281+
TORCHTRT_CHECK(
282+
(exec_ctx.get() != nullptr),
283+
"Unable to recreate TensorRT execution context after setting new device memory budget");
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if (profile_execution) {
285+
enable_profiling();
286+
}
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return result;
288+
}
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// Returns 0 if BuilderFlag::kWEIGHT_STREAMING is unset during engine building.
291+
int64_t TRTEngine::get_streamable_device_memory_budget() {
292+
return cuda_engine->getStreamableWeightsSize();
293+
}
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295+
int64_t TRTEngine::get_automatic_device_memory_budget() {
296+
return cuda_engine->getWeightStreamingAutomaticBudget();
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}
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std::string TRTEngine::to_str() const {
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// clang-format off
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std::stringstream ss;

‎core/runtime/TRTEngine.h

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@@ -71,6 +71,10 @@ struct TRTEngine : torch::CustomClassHolder {
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std::string get_engine_layer_info();
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void dump_engine_layer_info_to_file(const std::string& path);
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void dump_engine_layer_info();
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int64_t get_device_memory_budget();
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bool set_device_memory_budget(int64_t budget);
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int64_t get_streamable_device_memory_budget();
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int64_t get_automatic_device_memory_budget();
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friend std::ostream& operator<<(std::ostream& os, const TRTEngine& engine);
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static const char BINDING_DELIM = '%';
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‎core/runtime/register_jit_hooks.cpp

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@@ -86,6 +86,12 @@ static auto TORCHTRT_UNUSED TRTEngineTSRegistrtion =
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.def("dump_engine_layer_info_to_file", &TRTEngine::dump_engine_layer_info_to_file)
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.def("dump_engine_layer_info", &TRTEngine::dump_engine_layer_info)
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.def("get_engine_layer_info", &TRTEngine::get_engine_layer_info)
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.def_property(
90+
"device_memory_budget",
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&TRTEngine::get_device_memory_budget,
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&TRTEngine::set_device_memory_budget)
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.def_property("streamable_device_memory_budget", &TRTEngine::get_streamable_device_memory_budget)
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.def_property("automatic_device_memory_budget", &TRTEngine::get_automatic_device_memory_budget)
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.def_pickle(
9096
[](const c10::intrusive_ptr<TRTEngine>& self) -> std::vector<std::string> {
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// Serialize TensorRT engine

‎docs/_cpp_api/classtorch__tensorrt_1_1DataType.html

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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/converter_overloading.html">Overloading Torch-TensorRT Converters with Custom Converters</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/weight_streaming_example.html">Weight Streaming</a></li>
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‎docs/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html

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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
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‎docs/_cpp_api/classtorch__tensorrt_1_1TensorFormat.html

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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
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‎docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html

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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
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‎docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html

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‎docs/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.html

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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
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‎docs/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html

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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/converter_overloading.html">Overloading Torch-TensorRT Converters with Custom Converters</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
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‎docs/_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html

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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
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