[GitHub] [tvm] trevor-m commented on a change in pull request #8172: [BYOC][TensorRT] Reuse TRT engines based on max_batch_size for dynamic batching, improve device buffer allocation

[GitBox <gi...@apache.org>]

trevor-m commented on a change in pull request #8172:
URL: https://github.com/apache/tvm/pull/8172#discussion_r643553887



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File path: src/runtime/contrib/tensorrt/tensorrt_runtime.cc
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@@ -174,25 +176,50 @@ class TensorRTRuntime : public JSONRuntimeBase {
       int binding_index = engine->getBindingIndex(name.c_str());
       ICHECK_NE(binding_index, -1);
       if (data_entry_[eid]->device.device_type != kDLCUDA) {
-        device_buffers[binding_index].CopyTo(const_cast<DLTensor*>(data_entry_[eid]));
+        auto device_buffer = GetOrAllocateDeviceBuffer(eid, binding_index);
+        device_buffer.CopyTo(const_cast<DLTensor*>(data_entry_[eid]));
       }
     }
   }
 
  private:
+  /*! \brief Get batch size for engine from the runtime input shapes. */
+  int GetBatchSize() {
+    return data_entry_[input_var_eid_[0]]->ndim == 0 ? 1 : data_entry_[input_var_eid_[0]]->shape[0];
+  }
+
+  /*! \brief TensorRT engines are built for a maximum batch size. If an engine doesn't exist for a
+   * certain batch size already, see if we can reuse an engine built for a higher batch size. */
+  bool FindCompatibleEngine(int batch_size, int* compatible_engine_batch_size) {
+    // Check for exact match
+    if (trt_engine_cache_.count(std::make_pair(symbol_name_, batch_size))) {
+      *compatible_engine_batch_size = batch_size;
+      return true;
+    }

Review comment:
       Thanks for the review! Yes, I suppose we could do that instead. 




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