Fix: Fix GitHub Issue #4608: TensorRT engine hangs when calling get_binding_shape with PyTorch imported ...

ISSUE_4608_SOLUTION.md

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+# Solution for GitHub Issue #4608: TensorRT Engine Hangs with PyTorch and PyCUDA
+
+## Problem Summary
+
+When importing PyTorch before initializing a TensorRT engine with PyCUDA, the program hangs at `engine.get_binding_shape()`. This is caused by a CUDA context conflict between PyTorch and PyCUDA/TensorRT.
+
+## Root Cause
+
+The issue occurs because:
+
+1. **PyTorch creates its own CUDA context** when imported, which becomes the active context
+2. **PyCUDA expects to manage its own CUDA context** and may conflict with PyTorch's context
+3. **TensorRT operations** (like `get_binding_shape()`) require a valid CUDA context, and when there's a conflict between PyTorch's and PyCUDA's contexts, the operation hangs
+
+## Solutions
+
+### Solution 1: Use `cuda-python` Instead of PyCUDA (Recommended)
+
+The TensorRT team has migrated samples from PyCUDA to `cuda-python` to avoid these conflicts and support newer GPUs. This is the **recommended approach**.
+
+**Benefits:**
+- No context conflicts with PyTorch
+- Better support for modern GPUs
+- Official NVIDIA CUDA Python bindings
+- More maintainable and future-proof
+
+**Implementation:**
+
+```python
+import torch  # Can import PyTorch without issues
+import tensorrt as trt
+from cuda import cudart  # Use cuda-python instead of pycuda
+import numpy as np
+
+def check_cuda_error(error):
+    """Helper function to check CUDA errors"""
+    if isinstance(error, tuple):
+        error = error[0]
+    if error != cudart.cudaError_t.cudaSuccess:
+        error_name = cudart.cudaGetErrorName(error)[1]
+        error_string = cudart.cudaGetErrorString(error)[1]
+        raise RuntimeError(f"CUDA Error: {error_name} ({error_string})")
+
+class TRTInference:
+    def __init__(self, engine_path: str):
+        # Initialize TensorRT logger and runtime
+        self.logger = trt.Logger(trt.Logger.ERROR)
+
+        # Load the TensorRT engine
+        with open(engine_path, "rb") as f:
+            runtime = trt.Runtime(self.logger)
+            self.engine = runtime.deserialize_cuda_engine(f.read())
+
+        if self.engine is None:
+            raise RuntimeError("Failed to load TensorRT engine")
+
+        # Create execution context
+        self.context = self.engine.create_execution_context()
+
+        # Get binding information - this now works without hanging
+        self.bindings = []
+        self.allocations = []
+
+        for i in range(self.engine.num_io_tensors):
+            name = self.engine.get_tensor_name(i)
+            dtype = self.engine.get_tensor_dtype(name)
+            shape = self.engine.get_tensor_shape(name)
+            is_input = self.engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT
+
+            # Calculate size and allocate GPU memory
+            size = np.dtype(trt.nptype(dtype)).itemsize
+            for s in shape:
+                size *= s
+
+            err, allocation = cudart.cudaMalloc(size)
+            check_cuda_error(err)
+
+            binding = {
+                "name": name,
+                "dtype": np.dtype(trt.nptype(dtype)),
+                "shape": list(shape),
+                "allocation": allocation,
+                "size": size,
+                "is_input": is_input
+            }
+
+            self.bindings.append(binding)
+            self.allocations.append(allocation)
+
+        # Create CUDA stream
+        err, self.stream = cudart.cudaStreamCreate()
+        check_cuda_error(err)
+
+    def infer(self, input_data: np.ndarray) -> np.ndarray:
+        """Run inference on input data"""
+        # Ensure input is contiguous
+        input_data = np.ascontiguousarray(input_data)
+
+        # Copy input to GPU
+        input_binding = [b for b in self.bindings if b["is_input"]][0]
+        err = cudart.cudaMemcpy(
+            input_binding["allocation"],
+            input_data.ctypes.data,
+            input_binding["size"],
+            cudart.cudaMemcpyKind.cudaMemcpyHostToDevice
+        )
+        check_cuda_error(err)
+
+        # Set tensor addresses
+        for i, binding in enumerate(self.bindings):
+            self.context.set_tensor_address(binding["name"], self.allocations[i])
+
+        # Execute inference
+        self.context.execute_async_v3(stream_handle=self.stream)
+        err = cudart.cudaStreamSynchronize(self.stream)
+        check_cuda_error(err)
+
+        # Copy output from GPU
+        output_binding = [b for b in self.bindings if not b["is_input"]][0]
+        output = np.empty(output_binding["shape"], dtype=output_binding["dtype"])
+        err = cudart.cudaMemcpy(
+            output.ctypes.data,
+            output_binding["allocation"],
+            output_binding["size"],
+            cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost
+        )
+        check_cuda_error(err)
+
+        return output
+
+    def __del__(self):
+        """Cleanup GPU resources"""
+        # Free GPU memory
+        for allocation in self.allocations:
+            cudart.cudaFree(allocation)
+
+        # Destroy stream
+        if hasattr(self, 'stream'):
+            cudart.cudaStreamDestroy(self.stream)
+
+# Example usage
+if __name__ == "__main__":
+    # PyTorch can be imported and used without conflicts
+    import torch
+
+    # Create some PyTorch tensors (optional)
+    torch_tensor = torch.randn(1, 3, 224, 224).cuda()
+    print(f"PyTorch tensor shape: {torch_tensor.shape}")
+
+    # Initialize TensorRT inference - no hanging!
+    trt_inference = TRTInference("model.trt")
+
+    # Run inference
+    input_data = np.random.randn(1, 3, 224, 224).astype(np.float32)
+    output = trt_inference.infer(input_data)
+    print(f"TensorRT output shape: {output.shape}")
+```
+
+**Installation:**
+
+```bash
+pip install cuda-python tensorrt
+```
+
+### Solution 2: Proper PyCUDA Context Management (Alternative)
+
+If you must use PyCUDA, you need to properly manage CUDA contexts to avoid conflicts with PyTorch.
+
+**Implementation:**
+
+```python
+import tensorrt as trt
+import numpy as np
+
+# IMPORTANT: Import torch AFTER pycuda initialization
+import pycuda.driver as cuda
+import pycuda.autoinit  # This initializes CUDA context
+
+# NOW import torch
+import torch
+
+class TRTInference:
+    def __init__(self, engine_path: str):
+        # Make sure PyCUDA context is active
+        cuda.init()
+
+        # Get the current context (created by pycuda.autoinit)
+        self.cuda_ctx = cuda.Device(0).retain_primary_context()
+        self.cuda_ctx.push()
+
+        # Initialize TensorRT
+        self.logger = trt.Logger(trt.Logger.ERROR)
+
+        with open(engine_path, "rb") as f:
+            runtime = trt.Runtime(self.logger)
+            self.engine = runtime.deserialize_cuda_engine(f.read())
+
+        if self.engine is None:
+            raise RuntimeError("Failed to load TensorRT engine")
+
+        # Now get_binding_shape should work
+        self.bindings = []
+        for i in range(self.engine.num_bindings):
+            shape = tuple(self.engine.get_binding_shape(i))
+            dtype = trt.nptype(self.engine.get_binding_dtype(i))
+            name = self.engine.get_binding_name(i)
+            is_input = self.engine.binding_is_input(i)
+
+            self.bindings.append({
+                "name": name,
+                "shape": shape,
+                "dtype": dtype,
+                "is_input": is_input
+            })
+
+        self.context = self.engine.create_execution_context()
+
+    def infer(self, input_data: np.ndarray) -> np.ndarray:
+        """Run inference"""
+        # Ensure context is active
+        self.cuda_ctx.push()
+
+        try:
+            # Allocate device memory
+            d_input = cuda.mem_alloc(input_data.nbytes)
+
+            # Get output shape and allocate
+            output_shape = [b["shape"] for b in self.bindings if not b["is_input"]][0]
+            output_dtype = [b["dtype"] for b in self.bindings if not b["is_input"]][0]
+            output = np.empty(output_shape, dtype=output_dtype)
+            d_output = cuda.mem_alloc(output.nbytes)
+
+            # Copy input to device
+            cuda.memcpy_htod(d_input, input_data)
+
+            # Create bindings list
+            bindings = [int(d_input), int(d_output)]
+
+            # Execute
+            self.context.execute_v2(bindings=bindings)
+
+            # Copy output to host
+            cuda.memcpy_dtoh(output, d_output)
+
+            return output
+        finally:
+            self.cuda_ctx.pop()
+
+    def __del__(self):
+        """Cleanup"""
+        if hasattr(self, 'cuda_ctx'):
+            self.cuda_ctx.pop()
+
+# Example usage
+if __name__ == "__main__":
+    # Initialize TensorRT first
+    trt_inference = TRTInference("model.trt")
+
+    # Now you can use PyTorch
+    torch_tensor = torch.randn(1, 3, 224, 224).cuda()
+
+    # Run TensorRT inference
+    input_data = np.random.randn(1, 3, 224, 224).astype(np.float32)
+    output = trt_inference.infer(input_data)
+```
+
+### Solution 3: Separate Processes (For Complex Scenarios)
+
+If you need to use both PyTorch and TensorRT extensively, consider running them in separate processes:
+
+```python
+import multiprocessing as mp
+import numpy as np
+
+def pytorch_process(input_queue, output_queue):
+    """Process that handles PyTorch operations"""
+    import torch
+
+    while True:
+        data = input_queue.get()
+        if data is None:
+            break
+
+        # PyTorch operations
+        tensor = torch.from_numpy(data).cuda()
+        result = tensor.cpu().numpy()
+        output_queue.put(result)
+
+def tensorrt_process(input_queue, output_queue):
+    """Process that handles TensorRT operations"""
+    import tensorrt as trt
+    from cuda import cudart
+
+    # Initialize TensorRT (no PyTorch imported here)
+    # ... TensorRT inference code ...
+
+    while True:
+        data = input_queue.get()
+        if data is None:
+            break
+
+        # TensorRT inference
+        result = run_trt_inference(data)
+        output_queue.put(result)
+
+# Main process coordinates between PyTorch and TensorRT
+if __name__ == "__main__":
+    pytorch_in = mp.Queue()
+    pytorch_out = mp.Queue()
+    tensorrt_in = mp.Queue()
+    tensorrt_out = mp.Queue()
+
+    # Start processes
+    p1 = mp.Process(target=pytorch_process, args=(pytorch_in, pytorch_out))
+    p2 = mp.Process(target=tensorrt_process, args=(tensorrt_in, tensorrt_out))
+
+    p1.start()
+    p2.start()
+
+    # Use both without conflicts
+    # ...
+```
+
+## Recommended Migration Path
+
+1. **Install cuda-python**: `pip install cuda-python`
+2. **Replace PyCUDA imports** with cuda-python equivalents:
+   - `import pycuda.driver as cuda` → `from cuda import cudart`
+   - `cuda.mem_alloc()` → `cudart.cudaMalloc()`
+   - `cuda.memcpy_htod()` → `cudart.cudaMemcpy(..., cudaMemcpyHostToDevice)`
+   - `cuda.memcpy_dtoh()` → `cudart.cudaMemcpy(..., cudaMemcpyDeviceToHost)`
+3. **Update TensorRT API calls** to use modern APIs:
+   - Use `engine.num_io_tensors` instead of `engine.num_bindings`
+   - Use `engine.get_tensor_name()` instead of `engine.get_binding_name()`
+   - Use `context.execute_async_v3()` instead of `context.execute_v2()`
+
+## References
+
+- [TensorRT Refactored Samples](samples/python/refactored/) - Examples using cuda-python
+- [TensorRT Changelog](CHANGELOG.md) - See 10.13.0 GA release notes about cuda-python migration
+- [CUDA Python Documentation](https://nvidia.github.io/cuda-python/)
+- [TensorRT Python API](https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/)
+
+## Additional Notes
+
+- The TensorRT team officially migrated from PyCUDA to cuda-python in version 10.13.0
+- cuda-python provides better support for modern GPUs and CUDA versions
+- The quickstart guide explicitly warns: "TensorRT and PyTorch can not be loaded into your Python processes at the same time" when using PyCUDA
+- Using cuda-python resolves this limitation
+
+## Testing the Solution
+
+To verify the fix works:
+
+```python
+# This should NOT hang anymore
+import torch  # Import PyTorch first
+import tensorrt as trt
+from cuda import cudart
+
+# Initialize CUDA
+err = cudart.cudaSetDevice(0)
+assert err[0] == cudart.cudaError_t.cudaSuccess
+
+# Load TensorRT engine
+logger = trt.Logger(trt.Logger.ERROR)
+with open("model.trt", "rb") as f:
+    runtime = trt.Runtime(logger)
+    engine = runtime.deserialize_cuda_engine(f.read())
+
+# This should work without hanging
+for i in range(engine.num_io_tensors):
+    name = engine.get_tensor_name(i)
+    shape = engine.get_tensor_shape(name)
+    print(f"Tensor {i}: {name}, shape: {shape}")
+
+print("Success! No hanging occurred.")
+```