🐛 [Bug] Error Code 2: OutOfMemory (no further information)

[wuhongsheng]

Bug Description

To Reproduce

Steps to reproduce the behavior:

input_data = torch.rand([1, 3, 1280, 720]).cuda(device) print(type(input_data))

# input_data = input_data.to(device)

# Trace the module with example data
traced_model = torch.jit.trace(model, [input_data])
print("torch trace success")

trt_ts_module = torch_tensorrt.compile(
    traced_model, inputs=[input_data], ir= "ts", enabled_precisions = {torch.half})
print("torch compile success")

Expected behavior

Environment

Build information about Torch-TensorRT can be found by turning on debug messages

• Torch-TensorRT Version (e.g. 1.0.0): 2.0.1
• PyTorch Version (e.g. 1.0):1.4.0
• CPU Architecture:
• OS (e.g., Linux):
• How you installed PyTorch (conda, pip, libtorch, source):
• Build command you used (if compiling from source):
• Are you using local sources or building from archives:
• Python version:3.8
• CUDA version:
• GPU models and configuration:rtx3090
• Any other relevant information:

Additional context

[narendasan]

This is model dependent, if you have a large model, you might run out of memory. You can try adjusting workspace size to see if that helps