content: """
  .. _torch_compile_transformer:
  
  Compiling a Transformer using torch.compile and TensorRT
  ==============================================================
  
  This interactive script is intended as a sample of the `torch_tensorrt.dynamo.compile` workflow on a transformer-based model.
  """
  
  # %%
  # Imports and Model Definition
  # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  
  import torch
  import torch_tensorrt
  from transformers import BertModel
  
  # %%
  
  # Initialize model with float precision and sample inputs
  model = BertModel.from_pretrained("bert-base-uncased").eval().to("cuda")
  inputs = [
      torch.randint(0, 2, (1, 14), dtype=torch.int32).to("cuda"),
      torch.randint(0, 2, (1, 14), dtype=torch.int32).to("cuda"),
  ]
  
  
  # %%
  # Optional Input Arguments to `torch_tensorrt.dynamo.compile`
  # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  
  # Enabled precision for TensorRT optimization
  enabled_precisions = {torch.float}
  
  # Whether to print verbose logs
  debug = True
  
  # Workspace size for TensorRT
  workspace_size = 20 << 30
  
  # Maximum number of TRT Engines
  # (Lower value allows more graph segmentation)
  min_block_size = 3
  
  # Operations to Run in Torch, regardless of converter support
  torch_executed_ops = {}
  
  # %%
  # Compilation with `torch_tensorrt.dynamo.compile`
  # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  
  # Build and compile the model with torch.compile, using tensorrt backend
  optimized_model = torch_tensorrt.dynamo.compile(
      model,
      inputs,
      enabled_precisions=enabled_precisions,
      debug=debug,
      workspace_size=workspace_size,
      min_block_size=min_block_size,
      torch_executed_ops=torch_executed_ops,
  )
  
  # %%
  # Equivalently, we could have run the above via the convenience frontend, as so:
  # `torch_tensorrt.compile(model, ir="dynamo_compile", inputs=inputs, ...)`
  
  # %%
  # Inference
  # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  
  # Does not cause recompilation (same batch size as input)
  new_inputs = [
      torch.randint(0, 2, (1, 14), dtype=torch.int32).to("cuda"),
      torch.randint(0, 2, (1, 14), dtype=torch.int32).to("cuda"),
  ]
  new_outputs = optimized_model(*new_inputs)
  
  # %%
  
  # Does cause recompilation (new batch size)
  new_inputs = [
      torch.randint(0, 2, (4, 14), dtype=torch.int32).to("cuda"),
      torch.randint(0, 2, (4, 14), dtype=torch.int32).to("cuda"),
  ]
  new_outputs = optimized_model(*new_inputs)
  
  # %%
  # Cleanup
  # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  
  # Finally, we use Torch utilities to clean up the workspace
  torch._dynamo.reset()
  
  with torch.no_grad():
      torch.cuda.empty_cache()
  