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fix: Repair integer inputs in dynamic shape cases
- Generative inference with HF text generation models such as gpt2 can fail if graph segmentation causes a symbolic integer to be passed from Torch to TRT, since the Torch output is an integer, while TRT expects a tensor - Added logic to the modules to address this case - Added test cases to validate generation with both Python and C++ runtimes
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,132 @@ | ||
| import pytest | ||
| import torch | ||
| import torch_tensorrt | ||
| from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteriaList | ||
| from transformers.generation.stopping_criteria import ( | ||
| EosTokenCriteria, | ||
| MaxLengthCriteria, | ||
| ) | ||
|
|
||
|
|
||
| @pytest.mark.unit | ||
| def test_dynamic_generation_python_rt(): | ||
| """ | ||
| Tests HuggingFace Generate Code with dynamic shapes | ||
| Code Credit: @peri044 | ||
| """ | ||
| # Define tokenizer and model | ||
| tokenizer = AutoTokenizer.from_pretrained("gpt2") | ||
| model = ( | ||
| AutoModelForCausalLM.from_pretrained( | ||
| "gpt2", pad_token_id=tokenizer.eos_token_id, use_cache=False | ||
| ) | ||
| .eval() | ||
| .to("cuda") | ||
| ) | ||
|
|
||
| # Input prompt | ||
| model_inputs = tokenizer(("Repeat " * 128)[:-1], return_tensors="pt").to("cuda") | ||
| input_ids = model_inputs["input_ids"] | ||
| max_tokens = 40 | ||
|
|
||
| # Pyt model outputs | ||
| greedy_output = model.generate(**model_inputs, max_new_tokens=max_tokens) | ||
| print( | ||
| "Pytorch model generated text: ", | ||
| tokenizer.decode(greedy_output[0], skip_special_tokens=True), | ||
| ) | ||
|
|
||
| # Compile Torch-TRT model | ||
| torch._dynamo.mark_dynamic(input_ids, 1, min=2, max=1023) | ||
| model.forward = torch.compile( | ||
| model.forward, | ||
| backend="tensorrt", | ||
| dynamic=None, | ||
| options={ | ||
| "enabled_precisions": {torch.float}, | ||
| "torch_executed_ops": {"torch.ops.aten.slice.Tensor"}, | ||
| "use_python_runtime": True, | ||
| "optimization_level": 0, | ||
| "min_block_size": 29, | ||
| }, | ||
| ) | ||
|
|
||
| # Auto-regressive generation loop for greedy search | ||
| stopping_criteria = StoppingCriteriaList( | ||
| [ | ||
| MaxLengthCriteria(max_length=max_tokens), | ||
| EosTokenCriteria(eos_token_id=tokenizer.eos_token_id), | ||
| ] | ||
| ) | ||
| while True: | ||
| trt_outputs = model(input_ids) | ||
| logits = trt_outputs.logits | ||
| next_token_logits = logits[:, -1, :] | ||
| next_tokens = torch.argmax(next_token_logits, dim=-1) | ||
| input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) | ||
| if stopping_criteria(input_ids, logits).item(): | ||
| break | ||
|
|
||
| # TODO: Add test for correctness | ||
|
|
||
|
|
||
| @pytest.mark.unit | ||
| def test_dynamic_generation_cpp_rt(): | ||
| """ | ||
| Tests HuggingFace Generate Code with dynamic shapes | ||
| Code Credit: @peri044 | ||
| """ | ||
| # Define tokenizer and model | ||
| tokenizer = AutoTokenizer.from_pretrained("gpt2") | ||
| model = ( | ||
| AutoModelForCausalLM.from_pretrained( | ||
| "gpt2", pad_token_id=tokenizer.eos_token_id, use_cache=False | ||
| ) | ||
| .eval() | ||
| .to("cuda") | ||
| ) | ||
|
|
||
| # Input prompt | ||
| model_inputs = tokenizer(("Repeat " * 128)[:-1], return_tensors="pt").to("cuda") | ||
| input_ids = model_inputs["input_ids"] | ||
| max_tokens = 40 | ||
|
|
||
| # Pyt model outputs | ||
| greedy_output = model.generate(**model_inputs, max_new_tokens=max_tokens) | ||
| print( | ||
| "Pytorch model generated text: ", | ||
| tokenizer.decode(greedy_output[0], skip_special_tokens=True), | ||
| ) | ||
|
|
||
| # Compile Torch-TRT model | ||
| torch._dynamo.mark_dynamic(input_ids, 1, min=2, max=1023) | ||
| model.forward = torch.compile( | ||
| model.forward, | ||
| backend="tensorrt", | ||
| dynamic=None, | ||
| options={ | ||
| "enabled_precisions": {torch.float}, | ||
| "torch_executed_ops": {"torch.ops.aten.slice.Tensor"}, | ||
| "use_python_runtime": False, | ||
| "optimization_level": 0, | ||
| "min_block_size": 29, | ||
| }, | ||
| ) | ||
|
|
||
| # Auto-regressive generation loop for greedy search | ||
| stopping_criteria = StoppingCriteriaList( | ||
| [ | ||
| MaxLengthCriteria(max_length=max_tokens), | ||
| EosTokenCriteria(eos_token_id=tokenizer.eos_token_id), | ||
| ] | ||
| ) | ||
| while True: | ||
| trt_outputs = model(input_ids) | ||
| logits = trt_outputs.logits | ||
| next_token_logits = logits[:, -1, :] | ||
| next_tokens = torch.argmax(next_token_logits, dim=-1) | ||
| input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) | ||
| if stopping_criteria(input_ids, logits).item(): | ||
| break | ||
|
|
||
| # TODO: Add test for correctness |