fix: cumsum add_constant bug fix (add dtype for np zeros)

[chohk88]

Description

When compiling the roberta-base model from Hugging Face (https://huggingface.co/FacebookAI/roberta-base), a TypeError occurs in the cumsum operation. For static shape input, the default datatype of np.zeros(new_dims) function is np.float64 which is not handled properly by the create_constant utility function.

Fixes # (issue)

  File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/dynamo/conversion/aten_ops_converters.py", line 934, in aten_ops_cumsum
    return impl.slice.cumsum(
  File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/dynamo/conversion/impl/slice/ops.py", line 387, in cumsum
    zero_trttensor = get_trt_tensor(ctx, zeros, f"{name}_initial_value")
  File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/dynamo/conversion/converter_utils.py", line 388, in get_trt_tensor
    return create_constant(ctx, input_val, name, dtype, min_rank)
  File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/dynamo/conversion/converter_utils.py", line 349, in create_constant
    constant = ctx.net.add_constant(
torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
TypeError: add_constant(): incompatible function arguments. The following argument types are supported:
    1. (self: tensorrt.tensorrt.INetworkDefinition, shape: tensorrt.tensorrt.Dims, weights: tensorrt.tensorrt.Weights) -> tensorrt.tensorrt.IConstantLayer
Invoked with: <tensorrt.tensorrt.INetworkDefinition object at 0x7fee84ebd770>, (1,), array([0.])

Reproduction Code:

# https://huggingface.co/FacebookAI/roberta-base
import torch
from transformers import RobertaTokenizer, RobertaModel
import torch_tensorrt

backend = "torch_tensorrt"
device = "cuda:0"

# Load tokenizer and model
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaModel.from_pretrained('roberta-base')
model = model.to(device)

# Tokenize input text
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
encoded_input = {k: v.to(device) for k, v in encoded_input.items()} 

# Compile model with Torch-TensorRT
model = torch.compile(
    model,
    backend=backend,
    options={
        "truncate_long_and_double": True,
        "enabled_precisions": {torch.float16},
    },
    dynamic=False,
)

# Run inference
output = model(**encoded_input)
print(output)

Type of change

• Bug fix (non-breaking change which fixes an issue)

Checklist:

• My code follows the style guidelines of this project (You can use the linters)
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas and hacks
• I have made corresponding changes to the documentation
• I have added tests to verify my fix or my feature
• New and existing unit tests pass locally with my changes
• I have added the relevant labels to my PR in so that relevant reviewers are notified

[peri044]

should this dtype be dependent on input dtype or always float32 ?