[Bug] operator.gt got an object with type <class 'int'>

[ruofan-wu]

Hi @yaoyaoding, I encounter a bug when I run T5Model with hidet:

DEBUG:hidet.graph.frontend.torch.interpreter:interpreting node 34: %gt : [#users=1] = call_function[target=operator.gt](args = (%sub_1, 0), kwargs = {})
Traceback (most recent call last):
  File "hidet-path/python/hidet/graph/frontend/torch/interpreter.py", line 260, in forward
    hidet_env[node.name] = hidet_func(*hidet_args, **hidet_kwargs)
  File "hidet-path/python/hidet/graph/frontend/torch/register_functions.py", line 694, in gt
    return ops.greater(a, b)
  File "hidet-path/python/hidet/graph/ops/definitions/compare.py", line 85, in greater
    return GreaterOp(x, y).get_output(0)
  File "hidet-path/python/hidet/graph/ops/definitions/compare.py", line 34, in __init__
    super().__init__(x, y, lambda a, b: a > b, name='gt')
  File "hidet-path/python/hidet/graph/ops/definitions/arithmetic.py", line 125, in __init__
    task=BinaryElementwiseTask(name, input_like(x, 'x'), input_like(y, 'y'), op=op),
  File "hidet-path/python/hidet/graph/ops/definitions/utils/tensor_utils.py", line 26, in input_like
    raise TypeError('Expect a hidet.Tensor, but got an object with type {}'.format(type(tensor)))
TypeError: Expect a hidet.Tensor, but got an object with type <class 'int'>

Could you please help me fix it?

[yaoyaoding]

Hi @GisellWu,

Any minimal reproducible example to reproduce the error?

[ruofan-wu]

import torch
from transformers import T5Tokenizer, T5Model
import hidet

model_name = 't5-base' 
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5Model.from_pretrained(model_name).to(device="cuda:0")

model = torch.compile(model, backend='hidet')

input_text = ["translate English to French: Hello, how are you?"]
tokens = tokenizer.encode_plus(input_text, add_special_tokens=True, return_tensors='pt',
                               padding='max_length', truncation=True, max_length=128).to(device="cuda:0")

outputs = model(input_ids=tokens.input_ids, decoder_input_ids=tokens.input_ids)
logits = outputs.last_hidden_state
print("Logits Shape:", logits.shape)

[ruofan-wu]

Furthermore, I added some functions to register_functions.py in order to run through T5Model:

@register_function(torch.abs)
def abs(x: Tensor, *, out: Optional[Tensor] = None) -> Tensor:
    if out is not None:
        raise NotImplementedError("hidet: does not support torch.abs(..., out=...)")
    return ops.abs(x)

@register_function(torch.log)
def log(x: Tensor, *, out: Optional[Tensor] = None) -> Tensor:
    if out is not None:
        raise NotImplementedError("hidet: does not support torch.log(..., out=...)")
    return ops.log(x)

@register_function(torch.full_like)
def full_like(input, fill_value, *, dtype=None, layout=torch.strided, device=None, requires_grad=False, memory_format=torch.preserve_format):
    if layout not in [None, torch.strided]:
        raise NotImplementedError("hidet: does not support torch.full(..., layout=..., ...)")
    if requires_grad and torch.is_grad_enabled():
        warnings.warn_once("hidet: requires_grad=True when torch.is_grad_enabled(), treating as requires_grad=False")
    hidet_device: Device = device_from_torch(torch_device=device)
    hidet_dtype: DataType = dtype_from_torch(torch_dtype=dtype)
    return ops.full(input.size(), fill_value, dtype=hidet_dtype, device=hidet_device)

@register_function(torch.zeros_like)
def zeros_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format):
    import hidet

    if layout is not None:
        raise NotImplementedError("layout is not None")
    
    size = input.size()
    if len(size) == 1:
        if isinstance(size[0], (list, tuple)):
            size = size[0]
    shape = [int(v) for v in size]

    if dtype is None:
        dtype = torch.get_default_dtype()

    _ = requires_grad

    return hidet.zeros(shape, dtype=dtype_from_torch(dtype), device=device_from_torch(device))

[yaoyaoding]

Hi @GisellWu,

I added the missing operators and fixed some bugs in #322 for T5 model. Could you give a try again?

[ruofan-wu]

Thanks for your help! I successfully ran it. Close the issue :)

[ruofan-wu]

Hi @yaoyaoding ,

Sorry to bother you again, I’m trying T5Model with float16. There are some new unsupported functions. Could you please help me fix it?

The error is:

NotImplementedError: The following modules/functions are not supported by hidet yet:
  torch.clamp
  torch.isinf

And the example code is:

import torch
from transformers import T5Tokenizer, T5Model
import hidet

model_name = 't5-base' 
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5Model.from_pretrained(model_name, torch_dtype=torch.float16).to(device="cuda:0")

model = torch.compile(model, backend='hidet')

input_text = ["translate English to French: Hello, how are you?"]
tokens = tokenizer.encode_plus(input_text, add_special_tokens=True, return_tensors='pt',
                               padding='max_length', truncation=True, max_length=128).to(device="cuda:0")

outputs = model(input_ids=tokens.input_ids, decoder_input_ids=tokens.input_ids)
logits = outputs.last_hidden_state
print("Logits Shape:", logits.shape)

[yaoyaoding]

Hi @GisellWu,

I added the missing operators in #343, could you give it a try? Thanks!

[ruofan-wu]

Hi @yaoyaoding,

That's ok. Appreciate your help again!