Converting PyTorch model to TorchScript, ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds

nlp
1

I’m converting the SwitchTransformer model from HuggingFace to TorchScript.

But I encountered the following error message

ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds.

In some other QnAs in StackOverflow, it said that Enc-Dec model cannot be converted at once and it should be divided into encoder and decoder, then it can be converted, but I still cannot understand. The model itself is integrated at the function level.

Does that mean that I should divide it making a new function?

Here are the two links that I referred to.

Also, in the answer in the above link, someone said that the converted model doesn’t have generate function, then how can I make an inference?

Here’s the code that I’m using now.

from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
from transformers import AutoTokenizer, SwitchTransformersConfig
import torch

torch.set_printoptions(threshold=10_000)

# Tokenizer
tokenizer = AutoTokenizer.from_pretrained(
    "google/switch-base-8", resume_download=True, torchscript=True)

input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
# To use GPU, add .to(0)
input_ids = tokenizer(input_text, return_tensors="pt", padding=True).input_ids.to(0)

# To use GPU, add args device_map="auto"
model = SwitchTransformersForConditionalGeneration.from_pretrained(
    "google/switch-base-8",
    device_map="auto",
    resume_download=True, torch_dtype=torch.bfloat16,
    torchscript = True,
    )

# This is for TorchScript
model.eval()

model = torch.jit.trace(model, (input_ids))

outputs = model.generate(
    input_ids,
    decoder_start_token_id=0,
    bos_token_id=2,
    )