The size of tensor a (882) must match the size of tensor b (568) at non-singleton dimension 1

🤗Transformers
1

I am using quite a standard pipeline to train reward modelling with an implicit preference dataset, but I run into the issue of tensor dimension mismatch. May I ask what might be the issue here, and what debugging steps I can take to resolve this issue?

import torch
from datasets import load_dataset
from trl import RewardTrainer, RewardConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
torch.set_default_device('cuda')
model = AutoModelForCausalLM.from_pretrained("gemma3", attn_implementation='eager')
tokenizer = AutoTokenizer.from_pretrained("gemma3")

# load training data, and process it so it becomes an implicit preference dataset ("chosen" and "rejected")
train_dataset = load_dataset("json", data_files="custom_training_data.json", split="train")
def prefix_with_input(example):
    example['chosen'] = example['input'] + " " + example['chosen']
    example['rejected'] = example['input'] + " " + example['rejected'][0]
    return example
train_dataset = train_dataset.map(prefix_with_input)
train_dataset = train_dataset.remove_columns(["input"])

training_args = RewardConfig()
tokenizer.pad_token = tokenizer.eos_token
training_args.dataloader_pin_memory=False
training_args.per_device_train_batch_size = 1

trainer = RewardTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset
)
trainer.train()

Error message below:

The size of tensor a (882) must match the size of tensor b (568) at non-singleton dimension 1
  File "train.py", line 109, in <module>
    trainer.train()
RuntimeError: The size of tensor a (882) must match the size of tensor b (568) at non-singleton dimension 1
2

In the simplest case, it seems that the problem can be fixed by setting tokenizer.model_max_length = 512.

The error you’re encountering, “The size of tensor a (882) must match the size of tensor b (568) at non-singleton dimension 1,” indicates a mismatch in tensor dimensions during the training process. This is a common issue in deep learning when tensors of different shapes are combined or compared. Below, I’ll guide you through potential causes and debugging steps to resolve this issue.

Potential Causes

  1. Mismatched Input Sizes:

    • The tensors being passed to the model (e.g., chosen and rejected examples) might have inconsistent shapes.
    • For example, the chosen and rejected sequences could have different lengths after tokenization.

  2. Batching Issues:

    • The RewardTrainer might be expecting batches of consistent size, but the data loader is providing batches with varying tensor dimensions.

  3. Tokenization Differences:

    • The chosen and rejected examples might not be tokenized to the same maximum length, causing tensor shape mismatches.

  4. Inconsistent Dataset Processing:

    • The prefix_with_input function could be introducing irregularities in the dataset, leading to inconsistent tensor shapes.

Debugging Steps

1. Verify Input Tensor Shapes

  • Add print statements or use debugging tools to inspect the shapes of tensors before and after processing.
  • For example, in the prefix_with_input function, check the lengths of chosen and rejected sequences:
    def prefix_with_input(example):
    example['chosen'] = example['input'] + " " + example['chosen']
    example['rejected'] = example['input'] + " " + example['rejected'][0]
    print(f"Chosen length: {len(example['chosen'].split())}")
    print(f"Rejected length: {len(example['rejected'].split())}")
    return example
  • This will help identify if the sequences have mismatched lengths.

2. Ensure Consistent Tokenization

  • The tokenizer might not be padding or truncating sequences to the same length. Try setting a fixed maximum sequence length:
    from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("gemma3")
tokenizer.model_max_length = 512  # Set a fixed maximum length
  • When tokenizing, ensure that both chosen and rejected examples are padded or truncated to the same length:
    train_dataset = train_dataset.map(prefix_with_input).map(
    lambda x: tokenizer(
        x['chosen'], max_length=tokenizer.model_max_length,
        padding='max_length', truncation=True
    ),
    batched=True
)

3. Inspect Batch Sizes

  • Check if the data loader is producing batches with consistent tensor shapes. You can modify the RewardConfig to include:
    training_args = RewardConfig(
    dataloader_pin_memory=False,
    per_device_train_batch_size=1,
    max_steps=1  # Process only one batch to inspect shapes
)
  • After training, inspect the shapes of the input tensors:
    for batch in trainer.get_train_dataloader():
    print(f"Batch shapes: {batch['input_ids'].shape}")
    break  # Exit after the first batch

4. Check the Reward Model’s Input Requirements

  • Ensure that the reward model expects inputs of the same shape. You can print the model’s input requirements:
    print(model)

5. Modify the Dataset Processing

  • The prefix_with_input function might be introducing inconsistencies. Try simplifying it to ensure consistent processing:
    def prefix_with_input(example):
    example['chosen'] = example['input'] + " " + example['chosen']
    example['rejected'] = example['input'] + " " + example['rejected'][0]
    # Ensure both sequences have the same format
    assert isinstance(example['chosen'], str) and isinstance(example['rejected'], str)
    return example

Example Solution

Based on the error message, the mismatch is likely due to inconsistent tokenization or batching. Here’s a modified version of your code with potential fixes:

import torch
from datasets import load_dataset
from trl import RewardTrainer, RewardConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

torch.set_default_device('cuda')
model = AutoModelForCausalLM.from_pretrained("gemma3", attn_implementation='eager')
tokenizer = AutoTokenizer.from_pretrained("gemma3")
tokenizer.pad_token = tokenizer.eos_token
tokenizer.model_max_length = 512  # Fixed maximum sequence length

# Load and process the dataset
train_dataset = load_dataset("json", data_files="custom_training_data.json", split="train")

def prefix_with_input(example):
    example['chosen'] = example['input'] + " " + example['chosen']
    example['rejected'] = example['input'] + " " + example['rejected'][0]
    return example

# Apply the prefix function
train_dataset = train_dataset.map(prefix_with_input, num_proc=4)

# Tokenize the dataset
train_dataset = train_dataset.map(
    lambda x: tokenizer(
        x['chosen'], max_length=tokenizer.model_max_length,
        padding='max_length', truncation=True
    ),
    batched=True
)

# Remove unnecessary columns
train_dataset = train_dataset.remove_columns(["input"])

# Initialize training arguments
training_args = RewardConfig(
    dataloader_pin_memory=False,
    per_device_train_batch_size=1
)

# Initialize the trainer
trainer = RewardTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset
)

# Debugging: Print batch shapes
for batch in trainer.get_train_dataloader():
    print(f"Batch shapes: {batch['input_ids'].shape}")
    break

# Train the model
trainer.train()

Final Notes

  • If the issue persists, consider reducing the batch size (per_device_train_batch_size) or experimenting with different maximum sequence lengths.
  • To gain more insights, you can also enable detailed error messages by setting os.environ['HYDRA_FULL_ERROR'] = '1' at the beginning of your script.

By following these steps, you should be able to identify and resolve the tensor dimension mismatch issue in your reward modeling pipeline.