Dimension Mismatch in Tensors | Generated by AI

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It looks like you’ve encountered an error while training a reward model as part of a Reinforcement Learning from Human Feedback (RLHF) setup using PyTorch and the Transformers library. The error message indicates a dimension mismatch in the MarginRankingLoss function, which expects all input tensors (input1, input2, and target) to have the same size. Let’s break this down and fix it.

The Error

The traceback shows:

RuntimeError: margin_ranking_loss : All input tensors should have same dimension but got sizes: input1: torch.Size([1, 1]), input2: torch.Size([1, 1]), target: torch.Size([1])

The MarginRankingLoss expects the inputs and target to have compatible dimensions. Here, the extra dimension in preferred_reward and less_preferred_reward (i.e., [1, 1] vs. [1]) is causing the mismatch. This is likely because the reward model outputs a tensor with an additional dimension from the linear layer (self.v_head).

Why This Happens

In your RewardModel:

reward = self.v_head(last_hidden_states[:, -1])  # Shape: [batch_size, 1]

The v_head layer outputs a reward score with shape [batch_size, 1] (e.g., [1, 1] for a batch size of 1). Meanwhile, labels is created as:

labels = torch.ones(preferred_reward.size(0)).to(device)  # Shape: [batch_size]

This gives labels a shape of [1], which doesn’t match the [1, 1] of the rewards.

The Fix

To resolve this, you need to ensure the reward tensors and the target tensor have compatible shapes. Since MarginRankingLoss expects 1D tensors (or at least matching shapes), you can squeeze the extra dimension from the reward outputs. Here’s how to modify the training loop:

Updated Code Snippet

# Train the Reward Model
num_reward_epochs = 3
for epoch in range(num_reward_epochs):
    for prompt, preferred, less_preferred in preference_data:
        preferred_tokens = tokenizer(prompt + preferred, return_tensors="pt", truncation=True, max_length=128).to(device)
        less_preferred_tokens = tokenizer(prompt + less_preferred, return_tensors="pt", truncation=True, max_length=128).to(device)

        preferred_reward = reward_model(**preferred_tokens).squeeze()  # Remove extra dimension
        less_preferred_reward = reward_model(**less_preferred_tokens).squeeze()  # Remove extra dimension

        labels = torch.ones(preferred_reward.size(0)).to(device)  # Shape: [batch_size], e.g., [1]
        loss = reward_criterion(preferred_reward, less_preferred_reward, labels)

        reward_optimizer.zero_grad()
        loss.backward()
        reward_optimizer.step()
    print(f"Reward Epoch {epoch+1}, Loss: {loss.item()}")

Key Change

Additional Notes

  1. Batch Size Consideration:
    • If you later increase the batch size (e.g., process multiple examples at once), ensure that preferred_tokens and less_preferred_tokens are batched correctly, and the shapes will still align after squeezing.
  2. Reward Model Output:
    • The current reward model takes the last hidden state (last_hidden_states[:, -1]) and passes it through a linear layer. This is fine for a simple reward model, but you might experiment with aggregating hidden states (e.g., mean pooling) for more nuanced scoring.
  3. RL Loop:
    • Your RL fine-tuning loop uses a simplified loss (-reward.mean()). In practice, RLHF typically employs algorithms like PPO (Proximal Policy Optimization), which require a more complex setup (e.g., a value function, clipped objectives). For now, your approach works as a proof of concept.
  4. Tokenizer Padding:
    • You might want to add padding=True to the tokenizer calls to ensure consistent input lengths, though truncation=True with max_length should handle most cases here.

Full Working Example (Simplified)

Here’s a corrected and minimal version of your reward training loop:

import torch
import torch.nn as nn
import torch.optim as optim
from transformers import AutoModelForCausalLM, AutoTokenizer

# Sample data
preference_data = [
    ("Write a story about a cat.", "Whiskers the cat lived in a cozy cottage...", "A cat story. The end."),
]

# Load model and tokenizer
model_name = "gpt2"
policy_model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
policy_model.to(device)

# Reward Model
class RewardModel(nn.Module):
    def __init__(self, base_model):
        super().__init__()
        self.base_model = base_model.transformer
        self.v_head = nn.Linear(base_model.config.n_embd, 1)

    def forward(self, input_ids, attention_mask):
        outputs = self.base_model(input_ids=input_ids, attention_mask=attention_mask)
        last_hidden_states = outputs.last_hidden_state
        reward = self.v_head(last_hidden_states[:, -1])
        return reward

reward_model = RewardModel(policy_model).to(device)
reward_optimizer = optim.AdamW(reward_model.parameters(), lr=1e-5)
reward_criterion = nn.MarginRankingLoss(margin=1.0)

# Train Reward Model
for epoch in range(3):
    for prompt, preferred, less_preferred in preference_data:
        preferred_tokens = tokenizer(prompt + preferred, return_tensors="pt", truncation=True, max_length=128).to(device)
        less_preferred_tokens = tokenizer(prompt + less_preferred, return_tensors="pt", truncation=True, max_length=128).to(device)

        preferred_reward = reward_model(**preferred_tokens).squeeze()
        less_preferred_reward = reward_model(**less_preferred_tokens).squeeze()

        labels = torch.ones(preferred_reward.size(0)).to(device)
        loss = reward_criterion(preferred_reward, less_preferred_reward, labels)

        reward_optimizer.zero_grad()
        loss.backward()
        reward_optimizer.step()
    print(f"Reward Epoch {epoch+1}, Loss: {loss.item()}")

This should resolve the dimension mismatch and allow your reward model to train successfully. Let me know if you need further clarification or help with the RL part!

Understanding and Fixing the IndexError in Your PyTorch Script

The error message you’ve encountered in your Python script is:

IndexError: Dimension specified as 0 but tensor has no dimensions

This occurs on line 44 of your script, rl.py, within the following line:

labels = torch.ones(preferred_reward.size(0)).to(device)

The error indicates that preferred_reward is a scalar tensor (i.e., it has no dimensions, with a shape of []), and attempting to access its first dimension with .size(0) is invalid because a scalar has no dimensions to index.

Let’s break this down step-by-step and provide a solution.

Why This Error Occurs

In your script, you’re likely training a reward model using a loss function like MarginRankingLoss, which expects its inputs (preferred_reward, less_preferred_reward, and labels) to be tensors of compatible shapes—typically 1D tensors where each element corresponds to a sample in a batch. Here’s what’s happening:

  1. Origin of preferred_reward:
    • preferred_reward is the output of a reward_model forward pass, e.g., reward_model(**preferred_tokens).
    • Assuming your reward model outputs a single value per sample, for a batch size of 1, the output shape is [1, 1] (batch size × output dimension).
  2. Squeezing the Tensor:
    • In your original code, you apply .squeeze() to preferred_reward: 
      preferred_reward = reward_model(**preferred_tokens).squeeze()
    • The .squeeze() method removes all dimensions of size 1. For a tensor of shape [1, 1], this reduces it to []—a scalar tensor with no dimensions.
  3. Accessing the Size:
    • Later, you attempt to create a labels tensor with the same batch size as preferred_reward: 
      labels = torch.ones(preferred_reward.size(0)).to(device)
    • For a scalar tensor ([]), preferred_reward.size() returns torch.Size([]), an empty size tuple. Trying to access the first dimension with .size(0) raises the IndexError because there are no dimensions to access.
  4. Expected Behavior:
    • The MarginRankingLoss function requires its inputs (preferred_reward, less_preferred_reward, and labels) to have the same shape, typically 1D tensors like [batch_size]. For a batch size of 1, this should be [1], not a scalar [].

The root cause is that .squeeze() is too aggressive—it removes all singleton dimensions, turning [1, 1] into a scalar [] when the batch size is 1, which breaks the subsequent .size(0) call.

The Fix

To resolve this, you need to ensure that preferred_reward and less_preferred_reward remain 1D tensors with shape [batch_size], even when batch_size=1. Instead of using .squeeze(), which removes all dimensions of size 1, use .squeeze(-1) to remove only the last dimension. This transforms [1, 1] into [1], preserving the batch dimension.

Here’s the corrected code snippet for your reward model training loop:

# Train the Reward Model
num_reward_epochs = 3
for epoch in range(num_reward_epochs):
    for prompt, preferred, less_preferred in preference_data:
        # Tokenize inputs
        preferred_tokens = tokenizer(prompt + preferred, return_tensors="pt", truncation=True, max_length=128).to(device)
        less_preferred_tokens = tokenizer(prompt + less_preferred, return_tensors="pt", truncation=True, max_length=128).to(device)

        # Compute rewards, squeezing only the last dimension
        preferred_reward = reward_model(**preferred_tokens).squeeze(-1)  # Shape: [1]
        less_preferred_reward = reward_model(**less_preferred_tokens).squeeze(-1)  # Shape: [1]

        # Create labels tensor based on batch size
        labels = torch.ones(preferred_reward.size(0)).to(device)  # Shape: [1]
        
        # Compute loss
        loss = reward_criterion(preferred_reward, less_preferred_reward, labels)

        # Backpropagation
        reward_optimizer.zero_grad()
        loss.backward()
        reward_optimizer.step()
    
    print(f"Reward Epoch {epoch+1}, Loss: {loss.item()}")

How This Works

Alternative Approaches

While .squeeze(-1) is a clean and precise fix, here are two other methods that would also work:

  1. Using Indexing: 
    preferred_reward = reward_model(**preferred_tokens)[:, 0]  # Shape: [1]
less_preferred_reward = reward_model(**less_preferred_tokens)[:, 0]  # Shape: [1]
    • This selects the first (and only) element of the last dimension, converting [1, 1] to [1].
  2. Using .view(-1): 
    preferred_reward = reward_model(**preferred_tokens).view(-1)  # Shape: [1]
less_preferred_reward = reward_model(**less_preferred_tokens).view(-1)  # Shape: [1]
    • This flattens the tensor into a 1D tensor. For [1, 1], it becomes [1] since it has only one element.

Both alternatives achieve the same result as .squeeze(-1) in this context, but .squeeze(-1) is preferred because it explicitly targets the last dimension, aligning with the reward model’s output structure ([batch_size, 1]).

Additional Notes

Verification

For a batch size of 1:

The IndexError is eliminated, and your reward model training should now run smoothly.

Final Answer

To fix the IndexError: Dimension specified as 0 but tensor has no dimensions on line 44 of rl.py, replace .squeeze() with .squeeze(-1) when computing preferred_reward and less_preferred_reward. This ensures they remain 1D tensors with shape [1] (or [batch_size] for larger batches), allowing preferred_reward.size(0) to correctly retrieve the batch size for creating the labels tensor.

Apply this change in your reward model training loop as shown above, and the error will be resolved.

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