CPU vs. GPU for "lengths" Argument in PyTorch: A Troubleshooting Guide

• RuntimeError: This indicates an error that occurs during the execution of your PyTorch program.
• 'lengths' argument: The error points to an issue with the lengths argument passed to a function.
• should be a 1D CPU int64 tensor: The expected format for the lengths argument is a one-dimensional tensor (1D) residing on the CPU (Central Processing Unit). Its data type should be int64 (64-bit integers).
• but got 1D cuda:0 Long tensor: Instead, the function received a 1D tensor, but it's located on the GPU (Graphics Processing Unit) with device ID 0. The data type is Long, which is equivalent to int64 in PyTorch.

Explanation:

In PyTorch, some functions that work with sequences of variable lengths (like packed padded sequences) require a lengths argument. This argument specifies the length of each sequence in the batch. The pack_padded_sequence function is a common example that uses this concept.

This error occurs because the lengths argument is expected to be on the CPU for efficiency reasons. The function needs to manipulate this data quickly, and transferring it back and forth between CPU and GPU can be time-consuming. Additionally, some operations might not be supported directly on GPU tensors.

Solutions:

1. Move lengths to CPU:

2. Use a CPU list:

Additional Considerations:

• If you're working with large datasets and memory limitations on the CPU, transferring lengths might not be feasible. In such cases, explore alternative approaches like bucketing sequences by similar lengths or using a custom function that operates efficiently on GPU tensors (if possible).
• Double-check the documentation of the specific function you're using to ensure it supports GPU tensors for the lengths argument. Some functions might have specific requirements.

import torch

# Sample padded sequences (assuming they're already on GPU)
padded_sequences = torch.randn(16, 5, 10).cuda()  # Batch size 16, max seq length 5, embedding dim 10

# Incorrect usage (lengths on GPU)
lengths = torch.randint(1, 6, size=(16,))  # Random lengths on GPU (cuda:0)
try:
  packed_sequence = torch.nn.utils.rnn.pack_padded_sequence(padded_sequences, lengths)
except RuntimeError as e:
  print("Error:", e)  # This will print the "lengths" argument error

Solution 1: Move lengths to CPU

import torch

# Sample padded sequences (assuming they're already on GPU)
padded_sequences = torch.randn(16, 5, 10).cuda()

# Correct usage (lengths on CPU)
lengths = torch.randint(1, 6, size=(16,))  # Random lengths on CPU
lengths = lengths.cpu()  # Move lengths to CPU
packed_sequence = torch.nn.utils.rnn.pack_padded_sequence(padded_sequences, lengths)

Solution 2: Use a CPU list

import torch

# Sample padded sequences (assuming they're already on GPU)
padded_sequences = torch.randn(16, 5, 10).cuda()

# Correct usage (lengths as CPU list)
sequence_lengths = [len(seq) for seq in padded_sequences.cpu()]  # Get lengths on CPU
packed_sequence = torch.nn.utils.rnn.pack_padded_sequence(padded_sequences, sequence_lengths)

1. Function-Specific Support:

2. Custom Function (Advanced):

3. Bucketing Sequences (Large Datasets):