XLM-R model output changes with batch size

[ricardorei]

🐛 Bug

When using XLM-R the representations change depending on the batch size.

Code sample

from fairseq.models.roberta import XLMRModel
from torchnlp.encoders.text import stack_and_pad_tensors
import torch

torch.set_printoptions(precision=10)
def batch_encoder(samples, tokenizer):
    batch = []
    for sequence in samples:
        batch.append(tokenizer.encode(sequence))
    return stack_and_pad_tensors(batch, tokenizer.task.source_dictionary.__dict__["indices"]["<pad>"])
    
xlmr = XLMRModel.from_pretrained(
            "pretrained/xlmr.base", checkpoint_file="model.pt"
        )
xlmr.eval()

samples = [
    'the part of the regular expression within the forward slashes defines the pattern.', 
    'discards the current state and temporarily replaces it with the previous state.',
    'to convert a smooth point to a corner point without direction lines, click the smooth point.'
]

with torch.no_grad():
    big_batch_tokens, bb_lengths = batch_encoder(samples, xlmr)
    small_batch_tokens, sb_lengths = batch_encoder(samples[:2], xlmr)
    first_sample_tokens = xlmr.encode(samples[0])

    first_sample_last_layer = xlmr.extract_features(first_sample_tokens)
    print (first_sample_last_layer[:, 0, :][0][:5])

    small_batch_last_layer = xlmr.extract_features(tokens=small_batch_tokens)
    print (small_batch_last_layer[:, 0, :][0][:5])

    big_batch_last_layer = xlmr.extract_features(tokens=big_batch_tokens)
    print (big_batch_last_layer[:, 0, :][0][:5])

Expected behavior

tensor([ 0.0852593556,  0.1065494418,  0.0615975149, -0.0047241775, 0.0284897964])
tensor([ 0.0852593333,  0.1065494195,  0.0615975149, -0.0047241990, 0.0284897070])
tensor([ 0.0852593556,  0.1065494046,  0.0615975186, -0.0047241938, 0.0284897685])

Additional context

If I decide to average pool overall embeddings or if I max pool these differences are even bigger.

Am I doing something wrong? Is this behaviour expected?

[ngoyal2707]

This seems to be floating point math issue.
I get similar range of difference when trying on CPU, but on GPU it seems to be exactly the same till 10th digit.
Some discussion on pytorch thread: pytorch/pytorch#4914 (although that one has floating point issues on CUDA rather than CPU).

on CUDA:

tensor([-0.0130963037,  0.0021208122,  0.0833869055,  0.0168007165,
        -0.0006483230], device='cuda:0')
tensor([-0.0130963037,  0.0021208122,  0.0833869055,  0.0168007165,
        -0.0006483230], device='cuda:0')
tensor([-0.0130963037,  0.0021208122,  0.0833869055,  0.0168007165,
        -0.0006483230], device='cuda:0')

on CPU

tensor([-0.0130964424,  0.0021210182,  0.0833871067,  0.0168008748,
        -0.0006483837])
tensor([-0.0130964424,  0.0021210182,  0.0833871067,  0.0168008748,
        -0.0006483837])
tensor([-0.0130964629,  0.0021210436,  0.0833871216,  0.0168008823,
        -0.0006483909])