Different batch sizes lead to different inference results

Hi,

I found that when setting load_in_8bit=True, different batch sizes will lead to very different results, even if I’m doing inference-only. I found this phenomenon for several HF pretrained language models with int8. A simple example is as follow, where I got very different results when comparing out1 and out2.

Thank you!

GPU: 1 RTX3090, Driver version: 470.103.01
CUDA SETUP: Highest compute capability among GPUs detected: 8.6
CUDA SETUP: Detected CUDA version 114

from transformers import GPT2Tokenizer, AutoModelForCausalLM
import torch

tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt-1.3b")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-1.3b",
	device_map='auto', load_in_8bit=True)

#model.cuda()
model.eval()


@torch.no_grad()
def do_inference(model, input_ids, attention_mask):
  outputs = model(input_ids=input_ids.cuda(), attention_mask=attention_mask.cuda())
  return outputs.logits.cpu()


batch_sents = [
 'Review: luminous interviews and amazingly evocative film from three decades ago \nSentiment:',
 'Review: with fewer gags to break the tedium \nSentiment:',
 'Review: aims for poetry and ends up sounding like satire \nSentiment:',
 'Review: no way original \nSentiment:'
 ]
enc_inputs = tokenizer(batch_sents, return_tensors='pt', padding=True)


# run inference with batch_size = 2
out1 = []
for i in range(0, len(batch_sents), 2):
  out = do_inference(model, enc_inputs['input_ids'][i:i+2], enc_inputs['attention_mask'][i:i+2])
  out1.append(out)
out1 = torch.cat(out1)

# run inference with batch_size = 4
out2 = do_inference(model, enc_inputs['input_ids'], enc_inputs['attention_mask'])

print(torch.abs(out1-out2).max()) #got tensor(2.0664, dtype=torch.float16) on my machine

[terarachang]

Hi Tim,

Thank you so much for your reply.

I’d like to share my new findings. It seems like the results are dependent on the instances in the same batch:

# To avoid any potential issues in attention_mask, truncate all sents to the same length
enc_inputs = tokenizer(batch_sents, return_tensors='pt', max_length=5, truncation=True)

# run inference with batch_size = 2
out1 = []
for i in range(0, len(batch_sents), 2):
  out = do_inference(model, enc_inputs['input_ids'][i:i+2], enc_inputs['attention_mask'][i:i+2])
  out1.append(out)
out1 = torch.cat(out1)

# run inference with batch_size = 2 in a shuffled order
shuffled_order = [0,3,1,2]
input_ids_shf, attn_mask_shf = enc_inputs['input_ids'][shuffled_order], enc_inputs['attention_mask'][shuffled_order]

out4 = []
for i in range(0, len(batch_sents), 2):
  out = do_inference(model, input_ids_shf[i:i+2], attn_mask_shf[i:i+2])
  out4.append(out)
out4 = torch.cat(out4)

# shuffle back
ret_order = [0,2,3,1] # argsort(shuffled_order)
out4 = out4[ret_order]

print(torch.abs(out1-out4).max()) 
# got 1.0781 when load_in_8bit=True with the default threshold
# got 0 when load_in_8bit=True and int8_threshold = 0
# got 0 when load_in_8bit=False

A clarifying question: I wonder when I set int8_threshold=0, is it equivalent to the entire model in fp16 or in int8? My understanding is: hidden states values that are above this threshold are considered outliers and their operations will be done in fp16, so operation-wise, int8_threshold=0 is equivalent to the entire model in fp16, correct?

Thank you!

[mallorbc]

I do not know what is expected behavior after seeing this occur without using int8. When I was doing batch processing for GPTJ, I was using bfloat16, which is not unstable like fp16 can be. I have not tried this with fp32 but bfloat16 should be a drop in replacement.