Why does `generate` in `LlamaForCausalLM` give me _slightly_ lower logits than __call__?
I’m making some experiments on the probability of choosing a particular answer and I noticed that, even when using greedy decoding, the logits generated bymodel.generate(input_ids)are very slightly different than the ones called withmodel(cat([input_ids, answer]))with the same input.UsingLlama-3.1-8B-Instruct, I get the following values.input_ids = tensor([[128000,  16533,    279,   2768,   3488,    304,    264,   2478,   4339, 323,    449,    912,  37666,     13,   3639,    574,    279,   8250, 315,    578,   1050,    359,   2461,    315,    279,  21080,    555, 89315,  92898,     30,    578,   1050,    359,   2461,    315,    279, 21080,    555,  89315,  92898,  36513,    369]], device='cuda:0')
attention_mask = tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], device='cuda:0')

 gen_target_logits = tensor([[18.1250, 20.8750, 20.8750, 16.1250, 14.2500, 15.6875, 23.8750, 22.7500, 20.2500, 22.1250, 20.7500, 19.6250, 21.7500, 29.5000, 18.5000, 19.8750, 20.5000, 23.6250, 20.6250, 22.2500]], device='cuda:0')
call_target_logits = tensor([[18.2500, 20.8750, 20.8750, 16.2500, 12.1875, 12.4375, 12.7500, 12.9375, 12.8125, 13.0625, 13.1250, 12.8750, 12.8125, 12.4375, 12.1875, 12.1250, 11.9375, 12.3125, 12.6250, 13.0000]], device='cuda:0')As you can see,gen_target_logitsandcall_target_logitsarealmostequal. Each element is either the same or slightly lower in the version withgenerate.Why does this happen? Did I miss any regularisation done bygenerate, or should I just put my hands up and sayCUDA precision issues?Here’s the code I’m using to test this.@torch.no_grad()
def test(model: LlamaForCausalLM, input_ids: LongTensor, attention_mask: BoolTensor):
    generated = model.generate(
        input_ids = input_ids,
        attention_mask = attention_mask,
        max_new_tokens = 20,
        min_new_tokens = 20,
        do_sample = False,
        temperature = None,
        use_cache = False,
        top_p = None,
        top_k = None,
        output_logits = True,
        return_dict_in_generate = True,
    )

    # Get the target logits from calling `model.generate`.
    gen_logits = torch.stack(generated.logits, dim = 1)
    gen_target_logits = gen_logits.max(dim = 2)[0]
    
    # We can calculate the answer and its mask form these logits.
    gen_answer = gen_logits.argmax(dim = 2)
    gen_answer_mask = gen_answer != PAD

    # Combine the input query and answer to pass to __call__.
    combined_ids = torch.cat([input_ids, gen_answer], dim = 1)
    combined_mask = torch.cat([attention_mask, gen_answer_mask], dim = 1)

    # Call the model, and get the logits corresponding to the answer (shifted one position to the left).
    w0 = input_ids.shape[0]
    w1 = gen_answer.shape[1]
    call_logits = model(combined_ids, attention_mask = combined_mask).logits[:, w0 - 1 : w0 + w1 - 1]

    call_target_logits = call_logits.max(dim = 2)[0]
    call_answer = call_logits.argmax(dim = 2)

    # These two are exactly equal (which is good).
    print(gen_answer)
    print(call_target)

    # These two should be exactly equal, and they are _almost_ equal.
    print(gen_target_logits)
    print(call_target_logits)
2024-09-05T14:43:31Z
I found the problem: at some point in generation, there are two logits with the exact same score.gen_logits[0][1][18]
ipdb> tensor(20.8750, device='cuda:0')
gen_logits[0][1][19]
ipdb> tensor(20.8750, device='cuda:0')This produces the same probabilityup to this point, but the selected tokens and probabilities differ slightly in later cases.When there is a tie,torch.maxandtorch.argmaxalways choose the earliest token. It seems thatgeneratedoes not necessarily do this, and I cannot figure out what it does.I will open another thread with this question, since it’s interesting and will probably help other people to know the answer.