test_bert.py

import re
from collections import OrderedDict

import torch
import torch.nn.functional as F
import pytest

from einops import rearrange

from transformers import BertConfig
from transformers.models.bert.modeling_bert import BertModel as BertModelHF
from transformers.models.bert.modeling_bert import BertForPreTraining as BertForPreTrainingHF

from flash_attn.models.bert import BertModel, BertForPreTraining
from flash_attn.models.bert import state_dict_from_pretrained
from flash_attn.models.bert import remap_state_dict


@pytest.mark.parametrize('model_name', ["bert-base-uncased", "bert-large-uncased"])
# @pytest.mark.parametrize('model_name', ["bert-base-uncased"])
def test_bert_state_dict(model_name):
    config = BertConfig.from_pretrained(model_name)
    pretrained_state_dict = remap_state_dict(state_dict_from_pretrained(model_name), config)
    model = BertForPreTraining(config)
    state_dict = model.state_dict()
    assert state_dict.keys() == pretrained_state_dict.keys()
    for k in state_dict.keys():
        assert state_dict[k].shape == pretrained_state_dict[k].shape


def get_hf_models(model_name, config, dtype):
    pretrained_state_dict = state_dict_from_pretrained(model_name)
    def key_mapping_ln_gamma_beta(key):
        key = re.sub(r'LayerNorm.gamma$', 'LayerNorm.weight', key)
        key = re.sub(r'LayerNorm.beta$', 'LayerNorm.bias', key)
        return key
    pretrained_state_dict = OrderedDict((key_mapping_ln_gamma_beta(k), v)
                                        for k, v in pretrained_state_dict.items())
    model_hf = BertForPreTrainingHF(config)
    # Missing key(s) in state_dict: "bert.embeddings.position_ids", "cls.predictions.decoder.bias"
    # position_ids is a buffer, and predictions.decoder.bias is tied to predictions.bias.
    model_hf.load_state_dict(pretrained_state_dict, strict=False)
    model_hf.cuda().to(dtype=dtype)
    return model_hf


@pytest.mark.parametrize('model_name', ["bert-base-uncased", "bert-large-uncased"])
# @pytest.mark.parametrize('model_name', ["bert-base-uncased"])
def test_bert_non_optimized(model_name):
    """Check that our implementation of BERT (without any optimizations enabled) matches the
    HF implementation: the output of our forward pass in fp16 should be around the same as the HF
    forward pass in fp16, when compared to the HF forward pass in fp32.
    """
    dtype = torch.float16
    config = BertConfig.from_pretrained(model_name)
    # Our implementation assumes the activation is nn.GELU(approximate='tanh')
    # Huggingface calls it "gelu_new" or "gelu_fast".
    config.hidden_act = "gelu_new"

    model = BertForPreTraining.from_pretrained(model_name, config)
    model = model.cuda().to(dtype=dtype)

    model_ref = get_hf_models(model_name, config, torch.float32)
    model_hf = get_hf_models(model_name, config, torch.float16)

    model.eval()
    model_ref.eval()
    model_hf.eval()

    torch.manual_seed(0)
    batch_size = 4
    max_seqlen = 512
    seqlens = torch.randint(max_seqlen // 2, max_seqlen + 1, (batch_size,), device='cuda')
    attention_mask = torch.arange(max_seqlen, device='cuda')[None, :] < seqlens[:, None]
    input_ids = torch.randint(0, config.vocab_size, (batch_size, max_seqlen), dtype=torch.long,
                              device='cuda')
    sequence_output, pooled_output = model.bert(input_ids, attention_mask=attention_mask)
    out_hf = model_hf.bert(input_ids, attention_mask=attention_mask)
    sequence_output_hf, pooled_output_hf = out_hf.last_hidden_state, out_hf.pooler_output
    out_ref = model_ref.bert(input_ids, attention_mask=attention_mask)
    sequence_output_ref, pooled_output_ref = out_ref.last_hidden_state, out_ref.pooler_output

    print(f'Output max diff: {(sequence_output - sequence_output_ref).abs().max().item()}')
    print(f'Output mean diff: {(sequence_output - sequence_output_ref).abs().mean().item()}')
    print(f'HF fp16 max diff: {(sequence_output_hf - sequence_output_ref).abs().max().item()}')
    print(f'HF fp16 mean diff: {(sequence_output_hf - sequence_output_ref).abs().mean().item()}')
    assert (sequence_output - sequence_output_ref).abs().max().item() < 2 * (sequence_output_hf - sequence_output_ref).abs().max().item()
    assert (pooled_output - pooled_output_ref).abs().max().item() < 2 * (pooled_output_hf - pooled_output_ref).abs().max().item()


@pytest.mark.parametrize('model_name', ["bert-base-uncased", "bert-large-uncased"])
# @pytest.mark.parametrize('model_name', ["bert-base-uncased"])
def test_bert_optimized(model_name):
    """Check that our implementation of BERT (with all optimizations enabled) matches the
    HF implementation: the output of our forward pass in fp16 should be around the same as the HF
    forward pass in fp16, when compared to the HF forward pass in fp32.
    """
    dtype = torch.float16
    config = BertConfig.from_pretrained(model_name)
    # Our implementation assumes the activation is nn.GELU(approximate='tanh')
    # Huggingface calls it "gelu_new" or "gelu_fast".
    config.hidden_act = "gelu_new"
    config.use_flash_attn = True
    config.fused_bias_fc = True
    config.fused_dense_gelu_dense = True
    config.fused_dropout_add_ln = True

    model = BertForPreTraining.from_pretrained(model_name, config)
    model = model.cuda().to(dtype=dtype)

    model_ref = get_hf_models(model_name, config, torch.float32)
    model_hf = get_hf_models(model_name, config, torch.float16)

    model.eval()
    model_ref.eval()
    model_hf.eval()

    torch.manual_seed(0)
    batch_size = 4
    max_seqlen = 512
    seqlens = torch.randint(max_seqlen // 2, max_seqlen + 1, (batch_size,), device='cuda')
    attention_mask = torch.arange(max_seqlen, device='cuda')[None, :] < seqlens[:, None]
    input_ids = torch.randint(0, config.vocab_size, (batch_size, max_seqlen), dtype=torch.long,
                              device='cuda')
    sequence_output, pooled_output = model.bert(input_ids, attention_mask=attention_mask)
    out_hf = model_hf.bert(input_ids, attention_mask=attention_mask)
    sequence_output_hf, pooled_output_hf = out_hf.last_hidden_state, out_hf.pooler_output
    # Need to zero out the padded tokens in the sequence before comparison.
    sequence_output_hf[~attention_mask, :] = 0.0
    out_ref = model_ref.bert(input_ids, attention_mask=attention_mask)
    sequence_output_ref, pooled_output_ref = out_ref.last_hidden_state, out_ref.pooler_output
    sequence_output_ref[~attention_mask, :] = 0.0

    print(f'BertModel output max diff: {(sequence_output - sequence_output_ref).abs().max().item()}')
    print(f'BertModel output mean diff: {(sequence_output - sequence_output_ref).abs().mean().item()}')
    print(f'HF fp16 BertModel max diff: {(sequence_output_hf - sequence_output_ref).abs().max().item()}')
    print(f'HF fp16 BertModel mean diff: {(sequence_output_hf - sequence_output_ref).abs().mean().item()}')
    assert (sequence_output - sequence_output_ref).abs().max().item() < 4 * (sequence_output_hf - sequence_output_ref).abs().max().item()
    assert (pooled_output - pooled_output_ref).abs().max().item() < 4 * (pooled_output_hf - pooled_output_ref).abs().max().item()

    prediction_scores, seq_relationship_scores = model(input_ids, attention_mask=attention_mask)
    # Need to zero out the padded tokens in the sequence before comparison.
    prediction_scores = prediction_scores.clone()
    prediction_scores[~attention_mask, :] = 0.0
    out_hf = model_hf(input_ids, attention_mask=attention_mask)
    prediction_scores_hf, seq_relationship_scores_hf = out_hf.prediction_logits, out_hf.seq_relationship_logits
    prediction_scores_hf[~attention_mask, :] = 0.0
    out_ref = model_ref(input_ids, attention_mask=attention_mask)
    prediction_scores_ref, seq_relationship_scores_ref = out_ref.prediction_logits, out_ref.seq_relationship_logits
    prediction_scores_ref[~attention_mask, :] = 0.0

    print(f'prediction_scores max diff: {(prediction_scores - prediction_scores_ref).abs().max().item()}')
    print(f'prediction_scores mean diff: {(prediction_scores - prediction_scores_ref).abs().mean().item()}')
    print(f'HF fp16 prediction_scoresff: {(prediction_scores_hf - prediction_scores_ref).abs().max().item()}')
    print(f'HF fp16 prediction_scoresiff: {(prediction_scores_hf - prediction_scores_ref).abs().mean().item()}')
    assert (prediction_scores - prediction_scores_ref).abs().max().item() < 2 * (prediction_scores_hf - prediction_scores_ref).abs().max().item()
    assert (seq_relationship_scores - seq_relationship_scores_ref).abs().max().item() < 2 * (seq_relationship_scores_hf - seq_relationship_scores_ref).abs().max().item()


@pytest.mark.parametrize('model_name', ["bert-base-uncased", "bert-large-uncased"])
# @pytest.mark.parametrize('model_name', ["bert-base-uncased"])
def test_bert_dense_seq_output(model_name):
    """Check that our implementation of BERT (with all optimizations enabled) matches the
    HF implementation: the output of our forward pass in fp16 should be around the same as the HF
    forward pass in fp16, when compared to the HF forward pass in fp32.
    """
    dtype = torch.float16
    config = BertConfig.from_pretrained(model_name)
    # Our implementation assumes the activation is nn.GELU(approximate='tanh')
    # Huggingface calls it "gelu_new" or "gelu_fast".
    config.hidden_act = "gelu_new"
    config.use_flash_attn = True
    config.fused_bias_fc = True
    config.fused_dense_gelu_dense = True
    config.fused_dropout_add_ln = True
    config.dense_seq_output = True
    config.use_xentropy = True

    model = BertForPreTraining.from_pretrained(model_name, config)
    model = model.cuda().to(dtype=dtype)

    model_ref = get_hf_models(model_name, config, torch.float32)
    model_hf = get_hf_models(model_name, config, torch.float16)

    model.eval()
    model_ref.eval()
    model_hf.eval()

    torch.manual_seed(0)
    batch_size = 4
    max_seqlen = 512
    seqlens = torch.randint(max_seqlen // 2, max_seqlen + 1, (batch_size,), device='cuda')
    attention_mask = torch.arange(max_seqlen, device='cuda')[None, :] < seqlens[:, None]
    input_ids = torch.randint(0, config.vocab_size, (batch_size, max_seqlen), dtype=torch.long,
                              device='cuda')
    labels = torch.randint(0, config.vocab_size, (batch_size, max_seqlen), dtype=torch.long,
                           device='cuda')
    labels[(torch.rand(batch_size, max_seqlen, device='cuda') < 0.15) | ~attention_mask] = 0
    masked_tokens_mask = labels.flatten() > 0
    next_sequence_label = torch.randint(0, 2, (batch_size,), device='cuda')

    total_loss, prediction_scores, seq_relationship_scores, _, _ = model(
        input_ids, attention_mask=attention_mask,
        labels=labels, next_sentence_label=next_sequence_label
    )
    out_hf = model_hf(input_ids, attention_mask=attention_mask,
                      labels=labels, next_sentence_label=next_sequence_label)
    prediction_scores_hf, seq_relationship_scores_hf = out_hf.prediction_logits, out_hf.seq_relationship_logits
    prediction_scores_hf = rearrange(prediction_scores_hf, 'b s d -> (b s) d')[masked_tokens_mask]
    out_ref = model_ref(input_ids, attention_mask=attention_mask,
                        labels=labels, next_sentence_label=next_sequence_label)
    prediction_scores_ref, seq_relationship_scores_ref = out_ref.prediction_logits, out_ref.seq_relationship_logits
    prediction_scores_ref = rearrange(prediction_scores_ref, 'b s d -> (b s) d')[masked_tokens_mask]

    print(f'prediction_scores max diff: {(prediction_scores - prediction_scores_ref).abs().max().item()}')
    print(f'prediction_scores mean diff: {(prediction_scores - prediction_scores_ref).abs().mean().item()}')
    print(f'HF fp16 prediction_scoresff: {(prediction_scores_hf - prediction_scores_ref).abs().max().item()}')
    print(f'HF fp16 prediction_scoresiff: {(prediction_scores_hf - prediction_scores_ref).abs().mean().item()}')
    assert (prediction_scores - prediction_scores_ref).abs().max().item() < 2 * (prediction_scores_hf - prediction_scores_ref).abs().max().item()