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[Bugfix] handle alignment of arguments in convert_sparse_cross_attention_mask_to_dense (#12347)

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Signed-off-by: Travis Johnson <tsjohnso@us.ibm.com>
Signed-off-by: Wallas Santos <wallashss@ibm.com>
Co-authored-by: Wallas Santos <wallashss@ibm.com>
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Travis Johnson 2025-01-29 01:54:35 -07:00 committed by GitHub
parent ef001d98ef
commit 036ca94c25
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2 changed files with 222 additions and 4 deletions
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208
tests/models/encoder_decoder/vision_language/test_mllama.py
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@ -1,11 +1,15 @@
from typing import List, Optional, Tuple, Type, overload
import pytest
import torch
from transformers import (AutoConfig, AutoModelForVision2Seq, AutoTokenizer,
BatchEncoding)
from vllm.attention.backends.flash_attn import FlashAttentionMetadata
from vllm.attention.selector import (_Backend, _cached_get_attn_backend,
global_force_attn_backend_context_manager)
from vllm.model_executor.models.mllama import (MLLAMA_IMAGE_TOKEN_ID,
MllamaForConditionalGeneration)
from vllm.multimodal.image import rescale_image_size
from vllm.sequence import SampleLogprobs
@ -33,6 +37,29 @@ models = [
"meta-llama/Llama-3.2-11B-Vision-Instruct",
]
# Indices for inputs
TEXT_ONLY = '0'
IMAGE_AT_BEG = '1'
IMAGE_AT_MIDDLE = '2'
TWO_IMAGES = '3'
# Input tokenized
prompt_data = {
# Tell me a story
TEXT_ONLY: [41551, 757, 264, 3446],
# <|image|> What's the content of this image
IMAGE_AT_BEG:
[MLLAMA_IMAGE_TOKEN_ID, 3639, 596, 279, 2262, 315, 420, 2217, 220],
# Hello <|image|>What' the content of this image
IMAGE_AT_MIDDLE:
[9906, 220, MLLAMA_IMAGE_TOKEN_ID, 3923, 6, 279, 2262, 315, 420, 2217],
#<|image|>Is there a duck in this image?<|image|>What's the animal in this image? # noqa: E501
TWO_IMAGES: [
MLLAMA_IMAGE_TOKEN_ID, 3957, 1070, 264, 37085, 304, 420, 2217, 30,
MLLAMA_IMAGE_TOKEN_ID, 3923, 596, 279, 10065, 304, 420, 2217, 30
]
}
def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
Optional[SampleLogprobs]],
@ -365,3 +392,184 @@ def test_models_interleaved_images(hf_runner, vllm_runner, image_assets, model,
num_logprobs=num_logprobs,
tensor_parallel_size=1,
)
@large_gpu_test(min_gb=48)
@pytest.mark.core_model
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize("attn_backend", LIST_ENC_DEC_SUPPORTED_BACKENDS)
def test_regression(vllm_runner, image_assets, model, dtype, max_tokens,
num_logprobs, attn_backend: _Backend) -> None:
stop_sign = image_assets[0].pil_image
with global_force_attn_backend_context_manager(attn_backend), vllm_runner(
model,
dtype=dtype,
max_model_len=4096,
max_num_seqs=2,
tensor_parallel_size=1,
enforce_eager=True,
limit_mm_per_prompt={"image":
_LIMIT_IMAGE_PER_PROMPT}) as vllm_model:
# Regression tests for https://github.com/vllm-project/vllm/issues/10648
# Number of image tags is greater than the number of images provided
prompt = "<|begin_of_text|><|image|><|image|> Compare the two images" # noqa: E501
image = stop_sign
with pytest.raises(ValueError):
vllm_model.generate_greedy_logprobs([prompt],
max_tokens,
num_logprobs,
images=[image])
# Batch of a text-only and image request that requires cross-attention
prompts = [
"What is the capital of spain?",
"Text before the image...<|image|>What is in the image?", # noqa: E501
]
images = [
None,
[stop_sign],
]
vllm_model.generate_greedy_logprobs(prompts,
max_tokens,
num_logprobs,
images=images)
# Test the reverse order too for good measure
prompts = [
"<|begin_of_text|>Text before the image...<|image|>What is in the image?", # noqa: E501
"<|begin_of_text|>Hello!",
]
images = [
[stop_sign],
None,
]
vllm_model.generate_greedy_logprobs(prompts,
max_tokens,
num_logprobs,
images=images)
@pytest.mark.core_model
@pytest.mark.parametrize(
"input_indices_and_output",
# inputs, (cross_attention_mask, kv_range_for_decode)
[([TEXT_ONLY], (None, None)), ([IMAGE_AT_BEG], (None, None)),
([TEXT_ONLY, IMAGE_AT_BEG], (None, None)),
([IMAGE_AT_MIDDLE], ((10, 12), [[0, 6]])),
([TEXT_ONLY, IMAGE_AT_MIDDLE], ((14, 12), [[0, 6]])),
([TEXT_ONLY, IMAGE_AT_BEG, IMAGE_AT_MIDDLE],
((23, 24), [[0, 6], [6, 12]])),
([IMAGE_AT_MIDDLE, TEXT_ONLY], ((14, 12), [[0, 6]])),
([TWO_IMAGES], ((18, 12), [[6, 12]])),
([TEXT_ONLY, TWO_IMAGES], ((22, 12), [[6, 12]]))])
def test_get_cross_attention_mask(input_indices_and_output) -> None:
input_indices, expected_output = input_indices_and_output
sequences = [torch.tensor(prompt_data[i]) for i in input_indices]
num_tiles = [[2, 2] if i != TEXT_ONLY else [] for i in input_indices
if i != TEXT_ONLY]
input = torch.cat(sequences)
seq_lens = [len(s) for s in sequences]
attn_data = FlashAttentionMetadata(
seq_lens=seq_lens,
# Dummy values
enable_kv_scales_calculation=False,
num_prefills=0,
num_prefill_tokens=0,
num_decode_tokens=0,
slot_mapping=0,
multi_modal_placeholder_index_maps=None,
seq_lens_tensor=0,
max_prefill_seq_len=0,
max_decode_seq_len=0,
context_lens_tensor=None,
block_tables=None,
use_cuda_graph=False,
)
dummy: dict[str, str] = {}
cross_attention_mask, kv_range_for_decode = MllamaForConditionalGeneration\
.get_cross_attention_mask(dummy,
input,
attn_data,
num_tiles=num_tiles,
num_tokens_per_tile=3,
dtype=torch.bfloat16)
expected_cross_attention_mask, expected_kv_range_for_decode = \
expected_output
assert kv_range_for_decode == expected_kv_range_for_decode
if expected_cross_attention_mask is not None:
assert cross_attention_mask is not None
assert cross_attention_mask.shape == expected_cross_attention_mask
else:
assert cross_attention_mask is None
@pytest.mark.core_model
@pytest.mark.parametrize(
"input_indices",
[[TEXT_ONLY], [IMAGE_AT_BEG], [TEXT_ONLY, IMAGE_AT_BEG], [IMAGE_AT_MIDDLE],
[TEXT_ONLY, IMAGE_AT_MIDDLE], [TEXT_ONLY, IMAGE_AT_BEG, IMAGE_AT_MIDDLE],
[IMAGE_AT_MIDDLE, TEXT_ONLY], [TWO_IMAGES], [TEXT_ONLY, TWO_IMAGES]])
def test_get_full_text_row_masked_out_mask(input_indices) -> None:
sequences = [torch.tensor(prompt_data[i]) for i in input_indices]
seq_lens = [len(s) for s in sequences]
num_prefill_tokens = sum(seq_lens)
# TEXT_ONLY is zero, so it will be masked out,
# other instances should not be.
encoder_seq_lens = [int(i) for i in input_indices]
attn_data = FlashAttentionMetadata(
seq_lens=seq_lens,
encoder_seq_lens=encoder_seq_lens,
num_prefill_tokens=num_prefill_tokens,
# Dummy values
enable_kv_scales_calculation=False,
num_prefills=0,
num_decode_tokens=0,
slot_mapping=0,
multi_modal_placeholder_index_maps=None,
seq_lens_tensor=0,
max_prefill_seq_len=0,
max_decode_seq_len=0,
context_lens_tensor=None,
block_tables=None,
use_cuda_graph=False,
)
dummy: dict[str, str] = {}
full_text_row_masked_out_mask = MllamaForConditionalGeneration\
.get_full_text_row_masked_out_mask(dummy,
attn_data,
torch.get_default_device())
full_text_row_masked_out_mask = full_text_row_masked_out_mask.squeeze()
full_text_row_masked_out_mask = full_text_row_masked_out_mask.tolist()
idx = 0
assert len(full_text_row_masked_out_mask) == num_prefill_tokens
for i, seq_len in enumerate(seq_lens):
must_be_masked = input_indices[i] != TEXT_ONLY
for _ in range(seq_len):
assert full_text_row_masked_out_mask[idx] == must_be_masked, \
f"full_text_row_masked_out_mask[{idx}] must be " \
f"'{must_be_masked}' "
idx += 1

18
vllm/model_executor/models/mllama.py
View File

@ -1485,14 +1485,23 @@ def convert_sparse_cross_attention_mask_to_dense(
total_length = sum(lengths)
total_tiles = sum([sum(tiles) for tiles in num_tiles])
dense_mask = np.zeros(shape=(total_length, total_tiles), dtype=np.int64)
# A list of ranges, range[i] = [start, end] means
# if the i-th sample has N tiles in total, the tiles[start, end]
# will be used for cross-attention decoding.
# A list of ranges, range[i] = [start, end] means that the i-th image will
# use tiles[start, end] for cross-attention decoding.
tile_range_for_decode = []
seq_start = 0
tile_start = 0
for masks, tiles, length in zip(sparse_mask, num_tiles, lengths):
# sparse_mask has an [] entry for each sequence that does not have images,
# but num_tiles does not have these entries...
num_tiles_idx = 0
for masks, length in zip(sparse_mask, lengths):
if len(masks) == 0:
# Text only
continue
tiles = num_tiles[num_tiles_idx]
num_tiles_idx += 1
ts, td = -1, 0
for mask, tile in zip(masks, tiles):
if len(mask) != 2:
@ -1512,6 +1521,7 @@ def convert_sparse_cross_attention_mask_to_dense(
assert td != 0
tile_range_for_decode.append((ts, ts + td))
seq_start += length
assert num_tiles_idx == len(num_tiles)
return dense_mask, tile_range_for_decode