[Bugfix] Comprehensively test and fix LLaVA-NeXT feature size calculation (#11800)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

requirements-test.in

@@ -13,6 +13,7 @@ einops # required for MPT, qwen-vl and Mamba
 httpx
 librosa # required for audio tests
 peft
+pqdm
 ray[adag]==2.40.0
 sentence-transformers # required for embedding tests
 soundfile # required for audio tests

requirements-test.txt

@@ -48,6 +48,8 @@ botocore==1.35.57
     #   awscli
     #   boto3
     #   s3transfer
+bounded-pool-executor==0.0.3
+    # via pqdm
 buildkite-test-collector==0.1.9
     # via -r requirements-test.in
 certifi==2024.8.30
@@ -342,6 +344,8 @@ pooch==1.8.2
     # via librosa
 portalocker==2.10.1
     # via sacrebleu
+pqdm==0.2.0
+    # via -r requirements-test.in
 propcache==0.2.0
     # via yarl
 protobuf==5.28.3

tests/models/decoder_only/vision_language/processing/test_llava_next.py

@@ -1,8 +1,13 @@
+import itertools
+from functools import partial
+
 import pytest
 from PIL import Image
+from pqdm.threads import pqdm
 from transformers import AutoTokenizer
 
 from vllm.inputs import InputProcessingContext
+from vllm.multimodal.parse import ImageSize
 
 from ....utils import build_model_context
 
@@ -15,20 +20,69 @@ def processor_for_llava_next():
     return LlavaNextMultiModalProcessor
 
 
+def _validate_image_prompt_replacements_one(
+    processor,
+    num_imgs: int,
+    failed_size_excs: list[tuple[ImageSize, Exception]],
+    image_size: ImageSize,
+) -> None:
+    prompt = "<image>" * num_imgs
+    image = Image.new("RGB", size=image_size)
+    mm_data = {"image": [image] * num_imgs}
+
+    try:
+        # The processor will throw an error if there is a mismatch
+        # in the prompt replacements
+        processed_inputs = processor.apply(prompt, mm_data, {})
+
+        image_placeholders = processed_inputs["mm_placeholders"]["image"]
+        assert len(image_placeholders) == num_imgs
+
+        first_placeholder = image_placeholders[0]
+
+        # NOTE: There is a BOS token
+        assert first_placeholder["offset"] == 1
+        assert first_placeholder["length"] == (
+            len(processed_inputs["prompt_token_ids"]) - 1) // num_imgs
+
+    except Exception as exc:
+        failed_size_excs.append((image_size, exc))
+
+
+def _test_image_prompt_replacements(
+    processor,
+    *,
+    num_imgs: int,
+    image_sizes: list[ImageSize],
+) -> None:
+    """
+    Ensure LlavaNextMultiModalProcessor
+    handles prompt replacement properly for input images.
+    """
+    failed_size_excs = list[tuple[ImageSize, Exception]]()
+
+    validate_one = partial(
+        _validate_image_prompt_replacements_one,
+        processor,
+        num_imgs,
+        failed_size_excs,
+    )
+    pqdm(image_sizes, validate_one, n_jobs=8, desc="Validating image sizes")
+
+    if failed_size_excs:
+        msg = "Found failing image sizes:" \
+            + "\n========\n".join(f"[{size}]\n{exc}"
+                                  for size, exc in failed_size_excs)
+        raise AssertionError(msg)
+
+
 @pytest.mark.parametrize("model_id", ["llava-hf/llava-v1.6-mistral-7b-hf"])
-@pytest.mark.parametrize("image_size", [(1669, 2560), (2560, 1669), (183, 488),
-                                        (488, 183), (198, 176), (176, 198),
-                                        (161, 184), (184, 161)])
 @pytest.mark.parametrize("num_imgs", [1, 2])
-def test_processor_prompt_replacements(
+def test_processor_prompt_replacements_regression(
     processor_for_llava_next,
     model_id: str,
-    image_size: tuple[int, int],
     num_imgs: int,
 ):
-    """
-    Ensure LlavaNextMultiModalProcessor handles prompt replacement properly.
-    """
     ctx = build_model_context(
         model_name=model_id,
         tokenizer_name=model_id,
@@ -37,22 +91,55 @@ def test_processor_prompt_replacements(
     )
     tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
     ctx = InputProcessingContext(ctx.model_config, tokenizer)
-
-    # Build the image str / prompt based on the number of images we pass
-    prompt = "<image>" * num_imgs
-    mm_data = {"image": [Image.new("RGB", size=image_size)] * num_imgs}
-
-    # The processor will throw an error if there is a mismatch
-    # in the prompt replacements
     processor = processor_for_llava_next(ctx)
-    processed_inputs = processor.apply(prompt, mm_data, {})
 
-    image_placeholders = processed_inputs["mm_placeholders"]["image"]
-    assert len(image_placeholders) == num_imgs
+    image_ratios = [(171, 152), (184, 161), (198, 176), (333, 296), (369, 328),
+                    (488, 183), (2560, 1669)]
+    image_sizes = [
+        size for w, h in image_ratios
+        for size in [ImageSize(w, h), ImageSize(h, w)]
+    ]
 
-    first_placeholder = image_placeholders[0]
+    _test_image_prompt_replacements(
+        processor,
+        num_imgs=num_imgs,
+        image_sizes=image_sizes,
+    )
 
-    # NOTE: There is a BOS token
-    assert first_placeholder["offset"] == 1
-    assert first_placeholder["length"] == (
-        len(processed_inputs["prompt_token_ids"]) - 1) // num_imgs
+
+@pytest.mark.skip("This test takes around 2 hours to run. "
+                  "Comment this out to run it manually.")
+@pytest.mark.parametrize("model_id", ["llava-hf/llava-v1.6-mistral-7b-hf"])
+@pytest.mark.parametrize("num_imgs", [1])
+def test_processor_prompt_replacements_all(
+    processor_for_llava_next,
+    model_id: str,
+    num_imgs: int,
+):
+    ctx = build_model_context(
+        model_name=model_id,
+        tokenizer_name=model_id,
+        mm_processor_kwargs=None,
+        limit_mm_per_prompt={"image": num_imgs},
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+    ctx = InputProcessingContext(ctx.model_config, tokenizer)
+    processor = processor_for_llava_next(ctx)
+
+    seen_aspect_ratios = set[float]()
+    image_sizes = list[ImageSize]()
+
+    # The aspect ratio of the grid layout is between 1 and 2
+    # NOTE: Assumes that feature size calculation is the same if we
+    # swap the width and height of the image
+    for w, h in itertools.product(range(64, 1024), repeat=2):
+        aspect_ratio = w / h
+        if 1 <= aspect_ratio <= 2 and aspect_ratio not in seen_aspect_ratios:
+            image_sizes.append(ImageSize(w, h))
+            seen_aspect_ratios.add(aspect_ratio)
+
+    _test_image_prompt_replacements(
+        processor,
+        num_imgs=num_imgs,
+        image_sizes=image_sizes,
+    )

tests/models/decoder_only/vision_language/processing/test_llava_onevision.py

@@ -1,8 +1,13 @@
+import itertools
+from functools import partial
+
 import pytest
 from PIL import Image
+from pqdm.threads import pqdm
 from transformers import AutoTokenizer
 
 from vllm.inputs import InputProcessingContext
+from vllm.multimodal.parse import ImageSize
 
 from ....utils import build_model_context
 
@@ -15,22 +20,68 @@ def processor_for_llava_onevision():
     return LlavaOnevisionMultiModalProcessor
 
 
+def _validate_image_prompt_replacements_one(
+    processor,
+    num_imgs: int,
+    failed_size_excs: list[tuple[ImageSize, Exception]],
+    image_size: ImageSize,
+) -> None:
+    prompt = "<image>" * num_imgs
+    image = Image.new("RGB", size=image_size)
+    mm_data = {"image": [image] * num_imgs}
+
+    try:
+        # The processor will throw an error if there is a mismatch
+        # in the prompt replacements
+        processed_inputs = processor.apply(prompt, mm_data, {})
+
+        image_placeholders = processed_inputs["mm_placeholders"]["image"]
+        assert len(image_placeholders) == num_imgs
+
+        first_placeholder = image_placeholders[0]
+
+        assert first_placeholder["offset"] == 0
+        assert first_placeholder["length"] == len(
+            processed_inputs["prompt_token_ids"]) // num_imgs
+    except Exception as exc:
+        failed_size_excs.append((image_size, exc))
+
+
+def _test_image_prompt_replacements(
+    processor,
+    *,
+    num_imgs: int,
+    image_sizes: list[ImageSize],
+) -> None:
+    """
+    Ensure LlavaOnevisionMultiModalProcessor
+    handles prompt replacement properly for input images.
+    """
+    failed_size_excs = list[tuple[ImageSize, Exception]]()
+
+    validate_one = partial(
+        _validate_image_prompt_replacements_one,
+        processor,
+        num_imgs,
+        failed_size_excs,
+    )
+    pqdm(image_sizes, validate_one, n_jobs=8, desc="Validating image sizes")
+
+    if failed_size_excs:
+        msg = "Found failing image sizes:" \
+            + "\n========\n".join(f"[{size}]\n{exc}"
+                                  for size, exc in failed_size_excs)
+        raise AssertionError(msg)
+
+
 @pytest.mark.parametrize("model_id",
                          ["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"])
-@pytest.mark.parametrize("image_size", [(1669, 2560), (2560, 1669), (183, 488),
-                                        (488, 183), (198, 176), (176, 198),
-                                        (161, 184), (184, 161)])
 @pytest.mark.parametrize("num_imgs", [1, 2])
-def test_processor_prompt_replacements(
+def test_processor_prompt_replacements_regression(
     processor_for_llava_onevision,
     model_id: str,
-    image_size: tuple[int, int],
     num_imgs: int,
 ):
-    """
-    Ensure LlavaOnevisionMultiModalProcessor handles prompt replacement
-    properly.
-    """
     ctx = build_model_context(
         model_name=model_id,
         tokenizer_name=model_id,
@@ -39,22 +90,56 @@ def test_processor_prompt_replacements(
     )
     tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
     ctx = InputProcessingContext(ctx.model_config, tokenizer)
-
-    # Build the image str / prompt based on the number of images we pass
-    prompt = "<image>" * num_imgs
-    mm_data = {"image": [Image.new("RGB", size=image_size)] * num_imgs}
-
-    # The processor will throw an error if there is a mismatch
-    # in the prompt replacements
     processor = processor_for_llava_onevision(ctx)
-    processed_inputs = processor.apply(prompt, mm_data, {})
 
-    image_placeholders = processed_inputs["mm_placeholders"]["image"]
-    assert len(image_placeholders) == num_imgs
+    image_ratios = [(171, 152), (184, 161), (198, 176), (333, 296), (369, 328),
+                    (488, 183), (2560, 1669)]
+    image_sizes = [
+        size for w, h in image_ratios
+        for size in [ImageSize(w, h), ImageSize(h, w)]
+    ]
 
-    first_placeholder = image_placeholders[0]
+    _test_image_prompt_replacements(
+        processor,
+        num_imgs=num_imgs,
+        image_sizes=image_sizes,
+    )
 
-    # NOTE: There is a BOS token
-    assert first_placeholder["offset"] == 0
-    assert first_placeholder["length"] == len(
-        processed_inputs["prompt_token_ids"]) // num_imgs
+
+@pytest.mark.skip("This test takes around 2 hours to run. "
+                  "Comment this out to run it manually.")
+@pytest.mark.parametrize("model_id",
+                         ["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"])
+@pytest.mark.parametrize("num_imgs", [1])
+def test_processor_prompt_replacements_all(
+    processor_for_llava_onevision,
+    model_id: str,
+    num_imgs: int,
+):
+    ctx = build_model_context(
+        model_name=model_id,
+        tokenizer_name=model_id,
+        mm_processor_kwargs=None,
+        limit_mm_per_prompt={"image": num_imgs},
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+    ctx = InputProcessingContext(ctx.model_config, tokenizer)
+    processor = processor_for_llava_onevision(ctx)
+
+    seen_aspect_ratios = set[float]()
+    image_sizes = list[ImageSize]()
+
+    # The aspect ratio of the grid layout is between 1 and 6
+    # NOTE: Assumes that feature size calculation is the same if we
+    # swap the width and height of the image
+    for w, h in itertools.product(range(64, 1024), repeat=2):
+        aspect_ratio = w / h
+        if 1 <= aspect_ratio <= 6 and aspect_ratio not in seen_aspect_ratios:
+            image_sizes.append(ImageSize(w, h))
+            seen_aspect_ratios.add(aspect_ratio)
+
+    _test_image_prompt_replacements(
+        processor,
+        num_imgs=num_imgs,
+        image_sizes=image_sizes,
+    )

vllm/model_executor/models/llava_next.py

@@ -2,7 +2,6 @@ from functools import cached_property
 from typing import (Final, Iterable, List, Literal, Mapping, Optional,
                     Protocol, Set, Tuple, TypedDict, Union)
 
-import numpy as np
 import torch
 import torch.nn as nn
 from transformers import BatchFeature, LlavaNextConfig, LlavaNextProcessor
@@ -74,7 +73,7 @@ class LlavaNextProcessingMixin(BaseLlavaProcessingMixin):
     def _get_hf_processor(self):
         return self.ctx.get_hf_processor(LlavaNextProcessor)
 
-    # Based on: https://github.com/huggingface/text-generation-inference/blob/v2.2.0/server/text_generation_server/models/vlm_causal_lm.py#L106
+    # Based on: https://github.com/huggingface/text-generation-inference/blob/v3.0.1/server/text_generation_server/models/vlm_causal_lm.py#L113
     def _get_num_image_tokens(
         self,
         *,
@@ -111,7 +110,7 @@ class LlavaNextProcessingMixin(BaseLlavaProcessingMixin):
 
         return unpadded_feature_size + newline_feature_size + base_feature_size
 
-    # Based on: https://github.com/huggingface/text-generation-inference/blob/v2.2.0/server/text_generation_server/models/vlm_causal_lm.py#L79
+    # Based on: https://github.com/huggingface/text-generation-inference/blob/v3.0.1/server/text_generation_server/models/vlm_causal_lm.py#L86
     def _get_num_unpadded_features(
         self,
         *,
@@ -121,29 +120,23 @@ class LlavaNextProcessingMixin(BaseLlavaProcessingMixin):
         num_patch_height: int,
         num_patch_width: int,
     ) -> tuple[int, int]:
-        # NOTE: Use float32 to remain consistent with HF output
-        current_height_f = np.float32(npatches * num_patch_height)
-        current_width_f = np.float32(npatches * num_patch_width)
+        current_height = npatches * num_patch_height
+        current_width = npatches * num_patch_width
 
-        original_width_f = np.float32(original_width)
-        original_height_f = np.float32(original_height)
+        aspect_ratio = original_width / original_height
+        current_aspect_ratio = current_width / current_height
 
-        original_aspect_ratio = original_width_f / original_height_f
-        current_aspect_ratio = current_width_f / current_height_f
-
-        if original_aspect_ratio > current_aspect_ratio:
-            scale_factor = current_width_f / original_width_f
-            new_height = int(original_height_f * scale_factor)
-            padding = (current_height_f - new_height) // 2
-            current_height_f -= 2 * padding
+        if aspect_ratio > current_aspect_ratio:
+            new_height = (original_height * current_width) // original_width
+            padding = (current_height - new_height) // 2
+            current_height = current_height - (2 * padding)
         else:
-            scale_factor = current_height_f / original_height_f
-            new_width = int(original_width_f * scale_factor)
-            padding = (current_width_f - new_width) // 2
-            current_width_f -= 2 * padding
+            new_width = (original_width * current_height) // original_height
+            padding = (current_width - new_width) // 2
+            current_width = current_width - (2 * padding)
 
-        unpadded_features = int(current_height_f * current_width_f)
-        newline_features = int(current_height_f)
+        unpadded_features = current_height * current_width
+        newline_features = current_height
 
         return (unpadded_features, newline_features)
 

vllm/model_executor/models/llava_onevision.py

@@ -3,7 +3,6 @@ from functools import cached_property
 from typing import (Final, Iterable, List, Literal, Mapping, Optional,
                     Protocol, Set, Tuple, TypedDict, Union)
 
-import numpy as np
 import torch
 import torch.nn as nn
 from transformers import (BatchFeature, LlavaOnevisionConfig,
@@ -98,6 +97,8 @@ class LlavaOnevisionProcessingMixin(LlavaNextProcessingMixin):
     def _get_hf_processor(self):
         return self.ctx.get_hf_processor(LlavaOnevisionProcessor)
 
+    # Based on: https://github.com/huggingface/text-generation-inference/blob/v3.0.1/server/text_generation_server/models/vlm_causal_lm.py#L86
+    # with additional logic afterwards taken from LlavaOnevisionProcessor
     def _get_num_unpadded_features(
         self,
         *,
@@ -107,35 +108,28 @@ class LlavaOnevisionProcessingMixin(LlavaNextProcessingMixin):
         num_patch_height: int,
         num_patch_width: int,
     ) -> tuple[int, int]:
-        # NOTE: Use float32 to remain consistent with HF output
-        current_height_f = np.float32(npatches * num_patch_height)
-        current_width_f = np.float32(npatches * num_patch_width)
+        current_height = npatches * num_patch_height
+        current_width = npatches * num_patch_width
 
-        original_width_f = np.float32(original_width)
-        original_height_f = np.float32(original_height)
+        aspect_ratio = original_width / original_height
+        current_aspect_ratio = current_width / current_height
 
-        original_aspect_ratio = original_width_f / original_height_f
-        current_aspect_ratio = current_width_f / current_height_f
-
-        if original_aspect_ratio > current_aspect_ratio:
-            scale_factor = current_width_f / original_width_f
-            new_height = int(original_height_f * scale_factor)
-            padding = (current_height_f - new_height) // 2
-            current_height_f -= 2 * padding
+        if aspect_ratio > current_aspect_ratio:
+            new_height = (original_height * current_width) // original_width
+            padding = (current_height - new_height) // 2
+            current_height = current_height - (2 * padding)
         else:
-            scale_factor = current_height_f / original_height_f
-            new_width = int(original_width_f * scale_factor)
-            padding = (current_width_f - new_width) // 2
-            current_width_f -= 2 * padding
+            new_width = (original_width * current_height) // original_height
+            padding = (current_width - new_width) // 2
+            current_width = current_width - (2 * padding)
 
-        unpadded_features = int(current_height_f * current_width_f)
-        newline_features = int(current_height_f)
+        unpadded_features = current_height * current_width
+        newline_features = current_height
 
-        ratio = math.sqrt(current_height_f * current_width_f /
-                          (9 * npatches**2))
+        ratio = math.sqrt(current_height * current_width / (9 * npatches**2))
         if ratio > 1.1:
-            height_factor = int(current_height_f // ratio)
-            width_factor = int(current_width_f // ratio)
+            height_factor = int(current_height // ratio)
+            width_factor = int(current_width // ratio)
             unpadded_features = height_factor * width_factor
             newline_features = height_factor