[Hotfix][Pixtral] Fix multiple images bugs (vllm-project#8415)

Signed-off-by: Amit Garg <mitgarg17495@gmail.com>

tests/models/test_pixtral.py

@@ -2,63 +2,167 @@
 
 Run `pytest tests/models/test_mistral.py`.
 """
+import pickle
+import uuid
+from typing import Any, Dict, List
+
 import pytest
+from mistral_common.protocol.instruct.messages import ImageURLChunk
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.tokens.tokenizers.multimodal import image_from_chunk
+
+from vllm import EngineArgs, LLMEngine, SamplingParams, TokensPrompt
+from vllm.multimodal import MultiModalDataBuiltins
 
-from vllm.sampling_params import SamplingParams
+from .utils import check_logprobs_close
 
 pytestmark = pytest.mark.vlm
 
 MODELS = ["mistralai/Pixtral-12B-2409"]
+IMG_URLS = [
+    "https://picsum.photos/id/237/400/300",
+    "https://picsum.photos/id/231/200/300",
+    "https://picsum.photos/id/27/500/500",
+    "https://picsum.photos/id/17/150/600",
+]
+PROMPT = "Describe each image in one short sentence."
+
+
+def _create_msg_format(urls: List[str]) -> List[Dict[str, Any]]:
+    return [{
+        "role":
+        "user",
+        "content": [{
+            "type": "text",
+            "text": PROMPT,
+        }] + [{
+            "type": "image_url",
+            "image_url": {
+                "url": url
+            }
+        } for url in urls],
+    }]
+
+
+def _create_engine_inputs(urls: List[str]) -> TokensPrompt:
+    msg = _create_msg_format(urls)
+
+    tokenizer = MistralTokenizer.from_model("pixtral")
+
+    request = ChatCompletionRequest(messages=msg)  # type: ignore[type-var]
+    tokenized = tokenizer.encode_chat_completion(request)
+
+    engine_inputs = TokensPrompt(prompt_token_ids=tokenized.tokens)
+
+    images = []
+    for chunk in request.messages[0].content:
+        if isinstance(chunk, ImageURLChunk):
+            images.append(image_from_chunk(chunk))
+
+    mm_data = MultiModalDataBuiltins(image=images)
+    engine_inputs["multi_modal_data"] = mm_data
+
+    return engine_inputs
+
+
+MSGS = [
+    _create_msg_format(IMG_URLS[:1]),
+    _create_msg_format(IMG_URLS[:2]),
+    _create_msg_format(IMG_URLS),
+]
+ENGINE_INPUTS = [
+    _create_engine_inputs(IMG_URLS[:1]),
+    _create_engine_inputs(IMG_URLS[:2]),
+    _create_engine_inputs(IMG_URLS),
+]
+
+SAMPLING_PARAMS = SamplingParams(max_tokens=512, temperature=0.0, logprobs=5)
+LIMIT_MM_PER_PROMPT = dict(image=4)
+
+MAX_MODEL_LEN = [8192, 65536]
+FIXTURE_LOGPROBS_CHAT = "tests/models/fixtures/pixtral_chat.pickle"
+FIXTURE_LOGPROBS_ENGINE = "tests/models/fixtures/pixtral_chat_engine.pickle"
+
+
+def load_logprobs(filename: str) -> Any:
+    with open(filename, 'rb') as f:
+        return pickle.load(f)
 
 
 @pytest.mark.skip(
     reason=
     "Model is too big, test passed on A100 locally but will OOM on CI machine."
 )
 @pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_model_len", MAX_MODEL_LEN)
 @pytest.mark.parametrize("dtype", ["bfloat16"])
-@pytest.mark.parametrize("max_tokens", [64])
-@pytest.mark.parametrize("num_logprobs", [5])
-def test_models(
+def test_chat(
     vllm_runner,
-    example_prompts,
+    max_model_len: int,
     model: str,
     dtype: str,
-    max_tokens: int,
-    num_logprobs: int,
 ) -> None:
-    image_urls = [
-        "https://picsum.photos/id/237/200/300",
-        "https://picsum.photos/seed/picsum/200/300"
-    ]
-    expected = [
-        "The image depicts a black dog lying on a wooden surface, looking directly at the camera with a calm expression.",  # noqa
-        "The image depicts a serene landscape with a snow-covered mountain under a pastel-colored sky during sunset."  # noqa
-    ]
-    prompt = "Describe the image in one short sentence."
-
-    sampling_params = SamplingParams(max_tokens=512, temperature=0.0)
-
-    with vllm_runner(model, dtype=dtype,
-                     tokenizer_mode="mistral") as vllm_model:
-
-        for i, image_url in enumerate(image_urls):
-            messages = [
-                {
-                    "role":
-                    "user",
-                    "content": [{
-                        "type": "text",
-                        "text": prompt
-                    }, {
-                        "type": "image_url",
-                        "image_url": {
-                            "url": image_url
-                        }
-                    }]
-                },
-            ]
-
-            outputs = vllm_model.model.chat(messages,
-                                            sampling_params=sampling_params)
-            assert outputs[0].outputs[0].text == expected[i]
+    EXPECTED_CHAT_LOGPROBS = load_logprobs(FIXTURE_LOGPROBS_CHAT)
+    with vllm_runner(
+            model,
+            dtype=dtype,
+            tokenizer_mode="mistral",
+            enable_chunked_prefill=False,
+            max_model_len=max_model_len,
+            limit_mm_per_prompt=LIMIT_MM_PER_PROMPT,
+    ) as vllm_model:
+        outputs = []
+        for msg in MSGS:
+            output = vllm_model.model.chat(msg,
+                                           sampling_params=SAMPLING_PARAMS)
+
+            outputs.extend(output)
+
+    logprobs = vllm_runner._final_steps_generate_w_logprobs(outputs)
+    check_logprobs_close(outputs_0_lst=logprobs,
+                         outputs_1_lst=EXPECTED_CHAT_LOGPROBS,
+                         name_0="output",
+                         name_1="h100_ref")
+
+
+@pytest.mark.skip(
+    reason=
+    "Model is too big, test passed on A100 locally but will OOM on CI machine."
+)
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+def test_model_engine(vllm_runner, model: str, dtype: str) -> None:
+    EXPECTED_ENGINE_LOGPROBS = load_logprobs(FIXTURE_LOGPROBS_ENGINE)
+    args = EngineArgs(
+        model=model,
+        tokenizer_mode="mistral",
+        enable_chunked_prefill=False,
+        limit_mm_per_prompt=LIMIT_MM_PER_PROMPT,
+        dtype=dtype,
+    )
+    engine = LLMEngine.from_engine_args(args)
+
+    engine.add_request(uuid.uuid4().hex, ENGINE_INPUTS[0], SAMPLING_PARAMS)
+    engine.add_request(uuid.uuid4().hex, ENGINE_INPUTS[1], SAMPLING_PARAMS)
+
+    outputs = []
+    count = 0
+    while True:
+        out = engine.step()
+        count += 1
+        for request_output in out:
+            if request_output.finished:
+                outputs.append(request_output)
+
+        if count == 2:
+            engine.add_request(uuid.uuid4().hex, ENGINE_INPUTS[2],
+                               SAMPLING_PARAMS)
+        if not engine.has_unfinished_requests():
+            break
+
+    logprobs = vllm_runner._final_steps_generate_w_logprobs(outputs)
+    check_logprobs_close(outputs_0_lst=logprobs,
+                         outputs_1_lst=EXPECTED_ENGINE_LOGPROBS,
+                         name_0="output",
+                         name_1="h100_ref")

vllm/model_executor/models/pixtral.py

@@ -1,4 +1,3 @@
-import math
 from array import array
 from dataclasses import dataclass, fields
 from itertools import tee
@@ -15,11 +14,12 @@
 
 from vllm.attention import AttentionMetadata
 from vllm.config import CacheConfig, MultiModalConfig
-from vllm.inputs import INPUT_REGISTRY, InputContext
+from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.utils import merge_multimodal_embeddings
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.base import MultiModalInputs
@@ -48,23 +48,29 @@ def dummy_data_for_pixtral(ctx: InputContext, seq_len: int,
     tokenizer = cached_get_tokenizer(
         ctx.model_config.tokenizer,
         tokenizer_mode=ctx.model_config.tokenizer_mode)
-    mm_encoder = tokenizer.instruct.mm_encoder
 
-    mm_config = ctx.model_config.multimodal_config
-    max_num_images_per_request = mm_config.limit_per_prompt.get("image", 1)
+    mm_encoder = tokenizer.mistral.instruct_tokenizer.mm_encoder
+    patch_size = mm_encoder.mm_config.image_patch_size
+    image_token_id = mm_encoder.special_ids.img
 
-    # approximate image size
-    size = int(math.sqrt(seq_len) * mm_encoder.mm_config.image_patch_size)
+    mm_config = ctx.model_config.multimodal_config
+    num_images = mm_config.limit_per_prompt.get("image", 1)
 
+    # dummy size
+    size = 256
     image = Image.new("RGB", (size, size), color=0)
-    img_chunk = ImageChunk(image=image)
 
-    tokens = mm_encoder(img_chunk).tokens
-    token_ids = max_num_images_per_request * array(VLLM_TOKEN_ID_ARRAY_TYPE,
-                                                   tokens)
+    image_feature_size = (size**2) // (patch_size**2)
+
+    num_image_tokens = image_feature_size * num_images
+
+    token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE,
+                      [image_token_id]) * num_image_tokens
+    token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
+                       [0]) * (seq_len - num_image_tokens)
 
     seq_data = SequenceData(token_ids)
-    mm_data = {"image": max_num_images_per_request * [image]}
+    mm_data = {"image": num_images * [image]}
     return seq_data, mm_data
 
 
@@ -99,32 +105,31 @@ def input_mapper_for_pixtral(ctx: InputContext,
     return MultiModalInputs({"images": images})
 
 
-def merge_multimodal_embeddings(input_ids: torch.Tensor,
-                                inputs_embeds: torch.Tensor,
-                                image_features: Optional[List[torch.Tensor]],
-                                image_id: int) -> torch.Tensor:
-    text_locations = input_ids != image_id
-    image_locations = input_ids == image_id
-
-    seq_len = input_ids.shape[0]
+def input_processor_for_pixtral(ctx: InputContext, llm_inputs: LLMInputs):
+    multi_modal_data = llm_inputs.get("multi_modal_data")
+    if multi_modal_data is not None and "image" in multi_modal_data:
+        tokenizer = cached_get_tokenizer(
+            ctx.model_config.tokenizer,
+            tokenizer_mode=ctx.model_config.tokenizer_mode)
 
-    N_txt = text_locations.sum().item()
-    _, D_txt = inputs_embeds.shape
-    N_img, D_img = image_features.shape
+        mm_encoder = tokenizer.mistral.instruct_tokenizer.mm_encoder
+        image_token_id = mm_encoder.special_ids.img
 
-    assert (D_txt == D_img), (f"Text features dim {D_txt} should be equal "
-                              "to image features dim {D_img}")
-    assert (seq_len == N_txt +
-            N_img), (f"seq_len {seq_len} should be equal to N_txt + N_img "
-                     f"{(N_txt, N_img, image_locations.sum().item())}")
+        if image_token_id not in llm_inputs['prompt_token_ids']:
+            raise ValueError(
+                (f"You've passed {llm_inputs=} without {image_token_id=}"
+                 " Make sure to process your input via mistral_common's"
+                 " tokenizer or pass a chat completion request. For more"
+                 " For more info, see: "
+                 "https://github.com/vllm-project/vllm/issues/8411."))
 
-    inputs_embeds[image_locations, :] = image_features
-    return inputs_embeds
+    return llm_inputs
 
 
 @MULTIMODAL_REGISTRY.register_image_input_mapper(input_mapper_for_pixtral)
 @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_pixtral_image_tokens)
 @INPUT_REGISTRY.register_dummy_data(dummy_data_for_pixtral)
+@INPUT_REGISTRY.register_input_processor(input_processor_for_pixtral)
 class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal):
 
     def __init__(self,
@@ -201,11 +206,21 @@ def _parse_and_validate_image_input(
             return None
 
         if isinstance(images, torch.Tensor):
-            # always take last images
-            images = [images[-1][i] for i in range(images.size(1))]
+            # if passed as batch take all images
+            N, B, C, W, H = images.shape
+            images = images.reshape(N * B, C, W, H)
+            images = [images[i] for i in range(images.size(0))]
         elif isinstance(images, list):
-            # always take last images
-            images = [images[-1][i] for i in range(len(images[0]))]
+            # if passed as list flatten lists of tensors
+            flatten_images = []
+            for imgs_per_req in images:
+                imgs_per_req = [
+                    imgs_per_req[i] for i in range(imgs_per_req.size(0))
+                ] if isinstance(imgs_per_req, torch.Tensor) else imgs_per_req
+
+                flatten_images.extend(imgs_per_req)
+
+            images = flatten_images
 
         return images