[BUGFIX] Fix Pixtral consolidated format vision weight loading (#39916)

Signed-off-by: Julien Denize <julien.denize@mistral.ai>
Signed-off-by: juliendenize <julien.denize@mistral.ai>

tests/models/fixtures/ministral_3b_chat.json

+[[[4380, 3937, 6122, 1261, 7244, 10575, 18970, 41132, 3923, 1408, 1261, 32656, 4691, 1454, 2246, 22131, 15179, 11521, 17277, 1046, 2], "This image shows a black dog sitting attentively on a wooden surface with its gaze directed straight ahead.", [{"4380": {"logprob": -1.0445597171783447, "rank": 1, "decoded_token": "This"}, "1784": {"logprob": -1.5445597171783447, "rank": 2, "decoded_token": "The"}, "1065": {"logprob": -1.7945597171783447, "rank": 3, "decoded_token": "A"}, "1785": {"logprob": -2.2945597171783447, "rank": 4, "decoded_token": "In"}, "4051": {"logprob": -4.357059478759766, "rank": 5, "decoded_token": "An"}}, {"3937": {"logprob": -0.012832445092499256, "rank": 1, "decoded_token": " image"}, "13083": {"logprob": -6.8253326416015625, "rank": 2, "decoded_token": " picture"}, "16649": {"logprob": -6.8878326416015625, "rank": 3, "decoded_token": " photo"}, "1395": {"logprob": -7.2003326416015625, "rank": 4, "decoded_token": " is"}, "7244": {"logprob": -7.5128326416015625, 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The first image shows a black dog sitting attentively on a wooden surface.\n   - The second image (not shown in your question) appears to be a mountain landscape with rugged terrain and distant peaks.\n\n2. The third image depicts a serene beach scene with gentle waves meeting the shore and a lone person walking along the shore.\n\n3. The fourth image features a winding gravel path bordered by grass and trees in a picturesque outdoor setting.\n\n4. The fifth image (not applicable as per the initial request) would be the one with the dog again, but since it's already described, here\u2019s a different focus if we consider the following hypothetical:\n   - The initial provided image is a black dog looking up with a calm expression on a rustic wooden plank background.\n\nTo clarify, here\u2019s the correct description for the provided black dog image:\nA black dog gazes intently upward with a calm expression on a rustic wooden plank background.", [{"1049": {"logprob": -0.3098178803920746, "rank": 1, "decoded_token": "1"}, "1784": {"logprob": -2.1848177909851074, "rank": 2, "decoded_token": "The"}, "11745": {"logprob": -2.6223177909851074, "rank": 3, "decoded_token": "Here"}, "2757": {"logprob": -6.059817790985107, "rank": 4, "decoded_token": "It"}, "69957": {"logprob": -6.122317790985107, "rank": 5, "decoded_token": "Sure"}}, {"1046": {"logprob": -0.16265615820884705, "rank": 1, "decoded_token": "."}, "1626": {"logprob": 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"decoded_token": " wide"}, "26517": {"logprob": -3.888719081878662, "rank": 5, "decoded_token": " calm"}}, {"26517": {"logprob": -1.2183846235275269, "rank": 1, "decoded_token": " calm"}, "2169": {"logprob": -2.8433847427368164, "rank": 2, "decoded_token": " ser"}, "16318": {"logprob": -3.0933847427368164, "rank": 3, "decoded_token": " neutral"}, "14781": {"logprob": -3.2183847427368164, "rank": 4, "decoded_token": " focused"}, "6444": {"logprob": -3.2183847427368164, "rank": 5, "decoded_token": " soft"}}, {"4818": {"logprob": -0.4964141249656677, "rank": 1, "decoded_token": " expression"}, "1321": {"logprob": -1.9339141845703125, "rank": 2, "decoded_token": " and"}, "1311": {"logprob": -2.4339141845703125, "rank": 3, "decoded_token": " de"}, "1044": {"logprob": -3.3089141845703125, "rank": 4, "decoded_token": ","}, "2985": {"logprob": -3.4964141845703125, "rank": 5, "decoded_token": " look"}}, {"1408": {"logprob": -0.8785426616668701, "rank": 1, "decoded_token": " on"}, "1562": {"logprob": -2.06604266166687, "rank": 2, "decoded_token": " from"}, "18970": {"logprob": -2.31604266166687, "rank": 3, "decoded_token": " sitting"}, "3675": {"logprob": -2.62854266166687, "rank": 4, "decoded_token": " against"}, "38235": {"logprob": -3.06604266166687, "rank": 5, "decoded_token": " resting"}}, {"1261": {"logprob": -0.3605937659740448, "rank": 1, "decoded_token": " a"}, "32656": {"logprob": -2.923093795776367, "rank": 2, "decoded_token": " wooden"}, "17253": {"logprob": -3.110593795776367, "rank": 3, "decoded_token": " weather"}, "3403": {"logprob": -3.173093795776367, "rank": 4, "decoded_token": " text"}, "44130": {"logprob": -3.298093795776367, "rank": 5, "decoded_token": " rust"}}, {"44130": {"logprob": -1.4187138080596924, "rank": 1, "decoded_token": " rust"}, "32656": {"logprob": -1.4812138080596924, "rank": 2, "decoded_token": " wooden"}, "3403": {"logprob": -1.6687138080596924, "rank": 3, "decoded_token": " text"}, "17253": {"logprob": -2.4812138080596924, "rank": 4, "decoded_token": " weather"}, "8500": {"logprob": -4.168713569641113, "rank": 5, "decoded_token": " dark"}}, {"1290": {"logprob": -0.28350138664245605, "rank": 1, "decoded_token": "ic"}, "1970": {"logprob": -2.283501386642456, "rank": 2, "decoded_token": "led"}, "1121": {"logprob": -2.971001386642456, "rank": 3, "decoded_token": "y"}, "2981": {"logprob": -4.283501625061035, "rank": 4, "decoded_token": "ically"}, "11395": {"logprob": -4.471001625061035, "rank": 5, "decoded_token": "iced"}}, {"32656": {"logprob": -0.7727869749069214, "rank": 1, "decoded_token": " wooden"}, "1044": {"logprob": -1.4602869749069214, "rank": 2, "decoded_token": ","}, "3403": {"logprob": -2.585287094116211, "rank": 3, "decoded_token": " text"}, "1615": {"logprob": -2.835287094116211, "rank": 4, "decoded_token": " pl"}, "12603": {"logprob": -3.335287094116211, "rank": 5, "decoded_token": " wood"}}, {"1615": {"logprob": -0.7352191805839539, "rank": 1, "decoded_token": " pl"}, "4691": {"logprob": -2.1102192401885986, "rank": 2, "decoded_token": " surface"}, "9710": {"logprob": -2.6102192401885986, "rank": 3, "decoded_token": " board"}, "7042": {"logprob": -2.9852192401885986, "rank": 4, "decoded_token": " background"}, "92504": {"logprob": -3.1102192401885986, "rank": 5, "decoded_token": " backdrop"}}, {"2395": {"logprob": -0.13373544812202454, "rank": 1, "decoded_token": "ank"}, "5933": {"logprob": -2.133735418319702, "rank": 2, "decoded_token": "anks"}, "122370": {"logprob": -6.883735656738281, "rank": 3, "decoded_token": "anking"}, "11847": {"logprob": -8.071235656738281, "rank": 4, "decoded_token": "anned"}, "2077": {"logprob": -8.071235656738281, "rank": 5, "decoded_token": "ink"}}, {"7042": {"logprob": -1.197163701057434, "rank": 1, "decoded_token": " background"}, "4691": {"logprob": -1.572163701057434, "rank": 2, "decoded_token": " surface"}, "92504": {"logprob": -1.884663701057434, "rank": 3, "decoded_token": " backdrop"}, "26228": {"logprob": -3.3221635818481445, "rank": 4, "decoded_token": " texture"}, "9436": {"logprob": -3.3846635818481445, "rank": 5, "decoded_token": " setting"}}, {"1046": {"logprob": -0.13945358991622925, "rank": 1, "decoded_token": "."}, "1338": {"logprob": -2.576953649520874, "rank": 2, "decoded_token": ".\n\n"}, "1294": {"logprob": -4.764453411102295, "rank": 3, "decoded_token": " in"}, "1044": {"logprob": -5.701953411102295, "rank": 4, "decoded_token": ","}, "1319": {"logprob": -5.889453411102295, "rank": 5, "decoded_token": " ("}}, {"2": {"logprob": -0.5447223782539368, "rank": 1, "decoded_token": "</s>"}, "1319": {"logprob": -2.607222318649292, "rank": 2, "decoded_token": " ("}, "9380": {"logprob": -3.669722318649292, "rank": 3, "decoded_token": " Here"}, "1531": {"logprob": -3.919722318649292, "rank": 4, "decoded_token": " The"}, "2898": {"logprob": -4.107222557067871, "rank": 5, "decoded_token": " For"}}]]]
\ No newline at end of file

tests/models/multimodal/generation/test_pixtral.py

@@ -25,6 +25,7 @@ if TYPE_CHECKING:
 
 PIXTRAL_ID = "mistralai/Pixtral-12B-2409"
 MISTRAL_SMALL_3_1_ID = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
+MINISTRAL_3B_ID = "mistralai/Ministral-3-3B-Instruct-2512"
 
 MODELS = [PIXTRAL_ID, MISTRAL_SMALL_3_1_ID]
 
@@ -116,6 +117,7 @@ assert FIXTURES_PATH.exists()
 FIXTURE_LOGPROBS_CHAT = {
     PIXTRAL_ID: FIXTURES_PATH / "pixtral_chat.json",
     MISTRAL_SMALL_3_1_ID: FIXTURES_PATH / "mistral_small_3_chat.json",
+    MINISTRAL_3B_ID: FIXTURES_PATH / "ministral_3b_chat.json",
 }
 
 OutputsLogprobs = list[tuple[list[int], str, SampleLogprobs | None]]
@@ -209,3 +211,41 @@ def test_chat(
         name_0="h100_ref",
         name_1="output",
     )
+
+
+@large_gpu_test(min_gb=16)
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+def test_chat_consolidated(vllm_runner, dtype: str, local_asset_server) -> None:
+    EXPECTED_CHAT_LOGPROBS = load_outputs_w_logprobs(
+        FIXTURE_LOGPROBS_CHAT[MINISTRAL_3B_ID]
+    )
+    with vllm_runner(
+        MINISTRAL_3B_ID,
+        dtype=dtype,
+        tokenizer_mode="mistral",
+        load_format="mistral",
+        config_format="mistral",
+        max_model_len=8192,
+        limit_mm_per_prompt=LIMIT_MM_PER_PROMPT,
+    ) as vllm_model:
+        outputs = []
+        urls_all = [local_asset_server.url_for(u) for u in IMG_URLS]
+        msgs = [
+            _create_msg_format(urls_all[:1]),
+            _create_msg_format(urls_all[:2]),
+            _create_msg_format(urls_all),
+        ]
+        for msg in msgs:
+            output = vllm_model.llm.chat(msg, sampling_params=SAMPLING_PARAMS)
+            outputs.extend(output)
+
+    logprobs = vllm_runner._final_steps_generate_w_logprobs(outputs)
+    for i in range(len(logprobs)):
+        assert logprobs[i][-1] is None
+        logprobs[i] = logprobs[i][:-1]
+    check_logprobs_close(
+        outputs_0_lst=EXPECTED_CHAT_LOGPROBS,
+        outputs_1_lst=logprobs,
+        name_0="h100_ref",
+        name_1="output",
+    )

vllm/model_executor/models/pixtral.py

@@ -458,13 +458,27 @@ class PixtralForConditionalGeneration(
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
         _vision_encoder_stacked_params = [
             # (param_name, shard_name, shard_id)
+            # HF format
             (".qkv_proj", ".q_proj", "q"),
             (".qkv_proj", ".k_proj", "k"),
             (".qkv_proj", ".v_proj", "v"),
             (".gate_up_proj", ".gate_proj", 0),
             (".gate_up_proj", ".up_proj", 1),
+            # Mistral native (consolidated) format
+            (".qkv_proj", ".wq", "q"),
+            (".qkv_proj", ".wk", "k"),
+            (".qkv_proj", ".wv", "v"),
+            (".gate_up_proj", ".w1", 0),
+            (".gate_up_proj", ".w3", 1),
         ]
 
+        # Remap Mistral native names to HF-style names
+        # used by the vLLM vision encoder modules.
+        _vision_encoder_name_remap = {
+            ".wo.": ".o_proj.",
+            ".w2.": ".down_proj.",
+        }
+
         def is_vision_encoder_weights(weight: tuple[str, torch.Tensor]):
             return weight[0].startswith(("vision_encoder", "vision_tower"))
 
@@ -518,6 +532,11 @@ class PixtralForConditionalGeneration(
                             weight_loader(param, w, shard_id)
                             break
                     else:
+                        for old, new in _vision_encoder_name_remap.items():
+                            if old in trimmed_name:
+                                trimmed_name = trimmed_name.replace(old, new)
+                                break
+
                         param = vision_encoder_dict.get(trimmed_name)
                         if param is not None:
                             weight_loader = getattr(