[BUGFIX] Fix Pixtral consolidated format vision weight loading

tests/models/multimodal/generation/test_pixtral.py

@@ -25,6 +25,7 @@
 
 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 @@ def _create_engine_inputs_hf(urls: list[str]) -> TextPrompt:
 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 @@ def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
     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 @@ def llm_weights_generator():
                             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(