[Bugfix] Fix the acceptance rates dorp issue when applying eagle3 to QuaRot model (#6914)

### What this PR does / why we need it?
When using the target model after rotational quantization, the
acceptance rate decreases because the fc weight of the draft model has
not undergone rotational quantization(issue: #6445). We fixed this issue
by performing rotation quantization on the fc weight of the draft model
in the same way as the main model when loading draft model.

- vLLM version: v0.16.0
- vLLM main:
15d76f74e2

Signed-off-by: zhaomingyu <zhaomingyu13@h-partners.com>

vllm_ascend/patch/__init__.py

@@ -305,3 +305,14 @@
 #       https://github.com/vllm-project/vllm/pull/34336
 #    Future Plan:
 #       Remove this patch when vLLM merges the PR.
+# ** 16. File: worker/patch_qwen3_quarot.py**
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#   1. `vllm.model_executor.models.llama_eagle3.Eagle3LlamaForCausalLM.load_weights`
+#    Why:
+#       vllm-ascend reused the loading logic of drafter model from vllm,
+#       but vllm doesn't need to apply to Ascend quantization.
+#    How：
+#       Dynamically replace the `load_weights` function at runtime,
+#       and fix `target_config` into the new implementation with a closure.
+#    Future Plan:
+#       Remove this patch when vLLM merges the PR.

vllm_ascend/patch/worker/__init__.py

@@ -35,3 +35,4 @@ import vllm_ascend.patch.worker.patch_huanyuan_vl  # noqa
 import vllm_ascend.patch.worker.patch_routed_experts_capturer  # noqa
 import vllm_ascend.patch.worker.patch_npugraph_ex_triton  # noqa
 import vllm_ascend.patch.worker.patch_kimi_k25  # noqa
+import vllm_ascend.patch.worker.patch_qwen3_quarot  # noqa

vllm_ascend/patch/worker/patch_qwen3_quarot.py

@@ -0,0 +1,79 @@
+import logging
+from collections.abc import Iterable
+from pathlib import Path
+
+import torch
+from safetensors.torch import load_file
+from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
+from vllm.model_executor.models.utils import (
+    AutoWeightsLoader,
+    process_eagle_weight,
+)
+
+
+def patch_load_weights(target_config):
+    Eagle3LlamaForCausalLM.load_weights = make_load_weights(target_config)
+
+
+def make_load_weights(target_config):
+    logger = logging.getLogger(__name__)
+    quant_cfg = target_config.quant_config
+    rotation_matrix3 = None
+
+    model_path = target_config.model_config.model
+    try:
+        rotation_rel_path = quant_cfg.quant_description["optional"]["quarot"]["rotation_map"]["global_rotation"]
+    except KeyError as e:
+        logger.error(
+            "Invalid quant_config: missing key "
+            "quant_description['optional']['quarot']['rotation_map']['global_rotation']. "
+            "If you don't use quarot model, please ignore it. "
+            f"Error: {e}"
+        )
+    else:
+        rotation_path = Path(model_path) / rotation_rel_path
+        try:
+            safetensor_data = load_file(rotation_path)
+            Q = safetensor_data["global_rotation"]
+            rotation_matrix3 = torch.block_diag(Q, Q, Q)
+        except Exception as e:
+            logger.error(
+                f"Failed to load rotation weight from '{rotation_path}'. "
+                "If you don't use quarot model, please ignore it. "
+                f"Error: {e}"
+            )
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        model_weights = {}
+        includes_draft_id_mapping = False
+        includes_embed_tokens = False
+        for name, loaded_weight in weights:
+            if "t2d" in name:
+                continue
+            if "d2t" in name:
+                name = name.replace("d2t", "draft_id_to_target_id")
+                includes_draft_id_mapping = True
+            elif "lm_head" not in name:
+                name = "model." + name
+            if "fc." in name and rotation_matrix3 is not None:
+                loaded_weight = loaded_weight @ rotation_matrix3.to(loaded_weight.dtype)
+            if "embed_tokens" in name:
+                includes_embed_tokens = True
+            model_weights[name] = loaded_weight
+            process_eagle_weight(self, name)
+
+        skip_substrs = []
+        if not includes_draft_id_mapping:
+            skip_substrs.append("draft_id_to_target_id")
+        if not includes_embed_tokens:
+            skip_substrs.append("embed_tokens")
+        if not self.model.use_aux_hidden_state:
+            skip_substrs.append("fc.")
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=None,
+            skip_substrs=skip_substrs,
+        )
+        loader.load_weights(model_weights.items())
+
+    return load_weights

vllm_ascend/worker/model_runner_v1.py

@@ -106,6 +106,7 @@ from vllm_ascend.eplb.eplb_updator import EplbUpdator
 from vllm_ascend.eplb.utils import model_register
 from vllm_ascend.ops.rotary_embedding import set_cos_and_sin, update_cos_sin
 from vllm_ascend.patch.worker.patch_module import patch_torch_npu_argsort
+from vllm_ascend.patch.worker.patch_qwen3_quarot import patch_load_weights
 from vllm_ascend.sample.sampler import AscendSampler
 from vllm_ascend.spec_decode import get_spec_decode_method
 from vllm_ascend.spec_decode.eagle_proposer import EagleProposer
@@ -2422,6 +2423,8 @@ class NPUModelRunner(GPUModelRunner):
                 model_register(self.model)
             if self.drafter:
                 logger.info("Loading drafter model...")
+                if self.vllm_config.quant_config is not None:
+                    patch_load_weights(self.vllm_config)
                 with get_tp_context(self.drafter):
                     self.drafter.load_model(self.model)
                 if self.use_aux_hidden_state_outputs: