[XPU] Fix Eagle3 initialization on XPU (#43957)

Signed-off-by: Chaojun Zhang <chaojun.zhang@intel.com>
Co-authored-by: Andreas Karatzas <akaratza@amd.com>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
Co-authored-by: Kunshang Ji <kunshang.ji@intel.com>

.buildkite/intel_jobs/misc_intel.yaml

@@ -76,6 +76,26 @@ steps:
       pytest -v -s v1/sample/test_logprobs.py &&
       pytest -v -s v1/sample/test_logprobs_e2e.py'
 
+- label: Basic Models Tests (Initialization)
+  timeout_in_minutes: 60
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/
+    - tests/models/test_initialization.py
+    - tests/models/registry.py
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'export VLLM_XPU_FUSED_MOE_USE_REF=1 &&
+      cd tests &&
+      pytest -v -s models/test_initialization.py::test_can_initialize_large_subset[Eagle3MiniMaxM2ForCausalLM]'
+
 - label: XPU CPU Offload
   timeout_in_minutes: 60
   device: intel_gpu

vllm/model_executor/models/llama_eagle3.py

@@ -233,7 +233,10 @@ class LlamaModel(nn.Module):
     ) -> tuple[torch.Tensor, torch.Tensor]:
         if input_embeds is None:
             input_embeds = self.embed_input_ids(input_ids)
-        assert hidden_states.shape[-1] == input_embeds.shape[-1]
+        torch._assert(
+            hidden_states.shape[-1] == input_embeds.shape[-1],
+            "hidden_states and input_embeds must have the same last dimension",
+        )
 
         residual = None
         for layer in self.layers:

vllm/v1/spec_decode/llm_base_proposer.py

@@ -1468,6 +1468,26 @@ class SpecDecodeBaseProposer:
                     "Sharing target model embedding weights with the draft model."
                 )
 
+            if share_embeddings:
+                draft_embed = self.model.model.embed_tokens
+                # Only share when both models use the same embedding width.
+                # Guard with isinstance so non-Tensor weights (e.g. in tests)
+                # are not affected — mirrors the weight-equality check above.
+                if isinstance(target_embed_tokens.weight, torch.Tensor) and isinstance(
+                    draft_embed.weight, torch.Tensor
+                ):
+                    target_dim = target_embed_tokens.weight.shape[-1]
+                    draft_dim = draft_embed.weight.shape[-1]
+                    if target_dim != draft_dim:
+                        share_embeddings = False
+                        logger.info(
+                            "Target embedding dim (%d) differs from draft "
+                            "embedding dim (%d). Keeping separate embedding "
+                            "weights.",
+                            target_dim,
+                            draft_dim,
+                        )
+
             if share_embeddings:
                 if hasattr(self.model.model, "embed_tokens"):
                     del self.model.model.embed_tokens