diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py
index 4bea37c853031..5a185e7451ade 100644
--- a/vllm/worker/model_runner.py
+++ b/vllm/worker/model_runner.py
@@ -1785,24 +1785,32 @@ class ModelRunner(GPUModelRunnerBase[ModelInputForGPUWithSamplingMetadata]):
 
         if model_input.inputs_embeds is not None:
             if self.is_driver_worker:
-                sampled = broadcast_tensor_dict(
-                    {"token_ids": output.sampled_token_ids})
+                sampled_token_ids = []
+                valid_outputs = []
+                for sequence_group_output in output.outputs:
+                    if len(sequence_group_output.samples) == 0:
+                        continue
+                    assert len(sequence_group_output.samples) == 1
+                    valid_outputs.append(sequence_group_output)
+                    sampled_token_ids.append(
+                        sequence_group_output.samples[0].output_token)
+                sampled_token_ids = torch.tensor(sampled_token_ids).to(
+                    self.device)
+                sampled_token_ids = broadcast_tensor_dict(
+                    {"sampled_token_ids":
+                     sampled_token_ids})["sampled_token_ids"]
             else:
-                sampled = broadcast_tensor_dict()
-            if sampled["token_ids"] is not None:
-                sampled_token_embeds = self.model.get_input_embeddings(
-                    sampled["token_ids"].squeeze(1))
+                sampled_token_ids = broadcast_tensor_dict(
+                )["sampled_token_ids"]
+            if len(sampled_token_ids) > 0:
+                sampled_token_embeds = \
+                    self.model.get_input_embeddings(sampled_token_ids)
                 if self.is_driver_worker:
                     self.sampler.include_gpu_probs_tensor = \
                         orig_include_gpu_probs
-
-                    output.sampled_token_embeds = sampled_token_embeds
-
-                    for token_embed, sequence_group_output in zip(
-                            output.sampled_token_embeds, output.outputs):
-                        assert len(sequence_group_output.samples) == 1
-                        sequence_group_output.samples[
-                            0].output_embed = token_embed
+                    for i, sequence_group_output in enumerate(valid_outputs):
+                        sequence_group_output.samples[0].output_embed = \
+                            sampled_token_embeds[i]
 
         if not self.is_driver_worker:
             return []