Fix invalid logprobs with MTP enabled and sync scheduling (#38711)

Signed-off-by: Daniel Serebrenik <daserebrenik@nvidia.com>

vllm/v1/worker/gpu_model_runner.py

@@ -4192,6 +4192,7 @@ class GPUModelRunner(
         spec_config = self.speculative_config
         propose_drafts_after_bookkeeping = False
         if spec_config is not None:
+            # Decide whether to run the drafter or zero out draft tokens.
             input_fits_in_drafter = spec_decode_common_attn_metadata is not None and (
                 spec_decode_common_attn_metadata.max_seq_len + self.num_spec_tokens
                 <= self.effective_drafter_max_model_len
@@ -4227,10 +4228,6 @@ class GPUModelRunner(
                     self._copy_valid_sampled_token_count(
                         next_token_ids, valid_sampled_tokens_count
                     )
-                    self._draft_token_ids = torch.zeros(
-                        1, device=self.device, dtype=torch.int32
-                    ).expand(len(self.input_batch.req_ids), self.num_spec_tokens)
-                    self._copy_draft_token_ids_to_cpu(scheduler_output, zeros_only=True)
             elif (
                 spec_config.use_ngram_gpu()
                 and not spec_config.disable_padded_drafter_batch
@@ -4253,14 +4250,19 @@ class GPUModelRunner(
                     self._copy_valid_sampled_token_count(
                         next_token_ids, valid_sampled_tokens_count
                     )
-                    # Since we couldn't run the drafter,
-                    # just use zeros for the draft tokens.
+            else:
+                propose_drafts_after_bookkeeping = input_fits_in_drafter
+
+            if not input_fits_in_drafter:
+                # Zero out draft tokens so the scheduler doesn't schedule
+                # stale drafts from the previous step.
+                # For Nemotron-H: it is necessary to zero out the draft tokens,
+                # otherwise the stale tokens will corrupt Mamba recurrent
+                # state and logprobs for sequences near max_model_len.
                 self._draft_token_ids = torch.zeros(
                     1, device=self.device, dtype=torch.int32
                 ).expand(len(self.input_batch.req_ids), self.num_spec_tokens)
                 self._copy_draft_token_ids_to_cpu(scheduler_output, zeros_only=True)
-            else:
-                propose_drafts_after_bookkeeping = input_fits_in_drafter
 
         with record_function_or_nullcontext("gpu_model_runner: bookkeep"):
             (