[Fix] Fixes speculative decode indexing and unpad condition for attention metadata (#5626)

### What this PR does / why we need it?
This addresses the issue brought up by #5356 and #4963, and we believe
the unnecessary conditions are the root cause.

Change the unpad trigger to be driven by actual size mismatches
(num_reqs vs base_num_reqs or scheduled vs input token counts) rather
than specific speculative-method flags. Then remove brittle workarounds
that forced request counts and sliced query start locations.

This prevents incorrect indexing and length mismatches during
speculative decoding and makes metadata unpadding more robust across
scheduling modes.

### Does this PR introduce _any_ user-facing change?
None.

### How was this patch tested?
Tested by existing cases.

- vLLM version: v0.13.0
- vLLM main:
8be6432bda

---------

Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>

vllm_ascend/spec_decode/eagle_proposer.py

@@ -749,13 +749,6 @@ class EagleProposer(VllmEagleProposer):
             num_reqs = common_attn_metadata.num_reqs
             device = valid_sampled_tokens_count.device
 
-            if num_reqs != spec_decode_metadata.cu_num_draft_tokens.shape[0]:
-                # TODO: This is a serious issue and should be taken care of ASAP
-                # In short, why input_batch.num_reqs != attn_metadata.num_reqs?
-                # Previously in #4963, we modified `query_start_loc`, but this
-                # problem remains unsolved.
-                num_reqs = spec_decode_metadata.cu_num_draft_tokens.shape[0]
-
             token_indices_to_sample = torch.empty((num_reqs, ),
                                                   dtype=torch.int32,
                                                   device=device)
@@ -787,9 +780,9 @@ class EagleProposer(VllmEagleProposer):
                 torch.zeros_like(num_draft_tokens_gpu),
             )
 
-            query_start_loc = common_attn_metadata.query_start_loc[
-                1:1 + num_rejected_tokens_gpu.shape[0]]
-            token_indices_to_sample = query_start_loc - 1 - num_rejected_tokens_gpu
+            token_indices_to_sample = (
+                common_attn_metadata.query_start_loc[1:] - 1 -
+                num_rejected_tokens_gpu)
 
         query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu