[SpecDecode] Fix Draft model proposer (#7230)

### What this PR does / why we need it?
This pr fix the Unified draft parallel feature. 
1. In Draft model proposer, there are exceed 1 attention layers in
target model, thus removing the assertion on layer number.
2. we should get block size through `draft_attn_groups` instead of
`attn_metadata_builder` after 0.17.0.
3. `attn_update_stack_num_spec_norm` shouldn't be done when unified
draft parallel is enabled

### How was this patch tested?
Test pass with
`tests/e2e/singlecard/spec_decode/test_v1_spec_decode.py::test_parallel_drafting_acceptance`,
which is already included in CI

- vLLM version: v0.17.0
- vLLM main:
4034c3d32e

Signed-off-by: MengqingCao <cmq0113@163.com>

vllm_ascend/spec_decode/eagle_proposer.py

@@ -195,7 +195,6 @@ class SpecDecodeBaseProposer(EagleProposer):
         all_indexer_layer_names = set(get_layers_from_vllm_config(self.vllm_config, DeepseekV32IndexerCache).keys())
         self._draft_attn_layer_names = set(all_attn_layers.keys()) - target_attn_layer_names - all_indexer_layer_names
 
-        assert len(self._draft_attn_layer_names) == 1
         self.attn_layer_names = list(sorted(self._draft_attn_layer_names))
         draft_attn_layers_dict = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
         self.kernel_block_size = (
@@ -699,6 +698,7 @@ class SpecDecodeBaseProposer(EagleProposer):
                         multi_steps_attn_metadata.append(per_layer_attn_metadata)
         else:
             # Copy the old attn_metadata and update
+            if not self.parallel_drafting:
                 for draft_step in range(1, self.num_speculative_tokens):
                     per_layer_attn_metadata = dict()
                     if vllm_version_is("0.17.0"):
@@ -1064,16 +1064,21 @@ class SpecDecodeBaseProposer(EagleProposer):
             # 2.
             # Recompute the slot mapping based on the new positions and
             # rejection mask.
-            builder = (
-                self._get_attention_metadata_builder()
-                if self.attn_metadata_builder is None
-                else self.attn_metadata_builder
-            )
+            if vllm_version_is("0.17.0"):
+                # Use the first draft attention group's kv_cache_spec for block_size
+                # (all draft layers share the same kv-cache group)
+                assert len(self.draft_attn_groups) > 0
+                block_size = self.draft_attn_groups[0].kv_cache_spec.block_size
+            else:
+                if self.attn_metadata_builder is None:
+                    block_size = self._get_attention_metadata_builder().kv_cache_spec.block_size
+                else:
+                    block_size = self.attn_metadata_builder.kv_cache_spec.block_size
             new_slot_mapping = compute_new_slot_mapping(
                 cad=cad,
                 new_positions=self.positions[:total_num_output_tokens],
                 is_rejected_token_mask=self.is_rejected_token_mask[:total_num_output_tokens],
-                block_size=builder.kv_cache_spec.block_size,
+                block_size=block_size,
                 num_new_tokens=self.net_num_new_slots_per_request,
                 max_model_len=self.max_model_len,
             )
@@ -1152,14 +1157,14 @@ class SpecDecodeBaseProposer(EagleProposer):
         # out-of-range access during the model execution. The draft tokens
         # generated with this adjustment should be ignored.
         if self.uses_mrope:
-            exceeds_max_model_len = used_update_positions[0] >= self.vllm_config.model_config.max_model_len
+            exceeds_max_model_len = used_update_positions[0] >= self.max_model_len
             # Mask out the position ids that exceed the max model length.
             # Otherwise, we may get out-of-range error in RoPE.
             clamped_positions = torch.where(
                 exceeds_max_model_len.unsqueeze(0), torch.zeros_like(used_update_positions), used_update_positions
             )
         else:
-            exceeds_max_model_len = used_update_positions >= self.vllm_config.model_config.max_model_len
+            exceeds_max_model_len = used_update_positions >= self.max_model_len
             clamped_positions = torch.where(exceeds_max_model_len, 0, used_update_positions)
 
         # For data integrity when async scheduling, we shouldn't use in place

vllm_ascend/worker/model_runner_v1.py

@@ -74,7 +74,6 @@ from vllm.v1.outputs import (
 from vllm.v1.sample.logits_processor import build_logitsprocs
 from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.sample.rejection_sampler import RejectionSampler
-from vllm.v1.spec_decode.draft_model import DraftModelProposer
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
 from vllm.v1.structured_output.utils import apply_grammar_bitmask
 from vllm.v1.utils import record_function_or_nullcontext
@@ -2561,7 +2560,7 @@ class NPUModelRunner(GPUModelRunner):
             if self.speculative_config and (
                 self.speculative_config.use_eagle() or self.speculative_config.uses_draft_model()
             ):
-                assert isinstance(self.drafter, AscendEagleProposer | DraftModelProposer)
+                assert isinstance(self.drafter, AscendEagleProposer | AscendDraftModelProposer)
                 self.drafter.initialize_attn_backend(kv_cache_config, self.kernel_block_sizes)
 
         if has_kv_transfer_group():