[BUGFIX] FIX mtp accuraccy when temperture is not 0

Signed-off-by: xuyexiong <xuyexiong@huawei.com>

Author: JC-ut0

tests/long_term/spec_decode_v1/test_v1_mtp_correctness.py

@@ -114,7 +114,8 @@ def test_mtp_torchair_correctness(
                       enforce_eager=False,
                       additional_config={
                           "torchair_graph_config": {
-                              "enabled": True
+                              "enabled": True,
+                              "graph_batch_size": [256]
                           },
                           "ascend_scheduler_config": {
                               "enabled": True
@@ -132,7 +133,8 @@ def test_mtp_torchair_correctness(
                        },
                        additional_config={
                            "torchair_graph_config": {
-                               "enabled": True
+                               "enabled": True,
+                               "graph_batch_size": [256]
                            },
                            "ascend_scheduler_config": {
                                "enabled": True

vllm_ascend/attention/mla_v1.py

@@ -329,7 +329,7 @@ def build_torchair_graph_dummy(
             num_reqs, block_table)
         num_tokens = num_reqs * self.runner.decode_token_per_req
         seq_lens = torch.zeros(num_reqs, dtype=torch.int32, device=device)
-        seq_lens_list = seq_lens.tolist()
+        seq_lens_list = [0] * num_reqs
         input_positions = torch.zeros(num_tokens,
                                       dtype=torch.int32,
                                       device=device).long()
@@ -471,7 +471,7 @@ def build(
         decode_metadata = None
         use_torchair_graph = num_token_pad_size != -1
         if self._num_decodes > 0:
-            actual_seq_q_lens = None
+            actual_seq_q_lens = query_start_loc[1:].tolist()
             max_seq_lens = seq_lens[:self._num_decodes].max().item()
             seq_lens = seq_lens[:self._num_decode_tokens]
             input_positions = input_positions[:self._num_decode_tokens]
@@ -980,11 +980,13 @@ def _forward_decode(
                                  self.qk_rope_head_dim)
                 input_layout = "BNSD"
 
-            # TorchAir's shape is [bs, num_heads_per_rank, q_seq_len, dim]
             if attn_metadata.attn_state == AscendAttentionState.SpecDecoding:
                 assert num_tokens % self.spec_token_num == 0
+                if self.enable_kv_nz:
+                    input_layout = "TND_NTD"
+                else:
+                    input_layout = "TND"
                 # [bs * q_seq_len, num_heads_per_rank, dim]
-                input_layout = "TND"
                 q_nope = q_nope.view(num_tokens, self.num_heads, -1)
                 q_pe = q_pe.view(num_tokens, self.num_heads, -1)
                 sparse_mode = 3

vllm_ascend/sample/rejection_sampler.py

@@ -432,7 +432,7 @@ def sample_recovered_tokens_pytorch(
 
             if IS_NGRAM:
                 draft_token_id = draft_token_ids[token_idx]
-                orig_prob = target_probs[token_idx, draft_token_id]
+                orig_prob = target_probs[token_idx, draft_token_id].item()
                 target_probs[token_idx, draft_token_id] = 0
                 prob = target_probs[token_idx].clone()
             else:

vllm_ascend/worker/model_runner_v1.py

@@ -1677,7 +1677,7 @@ def _dummy_run(
                         **model_kwargs)
             if self.speculative_config and self.speculative_config.method == "deepseek_mtp":
                 assert isinstance(self.drafter, MtpProposer)
-                self.drafter.dummy_run(num_reqs, with_prefill=with_prefill)
+                self.drafter.dummy_run(num_reqs)
             return hidden_states
 
     @contextmanager
@@ -2140,15 +2140,19 @@ def check_torchair_graph_batch_sizes(self):
             if self.torchair_graph_batch_sizes[-1] < self.max_num_reqs:
                 self.torchair_graph_batch_sizes.append(self.max_num_reqs)
 
+        # we need to make sure that we can deal with max_num_req when `self.decode_token_per_req` is not 1
+        self.torchair_graph_batch_sizes = [
+            graph_batch_size * self.decode_token_per_req
+            for graph_batch_size in self.torchair_graph_batch_sizes
+        ]
+
         # NOTE: when enable_expert_parallel, we need to check if `graph_batch_size` is divisible by `tp_size`
         tp_size = self.parallel_config.tensor_parallel_size
         if self.parallel_config.enable_expert_parallel:
             new_graph_batch_sizes = []
             for graph_batch_size in self.torchair_graph_batch_sizes:
                 cur_graph_batch_size = (graph_batch_size + tp_size -
                                         1) // tp_size * tp_size
-                # `graph_batch_size` need to be divisible by `self.decode_token_per_req`
-                cur_graph_batch_size = cur_graph_batch_size * self.decode_token_per_req
                 if cur_graph_batch_size not in new_graph_batch_sizes and \
                     cur_graph_batch_size <= self.scheduler_config.max_num_batched_tokens:
                     new_graph_batch_sizes.append(cur_graph_batch_size)

vllm_ascend/worker/mtp_proposer_v1.py

@@ -307,14 +307,9 @@ def load_model(self) -> None:
     def dummy_run(
         self,
         num_tokens: int,
-        with_prefill: bool = False,
     ) -> None:
-        if self.runner.torchair_graph_enabled and not with_prefill:
-            attn_metadata = self.runner.attn_metadata_builder.build_torchair_graph_dummy(
-                num_reqs=num_tokens, num_actual_tokens=1, is_mtp_model=True)
-        else:
-            attn_metadata = self.runner.attn_metadata_builder.build_torchair_graph_dummy(
-                num_reqs=num_tokens, num_actual_tokens=1, is_mtp_model=True)
+        attn_metadata = self.runner.attn_metadata_builder.build_torchair_graph_dummy(
+            num_reqs=num_tokens, num_actual_tokens=1, is_mtp_model=True)
         with set_ascend_forward_context(None,
                                         self.vllm_config,
                                         num_tokens=num_tokens):