[Spec] Internal rename per N2 v2 naming rule

python/sglang/srt/hardware_backend/npu/attention/ascend_gdn_backend.py

@@ -53,7 +53,7 @@ def prepare_gdn_inputs(
         spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
     ):
         cache_indices = self.forward_metadata.mamba_cache_indices
-        self.num_accepted_tokens = torch.ones(
+        self.num_accept_tokens = torch.ones(
             [bs], dtype=torch.int32, device=cache_indices.device
         )
         self.actual_seq_lengths = torch.ones(
@@ -237,7 +237,7 @@ def forward_extend(
                 seq_len = forward_batch.num_token_non_padded_cpu
 
             mixed_qkv_reshaped = mixed_qkv.view(batch_size, draft_token_num, -1)
-            num_accepted_tokens = torch.full(
+            num_accept_tokens = torch.full(
                 (batch_size,),
                 draft_token_num,
                 dtype=torch.int32,
@@ -249,7 +249,7 @@ def forward_extend(
                 conv_states,
                 cache_indices,
                 layer.bias,
-                num_accepted_tokens,
+                num_accept_tokens,
                 None,
                 layer.activation == "silu",
                 self.pad_slot_id,
@@ -391,15 +391,15 @@ def fused_recurrent_gated_delta_rule_update(
             )
 
         if self.graph_mode:
-            num_accepted_tokens = torch.full(
+            num_accept_tokens = torch.full(
                 [batch_size], 1, dtype=torch.int32, device=cache_indices.device
             )
             actual_seq_lengths = torch.full(
                 [batch_size], seq_len, dtype=torch.int32, device=cache_indices.device
             )
             ssm_state_indices = self.forward_metadata.mamba_cache_indices_gdn
         else:
-            num_accepted_tokens = self.num_accepted_tokens
+            num_accept_tokens = self.num_accept_tokens
             actual_seq_lengths = self.actual_seq_lengths
             ssm_state_indices = self.ssm_state_indices
 
@@ -414,7 +414,7 @@ def fused_recurrent_gated_delta_rule_update(
             nv=num_value_heads,
             intermediate_state=intermediate_state,
             cache_indices=cache_indices,
-            num_accepted_tokens=num_accepted_tokens,
+            num_accept_tokens=num_accept_tokens,
             g=g,
         )
 

python/sglang/srt/hardware_backend/npu/attention/ascend_hybrid_linear_attn_backend.py

@@ -219,7 +219,7 @@ def __init__(
 
     def update_mamba_state_after_mtp_verify(
         self,
-        accepted_steps: torch.Tensor,
+        accept_steps: torch.Tensor,
         mamba_track_indices: Optional[torch.Tensor],
         mamba_steps_to_track: Optional[torch.Tensor],
         model,
@@ -233,7 +233,7 @@ def update_mamba_state_after_mtp_verify(
         - index_select kernel launches
         - nonzero kernel launches
         """
-        request_number = accepted_steps.shape[0]
+        request_number = accept_steps.shape[0]
 
         state_indices_tensor = (
             self.linear_attn_backend.forward_metadata.mamba_cache_indices[
@@ -254,7 +254,7 @@ def update_mamba_state_after_mtp_verify(
             device=dst_indices_tensor.device,
             dtype=torch.int64,
         )
-        last_steps = accepted_steps.to(torch.int64)  # [N]
+        last_steps = accept_steps.to(torch.int64)  # [N]
 
         move_intermediate_cache(
             ssm_states,

python/sglang/srt/layers/attention/aiter_backend.py

@@ -1042,7 +1042,7 @@ def init_forward_metadata(self, forward_batch: ForwardBatch):
                     self.indices_updater_prefill.max_kv_len,
                 )
         elif forward_batch.forward_mode.is_draft_extend():
-            # EAGLE V1: DRAFT_EXTEND mode - uses spec_info.num_accepted_tokens
+            # EAGLE V1: DRAFT_EXTEND mode - uses spec_info.num_accept_tokens
             if self.use_mla:
                 kv_indices, kv_indptr, qo_indptr, custom_mask = (
                     spec_info.generate_attn_arg_prefill(
@@ -1110,7 +1110,7 @@ def init_forward_metadata(self, forward_batch: ForwardBatch):
                     )
                 )
                 kv_indices = kv_indices.to(torch.int64)
-                draft_max_extend_len = torch.max(spec_info.num_accepted_tokens).item()
+                draft_max_extend_len = torch.max(spec_info.num_accept_tokens).item()
 
                 self.forward_metadata = ForwardMetadata(
                     kv_indptr,
@@ -2240,10 +2240,10 @@ def init_forward_metadata_replay_cuda_graph(
                 num_kv_splits=num_kv_splits,
             )
         elif forward_mode.is_draft_extend():
-            # EAGLE V1: Uses spec_info.num_accepted_tokens
+            # EAGLE V1: Uses spec_info.num_accept_tokens
             num_tokens_per_bs = self.speculative_num_steps + 1
             seq_lens = seq_lens[:bs]
-            extend_lens = spec_info.num_accepted_tokens[:bs]
+            extend_lens = spec_info.num_accept_tokens[:bs]
             qo_indptr = self.qo_indptr[: bs + 1]
             qo_indptr[1 : bs + 1] = torch.cumsum(extend_lens, dim=0)
             kv_indptr = self.kv_indptr[: bs + 1]

python/sglang/srt/layers/attention/flashattention_backend.py

@@ -2162,9 +2162,9 @@ def init_forward_metadata_replay_cuda_graph(
             metadata.cu_seqlens_k[1:].copy_(
                 torch.cumsum(metadata.cache_seqlens_int32, dim=0, dtype=torch.int32)
             )
-            extend_lens = spec_info.num_accepted_tokens[:bs]
-            if spec_info.num_accepted_tokens_cpu:
-                metadata.max_seq_len_q = max(spec_info.num_accepted_tokens_cpu)
+            extend_lens = spec_info.num_accept_tokens[:bs]
+            if spec_info.num_accept_tokens_cpu:
+                metadata.max_seq_len_q = max(spec_info.num_accept_tokens_cpu)
             else:
                 metadata.max_seq_len_q = 1
 

python/sglang/srt/layers/attention/hybrid_linear_attn_backend.py

@@ -936,7 +936,7 @@ def forward(
 
     def update_mamba_state_after_mtp_verify(
         self,
-        accepted_steps: torch.Tensor,
+        accept_steps: torch.Tensor,
         mamba_track_indices: Optional[torch.Tensor],
         mamba_steps_to_track: Optional[torch.Tensor],
         model,
@@ -950,7 +950,7 @@ def update_mamba_state_after_mtp_verify(
         - index_select kernel launches
         - nonzero kernel launches
         """
-        request_number = accepted_steps.shape[0]
+        request_number = accept_steps.shape[0]
 
         state_indices_tensor = (
             self.linear_attn_backend.forward_metadata.mamba_cache_indices[
@@ -973,13 +973,13 @@ def update_mamba_state_after_mtp_verify(
             ssm_states,
             intermediate_state_cache,
             state_indices_tensor,
-            accepted_steps,
+            accept_steps,
         )
         fused_mamba_state_scatter_with_mask(
             conv_states,
             intermediate_conv_window_cache,
             state_indices_tensor,
-            accepted_steps,
+            accept_steps,
         )
 
         # Track indices used for tracking mamba states for prefix cache

python/sglang/srt/layers/attention/mamba/causal_conv1d_triton.py

@@ -576,7 +576,7 @@ def _causal_conv1d_update_kernel(
     conv_state_ptr,
     cache_seqlens_ptr,  # circular buffer
     conv_state_indices_ptr,
-    num_accepted_tokens_ptr,
+    num_accept_tokens_ptr,
     intermediate_conv_window_ptr,
     intermediate_state_indices_ptr,
     retrieve_next_token_ptr,
@@ -667,7 +667,7 @@ def _causal_conv1d_update_kernel(
         # - accept 1 tokens: [history2, ..., historyM, draft1]
         # - accept 2 tokens: [history3, ..., historyM, draft1, draft2]
         # - and so on.
-        conv_state_token_offset = tl.load(num_accepted_tokens_ptr + idx_seq) - 1
+        conv_state_token_offset = tl.load(num_accept_tokens_ptr + idx_seq) - 1
     else:
         conv_state_token_offset = 0
 
@@ -985,7 +985,7 @@ def causal_conv1d_update(
     activation: Union[bool, str, None] = None,
     cache_seqlens: Optional[torch.Tensor] = None,
     conv_state_indices: Optional[torch.Tensor] = None,
-    num_accepted_tokens: Optional[torch.Tensor] = None,
+    num_accept_tokens: Optional[torch.Tensor] = None,
     intermediate_conv_window: Optional[torch.Tensor] = None,
     intermediate_state_indices: Optional[torch.Tensor] = None,
     retrieve_next_token: Optional[torch.Tensor] = None,
@@ -1071,7 +1071,7 @@ def causal_conv1d_update(
         if intermediate_state_indices is not None
         else 0
     )
-    if num_accepted_tokens is not None:
+    if num_accept_tokens is not None:
         state_len = width - 1 + (seqlen - 1)  # effective state_len needed
     else:
         state_len = width - 1
@@ -1130,7 +1130,7 @@ def grid(META):
         conv_state,
         cache_seqlens,
         conv_state_indices,
-        num_accepted_tokens,
+        num_accept_tokens,
         intermediate_conv_window if intermediate_conv_window is not None else x,
         intermediate_state_indices,
         retrieve_next_token,
@@ -1174,7 +1174,7 @@ def grid(META):
         KERNEL_WIDTH=width,
         SILU_ACTIVATION=activation in ["silu", "swish"],
         IS_CONTINUOUS_BATCHING=conv_state_indices is not None,
-        IS_SPEC_DECODING=num_accepted_tokens is not None,
+        IS_SPEC_DECODING=num_accept_tokens is not None,
         NP2_STATELEN=np2_statelen,
         NP2_SEQLEN=np2_seqlen,
         USE_PAD_SLOT=pad_slot_id is not None,

python/sglang/srt/layers/attention/mamba/mamba_state_scatter_triton.py

@@ -17,7 +17,7 @@ def _fused_mamba_state_scatter_with_mask_kernel(
     dst_ptr,
     # Raw index arrays (before index_select)
     dst_indices_raw_ptr,  # [total_requests] - state_indices_tensor
-    step_indices_raw_ptr,  # [total_requests] - accepted_steps or mamba_steps_to_track
+    step_indices_raw_ptr,  # [total_requests] - accept_steps or mamba_steps_to_track
     elem_per_entry: tl.constexpr,
     src_layer_stride,
     src_req_stride,

python/sglang/srt/layers/attention/nsa/nsa_backend_mtp_precompute.py

@@ -261,9 +261,9 @@ def _precompute_draft_extend_mode(
         cache_seqlens = seq_lens.to(torch.int32)
         cu_seqlens_k = compute_cu_seqlens(cache_seqlens)
 
-        # Extend seqlens from spec_info: num_accepted_tokens already includes
+        # Extend seqlens from spec_info: num_accept_tokens already includes
         # the bonus token (drafts + 1).
-        extend_seq_lens = spec_info.num_accepted_tokens[:bs]
+        extend_seq_lens = spec_info.num_accept_tokens[:bs]
         extend_seq_lens_cpu = extend_seq_lens.tolist()
 
         # Page indices (repeated per accept length)

python/sglang/srt/layers/attention/nsa_backend.py

@@ -515,7 +515,7 @@ def init_forward_metadata(self, forward_batch: ForwardBatch):
                     page_table, repeats=self.speculative_num_draft_tokens, dim=0
                 )
             else:
-                # DRAFT_EXTEND (v1): V1 worker extends by (num_accepted_drafts + 1) per request
+                # DRAFT_EXTEND (v1): V1 worker extends by (num_correct_drafts + 1) per request
                 # after verification. Lengths vary per request based on how many tokens
                 # were accepted.
                 page_table = torch.repeat_interleave(
@@ -1053,7 +1053,7 @@ def init_forward_metadata_replay_cuda_graph(
                 torch.cumsum(cache_seqlens, dim=0, dtype=torch.int32)
             )
 
-            extend_seq_lens = spec_info.num_accepted_tokens[:bs]
+            extend_seq_lens = spec_info.num_accept_tokens[:bs]
             extend_seq_lens_cpu = extend_seq_lens.tolist()
 
             page_indices = self.req_to_token[req_pool_indices, :max_seqlen_k]

python/sglang/srt/layers/attention/triton_backend.py

@@ -424,9 +424,9 @@ def init_forward_metadata(self, forward_batch: ForwardBatch):
             kv_indices = kv_indices.to(torch.int64)
             mask_indptr = None
             # TODO(FIXME): This will trigger an invalid Eagle tree when using
-            # `max(spec_info.num_accepted_tokens_cpu)`.
+            # `max(spec_info.num_accept_tokens_cpu)`.
             # It might have been forgotten to update somewhere.
-            max_extend_len = torch.max(spec_info.num_accepted_tokens).item()
+            max_extend_len = torch.max(spec_info.num_accept_tokens).item()
             num_kv_splits = None
             attn_logits = None
             attn_lse = None

python/sglang/srt/layers/attention/trtllm_mha_backend.py

@@ -528,9 +528,9 @@ def init_forward_metadata_replay_cuda_graph(
             metadata.cu_seqlens_k[1:].copy_(
                 torch.cumsum(metadata.cache_seqlens_int32, dim=0, dtype=torch.int32)
             )
-            extend_lens = spec_info.num_accepted_tokens[:bs]
-            if spec_info.num_accepted_tokens_cpu:
-                metadata.max_seq_len_q = max(spec_info.num_accepted_tokens_cpu)
+            extend_lens = spec_info.num_accept_tokens[:bs]
+            if spec_info.num_accept_tokens_cpu:
+                metadata.max_seq_len_q = max(spec_info.num_accept_tokens_cpu)
             else:
                 metadata.max_seq_len_q = 1
 

python/sglang/srt/layers/attention/trtllm_mla_backend.py

@@ -131,7 +131,7 @@ def pad_draft_extend_query_kernel(
 def unpad_draft_extend_output_kernel(
     raw_out_ptr,  # Input raw output tensor (batch_size, token_per_batch, tp_q_head_num, v_head_dim)
     output_ptr,  # Output tensor (-1, tp_q_head_num, v_head_dim)
-    accept_length_ptr,  # Accept lengths for each sequence [batch_size]
+    num_accept_tokens_ptr,  # Accept lengths for each sequence [batch_size]
     cumsum_ptr,  # Cumulative sum of accept lengths [batch_size + 1]
     batch_size,
     token_per_batch,
@@ -151,7 +151,7 @@ def unpad_draft_extend_output_kernel(
         return
 
     # Load accept length for this batch
-    accept_len = tl.load(accept_length_ptr + batch_id)
+    accept_len = tl.load(num_accept_tokens_ptr + batch_id)
 
     if seq_pos >= accept_len:
         return
@@ -745,7 +745,7 @@ def unpad_draft_extend_output(
         unpad_draft_extend_output_kernel[grid](
             raw_out_ptr=raw_out,
             output_ptr=output,
-            accept_length_ptr=seq_lens_q,
+            num_accept_tokens_ptr=seq_lens_q,
             cumsum_ptr=cu_seqlens_q,
             batch_size=batch_size,
             token_per_batch=token_per_batch,
@@ -1006,7 +1006,7 @@ def forward_extend(
                 q = q.view(bs, -1, layer.tp_q_head_num, layer.head_dim)
                 needs_unpad = False
             else:
-                # draft_extend: handle varying num_accepted_drafts_per_req. If total_tokens % bs == 0,
+                # draft_extend: handle varying num_correct_drafts_per_req. If total_tokens % bs == 0,
                 # we can directly reshape q; otherwise, pad to max_seq_len_q.
                 total_tokens = q.shape[0]
                 tokens_per_seq = total_tokens // bs if bs > 0 else 0

python/sglang/srt/layers/attention/wave_backend.py

@@ -293,9 +293,9 @@ def init_forward_metadata(self, forward_batch: ForwardBatch):
             )
             mask_indptr = None
             # TODO(FIXME): This will trigger an invalid Eagle tree when using
-            # `max(spec_info.num_accepted_tokens_cpu)`.
+            # `max(spec_info.num_accept_tokens_cpu)`.
             # It might have been forgotten to update somewhere.
-            max_extend_len = torch.max(spec_info.num_accepted_tokens).item()
+            max_extend_len = torch.max(spec_info.num_accept_tokens).item()
             num_kv_splits = None
             attn_logits = None
             attn_lse = None

python/sglang/srt/layers/utils/logprob.py

@@ -338,11 +338,11 @@ def add_output_logprobs_for_spec_v1(
     if logits_output is None:
         logits_output = res.logits_output
 
-    if hasattr(res, "num_accepted_drafts_per_req_cpu"):
-        num_accepted_drafts_per_req_cpu = res.num_accepted_drafts_per_req_cpu
+    if hasattr(res, "num_correct_drafts_per_req_cpu"):
+        num_correct_drafts_per_req_cpu = res.num_correct_drafts_per_req_cpu
     else:
         # FIXME: Get a NgramVerifyOutput class and use that instead of this hack.
-        num_accepted_drafts_per_req_cpu = res.num_accepted_drafts.tolist()
+        num_correct_drafts_per_req_cpu = res.num_correct_drafts.tolist()
 
     top_logprobs_nums = batch.top_logprobs_nums
     token_ids_logprobs = batch.token_ids_logprobs
@@ -363,7 +363,7 @@ def add_output_logprobs_for_spec_v1(
             logits_output.next_token_logits / temperatures, dim=-1
         )
     batch_next_token_ids = res.accept_tokens
-    num_tokens_per_req = [accept + 1 for accept in num_accepted_drafts_per_req_cpu]
+    num_tokens_per_req = [accept + 1 for accept in num_correct_drafts_per_req_cpu]
 
     # We should repeat top_logprobs_nums to match num_tokens_per_req.
     top_logprobs_nums_repeat_interleaved = [

python/sglang/srt/managers/detokenizer_manager.py

@@ -362,8 +362,8 @@ def handle_batch_token_id_out(self, recv_obj: BatchTokenIDOutput):
             cached_tokens=recv_obj.cached_tokens,
             cached_tokens_details=recv_obj.cached_tokens_details,
             spec_verify_ct=recv_obj.spec_verify_ct,
-            spec_accepted_drafts=recv_obj.spec_accepted_drafts,
-            spec_acceptance_histogram=recv_obj.spec_acceptance_histogram,
+            spec_num_correct_drafts=recv_obj.spec_num_correct_drafts,
+            spec_correct_drafts_histogram=recv_obj.spec_correct_drafts_histogram,
             input_token_logprobs_val=recv_obj.input_token_logprobs_val,
             input_token_logprobs_idx=recv_obj.input_token_logprobs_idx,
             output_token_logprobs_val=recv_obj.output_token_logprobs_val,

python/sglang/srt/managers/io_struct.py

@@ -95,13 +95,13 @@ class SpeculativeDecodingMetricsMixin:
 
     # Accepted drafts: Number of accepted draft tokens during speculative decoding
     # (strict drafts-only count, excludes the bonus token).
-    spec_accepted_drafts: List[int]
+    spec_num_correct_drafts: List[int]
 
     # Acceptance histogram: List of lists, where each inner list represents histogram counts.
     # List index = number of accepted tokens in a step, List value = count of steps with that many accepted tokens.
     # Example: histogram[0] = 5 means 5 steps with 0 accepted tokens, histogram[3] = 10 means 10 steps with 3 accepted tokens.
     # Empty list [] when speculative decoding is disabled.
-    spec_acceptance_histogram: List[List[int]]
+    spec_correct_drafts_histogram: List[List[int]]
 
 
 # Parameters for a session