Clean Some Environment Variables for DeepSeek V32 (#15938)

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

@@ -22,9 +22,6 @@ from sglang.srt.layers.attention.nsa.transform_index import (
     transform_index_page_table_prefill,
 )
 from sglang.srt.layers.attention.nsa.utils import (
-    NSA_ENABLE_MTP_PRECOMPUTE_METADATA,
-    NSA_FLASHMLA_BACKEND_DECODE_COMPUTE_FP8,
-    NSA_FUSE_TOPK,
     can_nsa_prefill_cp_round_robin_split,
     compute_nsa_seqlens,
     is_nsa_enable_prefill_cp,
@@ -230,7 +227,7 @@ class NSAIndexerMetadata(BaseIndexerMetadata):
         else:
             page_table_size_1 = self.attn_metadata.page_table_1
 
-        if not NSA_FUSE_TOPK:
+        if not envs.SGLANG_NSA_FUSE_TOPK.get():
             return fast_topk_v2(logits, seq_lens_topk, topk, row_starts=ks)
         elif self.topk_transform_method == TopkTransformMethod.PAGED:
             # NOTE(dark): if fused, we return a transformed page table directly
@@ -1222,12 +1219,6 @@ class NativeSparseAttnBackend(
         assert q_rope is not None
         kv_cache = forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)
 
-        # when store in fp8 and compute in fp8, no need to convert dtype
-        if not (
-            NSA_FLASHMLA_BACKEND_DECODE_COMPUTE_FP8 and self.nsa_kv_cache_store_fp8
-        ):
-            kv_cache = kv_cache.to(q.dtype)
-
         if q_rope is not None:
             q_nope = q.view(-1, layer.tp_q_head_num, layer.v_head_dim)
             q_rope = q_rope.view(
@@ -1245,7 +1236,7 @@ class NativeSparseAttnBackend(
 
         # NOTE(dark): here, we use page size = 1
         topk_transform_method = self.get_topk_transform_method()
-        if NSA_FUSE_TOPK:
+        if envs.SGLANG_NSA_FUSE_TOPK.get():
             page_table_1 = topk_indices
         else:
             if topk_transform_method == TopkTransformMethod.RAGGED:
@@ -1283,8 +1274,6 @@ class NativeSparseAttnBackend(
             if q_rope is not None:
                 q_all = _concat_mla_absorb_q_general(q_nope, q_rope)
 
-            # NSA_FLASHMLA_BACKEND_DECODE_COMPUTE_FP8 has no effect here,
-            # because the flashmla_sparse kernel doesn't support fp8 compute
             if topk_transform_method == TopkTransformMethod.RAGGED:
                 if any(forward_batch.extend_prefix_lens_cpu):
                     page_table_1_flattened = (
@@ -1384,7 +1373,7 @@ class NativeSparseAttnBackend(
         if topk_indices is not None:
             topk_indices = self._pad_topk_indices(topk_indices, q_nope.shape[0])
 
-        if NSA_FUSE_TOPK:
+        if envs.SGLANG_NSA_FUSE_TOPK.get():
             page_table_1 = topk_indices
         else:
             page_table_1 = transform_index_page_table_decode(
@@ -1562,7 +1551,7 @@ class NativeSparseAttnBackend(
         kv_cache = kv_cache.view(-1, self.real_page_size, 1, self.kv_cache_dim)
         assert self.real_page_size == 64, "only page size 64 is supported"
 
-        if NSA_FLASHMLA_BACKEND_DECODE_COMPUTE_FP8 and not self.nsa_kv_cache_store_fp8:
+        if not self.nsa_kv_cache_store_fp8:
             # inefficiently quantize the whole cache
             kv_cache = quantize_k_cache(kv_cache)
 
@@ -1584,7 +1573,7 @@ class NativeSparseAttnBackend(
             block_table=torch.empty(
                 (q_all.shape[0], 0), dtype=torch.int32, device=q_all.device
             ),
-            is_fp8_kvcache=NSA_FLASHMLA_BACKEND_DECODE_COMPUTE_FP8,
+            is_fp8_kvcache=True,
         )
         return o
 
@@ -1787,7 +1776,7 @@ class NativeSparseAttnBackend(
 
     def get_topk_transform_method(self) -> TopkTransformMethod:
         """
-        NSA_FUSE_TOPK controls whether to fuse the topk transform into the topk kernel.
+        SGLANG_NSA_FUSE_TOPK controls whether to fuse the topk transform into the topk kernel.
         This method is used to select the topk transform method which can be fused or unfused.
         """
         if (
@@ -1819,7 +1808,7 @@ class NativeSparseAttnBackend(
             num_q_tokens_per_head_k=seq_len_q * self.num_q_heads // 1,
             num_heads_k=1,
             num_heads_q=self.num_q_heads,
-            is_fp8_kvcache=NSA_FLASHMLA_BACKEND_DECODE_COMPUTE_FP8,
+            is_fp8_kvcache=True,
             topk=self.nsa_index_topk,
         )
 
@@ -1871,7 +1860,7 @@ class NativeSparseAttnMultiStepBackend:
     def init_forward_metadata_replay_cuda_graph(
         self, forward_batch: ForwardBatch, bs: int
     ):
-        if NSA_ENABLE_MTP_PRECOMPUTE_METADATA:
+        if envs.SGLANG_NSA_ENABLE_MTP_PRECOMPUTE_METADATA.get():
             # Precompute metadata once (shared across all backends)
             precomputed = self.attn_backends[0]._precompute_replay_metadata(
                 bs=bs,