[Optimization][DeepSeekV3.2]Reducing slot_mapping compute frequency from twice per layer to a single pre-processing step.

custom_ops/gpu_ops/cpp_extensions.cc

@@ -540,12 +540,10 @@ std::vector<paddle::Tensor> count_tokens_per_expert_func(
     const paddle::Tensor& topk_ids,
     int64_t num_experts,
     bool compute_padded_cumsum = false);
-void GetPositionIdsAndMaskEncoderBatch(
-    const paddle::Tensor& seq_lens_encoder,
-    const paddle::Tensor& seq_lens_decoder,
-    const paddle::Tensor& seq_lens_this_time,
-    const paddle::Tensor& position_ids,
-    const paddle::Tensor& mask_encoder_batch);
+void GetPositionIdsAndMaskEncoderBatch(const paddle::Tensor& seq_lens_encoder,
+                                       const paddle::Tensor& seq_lens_decoder,
+                                       const paddle::Tensor& seq_lens_this_time,
+                                       const paddle::Tensor& position_ids);
 
 std::vector<paddle::Tensor> DecodeMLAWriteCacheKernel(
     const paddle::Tensor& kv_nope,

custom_ops/gpu_ops/get_position_ids_and_mask_encoder_batch.cu

@@ -20,8 +20,7 @@ __global__ void GetPositionIdsAndMaskEncoderBatchKernel(
     const int* seq_lens_decoder,  // [bsz] 每个批次的 decoder 长度
     const int* seq_lens_this_time,
     int* position_ids,  // 输出的一维 position_ids
-    int* mask_encoder_batch,
-    const int bsz) {  // 批次大小
+    const int bsz) {    // 批次大小
   // 当前线程索引（每个线程对应一个批次）
   int tid = threadIdx.x;
   if (tid >= bsz) return;
@@ -43,43 +42,38 @@ __global__ void GetPositionIdsAndMaskEncoderBatchKernel(
   // 写入 encoder 的 position_ids
   for (int i = 0; i < encoder_len; i++) {
     position_ids[offset + i] = i;
-    mask_encoder_batch[offset + i] = 1;
   }
   offset += encoder_len;
 
   // 写入 decoder 的 position_ids
   if (decoder_len > 0) {
     for (int i = 0; i < seq_len_this_time; i++) {
       position_ids[offset + i] = decoder_len + i;  // 使用 decoder 长度本身
-      mask_encoder_batch[offset + i] = 0;
     }
   }
 }
 
-void GetPositionIdsAndMaskEncoderBatch(
-    const paddle::Tensor& seq_lens_encoder,
-    const paddle::Tensor& seq_lens_decoder,
-    const paddle::Tensor& seq_lens_this_time,
-    const paddle::Tensor& position_ids,
-    const paddle::Tensor& mask_encoder_batch) {
+void GetPositionIdsAndMaskEncoderBatch(const paddle::Tensor& seq_lens_encoder,
+                                       const paddle::Tensor& seq_lens_decoder,
+                                       const paddle::Tensor& seq_lens_this_time,
+                                       const paddle::Tensor& position_ids) {
   const int bsz = seq_lens_this_time.shape()[0];
 
   GetPositionIdsAndMaskEncoderBatchKernel<<<1, bsz, 0, position_ids.stream()>>>(
       seq_lens_encoder.data<int>(),
       seq_lens_decoder.data<int>(),
       seq_lens_this_time.data<int>(),
       const_cast<int*>(position_ids.data<int>()),
-      const_cast<int*>(mask_encoder_batch.data<int>()),
       bsz);
 }
 
 PD_BUILD_STATIC_OP(get_position_ids_and_mask_encoder_batch)
-    .Inputs({"seq_lens_encoder",
-             "seq_lens_decoder",
-             "seq_lens_this_time",
-             "position_ids",
-             "mask_encoder_batch"})
-    .Outputs({"position_ids_out", "mask_encoder_batch_out"})
-    .SetInplaceMap({{"position_ids", "position_ids_out"},
-                    {"mask_encoder_batch", "mask_encoder_batch_out"}})
+    .Inputs({
+        "seq_lens_encoder",
+        "seq_lens_decoder",
+        "seq_lens_this_time",
+        "position_ids",
+    })
+    .Outputs({"position_ids_out"})
+    .SetInplaceMap({{"position_ids", "position_ids_out"}})
     .SetKernelFn(PD_KERNEL(GetPositionIdsAndMaskEncoderBatch));

fastdeploy/model_executor/forward_meta.py

@@ -160,7 +160,8 @@ class ForwardMeta:
 
     # for mla & dsa
     position_ids: Optional[paddle.Tensor] = None
-    mask_encoder_batch: Optional[paddle.Tensor] = None
+    # for kvcache slot
+    slot_mapping: Optional[paddle.Tensor] = None
 
     real_bsz: int = 0
 

fastdeploy/model_executor/layers/attention/dsa_attention_backend.py

@@ -54,33 +54,6 @@ def yarn_get_mscale(scale=1, mscale=1):
     return 0.1 * mscale * math.log(scale) + 1.0
 
 
-def compute_slot_mapping(
-    block_tables: paddle.Tensor,  # [num_reqs, max_blocks_per_req]
-    positions: paddle.Tensor,  # [num_tokens] 每个token的位置
-    batch_id_per_token: paddle.Tensor,  # [num_tokens] 每个token属于哪个请求
-    block_size: int,
-) -> paddle.Tensor:
-    """
-    计算 slot_mapping
-
-    公式: slot = block_id * block_size + offset_in_block
-    """
-    # 1. 计算每个 token 对应的 block 索引
-    block_idx = positions // block_size  # [num_tokens]
-
-    # 2. 从 block_tables 中查表获取 block_id
-    # block_tables[batch_id_per_token, block_idx]
-    block_ids = block_tables[batch_id_per_token, block_idx]  # [num_tokens]
-
-    # 3. 计算在 block 内的偏移
-    block_offset = positions % block_size  # [num_tokens]
-
-    # 4. 计算 slot_mapping
-    slot_mapping = block_ids * block_size + block_offset
-
-    return slot_mapping.cast(paddle.int64)
-
-
 @dataclass
 class DSAAttentionMetadata(AttentionMetadata):
     """
@@ -346,18 +319,11 @@ def forward_mixed(
 
         scale = paddle.abs(compressed_kv).max() / 200.0
 
-        slot_mapping = compute_slot_mapping(
-            forward_meta.block_tables,
-            forward_meta.position_ids,
-            forward_meta.batch_id_per_token,
-            self.block_size,
-        )
-
         dsk_attn_write_cache(
             compressed_kv,
             k_pe,
             latent_cache,
-            slot_mapping,
+            forward_meta.slot_mapping,
             scale.cast(paddle.float32),
             "fp8_ds_mla",
         )

fastdeploy/model_executor/models/deepseek_v3.py

@@ -46,6 +46,9 @@
 from fastdeploy.model_executor.layers.lm_head import ParallelLMHead
 from fastdeploy.model_executor.layers.moe.moe import FusedMoE
 from fastdeploy.model_executor.layers.normalization import LayerNorm, RMSNorm
+from fastdeploy.model_executor.layers.quantization.fp8_utils import (
+    per_token_group_quant_fp8,
+)
 from fastdeploy.model_executor.layers.rotary_embedding import (
     DeepseekScalingRotaryEmbedding,
 )
@@ -59,16 +62,6 @@
 )
 from fastdeploy.platforms import current_platform
 
-if current_platform.is_cuda() or current_platform.is_maca():
-    from fastdeploy.model_executor.ops.gpu import (
-        get_position_ids_and_mask_encoder_batch,
-    )
-
-from fastdeploy.model_executor.layers.quantization.fp8_utils import (
-    per_token_group_quant_fp8,
-)
-from fastdeploy.platforms import current_platform
-
 if current_platform.is_cuda():
     from fastdeploy.model_executor.ops.gpu import (
         cp_gather_indexer_k_quant_cache,
@@ -473,33 +466,6 @@ def forward(
         return output
 
 
-def compute_slot_mapping(
-    block_tables: paddle.Tensor,  # [num_reqs, max_blocks_per_req]
-    positions: paddle.Tensor,  # [num_tokens] 每个token的位置
-    batch_id_per_token: paddle.Tensor,  # [num_tokens] 每个token属于哪个请求
-    block_size: int,
-) -> paddle.Tensor:
-    """
-    计算 slot_mapping
-
-    公式: slot = block_id * block_size + offset_in_block
-    """
-    # 1. 计算每个 token 对应的 block 索引
-    block_idx = positions // block_size  # [num_tokens]
-
-    # 2. 从 block_tables 中查表获取 block_id
-    # block_tables[batch_id_per_token, block_idx]
-    block_ids = block_tables[batch_id_per_token, block_idx]  # [num_tokens]
-
-    # 3. 计算在 block 内的偏移
-    block_offset = positions % block_size  # [num_tokens]
-
-    # 4. 计算 slot_mapping
-    slot_mapping = block_ids * block_size + block_offset
-
-    return slot_mapping.cast(paddle.int64)
-
-
 import triton
 import triton.language as tl
 
@@ -688,17 +654,12 @@ def forward(
         weights = weights.unsqueeze(-1) * q_scale * self.softmax_scale * self.index_n_heads**-0.5
         weights = weights.squeeze(-1)
 
-        slot_mapping = compute_slot_mapping(
-            forward_meta.block_tables,
-            forward_meta.position_ids,
-            forward_meta.batch_id_per_token,
-            64,
-        )
-
         indexer_top_k = paddle.full([q_fp8.shape[0], self.index_topk], -1, dtype="int32")
 
         # indexer write_cache
-        indexer_k_quant_and_cache(k, self.indexer_cache, slot_mapping, self.quant_block_size, self.scale_fmt)
+        indexer_k_quant_and_cache(
+            k, self.indexer_cache, forward_meta.slot_mapping, self.quant_block_size, self.scale_fmt
+        )
 
         import deep_gemm
 
@@ -1174,12 +1135,6 @@ def __init__(self, fd_config: FDConfig):
             num_embeddings=fd_config.model_config.vocab_size,
             prefix="lm_head",
         )
-        self.position_ids_buffer = paddle.empty(
-            [fd_config.scheduler_config.max_num_batched_tokens], dtype=paddle.int32
-        )
-        self.mask_encoder_batch_buffer = paddle.empty(
-            [fd_config.scheduler_config.max_num_batched_tokens, 1], dtype=paddle.int32
-        )
 
     @classmethod
     def name(cls):
@@ -1276,25 +1231,6 @@ def compute_logits(self, hidden_states: paddle.Tensor, forward_meta: ForwardMeta
         logits[:, self.ori_vocab_size :] = -float("inf")
         return logits
 
-    def pre_process(self, forward_meta):
-        """ """
-        seq_lens_encoder = forward_meta.seq_lens_encoder
-        seq_lens_decoder = forward_meta.seq_lens_decoder
-        seq_lens_this_time = forward_meta.seq_lens_this_time
-
-        current_total_tokens = forward_meta.ids_remove_padding.shape[0]
-        position_ids = self.position_ids_buffer[:current_total_tokens]
-        mask_encoder_batch = self.mask_encoder_batch_buffer[:current_total_tokens]
-
-        get_position_ids_and_mask_encoder_batch(
-            seq_lens_encoder,
-            seq_lens_decoder,
-            seq_lens_this_time,
-            position_ids,
-            mask_encoder_batch,
-        )
-        return position_ids, mask_encoder_batch
-
     def empty_input_forward(self, forward_meta):
         """
         empty_input_forward
@@ -1315,7 +1251,6 @@ def forward(
         forward_meta: ForwardMeta,
     ):
         ids_remove_padding = inputs["ids_remove_padding"]
-        forward_meta.position_ids, forward_meta.mask_encoder_batch = self.pre_process(forward_meta)
         hidden_states = self.model(
             ids_remove_padding=ids_remove_padding,
             forward_meta=forward_meta,

fastdeploy/worker/gpu_model_runner.py

@@ -45,6 +45,12 @@
 from fastdeploy.model_executor.layers.attention.base_attention_backend import (
     AttentionBackend,
 )
+from fastdeploy.model_executor.layers.attention.dsa_attention_backend import (
+    DSAAttentionBackend,
+)
+from fastdeploy.model_executor.layers.attention.mla_attention_backend import (
+    MLAAttentionBackend,
+)
 from fastdeploy.model_executor.layers.moe.routing_indices_cache import (
     RoutingReplayManager,
 )
@@ -79,6 +85,7 @@
         speculate_schedule_cache,
         set_data_ipc,
         unset_data_ipc,
+        get_position_ids_and_mask_encoder_batch,
     )
 
 import zmq
@@ -1267,6 +1274,33 @@ def _prepare_inputs(self, cached_token_num=-1, cached_real_bsz=-1, is_dummy_or_p
         )
         return token_num, token_num_event
 
+    def _compute_position_ids_and_slot_mapping(self) -> None:
+        """Compute position_ids and slot_mapping for KV cache addressing.
+        This is a general computation based on sequence length info and block tables,
+        applicable to all models that need per-token KV cache physical slot addresses.
+        Results are stored in self.forward_meta.
+        """
+        # NOTE(zhushengguang): Only support MLAAttentionBackend and DSAAttentionBackend currently.
+        if not isinstance(self.attn_backends[0], (MLAAttentionBackend, DSAAttentionBackend)):
+            return
+        current_total_tokens = self.forward_meta.ids_remove_padding.shape[0]
+        position_ids = self.share_inputs["position_ids_buffer"][:current_total_tokens]
+        get_position_ids_and_mask_encoder_batch(
+            self.forward_meta.seq_lens_encoder,
+            self.forward_meta.seq_lens_decoder,
+            self.forward_meta.seq_lens_this_time,
+            position_ids,
+        )
+        block_size = self.cache_config.block_size
+        block_idx = position_ids // block_size  # [num_tokens]
+        assert self.forward_meta.batch_id_per_token.shape == block_idx.shape
+        block_ids = self.forward_meta.block_tables[self.forward_meta.batch_id_per_token, block_idx]  # [num_tokens]
+        block_offset = position_ids % block_size  # [num_tokens]
+        slot_mapping = self.share_inputs["slot_mapping_buffer"][:current_total_tokens]
+        paddle.assign((block_ids * block_size + block_offset).cast(paddle.int64), slot_mapping)
+        self.forward_meta.position_ids = position_ids
+        self.forward_meta.slot_mapping = slot_mapping
+
     def _process_reorder(self) -> None:
         if self.attn_backends and getattr(self.attn_backends[0], "enable_ids_reorder", False):
             self.share_inputs.enable_pd_reorder = True
@@ -1860,6 +1894,8 @@ def _dummy_run(
             # 2. Padding inputs for cuda graph
             self.forward_meta.step_use_cudagraph = in_capturing and self.forward_meta.step_use_cudagraph
             self.padding_cudagraph_inputs()
+            # Compute position_ids and slot_mapping
+            self._compute_position_ids_and_slot_mapping()
 
             model_inputs = {}
             model_inputs["ids_remove_padding"] = self.share_inputs["ids_remove_padding"]
@@ -2197,6 +2233,8 @@ def _preprocess(
 
         # Padding inputs for cuda graph
         self.padding_cudagraph_inputs()
+        # Compute position_ids and slot_mapping
+        self._compute_position_ids_and_slot_mapping()
 
         model_inputs = {}
         model_inputs["ids_remove_padding"] = self.share_inputs["ids_remove_padding"]

fastdeploy/worker/input_batch.py

@@ -188,6 +188,11 @@ def init_share_inputs(self):
         self.cu_seqlens_q = paddle.full([max_num_seqs + 1], 0, dtype="int32")
         self.cu_seqlens_k = paddle.full([max_num_seqs + 1], 0, dtype="int32")
 
+        # Initialize addressing buffers
+        _max_batched_tokens = self.scheduler_config.max_num_batched_tokens
+        self.position_ids_buffer = paddle.zeros([_max_batched_tokens], dtype=paddle.int32)
+        self.slot_mapping_buffer = paddle.zeros([_max_batched_tokens], dtype=paddle.int64)
+
         # Declare AttentionBackend buffers
         self.decoder_batch_ids = None
         self.decoder_tile_ids_per_batch = None

tests/distributed/chunked_moe.py

@@ -85,6 +85,7 @@ class SchedulerConfig:
         name = "default"
         splitwise_role = "mixed"
         max_num_seqs = 2
+        max_num_batched_tokens = 2048
 
     parallel_config = ParallelConfig()
     scheduler_config = SchedulerConfig()