[BugFix] fix prefixcache performance and accuracy on ascend

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

@@ -43,9 +43,13 @@ class ForwardMetadata:
     seq_lens: Optional[torch.Tensor] = None
     actual_seq_lengths_q: Optional[torch.Tensor] = None
 
+    # prefix cache
+    prefix_lens: Optional[torch.Tensor] = None
+    flatten_prefix_block_tables: Optional[torch.Tensor] = None
+
 
 class AscendAttnMaskBuilder:
-    def __init__(self, model_runner: ModelRunner, device, use_fia):
+    def __init__(self, model_runner: ModelRunner, device, use_fia, use_mla):
         """
         Initialize the AscendAttnMaskBuilder class.
 
@@ -76,6 +80,13 @@ def __init__(self, model_runner: ModelRunner, device, use_fia):
         self.mix_mask_cache = self.generate_attn_mask(mixed_chunk_cache_len, "mix")
         self.mix_seq_len_cached = self.mix_mask_cache.shape[0]
 
+        if use_mla:
+            # Initialize RingMla mask
+            ringmla_mask_len = 512
+            self.ringmla_mask = self.generate_attn_mask(
+                ringmla_mask_len, "norm", torch.bfloat16
+            ).to(self.device)
+
     @staticmethod
     def generate_mask_flag(max_seq_len):
         """
@@ -216,6 +227,7 @@ def __init__(self, model_runner: ModelRunner):
         if self.use_mla:
             self.kv_lora_rank = model_runner.model_config.kv_lora_rank
             self.qk_rope_head_dim = model_runner.model_config.qk_rope_head_dim
+            self.qk_nope_head_dim = model_runner.model_config.qk_nope_head_dim
             self.q_head_dim = (
                 self.qk_rope_head_dim + model_runner.model_config.qk_nope_head_dim
             )
@@ -229,14 +241,16 @@ def __init__(self, model_runner: ModelRunner):
             model_runner.server_args.speculative_num_draft_tokens
         )
         self.ascend_attn_mask_builder = AscendAttnMaskBuilder(
-            model_runner, self.device, self.use_fia
+            model_runner, self.device, self.use_fia, self.use_mla
         )
         self.mask, self.fia_mask, self.mtp_mask, self.mix_mask = (
             self.ascend_attn_mask_builder.mask,
             self.ascend_attn_mask_builder.fia_mask,
             self.ascend_attn_mask_builder.mtp_mask,
             self.ascend_attn_mask_builder.mix_mask_cache,
         )
+        if self.use_mla:
+            self.ringmla_mask = self.ascend_attn_mask_builder.ringmla_mask
 
     def get_verify_buffers_to_fill_after_draft(self):
         """
@@ -279,6 +293,33 @@ def init_forward_metadata(self, forward_batch: ForwardBatch):
 
         if forward_batch.forward_mode.is_target_verify():
             self.forward_metadata.seq_lens_cpu_int += self.speculative_num_draft_tokens
+
+        if (
+            self.use_mla
+            and forward_batch.forward_mode.is_extend()
+            and sum(forward_batch.extend_prefix_lens_cpu) > 0
+        ):
+            self.forward_metadata.prefix_lens = forward_batch.extend_prefix_lens.to(
+                "cpu"
+            )
+            seq_prefix_lens = self.forward_metadata.prefix_lens.tolist()
+            self.forward_metadata.flatten_prefix_block_tables = torch.empty(
+                0, dtype=torch.int32
+            ).to(self.device)
+            for req_idx, seq_len in zip(
+                forward_batch.req_pool_indices.tolist(), seq_prefix_lens
+            ):
+                req_indices = forward_batch.req_to_token_pool.req_to_token[req_idx]
+                req_prefix_block_tables = (
+                    req_indices[:seq_len][:: self.page_size] // self.page_size
+                )
+                self.forward_metadata.flatten_prefix_block_tables = torch.cat(
+                    (
+                        self.forward_metadata.flatten_prefix_block_tables,
+                        torch.flatten(req_prefix_block_tables),
+                    )
+                )
+
         if forward_batch.forward_mode.is_mixed():
             self.mix_mask = self.ascend_attn_mask_builder.update_mask(
                 self.forward_metadata
@@ -590,15 +631,99 @@ def forward_extend(
                         enable_gqa=use_gqa,
                         causal=causal,
                     )
+        elif sum(forward_batch.extend_prefix_lens_cpu) > 0:
+            q, k, v = [
+                data[: forward_batch.num_token_non_padded_cpu] for data in [q, k, v]
+            ]
+            q_nope, q_rope = q.split([layer.v_head_dim, self.qk_rope_head_dim], dim=-1)
+            k_nope, k_rope = k.split([layer.v_head_dim, self.qk_rope_head_dim], dim=-1)
+
+            # 1st, compute extend tokens to get attn_output and attn_lse
+            num_tokens = q_nope.size(0)
+            attn_output = torch.zeros(
+                num_tokens,
+                layer.tp_q_head_num,
+                layer.v_head_dim,
+                dtype=q_nope.dtype,
+                device=q_nope.device,
+            )
+            attn_lse = torch.zeros(
+                layer.tp_q_head_num,
+                num_tokens,
+                dtype=torch.float32,
+                device=q_nope.device,
+            )
+            torch_npu.atb.npu_ring_mla(
+                q_nope=q_nope,
+                q_rope=q_rope,
+                k_nope=k_nope,
+                k_rope=k_rope,
+                value=v,
+                mask=self.ringmla_mask,
+                seqlen=self.forward_metadata.extend_seq_lens_cpu_int,
+                head_num=layer.tp_q_head_num,
+                kv_head_num=layer.tp_k_head_num,
+                pre_out=None,
+                prev_lse=None,
+                qk_scale=layer.scaling,
+                kernel_type="kernel_type_high_precision",
+                mask_type="mask_type_triu",
+                calc_type="calc_type_first_ring",
+                output=attn_output,
+                softmax_lse=attn_lse,
+            )
+
+            # 2nd, load history kvcache(kv_a and k_pe) and calculate k_nope
+            k_buffer = forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)
+            v_buffer = forward_batch.token_to_kv_pool.get_value_buffer(layer.layer_id)
+            kv_cached = torch.index_select(
+                k_buffer, 0, self.forward_metadata.flatten_prefix_block_tables
+            )
+            k_rope_cached = torch.index_select(
+                v_buffer, 0, self.forward_metadata.flatten_prefix_block_tables
+            ).flatten(0, 1)
+
+            assert layer.kv_b_proj is not None
+            kv = layer.kv_b_proj(kv_cached)[0].view(
+                -1, layer.tp_k_head_num, self.qk_nope_head_dim + layer.v_head_dim
+            )
+            k_nope, v = kv.split([self.qk_nope_head_dim, layer.v_head_dim], dim=-1)
+
+            # 3rd, compute history kv to attn_out
+            k_rope = k_rope_cached.expand(-1, layer.tp_k_head_num, -1)
+            seq_len = torch.stack(
+                [
+                    self.forward_metadata.extend_seq_lens_cpu_int,
+                    self.forward_metadata.prefix_lens,
+                ]
+            )
+            torch_npu.atb.npu_ring_mla(
+                q_nope=q_nope,
+                q_rope=q_rope,
+                k_nope=k_nope,
+                k_rope=k_rope,
+                value=v,
+                mask=self.ringmla_mask,
+                seqlen=seq_len,
+                head_num=layer.tp_q_head_num,
+                kv_head_num=layer.tp_k_head_num,
+                pre_out=attn_output,
+                prev_lse=attn_lse,
+                qk_scale=layer.scaling,
+                kernel_type="kernel_type_high_precision",
+                mask_type="no_mask",
+                calc_type="calc_type_default",
+                output=attn_output,
+                softmax_lse=attn_lse,
+            )
+            attn_output = attn_output.reshape(
+                [-1, layer.tp_q_head_num, layer.v_head_dim]
+            )
         else:
             assert (
                 layer.qk_head_dim != layer.v_head_dim
             ), "FIA only supports qk_head_dim != v_head_dim"
 
-            # Wait for the KV transfer to complete before performing attention computation.
-            forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)
-            forward_batch.token_to_kv_pool.get_value_buffer(layer.layer_id)
-
             num_token_padding = q.shape[0]
             q, k, v = [
                 data[: forward_batch.num_token_non_padded_cpu] for data in [q, k, v]

python/sglang/srt/managers/cache_controller.py

@@ -503,7 +503,7 @@ def move_indices(self, op: CacheOperation):
             elif self.mem_pool_host.layout == "page_first_direct":
                 return host_indices, device_indices.cpu()
         elif self.io_backend == "kernel_ascend":
-            return host_indices, device_indices
+            return host_indices, device_indices.cpu()
         else:
             raise ValueError(f"Unsupported io backend")
 

python/sglang/srt/mem_cache/memory_pool_host.py

@@ -1,6 +1,7 @@
 import abc
 import logging
 import threading
+from collections import defaultdict
 from functools import wraps
 from typing import Optional
 
@@ -41,8 +42,6 @@
 
 logger = logging.getLogger(__name__)
 
-SUPPORT_PIN_MEMORY = not _is_npu
-
 
 def synchronized(func):
     @wraps(func)
@@ -53,6 +52,45 @@ def wrapper(self, *args, **kwargs):
     return wrapper
 
 
+def alloc_with_host_register(
+    dims,
+    dtype: torch.dtype,
+    device: str,
+    pin_memory: bool,
+) -> torch.Tensor:
+    """
+    Allocate tensor and register host memory with cudaHostRegister.
+    CudaHostRegister only applies when pin_memory=True.
+    """
+    buffer = torch.empty(dims, dtype=dtype, device=device)
+    if pin_memory:
+        torch.cuda.cudart().cudaHostRegister(
+            buffer.data_ptr(), buffer.numel() * buffer.element_size(), 0
+        )
+    return buffer
+
+
+def alloc_with_pin_memory(
+    dims,
+    dtype: torch.dtype,
+    device: str,
+    pin_memory: bool,
+) -> torch.Tensor:
+    """
+    Allocate tensor using PyTorch's built-in pin_memory flag.
+    """
+    buffer = torch.empty(dims, dtype=dtype, device=device, pin_memory=pin_memory)
+    return buffer
+
+
+ALLOC_MEMORY_FUNCS = defaultdict(
+    lambda: alloc_with_host_register,
+    {
+        "npu": alloc_with_pin_memory,
+    },
+)
+
+
 class HostKVCache(abc.ABC):
 
     def __init__(
@@ -68,7 +106,7 @@ def __init__(
         self.device_pool = device_pool
         self.page_size = page_size
         self.layout = layout
-        self.pin_memory = pin_memory and SUPPORT_PIN_MEMORY
+        self.pin_memory = pin_memory
         self.device = device
 
         self.dtype = device_pool.store_dtype
@@ -266,15 +304,11 @@ def init_kv_buffer(self):
             raise ValueError(f"Unsupported layout: {self.layout}")
         self.token_stride_size = self.head_num * self.head_dim * self.dtype.itemsize
         self.layout_dim = self.token_stride_size * self.layer_num
-        buffer = torch.empty(
-            dims,
-            dtype=self.dtype,
-            device=self.device,
+
+        alloc_func = ALLOC_MEMORY_FUNCS[self.device_pool.device]
+        buffer = alloc_func(
+            dims, dtype=self.dtype, device=self.device, pin_memory=self.pin_memory
         )
-        if self.pin_memory:
-            torch.cuda.cudart().cudaHostRegister(
-                buffer.data_ptr(), buffer.numel() * buffer.element_size(), 0
-            )
         return buffer
 
     @property
@@ -675,15 +709,18 @@ def init_kv_buffer(self):
                 self.page_size,
                 1,
             )
-            self.k_buffer = torch.empty(
+            alloc_func = ALLOC_MEMORY_FUNCS[self.device_pool.device]
+            self.k_buffer = alloc_func(
                 (*base_dims, self.kv_lora_rank),
                 dtype=self.dtype,
                 device=self.device,
+                pin_memory=self.pin_memory,
             )
-            self.v_buffer = torch.empty(
+            self.v_buffer = alloc_func(
                 (*base_dims, self.qk_rope_head_dim),
                 dtype=self.dtype,
                 device=self.device,
+                pin_memory=self.pin_memory,
             )
             # Return k_buffer to preserve original kv_buffer and data_refs init logic,
             # though Ascend doesn't use these parameters.
@@ -694,15 +731,11 @@ def init_kv_buffer(self):
             self.kv_lora_rank + self.qk_rope_head_dim
         ) * self.dtype.itemsize
         self.layout_dim = self.token_stride_size * self.layer_num
-        buffer = torch.empty(
-            dims,
-            dtype=self.dtype,
-            device=self.device,
+
+        alloc_func = ALLOC_MEMORY_FUNCS[self.device_pool.device]
+        buffer = alloc_func(
+            dims, dtype=self.dtype, device=self.device, pin_memory=self.pin_memory
         )
-        if self.pin_memory:
-            torch.cuda.cudart().cudaHostRegister(
-                buffer.data_ptr(), buffer.numel() * buffer.element_size(), 0
-            )
         return buffer
 
     def load_to_device_per_layer(