增加marlin对cache13的支持，以及新增flash mla的kvcache fp8的支持

vllm/attention/backends/flashmla.py

@@ -207,8 +207,9 @@ class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
                                       "FlashMLAImpl")
 
         if is_quantized_kv_cache(self.kv_cache_dtype):
+            if self.kv_cache_dtype != "fp8":
                 raise NotImplementedError(
-                "FlashMLA with FP8 KV cache not yet supported")
+                    "FlashMLA with other KV cache not yet supported")
 
     def _forward_decode(
         self,
@@ -216,6 +217,8 @@ class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
         q_pe: torch.Tensor,
         kv_c_and_k_pe_cache: torch.Tensor,
         attn_metadata: FlashMLAMetadata,
+        k_scale = None,
+        kv_cache_dtype = "auto",
     ) -> torch.Tensor:
         assert kv_c_and_k_pe_cache.numel() > 0
 
@@ -235,6 +238,8 @@ class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
             num_splits=decode_meta.decode_num_splits,
             softmax_scale=self.scale,
             causal=True,
+            k_scale = k_scale,
+            kv_cache_dtype = kv_cache_dtype,
         )
 
         return self._v_up_proj(o)

vllm/attention/backends/mla/common.py

@@ -1396,6 +1396,6 @@ class MLACommonImpl(MLAAttentionImpl[T], Generic[T]):
             decode_ql_nope = decode_ql_nope.transpose(0, 1)
 
             output[num_prefill_tokens:] = self._forward_decode(
-                decode_ql_nope, decode_q_pe, kv_cache, attn_metadata)
+                decode_ql_nope, decode_q_pe, kv_cache, attn_metadata, layer._k_scale, self.kv_cache_dtype)
 
         return output
\ No newline at end of file

vllm/attention/ops/flashmla.py

@@ -75,6 +75,8 @@ def flash_mla_with_kvcache(
     num_splits: torch.Tensor,
     softmax_scale: Optional[float] = None,
     causal: bool = False,
+    k_scale = None,
+    kv_cache_dtype = "auto",
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     """
     Arguments:
@@ -97,6 +99,22 @@ def flash_mla_with_kvcache(
     if softmax_scale is None:
         softmax_scale = q.shape[-1]**(-0.5)
     if current_platform.is_rocm():
+        if kv_cache_dtype == "fp8":         
+            out, softmax_lse = flash_mla_cuda.fwd_kvcache_quantization_mla(
+                q,
+                k_cache,
+                None,
+                head_dim_v,
+                cache_seqlens,
+                block_table,
+                softmax_scale,
+                causal,
+                tile_scheduler_metadata,
+                num_splits,
+                k_scale,
+                "fp8_e4m3",
+            )
+            return out, softmax_lse
         out, softmax_lse = flash_mla_cuda.fwd_kvcache_mla(
             q,
             k_cache,

vllm/model_executor/layers/fused_moe/fused_marlin_moe.py

@@ -17,6 +17,7 @@ from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     marlin_make_workspace_new, maybe_warn_marlin_atomic_add)
 from vllm.scalar_type import ScalarType, scalar_types
 from vllm.utils import direct_register_custom_op
+from vllm.model_executor.layers.fused_moe.fused_moe import get_moe_cache
 
 def get_scalar_type(num_bits: int, has_zp: bool):
     if has_zp:
@@ -104,7 +105,8 @@ def fused_marlin_moe(
     topk = topk_ids.shape[1] # 8
     
     #暂时固定为16384
-    CHUNK_SIZE = 16384
+    #CHUNK_SIZE = 16384
+    CHUNK_SIZE = envs.VLLM_FUSED_MOE_CHUNK_SIZE
 
     M = min(num_tokens, CHUNK_SIZE)
 
@@ -122,18 +124,21 @@ def fused_marlin_moe(
     if global_num_experts == -1:
         global_num_experts = E
     intermediate_cache2 = torch.empty( 
-        (M * topk_ids.shape[1], N),
+        (M * topk, N),
         device=hidden_states.device,
         dtype=hidden_states.dtype,
     )
+    if envs.VLLM_USE_GLOBAL_CACHE13:
+        intermediate_cache13 = get_moe_cache(topk, N, K, device=hidden_states.device, dtype=hidden_states.dtype)
+    else:    
         intermediate_cache13 = torch.empty(
-        (M * topk_ids.shape[1] * max(2 * N, K), ),
+            (M * topk * max(2 * N, K), ),
             device=hidden_states.device,
             dtype=hidden_states.dtype,
         )
-    intermediate_cache1 = intermediate_cache13[:M * topk_ids.shape[1] * 2 * N]
+    intermediate_cache1 = intermediate_cache13[:M * topk * 2 * N]
     intermediate_cache1 = intermediate_cache1.view(-1, 2 * N)
-    intermediate_cache3 = intermediate_cache13[:M * topk_ids.shape[1] * K] 
+    intermediate_cache3 = intermediate_cache13[:M * topk * K] 
     intermediate_cache3 = intermediate_cache3.view(-1, K) 
 
     use_atomic_add = hidden_states.dtype == torch.half or \

vllm/v1/attention/backends/mla/common.py

@@ -1325,6 +1325,6 @@ class MLACommonImpl(MLAAttentionImpl[M], Generic[M]):
             decode_ql_nope = decode_ql_nope.transpose(0, 1)
 
             output[:num_decode_tokens] = self._forward_decode(
-                decode_ql_nope, decode_q_pe, kv_cache, attn_metadata)
+                decode_ql_nope, decode_q_pe, kv_cache, attn_metadata, layer._k_scale, self.kv_cache_dtype)
 
         return output_padded
\ No newline at end of file

vllm/v1/attention/backends/mla/flashmla.py

@@ -145,8 +145,9 @@ class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
                                       "FlashMLAImpl")
 
         if is_quantized_kv_cache(self.kv_cache_dtype):
+            if self.kv_cache_dtype != "fp8":
                 raise NotImplementedError(
-                "FlashMLA V1 with FP8 KV cache not yet supported")
+                    "FlashMLA with other KV cache not yet supported")
 
     def _forward_decode(
         self,
@@ -154,6 +155,8 @@ class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
         q_pe: torch.Tensor,
         kv_c_and_k_pe_cache: torch.Tensor,
         attn_metadata: FlashMLAMetadata,
+        k_scale = None,
+        kv_cache_dtype = "auto",    
     ) -> torch.Tensor:
         assert kv_c_and_k_pe_cache.numel() > 0
         assert attn_metadata.decode is not None
@@ -172,6 +175,8 @@ class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
             num_splits=attn_metadata.decode.num_splits,
             softmax_scale=self.scale,
             causal=True,
+            k_scale = k_scale,
+            kv_cache_dtype = kv_cache_dtype,
         )
 
         return self._v_up_proj(o)