feat: gqa flash decode able to run

Author: PannenetsF

lightllm/models/llama/layer_infer/transformer_layer_infer.py

@@ -96,6 +96,11 @@ def _bind_attention(self):
                 LlamaTransformerLayerInfer._token_decode_attention_gqa_flashdecoding, self
             )
             self._copy_kv_to_mem_cache = partial(LlamaTransformerLayerInfer._copy_kv_to_mem_cache_normal, self)
+        elif "triton_gqa_flashdecoding_vsm" in self.mode:
+            self._token_attention_kernel = partial(
+                LlamaTransformerLayerInfer._token_decode_attention_gqa_flashdecoding_vsm, self
+            )
+            self._copy_kv_to_mem_cache = partial(LlamaTransformerLayerInfer._copy_kv_to_mem_cache_normal, self)
         else:
             self._token_attention_kernel = partial(LlamaTransformerLayerInfer._token_decode_attention_normal, self)
             self._copy_kv_to_mem_cache = partial(LlamaTransformerLayerInfer._copy_kv_to_mem_cache_normal, self)
@@ -587,3 +592,20 @@ def _token_decode_attention_ppl_int4kv_flashdecoding(
             out=out,
             alloc_tensor_func=self.alloc_tensor,
         )
+
+    def _token_decode_attention_gqa_flashdecoding_vsm( self, q, infer_state: LlamaInferStateInfo, layer_weight, out=None):
+        from lightllm.models.llama.triton_kernel.gqa_flash_decoding_vsm import gqa_token_decode_attention_flash_decoding_vsm
+
+        cache_k = infer_state.mem_manager.kv_buffer[self.layer_num_][:, 0 : self.tp_k_head_num_, :]
+        cache_v = infer_state.mem_manager.kv_buffer[self.layer_num_][
+            :, self.tp_k_head_num_ : self.tp_k_head_num_ + self.tp_v_head_num_, :
+        ]
+        q_shape = (infer_state.batch_size, self.tp_q_head_num_, self.head_dim_)
+        return gqa_token_decode_attention_flash_decoding_vsm(
+            q.view(q_shape),
+            cache_k,
+            cache_v,
+            infer_state,
+            out=out,
+            alloc_tensor_func=self.alloc_tensor,
+        )