【Inference Optimize】Support wint2 triton kernel about triton_utils_v2 (#2842)

* update supported_models doc

fastdeploy/model_executor/layers/moe/fused_moe_wint2_backend.py

@@ -20,6 +20,7 @@ from paddle import nn
 import fastdeploy
 from fastdeploy.distributed.communication_op import \
     tensor_model_parallel_all_reduce
+from fastdeploy.utils import ceil_div
 
 from ..quantization.quant_base import QuantMethodBase
 from ..utils import create_and_set_parameter, get_tensor
@@ -58,7 +59,7 @@ class Wint2MoeMethod(QuantMethodBase):
         pass
 
 
-class TritonWint2FusedMoeMethod(Wint2MoeMethod):
+class CutlassWint2FusedMoeMethod(Wint2MoeMethod):
     """
     Use Triton Group Gemm to compute Fused MoE.
     """
@@ -239,3 +240,177 @@ class TritonWint2FusedMoeMethod(Wint2MoeMethod):
             tensor_model_parallel_all_reduce(fused_moe_out)
 
         return fused_moe_out
+
+
+class TritonWint2FusedMoeMethod(CutlassWint2FusedMoeMethod):
+
+    def __init__(self, quant_config):
+        super().__init__(quant_config)
+        self.moe_quant_type = quant_config.moe_quant_type
+
+
+    def apply(
+        self,
+        layer: nn.Layer,
+        x: paddle.Tensor,
+        gate_out: paddle.Tensor,
+    ) -> paddle.Tensor:
+        """
+        Use Wint2 Triton Fusedmoe compute Fused MoE.
+        """
+
+        from fastdeploy.model_executor.ops.triton_ops import \
+            moe_wint2_ffn_kernel
+
+        topk_ids, topk_weights = fastdeploy.model_executor.ops.gpu.moe_topk_select(
+            gate_out,
+            layer.gate_correction_bias,
+            layer.top_k,
+            True,  # apply_norm_weight,
+            False,
+        )
+
+        num_tokens, K = x.shape
+        E, _, N = layer.moe_ffn1_weight.shape
+        M = num_tokens
+
+        top_k = topk_ids.shape[1]
+
+        intermediate_cache1 = paddle.empty(
+            [M, top_k, N],
+            dtype=x.dtype,
+        )
+        intermediate_cache3 = paddle.empty(
+            (M, top_k, K),
+            dtype=x.dtype,
+        )
+
+        double_quant = True
+        num_valid_tokens = topk_ids.shape[0] * topk_ids.shape[1]
+
+
+        config = {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 512,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 16,
+        }
+        from fastdeploy.model_executor.ops.gpu import tritonmoe_preprocess
+
+        sorted_token_ids, expert_ids, num_tokens_post_padded = tritonmoe_preprocess(
+            topk_ids, E, config["BLOCK_SIZE_M"])
+
+        max_possible_num_post_padded = sorted_token_ids.shape[0]
+        grid = (ceil_div(max_possible_num_post_padded, config["BLOCK_SIZE_M"]) *
+                ceil_div(N, config["BLOCK_SIZE_N"]), )
+
+        moe_wint2_ffn_kernel[grid](
+            x,
+            layer.moe_ffn1_weight,
+            intermediate_cache1,
+            layer.moe_ffn1_weight_scale,
+            layer.moe_ffn1_super_scales,
+            layer.moe_ffn1_code_scale,
+            layer.moe_ffn1_code_zp,
+            topk_weights,
+            sorted_token_ids,
+            expert_ids,
+            num_tokens_post_padded,
+            num_valid_tokens,
+            max_possible_num_post_padded,
+            # Matrix dimensions
+            N=layer.moe_ffn1_weight.shape[-1],
+            K=x.shape[-1],
+            # The stride variables represent how much to increase the ptr by when
+            # moving by 1 element in a particular dimension. E.g. `stride_am` is
+            # how much to increase `a_ptr` by to get the element one row down
+            # (A has M rows).
+            stride_am=x.strides[0],
+            stride_ak=x.strides[1],
+            stride_be=layer.moe_ffn1_weight.strides[0],
+            stride_bk=layer.moe_ffn1_weight.strides[1],
+            stride_bn=1,
+            stride_cm=intermediate_cache1.strides[-2],
+            stride_cn=1,
+            stride_bse=layer.moe_ffn1_weight_scale.strides[0],
+            stride_bsk=layer.moe_ffn1_weight_scale.strides[1],
+            stride_bsn=1,
+            stride_bce=layer.moe_ffn1_code_scale.strides[0],
+            stride_bck=1,
+            stride_bcn=1,
+            BLOCK_SIZE_M=config["BLOCK_SIZE_M"],
+            BLOCK_SIZE_N=config["BLOCK_SIZE_N"],
+            BLOCK_SIZE_K=config["BLOCK_SIZE_K"],
+            GROUP_SIZE_M=config["GROUP_SIZE_M"],
+            MUL_ROUTED_WEIGHT=False,
+            USE_DOUBLE_QUANT=double_quant,
+            top_k=top_k,
+        )
+
+        intermediate_cache2 = paddle.incubate.nn.functional.swiglu(intermediate_cache1.reshape([-1, N]))
+
+        config = {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 128,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 2,
+            "num_warps": 4,
+            "num_stages": 8,
+        }
+
+        grid = (ceil_div(max_possible_num_post_padded, config["BLOCK_SIZE_M"]) *
+                ceil_div(layer.moe_ffn2_weight.shape[-1], config["BLOCK_SIZE_N"]), )
+
+
+        moe_wint2_ffn_kernel[grid](
+            intermediate_cache2,
+            layer.moe_ffn2_weight,
+            intermediate_cache3,
+            layer.moe_ffn2_weight_scale,
+            layer.moe_ffn2_super_scales,
+            layer.moe_ffn2_code_scale,
+            layer.moe_ffn2_code_zp,
+            topk_weights,
+            sorted_token_ids,
+            expert_ids,
+            num_tokens_post_padded,
+            num_valid_tokens,
+            max_possible_num_post_padded,
+            # Matrix dimensions
+            N=layer.moe_ffn2_weight.shape[-1],
+            K=intermediate_cache2.shape[-1],
+            # The stride variables represent how much to increase the ptr by when
+            # moving by 1 element in a particular dimension. E.g. `stride_am` is
+            # how much to increase `a_ptr` by to get the element one row down
+            # (A has M rows).
+            stride_am=intermediate_cache2.strides[0],
+            stride_ak=1,
+            stride_be=layer.moe_ffn2_weight.strides[0],
+            stride_bk=layer.moe_ffn2_weight.strides[1],
+            stride_bn=1,
+            stride_cm=intermediate_cache3.strides[-2],
+            stride_cn=1,
+            stride_bse=layer.moe_ffn2_weight_scale.strides[0],
+            stride_bsk=layer.moe_ffn2_weight_scale.strides[1],
+            stride_bsn=1,
+            stride_bce=layer.moe_ffn2_code_scale.strides[0],
+            stride_bck=1,
+            stride_bcn=1,
+            BLOCK_SIZE_M=config["BLOCK_SIZE_M"],
+            BLOCK_SIZE_N=config["BLOCK_SIZE_N"],
+            BLOCK_SIZE_K=config["BLOCK_SIZE_K"],
+            GROUP_SIZE_M=config["GROUP_SIZE_M"],
+            MUL_ROUTED_WEIGHT=True,
+            USE_DOUBLE_QUANT=double_quant,
+            top_k=1,
+        )
+
+        fused_moe_out = paddle.sum(intermediate_cache3, axis=1)
+
+
+        if layer.tp_size > 1:
+            tensor_model_parallel_all_reduce(fused_moe_out)
+
+        return fused_moe_out

fastdeploy/model_executor/layers/quantization/wint2.py

@@ -126,7 +126,7 @@ class WINT2Config(QuantConfigBase):
             layer (Layer): The layer for which the quantization method should be retrieved.
 
         Returns:
-            QuantMethodBase: The quantization method associated with the given layer. 
+            QuantMethodBase: The quantization method associated with the given layer.
         """
         if isinstance(layer, FusedMoE):
             if layer.layer_idx <= self.moe_w4_quant_end_layer:
@@ -135,8 +135,8 @@ class WINT2Config(QuantConfigBase):
                         {}).get_quant_method(layer)
             else:
                 from fastdeploy.model_executor.layers.moe.fused_moe_wint2_backend import \
-                    TritonWint2FusedMoeMethod
-                return TritonWint2FusedMoeMethod(self)
+                    CutlassWint2FusedMoeMethod
+                return CutlassWint2FusedMoeMethod(self)
         else:
             return get_quantization_config(self.dense_quant_type).from_config(
                 {}).get_quant_method(layer)