[Feature] FD_USE_PHI_FP8_QUANT (#6320)

* add ut

* add use_fd_quant env

* rm mask_per_token_quant

* add make ops list

* USE_FD_FP8_QUANT -> FD_USE_PHI_FP8_QUANT 默认是true

* modify comments

* use bool type

* Add function declaration

fastdeploy/model_executor/layers/moe/fused_moe_deepgemm_backend.py

@@ -114,12 +114,20 @@ def m_grouped_fp8_gemm_nt_contiguous_custom_python_op(
     ffn_out = paddle.incubate.nn.functional.swiglu(ffn_out)
 
     # down_proj
-    ffn_in_x, ffn_in_x_scale_tensor = paddle.incubate.nn.functional.fp8_quant_blockwise(
-        ffn_out,
-        using_pow2_scale=not disable_ue8m0_cast,
-        using_ue8m0_scale=not disable_ue8m0_cast,
-    )
-    ffn_in_x_scale_tensor = ffn_in_x_scale_tensor.T[: ffn_in_x.shape[0]]
+    if not fastdeploy.envs.FD_USE_PHI_FP8_QUANT:
+        ffn_in_x, ffn_in_x_scale_tensor = fastdeploy.model_executor.ops.gpu.per_token_quant(
+            ffn_out, quant_config_weight_block_size_0
+        )
+
+        ffn_in_x_scale_tensor = ffn_in_x_scale_tensor.transpose([1, 0]).contiguous()
+        ffn_in_x_scale_tensor = ffn_in_x_scale_tensor.transpose([1, 0])
+    else:
+        ffn_in_x, ffn_in_x_scale_tensor = paddle.incubate.nn.functional.fp8_quant_blockwise(
+            ffn_out,
+            using_pow2_scale=not disable_ue8m0_cast,
+            using_ue8m0_scale=not disable_ue8m0_cast,
+        )
+        ffn_in_x_scale_tensor = ffn_in_x_scale_tensor.T[: ffn_in_x.shape[0]]
 
     ffn_out = paddle.empty(
         (permute_input.shape[0], layer_added_weight_attrs_1.shape[1]),
@@ -262,17 +270,22 @@ class DeepGemmFusedMoeMethod(MoEMethodBase):
             topk_ids_hookfunc(topk_ids=topk_idx)
 
         # 2. Dynamic compute blockwise quantization scales
-        x, x_scale_tensor = paddle.incubate.nn.functional.fp8_quant_blockwise(
-            x,
-            using_pow2_scale=self.quant_config.deepgemm_scale_ue8m0,
-            output_scale_transpose=self.quant_config.deepgemm_scale_ue8m0,
-            using_ue8m0_scale=self.quant_config.deepgemm_scale_ue8m0,
-        )
-        x_scale_tensor = (
-            x_scale_tensor[: x.shape[0]]
-            if not self.quant_config.deepgemm_scale_ue8m0
-            else x_scale_tensor.T[: x.shape[0]]
-        )
+        if not fastdeploy.envs.FD_USE_PHI_FP8_QUANT:
+            x, x_scale_tensor = fastdeploy.model_executor.ops.gpu.per_token_quant(
+                x, self.quant_config.weight_block_size[0]
+            )
+        else:
+            x, x_scale_tensor = paddle.incubate.nn.functional.fp8_quant_blockwise(
+                x,
+                using_pow2_scale=self.quant_config.deepgemm_scale_ue8m0,
+                output_scale_transpose=self.quant_config.deepgemm_scale_ue8m0,
+                using_ue8m0_scale=self.quant_config.deepgemm_scale_ue8m0,
+            )
+            x_scale_tensor = (
+                x_scale_tensor[: x.shape[0]]
+                if not self.quant_config.deepgemm_scale_ue8m0
+                else x_scale_tensor.T[: x.shape[0]]
+            )
 
         event = deep_ep.Buffer.capture()
         let_another_thread_run()
@@ -348,12 +361,18 @@ class DeepGemmFusedMoeMethod(MoEMethodBase):
             ffn_out = paddle.incubate.nn.functional.swiglu(ffn_out, None)
 
             # down_proj
-            ffn_in_x, ffn_in_x_scale_tensor = paddle.incubate.nn.functional.fp8_quant_blockwise(
-                ffn_out,
-                using_pow2_scale=self.quant_config.deepgemm_scale_ue8m0,
-                using_ue8m0_scale=self.quant_config.deepgemm_scale_ue8m0,
-            )
-            ffn_in_x_scale_tensor = ffn_in_x_scale_tensor.T[: ffn_in_x.shape[0]]
+            if not fastdeploy.envs.FD_USE_PHI_FP8_QUANT:
+                ffn_in_x, ffn_in_x_scale_tensor = fastdeploy.model_executor.ops.gpu.per_token_quant(
+                    ffn_out, self.quant_config.weight_block_size[0]
+                )
+                ffn_in_x_scale_tensor = ffn_in_x_scale_tensor.transpose([1, 0]).contiguous().transpose([1, 0])
+            else:
+                ffn_in_x, ffn_in_x_scale_tensor = paddle.incubate.nn.functional.fp8_quant_blockwise(
+                    ffn_out,
+                    using_pow2_scale=self.quant_config.deepgemm_scale_ue8m0,
+                    using_ue8m0_scale=self.quant_config.deepgemm_scale_ue8m0,
+                )
+                ffn_in_x_scale_tensor = ffn_in_x_scale_tensor.T[: ffn_in_x.shape[0]]
 
             del ffn_out
             ffn_out = paddle.empty(
@@ -505,17 +524,22 @@ class DeepGemmFusedMoeMethod(MoEMethodBase):
 
         tmp = count_tokens_per_expert_func(topk_ids, layer.num_experts)
 
-        recv_x, recv_x_scale = paddle.incubate.nn.functional.fp8_quant_blockwise(
-            x,
-            using_pow2_scale=self.quant_config.deepgemm_scale_ue8m0,
-            output_scale_transpose=self.quant_config.deepgemm_scale_ue8m0,
-            using_ue8m0_scale=self.quant_config.deepgemm_scale_ue8m0,
-        )
-        recv_x_scale = (
-            recv_x_scale[: recv_x.shape[0]]
-            if not self.quant_config.deepgemm_scale_ue8m0
-            else recv_x_scale.T[: recv_x.shape[0]]
-        )
+        if not fastdeploy.envs.FD_USE_PHI_FP8_QUANT:
+            recv_x, recv_x_scale = fastdeploy.model_executor.ops.gpu.per_token_quant(x, 128)
+        else:
+
+            recv_x, recv_x_scale = paddle.incubate.nn.functional.fp8_quant_blockwise(
+                x,
+                using_pow2_scale=self.quant_config.deepgemm_scale_ue8m0,
+                output_scale_transpose=self.quant_config.deepgemm_scale_ue8m0,
+                using_ue8m0_scale=self.quant_config.deepgemm_scale_ue8m0,
+            )
+            recv_x_scale = (
+                recv_x_scale[: recv_x.shape[0]]
+                if not self.quant_config.deepgemm_scale_ue8m0
+                else recv_x_scale.T[: recv_x.shape[0]]
+            )
+
         (
             permute_input,
             permute_scale,

fastdeploy/model_executor/layers/moe/fused_moe_triton_backend.py

@@ -1232,10 +1232,13 @@ def python_op_fused_moe_kernel_paddle(
 
     from .triton_moe_kernels import fused_moe_kernel_paddle
 
-    x_q, x_scale = paddle.incubate.nn.functional.fp8_quant_blockwise(
-        x, using_pow2_scale=False, output_scale_transpose=False
-    )
-    x_scale = x_scale[: x.shape[0]]
+    if not fastdeploy.envs.FD_USE_PHI_FP8_QUANT:
+        x_q, x_scale = fastdeploy.model_executor.ops.gpu.per_token_quant(x, quant_config.weight_block_size[0])
+    else:
+        x_q, x_scale = paddle.incubate.nn.functional.fp8_quant_blockwise(
+            x, using_pow2_scale=False, output_scale_transpose=False
+        )
+        x_scale = x_scale[: x.shape[0]]
 
     fused_moe_kernel_paddle[grid](
         x_q,
@@ -1285,11 +1288,15 @@ def python_op_fused_moe_kernel_paddle(
     intermediate_cache3 = cache13[: token_num * top_k * N2].view([token_num * top_k, N2])
 
     grid = (ceil_div(max_num_tokens_padded, config["BLOCK_SIZE_M"]) * ceil_div(hidden_size, config["BLOCK_SIZE_N"]),)
-
-    x_q, x_scale = paddle.incubate.nn.functional.fp8_quant_blockwise(
-        intermediate_cache2, using_pow2_scale=False, output_scale_transpose=False
-    )
-    x_scale = x_scale[: x_q.shape[0]]
+    if not fastdeploy.envs.FD_USE_PHI_FP8_QUANT:
+        x_q, x_scale = fastdeploy.model_executor.ops.gpu.per_token_quant(
+            intermediate_cache2, quant_config.weight_block_size[0]
+        )
+    else:
+        x_q, x_scale = paddle.incubate.nn.functional.fp8_quant_blockwise(
+            intermediate_cache2, using_pow2_scale=False, output_scale_transpose=False
+        )
+        x_scale = x_scale[: x_q.shape[0]]
 
     fused_moe_kernel_paddle[grid](
         x_q,