[Cleanup] Replace torch proxy alias with public compat API (#7348)

fastdeploy/model_executor/layers/attention/flash_attn_backend.py

@@ -84,7 +84,7 @@ def init_flash_attn_version():
         sm_version = get_sm_version()
         if sm_version >= 100:
             try:
-                paddle.compat.enable_torch_proxy(scope={"cutlass"})
+                paddle.enable_compat(scope={"cutlass"})
                 from flash_mask.cute.interface import flashmask_attention as fa4
 
                 global flashmask_attention_v4

fastdeploy/model_executor/layers/attention/mla_attention_backend.py

@@ -18,7 +18,7 @@ from __future__ import annotations
 
 import paddle
 
-paddle.enable_compat(scope={"flash_mla"})  # Enable torch proxy before importing flash_mla
+paddle.enable_compat(scope={"flash_mla"})  # Enable paddle.enable_compat before importing flash_mla
 import math
 import os
 from dataclasses import dataclass, field

fastdeploy/model_executor/layers/batch_invariant_ops/batch_invariant_ops.py

@@ -805,9 +805,9 @@ def enable_batch_invariant_mode():
     if _batch_invariant_MODE:
         return
 
-    if hasattr(paddle, "compat") and hasattr(paddle.compat, "enable_torch_proxy"):
-        paddle.compat.enable_torch_proxy()
-        # TODO(liujundong): Enabling torch proxy here has a global effect.
+    if hasattr(paddle, "enable_compat"):
+        paddle.enable_compat()
+        # TODO(liujundong): Enabling paddle.enable_compat() here has a global effect.
         # Do NOT call this function from module import time,
         # otherwise it may affect other test cases during pytest collection.
         # (ex: Could not import module 'PretrainedTokenizer' or No module named 'paddle.distributed.tensor')

fastdeploy/model_executor/layers/moe/ep.py

@@ -39,8 +39,8 @@ def load_deep_ep() -> ModuleType:
 
     try:
         if envs.FD_USE_PFCC_DEEP_EP:
-            # Enable torch proxy before importing deep_ep (required by PFCC/PaddleFleet variants)
-            paddle.compat.enable_torch_proxy(scope={"deep_ep"})
+            # Enable paddle.enable_compat before importing deep_ep (required by PFCC/PaddleFleet variants)
+            paddle.enable_compat(scope={"deep_ep"})
             try:
                 import paddlefleet.ops.deep_ep as deep_ep  # type: ignore
 

fastdeploy/model_executor/layers/quantization/fp8_utils.py

@@ -68,7 +68,7 @@ def load_deep_gemm():
     if current_platform.is_cuda():
         if get_sm_version() >= 100:
             # SM100 should use PFCC DeepGemm
-            paddle.compat.enable_torch_proxy(scope={"deep_gemm"})
+            paddle.enable_compat(scope={"deep_gemm"})
             try:
                 import logging
 

fastdeploy/model_executor/layers/quantization/mxfp4.py

@@ -35,7 +35,7 @@ from fastdeploy.utils import get_logger
 from ..moe import FusedMoE
 from .quant_base import QuantConfigBase, QuantMethodBase
 
-paddle.compat.enable_torch_proxy(scope={"flashinfer"})
+paddle.enable_compat(scope={"flashinfer"})
 
 logger = get_logger("config", "config.log")
 

fastdeploy/model_executor/layers/quantization/nvfp4.py

@@ -38,7 +38,7 @@ from .quant_base import QuantConfigBase, QuantMethodBase, is_nvfp4_supported
 
 # Only import flashinfer on supported GPUs (B卡)
 if is_nvfp4_supported():
-    paddle.compat.enable_torch_proxy(scope={"flashinfer"})
+    paddle.enable_compat(scope={"flashinfer"})
 
     from flashinfer import fp4_quantize, mm_fp4
     from flashinfer.fused_moe import cutlass_fused_moe as flashinfer_cutlass_fused_moe