Support MXFP4 for GPT-OSS (#5435)

* support mxfp4 in gpt-oss

* support mxfp4 in gpt-oss

* add scope for flashinfer

* remove torch code

* update envs.FD_MXFP4_BACKEND

* update process_weights_after_loading

* update env name

* support tp in gpt-oss, add e2e test

* add flashinfer-python-paddle in requirements

* fix import error

* add test

* add test

* add test

* add test

fastdeploy/model_executor/layers/utils.py

@@ -23,6 +23,7 @@ from paddle import Tensor, nn
 from paddle.framework import in_dynamic_mode
 from scipy.linalg import block_diag
 
+from fastdeploy.config import FDConfig
 from fastdeploy.platforms import current_platform
 
 if current_platform.is_cuda() and current_platform.available():
@@ -519,3 +520,20 @@ def vocab_range_from_per_partition_vocab_size(per_partition_vocab_size: int, ran
 def vocab_range_from_global_vocab_size(global_vocab_size: int, rank: int, world_size: int, offset: int = 0):
     per_partition_vocab_size = divide(global_vocab_size, world_size)
     return vocab_range_from_per_partition_vocab_size(per_partition_vocab_size, rank, offset=offset)
+
+
+def modules_to_convert(prefix: str, fd_config: FDConfig):
+    import fnmatch
+
+    if (
+        hasattr(fd_config.model_config, "quantization_config")
+        and fd_config.model_config.quantization_config is not None
+    ):
+        if "modules_to_not_convert" in fd_config.model_config.quantization_config:
+            patterns = fd_config.model_config.quantization_config["modules_to_not_convert"]
+            for p in patterns:
+                if fnmatch.fnmatch(prefix, p) or fnmatch.fnmatch(prefix, p + ".*"):
+                    return False
+        return True
+    else:
+        return True