chore: import upstream snapshot with attribution

swift/model/npu_patch/fsdp.py

@@ -0,0 +1,86 @@
+# Copyright (c) ModelScope Contributors. All rights reserved.
+from __future__ import annotations
+
+import accelerate.utils.fsdp_utils as fsdp_utils
+import torch
+from accelerate.accelerator import Accelerator
+from functools import wraps
+
+
+class NPUCastError(RuntimeError):
+    """Raised when fp32 casting fails during NPU FSDP2 preparation."""
+
+
+def _cast_module_to_fp32_for_npu_if_needed(module: torch.nn.Module, accelerator: Accelerator) -> torch.nn.Module:
+    if accelerator.device.type != 'npu':
+        return module
+
+    param = next(module.parameters(recurse=True), None)
+    if param is None:
+        return module
+
+    if not param.is_floating_point() or param.dtype == torch.float32:
+        return module
+
+    # Accelerate FSDP2 flattens and shards parameters during prepare. On NPU,
+    # entering that path with bf16/fp16 parameters can fail before mixed
+    # precision policy has a chance to manage runtime compute dtype. Cast early
+    # while parameters are still on CPU or meta, so only dtype changes here.
+
+    # GRPO with vLLM colocate mode may preload the model onto NPU before
+    # Accelerator.prepare() is called. In that case, casting fp32 on NPU
+    # would temporarily duplicate the full model (bf16 + fp32), causing OOM.
+    # We move the model back to CPU first to free NPU memory, then cast.
+    try:
+        if param.device.type == 'npu':
+            import torch_npu
+            module = module.cpu()
+            torch_npu.npu.synchronize()
+            torch_npu.npu.empty_cache()
+        return module.to(torch.float32)
+    except Exception as exc:
+        raise NPUCastError(f'Failed to cast {module.__class__.__name__} to fp32.') from exc
+
+
+_original_fsdp2_prepare_model = fsdp_utils.fsdp2_prepare_model
+
+
+@wraps(_original_fsdp2_prepare_model)
+def wrapped_fsdp2_prepare_model(
+    accelerator: Accelerator,
+    model: torch.nn.Module,
+):
+    # Public utility entry used by some code paths before Accelerator.prepare.
+    model = _cast_module_to_fp32_for_npu_if_needed(model, accelerator)
+    return _original_fsdp2_prepare_model(accelerator, model)
+
+
+_original_prepare_fsdp2 = Accelerator._prepare_fsdp2
+
+
+@wraps(_original_prepare_fsdp2)
+def wrapped_prepare_fsdp2(
+    self: Accelerator,
+    *args,
+    **kwargs,
+):
+    # Accelerator.prepare may receive one or more modules directly; patch this
+    # private entry too so all FSDP2 NPU preparation paths get the same fp32 cast.
+    patched_args = [
+        _cast_module_to_fp32_for_npu_if_needed(obj, self) if isinstance(obj, torch.nn.Module) else obj for obj in args
+    ]
+
+    return _original_prepare_fsdp2(self, *patched_args, **kwargs)
+
+
+_APPLIED = False
+
+
+def apply_patch() -> None:
+    global _APPLIED
+    if _APPLIED:
+        return
+
+    fsdp_utils.fsdp2_prepare_model = wrapped_fsdp2_prepare_model
+    Accelerator._prepare_fsdp2 = wrapped_prepare_fsdp2
+    _APPLIED = True