Cambricon MLUs support SDPA and flash_attn (#31102)

* add Cambricon MLUs support

* fix mlu device rng state

* up for quality check

* up mlu to support fp16

* fix mlu device dependency error

* fix mlu device dependency error

* enable mlu device for bf16

* fix mlu device memory tracker

* Cambricon support SDPA and flash_attn

src/transformers/utils/import_utils.py

@@ -329,6 +329,9 @@ def is_torch_sdpa_available():
     # NOTE: We require torch>=2.1 (and not torch>=2.0) to use SDPA in Transformers for two reasons:
     # - Allow the global use of the `scale` argument introduced in https://github.com/pytorch/pytorch/pull/95259
     # - Memory-efficient attention supports arbitrary attention_mask: https://github.com/pytorch/pytorch/pull/104310
+    # NOTE: MLU is OK with non-contiguous inputs.
+    if is_torch_mlu_available():
+        return version.parse(_torch_version) >= version.parse("2.1.0")
     # NOTE: We require torch>=2.1.1 to avoid a numerical issue in SDPA with non-contiguous inputs: https://github.com/pytorch/pytorch/issues/112577
     return version.parse(_torch_version) >= version.parse("2.1.1")
 
@@ -795,7 +798,7 @@ def is_flash_attn_2_available():
     # Let's add an extra check to see if cuda is available
     import torch
 
-    if not torch.cuda.is_available():
+    if not (torch.cuda.is_available() or is_torch_mlu_available()):
         return False
 
     if torch.version.cuda:
@@ -803,6 +806,8 @@ def is_flash_attn_2_available():
     elif torch.version.hip:
         # TODO: Bump the requirement to 2.1.0 once released in https://github.com/ROCmSoftwarePlatform/flash-attention
         return version.parse(importlib.metadata.version("flash_attn")) >= version.parse("2.0.4")
+    elif is_torch_mlu_available():
+        return version.parse(importlib.metadata.version("flash_attn")) >= version.parse("2.3.3")
     else:
         return False