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[intel] add for intel path for llama.py (#3012)

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* fix for intel path

* remove unuse code

* Update unsloth/models/llama.py

---------

Co-authored-by: Daniel Han <danielhanchen@gmail.com>
This commit is contained in:
Lei Zhenyuan 2025-07-22 18:33:26 +08:00 • committed by GitHub
commit 7d8acbdf86
1 changed files with 16 additions and 11 deletions
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27
unsloth/models/llama.py
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@ -92,6 +92,11 @@ def clean_gpu_cache():
else:
torch.cuda.empty_cache()
if DEVICE_TYPE == "xpu":
get_current_device = torch.xpu.current_device
else:
get_current_device = torch.cuda.current_device
def original_apply_qkv(self, X):
Q = self.q_proj(X)
K = self.k_proj(X)
@ -1365,8 +1370,8 @@ class LlamaRotaryEmbedding(torch.nn.Module):
self._set_cos_sin_cache(seq_len=self.current_rope_size, device=torch.device(device_idx), dtype=torch.get_default_dtype())
# dummy so that patch_utils doesn't fail for now
self.cos_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
self.sin_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
self.cos_cached = torch.empty(1, device=get_current_device(), dtype=torch.get_default_dtype())
self.sin_cached = torch.empty(1, device=get_current_device(), dtype=torch.get_default_dtype())
pass
def _set_cos_sin_cache(self, seq_len, device, dtype):
@ -1402,7 +1407,7 @@ class LlamaRotaryEmbedding(torch.nn.Module):
def get_cached(self, seq_len = None, device_index = None):
if device_index is None:
device_index = torch.cuda.current_device()
device_index = get_current_device()
return self.multi_gpu_cos_cached[device_index], self.multi_gpu_sin_cached[device_index]
pass
@ -1484,8 +1489,8 @@ class LlamaExtendedRotaryEmbedding(torch.nn.Module):
self._set_cos_sin_cache(seq_len=self.current_rope_size, device=torch.device(device_idx), dtype=torch.get_default_dtype())
# dummy so that patch_utils doesn't fail for now
self.cos_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
self.sin_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
self.cos_cached = torch.empty(1, device=get_current_device(), dtype=torch.get_default_dtype())
self.sin_cached = torch.empty(1, device=get_current_device(), dtype=torch.get_default_dtype())
pass
def _set_cos_sin_cache(self, seq_len, device, dtype):
@ -1518,7 +1523,7 @@ class LlamaExtendedRotaryEmbedding(torch.nn.Module):
def get_cached(self, seq_len = None, device_index = None):
if device_index is None:
device_index = torch.cuda.current_device()
device_index = get_current_device()
return self.multi_gpu_cos_cached[device_index], self.multi_gpu_sin_cached[device_index]
pass
@ -1631,10 +1636,10 @@ class LongRopeRotaryEmbedding(torch.nn.Module):
self.multi_gpu_short_sin_cached[device_idx] = sin_cached
# dummy so that patch_utils doesn't fail for now
self.short_cos_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
self.short_sin_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
self.long_cos_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
self.long_sin_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
self.short_cos_cached = torch.empty(1, device=get_current_device(), dtype=torch.get_default_dtype())
self.short_sin_cached = torch.empty(1, device=get_current_device(), dtype=torch.get_default_dtype())
self.long_cos_cached = torch.empty(1, device=get_current_device(), dtype=torch.get_default_dtype())
self.long_sin_cached = torch.empty(1, device=get_current_device(), dtype=torch.get_default_dtype())
pass
def _set_cos_sin_cache(self, seq_len, device, dtype):
@ -1675,7 +1680,7 @@ class LongRopeRotaryEmbedding(torch.nn.Module):
def get_cached(self, seq_len = None, device_index = None):
if device_index is None:
device_index = torch.cuda.current_device()
device_index = get_current_device()
if seq_len is not None and seq_len < self.original_max_position_embeddings:
return self.multi_gpu_short_cos_cached[device_index], self.multi_gpu_short_sin_cached[device_index]
return self.multi_gpu_long_cos_cached[device_index], self.multi_gpu_long_sin_cached[device_index]