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refactor attn impl replacement

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This commit is contained in:
Datta Nimmaturi 2026-03-11 06:10:29 +00:00
commit e5a2d39d57
3 changed files with 61 additions and 46 deletions
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81
unsloth/models/_utils.py
View file

@ -64,7 +64,7 @@ __all__ = [
"patch_compiled_autograd",
"process_vision_info",
"unsloth_compile_transformers",
"prefer_flex_attn_if_supported",
"determine_attention_implementation",
"patch_fast_lora",
"validate_loftq_config",
"RaiseUninitialized",
@ -222,36 +222,61 @@ def apply_unsloth_gradient_checkpointing(
return use_gradient_checkpointing
def prefer_flex_attn_if_supported(model_class, config):
if os.environ.get("UNSLOTH_ENABLE_FLEX_ATTENTION", "1") == "0":
return None
try:
from transformers.utils.import_utils import is_torch_flex_attn_available
def determine_attention_implementation(model_class, config):
model_type = getattr(config, "model_type", "").lower()
if not is_torch_flex_attn_available():
return None
if model_class is None or not getattr(
model_class, "_supports_flex_attn", False
):
return None
# GPT-OSS, Mllama and Gemma3N use eager/sdpa attention during
# inference since flex attention returns incorrect results or errors out.
# GPT-OSS: left padding issues cause incorrect outputs.
# Mllama: _update_causal_mask uses make_flex_block_causal_mask which
# creates BlockMask with Q_LEN=KV_LEN=total_seq_len, but during
# decode q_len=1, causing ValueError. Needs transformers update.
# Gemma3N: timm vision wrappers (eg Gemma3nVisionConfig) do not
# support flex_attention.
model_type = getattr(config, "model_type", "") if config else ""
if model_type in ("gpt_oss", "mllama") or str(model_type).startswith("gemma3n"):
return None
# 1. Flash Attention 2
if HAS_FLASH_ATTENTION and model_type not in ("gpt_oss", "mllama") and not model_type.startswith("gemma3"):
supports_fa2 = False
if model_class is not None:
supports_fa2 = getattr(model_class, "_supports_flash_attn_2", False) or getattr(model_class, "_supports_flash_attn", False)
if supports_fa2:
if config is not None:
setattr(config, "_attn_implementation", "flash_attention_2")
if hasattr(config, "attn_implementation"):
setattr(config, "attn_implementation", "flash_attention_2")
return "flash_attention_2"
# 2. Flex Attention
if os.environ.get("UNSLOTH_ENABLE_FLEX_ATTENTION", "1") != "0":
try:
from transformers.utils.import_utils import is_torch_flex_attn_available
if is_torch_flex_attn_available() and (model_class is not None) and getattr(
model_class, "_supports_flex_attn", False
):
# GPT-OSS, Mllama and Gemma3 use eager/sdpa attention during
# inference since flex attention returns incorrect results or errors out.
# GPT-OSS: left padding issues cause incorrect outputs.
# Mllama: _update_causal_mask uses make_flex_block_causal_mask which
# creates BlockMask with Q_LEN=KV_LEN=total_seq_len, but during
# decode q_len=1, causing ValueError. Needs transformers update.
# Gemma3N: timm vision wrappers (eg Gemma3nVisionConfig) do not
# support flex_attention.
if model_type not in ("gpt_oss", "mllama") and not model_type.startswith("gemma3"):
if config is not None:
setattr(config, "_attn_implementation", "flex_attention")
if hasattr(config, "attn_implementation"):
setattr(config, "attn_implementation", "flex_attention")
return "flex_attention"
except Exception:
pass
# 3. SDPA
if model_class is not None and getattr(model_class, "_supports_sdpa", False):
if config is not None:
setattr(config, "_attn_implementation", "flex_attention")
setattr(config, "_attn_implementation", "sdpa")
if hasattr(config, "attn_implementation"):
setattr(config, "attn_implementation", "flex_attention")
return "flex_attention"
except Exception:
return None
setattr(config, "attn_implementation", "sdpa")
return "sdpa"
# 4. Eager
if config is not None:
setattr(config, "_attn_implementation", "eager")
if hasattr(config, "attn_implementation"):
setattr(config, "attn_implementation", "eager")
return "eager"
def _run_temporary_patches(phase):

4
unsloth/models/llama.py
View file

@ -2341,9 +2341,7 @@ class FastLlamaModel:
model_function = MODEL_FOR_CAUSAL_LM_MAPPING[model_config.__class__]
IS_FALCON_H1 = model_config.model_type.startswith("falcon_h1")
preferred_attn_impl = (
prefer_flex_attn_if_supported(model_function, model_config) or "eager"
)
preferred_attn_impl = determine_attention_implementation(model_function, model_config)
has_rope_scaling = False
try:

22
unsloth/models/vision.py
View file

@ -597,8 +597,7 @@ class FastBaseModel:
custom_datatype = None
correct_dtype = None
# Stop SDPA for some archs like Pixtral / Mistral3
flex_attn_impl = None
# Unified hierarchical attention fallback: Flash > Flex > SDPA > Eager
if auto_config is None:
auto_config = AutoConfig.from_pretrained(
model_name,
@ -609,7 +608,8 @@ class FastBaseModel:
model_class = auto_model._model_mapping[auto_config.__class__]
except Exception:
model_class = None
flex_attn_impl = prefer_flex_attn_if_supported(model_class, auto_config)
attn_impl = determine_attention_implementation(model_class, auto_config)
# Handle FP8 models: get_model_name has already redirected this to BF16 sibling if the model ships with
# FP8 weights. We just need to update it here for sanity.
@ -620,20 +620,12 @@ class FastBaseModel:
except Exception:
model_class = None
model_type = str(getattr(auto_config, "model_type", "")).lower()
if model_type.startswith("gemma3n"):
# Gemma3N variants initialize timm-based vision towers which do
# not support flex_attention, so default to eager unless overridden.
default_attn_impl = "eager"
else:
default_attn_impl = "flex_attention" if flex_attn_impl else "sdpa"
if not ("attn_implementation" in kwargs):
kwargs["attn_implementation"] = default_attn_impl
kwargs["attn_implementation"] = attn_impl
if not supports_sdpa and kwargs.get("attn_implementation") == "sdpa":
if os.environ.get("UNSLOTH_ENABLE_FLEX_ATTENTION", "0") == "0":
print(
f"Unsloth: {model_type_arch.title()} does not support SDPA - switching to fast eager."
)
print(
f"Unsloth: {model_type_arch.title()} does not support SDPA - switching to fast eager."
)
del kwargs["attn_implementation"]
bnb_config = None