mostly gemma2

examples/modular-transformers/modeling_dummy.py

@@ -272,7 +272,7 @@ def forward(
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
         **kwargs: Unpack[FlashAttentionKwargs],
     ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
         residual = hidden_states

examples/modular-transformers/modeling_multimodal1.py

@@ -272,7 +272,7 @@ def forward(
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
         **kwargs: Unpack[FlashAttentionKwargs],
     ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
         residual = hidden_states

examples/modular-transformers/modeling_my_new_model2.py

@@ -207,7 +207,7 @@ def forward(
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
         **kwargs: Unpack[FlashAttentionKwargs],
     ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
         residual = hidden_states
@@ -245,6 +245,51 @@ def forward(
         return outputs
 
 
+MY_NEW_MODEL2_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`MyNewModel2Config`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare MyNewModel2 Model outputting raw hidden-states without any specific head on top.",
+    MY_NEW_MODEL2_START_DOCSTRING,
+)
+class MyNewModel2PreTrainedModel(PreTrainedModel):
+    config_class = MyNewModel2Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["MyNewModel2DecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
 class MyNewModel2RotaryEmbedding(nn.Module):
     def __init__(
         self,
@@ -310,51 +355,6 @@ def forward(self, x, position_ids):
         return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
 
 
-MY_NEW_MODEL2_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`MyNewModel2Config`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-
-@add_start_docstrings(
-    "The bare MyNewModel2 Model outputting raw hidden-states without any specific head on top.",
-    MY_NEW_MODEL2_START_DOCSTRING,
-)
-class MyNewModel2PreTrainedModel(PreTrainedModel):
-    config_class = MyNewModel2Config
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["MyNewModel2DecoderLayer"]
-    _skip_keys_device_placement = ["past_key_values"]
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _supports_cache_class = True
-    _supports_quantized_cache = True
-    _supports_static_cache = True
-
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-
 MY_NEW_MODEL2_INPUTS_DOCSTRING = r"""
     Args:
         input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):

examples/modular-transformers/modeling_super.py

@@ -269,7 +269,7 @@ def forward(
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
         **kwargs: Unpack[FlashAttentionKwargs],
     ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
         residual = hidden_states

src/transformers/integrations/flash_attention.py

@@ -1,13 +1,25 @@
+from typing import Optional
+
 import torch
 
 from ..modeling_flash_attention_utils import _flash_attention_forward
 
 
 def flash_attention_forward(
-    config, query, key, value, attentions_mask, target_dtype=torch.float16, training=False, layer_idx=0, **kwargs
+    module: torch.nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    dropout: float = 0.0,
+    scaling: Optional[float] = None,
+    sliding_window: Optional[int] = None,
+    softcap: Optional[float] = None,
+    target_dtype: torch.dtype = torch.float16,
+    **kwargs,
 ):
-    if attentions_mask is not None:
-        seq_len = attentions_mask.shape[1]
+    if attention_mask is not None:
+        seq_len = attention_mask.shape[1]
         query = query[:, :, :seq_len]
         value = value[:, :, :seq_len]
     else:
@@ -18,8 +30,6 @@ def flash_attention_forward(
     key = key.transpose(1, 2)
     value = value.transpose(1, 2)
 
-    dropout_rate = config.attention_dropout if training else 0.0
-
     input_dtype = query.dtype
     if input_dtype == torch.float32:
         query = query.to(target_dtype)
@@ -30,11 +40,14 @@ def flash_attention_forward(
         query,
         key,
         value,
-        attentions_mask,
+        attention_mask,
         seq_len,
-        config=config,
-        dropout=dropout_rate,
-        layer_idx=layer_idx,
+        module.is_causal,
+        dropout=dropout,
+        softmax_scale=scaling,
+        sliding_window=sliding_window,
+        softcap=softcap,
+        use_top_left_mask=module._flash_attn_uses_top_left_mask,
         **kwargs,
     )
 

src/transformers/integrations/flex_attention.py

@@ -1,16 +1,31 @@
+from typing import Optional
+
+import torch
+
 from ..utils import is_torch_greater_or_equal
 
 
 if is_torch_greater_or_equal("2.5"):
     from torch.nn.attention.flex_attention import flex_attention
 
 
-def flex_attention_forward(module, query, key, value, attention_mask, output_attentions=False, **_kwargs):
+def flex_attention_forward(
+    module: torch.nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    scaling: Optional[float] = None,
+    softcap: Optional[float] = None,
+    **kwargs,
+):
     causal_mask = attention_mask
     if causal_mask is not None:
         causal_mask = causal_mask[:, :, :, : key.shape[-2]]
 
     def causal_mod(score, b, h, q_idx, kv_idx):
+        if softcap is not None:
+            score = softcap * torch.tanh(score / softcap)
         if causal_mask is not None:
             score += causal_mask[b][0][q_idx][kv_idx]
         return score
@@ -21,8 +36,9 @@ def causal_mod(score, b, h, q_idx, kv_idx):
         value,
         score_mod=causal_mod,
         enable_gqa=True,
-        scale=module.scaling,
+        scale=scaling,
         return_lse=True,
     )
     attn_output = attn_output.transpose(1, 2).contiguous()
+
     return attn_output, attention_weights

src/transformers/integrations/sdpa_attention.py

@@ -1,3 +1,5 @@
+from typing import Optional
+
 import torch
 
 
@@ -13,7 +15,16 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
     return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
 
 
-def sdpa_attention_forward(module, query, key, value, attention_mask=None, **_kwargs):
+def sdpa_attention_forward(
+    module: torch.nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    dropout: float = 0.0,
+    scaling: Optional[float] = None,
+    **kwargs,
+):
     key = repeat_kv(key, module.num_key_value_groups)
     value = repeat_kv(value, module.num_key_value_groups)
 
@@ -31,9 +42,10 @@ def sdpa_attention_forward(module, query, key, value, attention_mask=None, **_kw
         key,
         value,
         attn_mask=causal_mask,
-        dropout_p=module.config.attention_dropout if module.training else 0.0,
+        dropout_p=dropout,
+        scale=scaling,
         is_causal=is_causal,
-        scale=module.scaling,
     )
     attn_output = attn_output.transpose(1, 2).contiguous()
+
     return attn_output, None

src/transformers/modeling_utils.py

@@ -30,7 +30,7 @@
 from dataclasses import dataclass
 from functools import partial, wraps
 from threading import Thread
-from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, TypeVar, Union
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
 from zipfile import is_zipfile
 
 import torch

src/transformers/models/aria/modeling_aria.py

@@ -588,7 +588,7 @@ def forward(
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
         **kwargs: Unpack[FlashAttentionKwargs],
     ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
         residual = hidden_states

src/transformers/models/cohere2/modeling_cohere2.py

@@ -659,10 +659,6 @@ def __init__(self, config: Cohere2Config):
             [Cohere2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
         )
         self.norm = Cohere2LayerNorm(hidden_size=(config.hidden_size), eps=config.layer_norm_eps)
-
-        self.gradient_checkpointing = False
-        if getattr(config, "pretraining_tp", 1) != 1:
-            logger.warn("`pretraining_tp` is deprecated, please use `model.tensor_parallel` instead.")
         self.rotary_emb = Cohere2RotaryEmbedding(config=config)
 
         # Initialize weights and apply final processing