Remove logits.float()

src/transformers/models/cohere/modeling_cohere.py

@@ -46,7 +46,6 @@
     add_start_docstrings_to_model_forward,
     is_flash_attn_2_available,
     is_flash_attn_greater_or_equal_2_10,
-    is_torchdynamo_compiling,
     logging,
     replace_return_docstrings,
 )
@@ -1169,13 +1168,8 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        if labels is None and not is_torchdynamo_compiling():
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        # TODO: remove the float() operation in v4.46
-        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
         logits = logits * self.logit_scale
 
         loss = None

src/transformers/models/gemma/modeling_gemma.py

@@ -43,7 +43,6 @@
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     is_flash_attn_greater_or_equal_2_10,
-    is_torchdynamo_compiling,
     logging,
     replace_return_docstrings,
 )
@@ -1081,13 +1080,8 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        if labels is None and not is_torchdynamo_compiling():
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        # TODO: remove the float() operation in v4.46
-        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
 
         loss = None
         if labels is not None:

src/transformers/models/gemma/modular_gemma.py

@@ -27,7 +27,7 @@
 from ...modeling_flash_attention_utils import _flash_attention_forward
 from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 from ...pytorch_utils import ALL_LAYERNORM_LAYERS
-from ...utils import is_torchdynamo_compiling, logging
+from ...utils import logging
 from ..llama.modeling_llama import (
     LlamaDecoderLayer,
     LlamaFlashAttention2,
@@ -833,13 +833,8 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        if labels is None and not is_torchdynamo_compiling():
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        # TODO: remove the float() operation in v4.46
-        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
 
         loss = None
         if labels is not None:

src/transformers/models/gemma2/modeling_gemma2.py

@@ -42,7 +42,6 @@
     add_start_docstrings_to_model_forward,
     is_flash_attn_greater_or_equal,
     is_flash_attn_greater_or_equal_2_10,
-    is_torchdynamo_compiling,
     logging,
     replace_return_docstrings,
 )
@@ -1058,19 +1057,13 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        if labels is None and not is_torchdynamo_compiling():
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
         logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
         if self.config.final_logit_softcapping is not None:
             logits = logits / self.config.final_logit_softcapping
             logits = torch.tanh(logits)
             logits = logits * self.config.final_logit_softcapping
 
-        # TODO: remove the float() operation in v4.46
-        logits = logits.float()
         loss = None
         if labels is not None:
             # Upcast to float if we need to compute the loss to avoid potential precision issues

src/transformers/models/gemma2/modular_gemma2.py

@@ -31,7 +31,6 @@
     is_flash_attn_2_available,
     is_flash_attn_greater_or_equal,
     is_flash_attn_greater_or_equal_2_10,
-    is_torchdynamo_compiling,
     logging,
 )
 from ..gemma.modeling_gemma import (
@@ -800,19 +799,13 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        if labels is None and not is_torchdynamo_compiling():
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
         logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
         if self.config.final_logit_softcapping is not None:
             logits = logits / self.config.final_logit_softcapping
             logits = torch.tanh(logits)
             logits = logits * self.config.final_logit_softcapping
 
-        # TODO: remove the float() operation in v4.46
-        logits = logits.float()
         loss = None
         if labels is not None:
             # Upcast to float if we need to compute the loss to avoid potential precision issues

src/transformers/models/idefics2/modeling_idefics2.py

@@ -34,7 +34,6 @@
     add_start_docstrings_to_model_forward,
     is_flash_attn_2_available,
     is_flash_attn_greater_or_equal_2_10,
-    is_torchdynamo_compiling,
     logging,
     replace_return_docstrings,
 )
@@ -1617,13 +1616,8 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        if labels is None and not is_torchdynamo_compiling():
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        # TODO: remove the float() operation in v4.46
-        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
 
         loss = None
         if labels is not None:

src/transformers/models/jamba/modeling_jamba.py

@@ -51,7 +51,6 @@
     is_flash_attn_2_available,
     is_flash_attn_greater_or_equal_2_10,
     is_mamba_ssm_available,
-    is_torchdynamo_compiling,
 )
 from .configuration_jamba import JambaConfig
 
@@ -1540,12 +1539,6 @@ def forward(
             logits = self.lm_head(hidden_states)
         else:
             logits = self.lm_head(hidden_states[..., -num_logits_to_keep:, :])
-        if labels is None and not is_torchdynamo_compiling:
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
-        # TODO: remove the float() operations in v4.46
-        logits = logits.float()
 
         loss = None
         if labels is not None:

src/transformers/models/jetmoe/modeling_jetmoe.py

@@ -38,7 +38,6 @@
     add_start_docstrings_to_model_forward,
     is_flash_attn_2_available,
     is_flash_attn_greater_or_equal_2_10,
-    is_torchdynamo_compiling,
     logging,
     replace_return_docstrings,
 )
@@ -1302,13 +1301,8 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        if labels is None and not is_torchdynamo_compiling():
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        # TODO: remove the float() operation in v4.46
-        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
 
         loss = None
         if labels is not None:

src/transformers/models/llama/modeling_llama.py

@@ -45,7 +45,6 @@
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     is_flash_attn_greater_or_equal_2_10,
-    is_torchdynamo_compiling,
     logging,
     replace_return_docstrings,
 )
@@ -1206,13 +1205,8 @@ def forward(
             logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
             logits = torch.cat(logits, dim=-1)
         else:
-            if labels is None and not is_torchdynamo_compiling():
-                logger.warning_once(
-                    "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-                )
             # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-            # TODO: remove the float() operation in v4.46
-            logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
+            logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
 
         loss = None
         if labels is not None:

src/transformers/models/mistral/modeling_mistral.py

@@ -43,7 +43,6 @@
     add_start_docstrings_to_model_forward,
     is_flash_attn_2_available,
     is_flash_attn_greater_or_equal_2_10,
-    is_torchdynamo_compiling,
     logging,
     replace_return_docstrings,
 )
@@ -1050,13 +1049,8 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        if labels is None and not is_torchdynamo_compiling():
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        # TODO: remove the float() operation in v4.46
-        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
 
         loss = None
         if labels is not None:

src/transformers/models/mixtral/modeling_mixtral.py

@@ -44,7 +44,6 @@
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     is_flash_attn_2_available,
-    is_torchdynamo_compiling,
     logging,
     replace_return_docstrings,
 )
@@ -1302,13 +1301,8 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        if labels is None and not is_torchdynamo_compiling():
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        # TODO: remove the float() operation in v4.46
-        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
 
         loss = None
         if labels is not None:

src/transformers/models/nemotron/modeling_nemotron.py

@@ -43,7 +43,6 @@
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     is_flash_attn_greater_or_equal_2_10,
-    is_torchdynamo_compiling,
     logging,
     replace_return_docstrings,
 )
@@ -1081,14 +1080,8 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        if labels is None and not is_torchdynamo_compiling():
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
         logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
-        # TODO: remove the float() operation in v4.46
-        logits = logits.float()
 
         loss = None
         if labels is not None:

src/transformers/models/olmo/modeling_olmo.py

@@ -43,7 +43,6 @@
     add_start_docstrings_to_model_forward,
     is_flash_attn_2_available,
     is_flash_attn_greater_or_equal_2_10,
-    is_torchdynamo_compiling,
     logging,
     replace_return_docstrings,
 )
@@ -1124,13 +1123,8 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        if labels is None and not is_torchdynamo_compiling():
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        # TODO: remove the float() operation in v4.46
-        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
 
         loss = None
         if labels is not None:

src/transformers/models/phi/modeling_phi.py

@@ -43,7 +43,6 @@
     get_torch_version,
     is_flash_attn_2_available,
     is_flash_attn_greater_or_equal_2_10,
-    is_torchdynamo_compiling,
     logging,
     replace_return_docstrings,
 )
@@ -1247,13 +1246,8 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        if labels is None and not is_torchdynamo_compiling():
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        # TODO: remove the float() operation in v4.46
-        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
 
         loss = None
         if labels is not None:

src/transformers/models/phi3/modeling_phi3.py

@@ -41,7 +41,6 @@
     add_start_docstrings_to_model_forward,
     is_flash_attn_2_available,
     is_flash_attn_greater_or_equal_2_10,
-    is_torchdynamo_compiling,
     logging,
     replace_return_docstrings,
 )
@@ -1277,13 +1276,8 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        if labels is None and not is_torchdynamo_compiling():
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        # TODO: remove the float() operation in v4.46
-        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
 
         loss = None
         if labels is not None:

src/transformers/models/qwen2/modeling_qwen2.py

@@ -44,7 +44,6 @@
     add_start_docstrings_to_model_forward,
     is_flash_attn_2_available,
     is_flash_attn_greater_or_equal_2_10,
-    is_torchdynamo_compiling,
     logging,
     replace_return_docstrings,
 )
@@ -1180,13 +1179,8 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        if labels is None and not is_torchdynamo_compiling():
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        # TODO: remove the float() operation in v4.46
-        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
 
         loss = None
         if labels is not None:

src/transformers/models/qwen2_moe/modeling_qwen2_moe.py

@@ -45,7 +45,6 @@
     add_start_docstrings_to_model_forward,
     is_flash_attn_2_available,
     is_flash_attn_greater_or_equal_2_10,
-    is_torchdynamo_compiling,
     logging,
     replace_return_docstrings,
 )
@@ -1368,13 +1367,8 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        if labels is None and not is_torchdynamo_compiling():
-            logger.warning_once(
-                "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
-            )
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        # TODO: remove the float() operation in v4.46
-        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
 
         loss = None
         if labels is not None: