v4.39 deprecations 🧼 (#29492)

docs/source/en/internal/generation_utils.md

@@ -336,12 +336,6 @@ A [`Constraint`] can be used to force the generation to include specific tokens
     - process
     - finalize
 
-## Utilities
-
-[[autodoc]] top_k_top_p_filtering
-
-[[autodoc]] tf_top_k_top_p_filtering
-
 ## Streamers
 
 [[autodoc]] TextStreamer

docs/source/ja/internal/generation_utils.md

@@ -335,12 +335,6 @@ generation_output[:2]
     - process
     - finalize
 
-## Utilities
-
-[[autodoc]] top_k_top_p_filtering
-
-[[autodoc]] tf_top_k_top_p_filtering
-
 ## Streamers
 
 [[autodoc]] TextStreamer

docs/source/zh/internal/generation_utils.md

@@ -330,12 +330,6 @@ generation_output[:2]
     - process
     - finalize
 
-## Utilities
-
-[[autodoc]] top_k_top_p_filtering
-
-[[autodoc]] tf_top_k_top_p_filtering
-
 ## Streamers
 
 [[autodoc]] TextStreamer

src/transformers/__init__.py

@@ -1409,7 +1409,6 @@ else:
             "TypicalLogitsWarper",
             "UnbatchedClassifierFreeGuidanceLogitsProcessor",
             "WhisperTimeStampLogitsProcessor",
-            "top_k_top_p_filtering",
         ]
     )
     _import_structure["generation_utils"] = []
@@ -3814,7 +3813,6 @@ else:
             "TFTemperatureLogitsWarper",
             "TFTopKLogitsWarper",
             "TFTopPLogitsWarper",
-            "tf_top_k_top_p_filtering",
         ]
     )
     _import_structure["generation_tf_utils"] = []
@@ -6206,7 +6204,6 @@ if TYPE_CHECKING:
             TypicalLogitsWarper,
             UnbatchedClassifierFreeGuidanceLogitsProcessor,
             WhisperTimeStampLogitsProcessor,
-            top_k_top_p_filtering,
         )
         from .modeling_utils import PreTrainedModel
         from .models.albert import (
@@ -8178,7 +8175,6 @@ if TYPE_CHECKING:
             TFTemperatureLogitsWarper,
             TFTopKLogitsWarper,
             TFTopPLogitsWarper,
-            tf_top_k_top_p_filtering,
         )
         from .keras_callbacks import KerasMetricCallback, PushToHubCallback
         from .modeling_tf_utils import (

src/transformers/activations.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import math
-import warnings
 from collections import OrderedDict
 
 import torch
@@ -138,14 +137,6 @@ class AccurateGELUActivation(nn.Module):
         return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044715 * torch.pow(input, 3))))
 
 
-class SiLUActivation(nn.SiLU):
-    def __init__(self, *args, **kwargs):
-        warnings.warn(
-            "The SiLUActivation class has been deprecated and will be removed in v4.39. Please use nn.SiLU instead.",
-        )
-        super().__init__(*args, **kwargs)
-
-
 class MishActivation(nn.Module):
     """
     See Mish: A Self-Regularized Non-Monotonic Activation Function (Misra., https://arxiv.org/abs/1908.08681). Also

src/transformers/generation/__init__.py

@@ -88,7 +88,6 @@ else:
     ]
     _import_structure["utils"] = [
         "GenerationMixin",
-        "top_k_top_p_filtering",
         "GreedySearchEncoderDecoderOutput",
         "GreedySearchDecoderOnlyOutput",
         "SampleEncoderDecoderOutput",
@@ -130,7 +129,6 @@ else:
     ]
     _import_structure["tf_utils"] = [
         "TFGenerationMixin",
-        "tf_top_k_top_p_filtering",
         "TFGreedySearchDecoderOnlyOutput",
         "TFGreedySearchEncoderDecoderOutput",
         "TFSampleEncoderDecoderOutput",
@@ -241,7 +239,6 @@ if TYPE_CHECKING:
             GreedySearchEncoderDecoderOutput,
             SampleDecoderOnlyOutput,
             SampleEncoderDecoderOutput,
-            top_k_top_p_filtering,
         )
 
     try:
@@ -279,7 +276,6 @@ if TYPE_CHECKING:
             TFGreedySearchEncoderDecoderOutput,
             TFSampleDecoderOnlyOutput,
             TFSampleEncoderDecoderOutput,
-            tf_top_k_top_p_filtering,
         )
 
     try:

src/transformers/generation/tf_utils.py

@@ -3088,68 +3088,6 @@ class TFGenerationMixin:
             return generated
 
 
-def tf_top_k_top_p_filtering(logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1):
-    """
-    Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
-
-    Args:
-        logits: logits distribution shape (batch size, vocabulary size)
-        top_k (`int`, *optional*, defaults to 0):
-            If > 0, only keep the top k tokens with highest probability (top-k filtering)
-        top_p (`float`, *optional*, defaults to 1.0):
-            If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus
-            filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
-        min_tokens_to_keep (`int`, *optional*, defaults to 1):
-            Minimumber of tokens we keep per batch example in the output.
-
-    From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
-    """
-
-    warnings.warn(
-        "`tf_top_k_top_p_filtering` is scheduled for deletion in v4.39. Use `TFTopKLogitsWarper` and "
-        "`TFTopPLogitsWarper` instead.",
-        DeprecationWarning,
-    )
-
-    logits_shape = shape_list(logits)
-
-    if top_k > 0:
-        top_k = min(max(top_k, min_tokens_to_keep), logits_shape[-1])  # Safety check
-        # Remove all tokens with a probability less than the last token of the top-k
-        indices_to_remove = logits < tf.math.top_k(logits, k=top_k)[0][..., -1, None]
-        logits = tf.where(indices_to_remove, filter_value, logits)
-    if top_p < 1.0:
-        sorted_indices = tf.argsort(logits, direction="DESCENDING")
-        sorted_logits = tf.gather(
-            logits, sorted_indices, axis=-1, batch_dims=1
-        )  # expects logits to be of dim (batch_size, vocab_size)
-
-        cumulative_probs = tf.math.cumsum(stable_softmax(sorted_logits, axis=-1), axis=-1)
-
-        # Remove tokens with cumulative probability above the threshold (token with 0 are kept)
-        sorted_indices_to_remove = cumulative_probs > top_p
-
-        if min_tokens_to_keep > 1:
-            # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
-            sorted_indices_to_remove = tf.concat(
-                [
-                    tf.zeros_like(sorted_indices_to_remove[:, :min_tokens_to_keep]),
-                    sorted_indices_to_remove[:, min_tokens_to_keep:],
-                ],
-                -1,
-            )
-
-        # Shift the indices to the right to keep also the first token above the threshold
-        sorted_indices_to_remove = tf.concat(
-            [tf.zeros_like(sorted_indices_to_remove[:, :1]), sorted_indices_to_remove[:, :-1]],
-            -1,
-        )
-        # scatter sorted tensors to original indexing
-        indices_to_remove = scatter_values_on_batch_indices(sorted_indices_to_remove, sorted_indices)
-        logits = tf.where(indices_to_remove, filter_value, logits)
-    return logits
-
-
 def scatter_values_on_batch_indices(values, batch_indices):
     shape = shape_list(batch_indices)
     # broadcast batch dim to shape

src/transformers/generation/utils.py

@@ -4810,47 +4810,6 @@ def _split_model_outputs(outputs, new_outputs, cur_len, added_len, is_decoder_at
     return outputs
 
 
-def top_k_top_p_filtering(
-    logits: torch.FloatTensor,
-    top_k: int = 0,
-    top_p: float = 1.0,
-    filter_value: float = -float("Inf"),
-    min_tokens_to_keep: int = 1,
-) -> torch.FloatTensor:
-    """
-    Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
-
-    Args:
-        logits: logits distribution shape (batch size, vocabulary size)
-        top_k (`int`, *optional*, defaults to 0):
-            If > 0, only keep the top k tokens with highest probability (top-k filtering)
-        top_p (`float`, *optional*, defaults to 1.0):
-            If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus
-            filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
-        min_tokens_to_keep (`int`, *optional*, defaults to 1):
-            Minimumber of tokens we keep per batch example in the output.
-
-    From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
-    """
-    warnings.warn(
-        "`top_k_top_p_filtering` is scheduled for deletion in v4.39. Use `TopKLogitsWarper` and `TopPLogitsWarper` "
-        "instead.",
-        DeprecationWarning,
-    )
-
-    if top_k > 0:
-        logits = TopKLogitsWarper(top_k=top_k, filter_value=filter_value, min_tokens_to_keep=min_tokens_to_keep)(
-            None, logits
-        )
-
-    if 0 <= top_p <= 1.0:
-        logits = TopPLogitsWarper(top_p=top_p, filter_value=filter_value, min_tokens_to_keep=min_tokens_to_keep)(
-            None, logits
-        )
-
-    return logits
-
-
 def _ranking_fast(
     context_hidden: torch.FloatTensor,
     next_hidden: torch.FloatTensor,

src/transformers/models/llama/modeling_llama.py

@@ -129,10 +129,7 @@ class LlamaRotaryEmbedding(nn.Module):
         return self._cos_cached
 
     @torch.no_grad()
-    def forward(self, x, position_ids, seq_len=None):
+    def forward(self, x, position_ids):
-        if seq_len is not None:
-            logger.warning_once("The `seq_len` argument is deprecated and unused. It will be removed in v4.39.")
-
         # x: [bs, num_attention_heads, seq_len, head_size]
         inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
         position_ids_expanded = position_ids[:, None, :].float()
@@ -151,17 +148,17 @@ class LlamaRotaryEmbedding(nn.Module):
 class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
     """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
 
-    def forward(self, x, position_ids, seq_len=None):
+    def forward(self, x, position_ids):
         # difference to the original RoPE: a scaling factor is aplied to the position ids
         position_ids = position_ids.float() / self.scaling_factor
-        cos, sin = super().forward(x, position_ids, seq_len)
+        cos, sin = super().forward(x, position_ids)
         return cos, sin
 
 
 class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
     """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
 
-    def forward(self, x, position_ids, seq_len=None):
+    def forward(self, x, position_ids):
         # difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
         seq_len = torch.max(position_ids) + 1
         if seq_len > self.max_position_embeddings:
@@ -173,7 +170,7 @@ class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
             )
             self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: this may break with compilation
 
-        cos, sin = super().forward(x, position_ids, seq_len)
+        cos, sin = super().forward(x, position_ids)
         return cos, sin
 
 

src/transformers/models/opt/modeling_opt.py

@@ -120,27 +120,10 @@ class OPTAttention(nn.Module):
     ):
         super().__init__()
         self.config = config
-
+        self.embed_dim = config.hidden_size
-        def _handle_deprecated_argument(config_arg_name, config, fn_arg_name, kwargs):
+        self.num_heads = config.num_attention_heads
-            """
+        self.dropout = config.attention_dropout
-            If a the deprecated argument `fn_arg_name` is passed, raise a deprecation
+        self.enable_bias = config.enable_bias
-            warning and return that value, otherwise take the equivalent config.config_arg_name
-            """
-            val = None
-            if fn_arg_name in kwargs:
-                logging.warning(
-                    "Passing in {fn_arg_name} to {self.__class__.__name__} is deprecated and won't be supported from "
-                    "v4.39. Please set it in the config instead"
-                )
-                val = kwargs.pop(fn_arg_name)
-            else:
-                val = getattr(config, config_arg_name)
-            return val
-
-        self.embed_dim = _handle_deprecated_argument("hidden_size", config, "embed_dim", kwargs)
-        self.num_heads = _handle_deprecated_argument("num_attention_heads", config, "num_heads", kwargs)
-        self.dropout = _handle_deprecated_argument("attention_dropout", config, "dropout", kwargs)
-        self.enable_bias = _handle_deprecated_argument("enable_bias", config, "bias", kwargs)
 
         self.head_dim = self.embed_dim // self.num_heads
         self.is_causal = True

src/transformers/utils/dummy_pt_objects.py

@@ -408,10 +408,6 @@ class WhisperTimeStampLogitsProcessor(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
-def top_k_top_p_filtering(*args, **kwargs):
-    requires_backends(top_k_top_p_filtering, ["torch"])
-
-
 class PreTrainedModel(metaclass=DummyObject):
     _backends = ["torch"]
 

src/transformers/utils/dummy_tf_objects.py

@@ -128,10 +128,6 @@ class TFTopPLogitsWarper(metaclass=DummyObject):
         requires_backends(self, ["tf"])
 
 
-def tf_top_k_top_p_filtering(*args, **kwargs):
-    requires_backends(tf_top_k_top_p_filtering, ["tf"])
-
-
 class KerasMetricCallback(metaclass=DummyObject):
     _backends = ["tf"]
 

tests/generation/test_tf_utils.py

@@ -41,7 +41,6 @@ if is_tf_available():
         TFBartForConditionalGeneration,
         TFLogitsProcessorList,
         TFMinLengthLogitsProcessor,
-        tf_top_k_top_p_filtering,
     )
     from transformers.modeling_tf_utils import keras
 
@@ -49,102 +48,6 @@ if is_tensorflow_text_available():
     import tensorflow_text as text
 
 
-@require_tf
-class UtilsFunctionsTest(unittest.TestCase):
-    # tests whether the top_k_top_p_filtering function behaves as expected
-    def test_top_k_top_p_filtering(self):
-        logits = tf.convert_to_tensor(
-            [
-                [
-                    8.2220991,  # 3rd highest value; idx. 0
-                    -0.5620044,
-                    5.23229752,
-                    4.0386393,
-                    -6.8798378,
-                    -0.54785802,
-                    -3.2012153,
-                    2.92777176,
-                    1.88171953,
-                    7.35341276,  # 5th highest value; idx. 9
-                    8.43207833,  # 2nd highest value; idx. 10
-                    -9.85711836,
-                    -5.96209236,
-                    -1.13039161,
-                    -7.1115294,
-                    -0.8369633,
-                    -5.3186408,
-                    7.06427407,
-                    0.81369344,
-                    -0.82023817,
-                    -5.9179796,
-                    0.58813443,
-                    -6.99778438,
-                    4.71551189,
-                    -0.18771637,
-                    7.44020759,  # 4th highest value; idx. 25
-                    9.38450987,  # 1st highest value; idx. 26
-                    2.12662941,
-                    -9.32562038,
-                    2.35652522,
-                ],  # cummulative prob of 5 highest values <= 0.6
-                [
-                    0.58425518,
-                    4.53139238,
-                    -5.57510464,
-                    -6.28030699,
-                    -7.19529503,
-                    -4.02122551,
-                    1.39337037,
-                    -6.06707057,
-                    1.59480517,
-                    -9.643119,
-                    0.03907799,
-                    0.67231762,
-                    -8.88206726,
-                    6.27115922,  # 4th highest value; idx. 13
-                    2.28520723,
-                    4.82767506,
-                    4.30421368,
-                    8.8275313,  # 2nd highest value; idx. 17
-                    5.44029958,  # 5th highest value; idx. 18
-                    -4.4735794,
-                    7.38579536,  # 3rd highest value; idx. 20
-                    -2.91051663,
-                    2.61946077,
-                    -2.5674762,
-                    -9.48959302,
-                    -4.02922645,
-                    -1.35416918,
-                    9.67702323,  # 1st highest value; idx. 27
-                    -5.89478553,
-                    1.85370467,
-                ],  # cummulative prob of 5 highest values <= 0.6
-            ],
-            dtype=tf.float32,
-        )
-
-        non_inf_expected_idx = tf.convert_to_tensor(
-            [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]],
-            dtype=tf.int32,
-        )  # expected non filtered idx as noted above
-
-        non_inf_expected_output = tf.convert_to_tensor(
-            [8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023],
-            dtype=tf.float32,
-        )  # expected non filtered values as noted above
-
-        output = tf_top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)
-
-        non_inf_output = output[output != -float("inf")]
-        non_inf_idx = tf.cast(
-            tf.where(tf.not_equal(output, tf.constant(-float("inf"), dtype=tf.float32))),
-            dtype=tf.int32,
-        )
-
-        tf.debugging.assert_near(non_inf_output, non_inf_expected_output, rtol=1e-12)
-        tf.debugging.assert_equal(non_inf_idx, non_inf_expected_idx)
-
-
 @require_tf
 class TFGenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMixin):
     # setting framework_dependent_parameters needs to be gated, just like its contents' imports

tests/generation/test_utils.py

@@ -52,7 +52,6 @@ if is_torch_available():
         GPT2Tokenizer,
         ImageGPTForCausalImageModeling,
         SpeechEncoderDecoderModel,
-        top_k_top_p_filtering,
     )
     from transformers.cache_utils import DynamicCache
     from transformers.generation import (
@@ -2345,133 +2344,6 @@ class GenerationTesterMixin:
 
 @require_torch
 class UtilsFunctionsTest(unittest.TestCase):
-    # tests whether the top_k_top_p function behaves as expected
-    def test_top_k_top_p_filtering(self):
-        logits = torch.tensor(
-            [
-                [
-                    8.2220991,  # 3rd highest value; idx. 0
-                    -0.5620044,
-                    5.23229752,
-                    4.0386393,
-                    -6.8798378,
-                    -0.54785802,
-                    -3.2012153,
-                    2.92777176,
-                    1.88171953,
-                    7.35341276,
-                    8.43207833,  # 2nd highest value; idx. 10
-                    -9.85711836,
-                    -5.96209236,
-                    -1.13039161,
-                    -7.1115294,
-                    -0.8369633,
-                    -5.3186408,
-                    7.06427407,
-                    0.81369344,
-                    -0.82023817,
-                    -5.9179796,
-                    0.58813443,
-                    -6.99778438,
-                    4.71551189,
-                    -0.18771637,
-                    7.44020759,  # 4th highest value; idx. 25
-                    9.38450987,  # 1st highest value; idx. 26
-                    2.12662941,
-                    -9.32562038,
-                    2.35652522,
-                ],  # cummulative prob of 4 highest values <= 0.6
-                [
-                    0.58425518,
-                    4.53139238,
-                    -5.57510464,
-                    -6.28030699,
-                    -7.19529503,
-                    -4.02122551,
-                    1.39337037,
-                    -6.06707057,
-                    1.59480517,
-                    -9.643119,
-                    0.03907799,
-                    0.67231762,
-                    -8.88206726,
-                    6.27115922,  # 4th highest value; idx. 13
-                    2.28520723,
-                    4.82767506,
-                    4.30421368,
-                    8.8275313,  # 2nd highest value; idx. 17
-                    5.44029958,
-                    -4.4735794,
-                    7.38579536,  # 3rd highest value; idx. 20
-                    -2.91051663,
-                    2.61946077,
-                    -2.5674762,
-                    -9.48959302,
-                    -4.02922645,
-                    -1.35416918,
-                    9.67702323,  # 1st highest value; idx. 27
-                    -5.89478553,
-                    1.85370467,
-                ],  # cummulative prob of 4 highest values <= 0.6
-            ],
-            dtype=torch.float,
-            device=torch_device,
-        )
-
-        non_inf_expected_idx = torch.tensor(
-            [[0, 0], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 20], [1, 27]],
-            dtype=torch.long,
-            device=torch_device,
-        )  # expected non filtered idx as noted above
-
-        non_inf_expected_output = torch.tensor(
-            [
-                8.2221,
-                8.4321,
-                7.4402,
-                9.3845,
-                6.2712,
-                8.8275,
-                7.3858,
-                9.6770,
-            ],  # expected non filtered values as noted above
-            dtype=torch.float,
-            device=torch_device,
-        )
-
-        output = top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)
-        non_inf_output = output[output != -float("inf")].to(device=torch_device)
-        non_inf_idx = (output != -float("inf")).nonzero().to(device=torch_device)
-
-        self.assertTrue(torch.allclose(non_inf_expected_output, non_inf_output, atol=1e-12))
-        self.assertTrue(torch.all(torch.eq(non_inf_expected_idx, non_inf_idx)))
-
-    # tests whether the function uses filter_value instead of default -inf
-    def test_top_k_top_p_filtering_with_filter_value(self):
-        logits = torch.tensor(
-            [
-                [
-                    1,
-                    1,
-                    1,
-                    0.99,  # get filtered by top-p filtering
-                    0.98,  # get filtered by top-k filtering
-                ]
-            ],
-            dtype=torch.float,
-            device=torch_device,
-        )
-
-        expected_output = torch.tensor(
-            [[1, 1, 1, 0, 0]],
-            dtype=torch.float,
-            device=torch_device,
-        )
-
-        output = top_k_top_p_filtering(logits, top_k=4, top_p=0.5, filter_value=0.0)
-
-        self.assertTrue(torch.allclose(expected_output, output, atol=1e-12))
-
     def test_speculative_sampling(self):
         # assume vocab size 10, input length 5 + 3 generated candidates
         candidate_input_ids = torch.tensor([[8, 0, 3, 9, 8, 1, 4, 5]])  # input tokens