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Unverified Commit ffe60fdc authored by Joao Gante's avatar Joao Gante Committed by GitHub
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v4.39 deprecations 🧼 (#29492)

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docs/source/en/internal/generation_utils.md
View file @ ffe60fdc
......@@ -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
View file @ ffe60fdc
......@@ -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
View file @ ffe60fdc
......@@ -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
View file @ ffe60fdc
......@@ -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
View file @ ffe60fdc
......@@ -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
View file @ ffe60fdc
......@@ -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
View file @ ffe60fdc
......@@ -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
View file @ ffe60fdc
......@@ -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
View file @ ffe60fdc
......@@ -129,10 +129,7 @@ class LlamaRotaryEmbedding(nn.Module):
return self._cos_cached
@torch.no_grad()
def forward(self, x, position_ids, seq_len=None):
if seq_len is not None:
logger.warning_once("The `seq_len` argument is deprecated and unused. It will be removed in v4.39.")
def forward(self, x, position_ids):
# 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
View file @ ffe60fdc
......@@ -120,27 +120,10 @@ class OPTAttention(nn.Module):
):
super().__init__()
self.config = config
def _handle_deprecated_argument(config_arg_name, config, fn_arg_name, kwargs):
"""
If a the deprecated argument `fn_arg_name` is passed, raise a deprecation
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.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.dropout = config.attention_dropout
self.enable_bias = config.enable_bias
self.head_dim = self.embed_dim // self.num_heads
self.is_causal = True
......
src/transformers/utils/dummy_pt_objects.py
View file @ ffe60fdc
......@@ -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
View file @ ffe60fdc
......@@ -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
View file @ ffe60fdc
......@@ -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
View file @ ffe60fdc
......@@ -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
......
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