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Cache: don't show warning in forward passes when past_key_values is None (#33541)

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Joao Gante 2024-09-19 12:02:46 +01:00 committed by GitHub
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33
docs/source/en/kv_cache.md
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@ -120,7 +120,7 @@ To enable quantization of the key-value cache, one needs to indicate `cache_impl
Quantization related arguments should be passed to the `generation_config` either as a `dict` or an instance of a [`~QuantizedCacheConfig`] class. Quantization related arguments should be passed to the `generation_config` either as a `dict` or an instance of a [`~QuantizedCacheConfig`] class.
One has to indicate which quantization backend to use in the [`~QuantizedCacheConfig`], the default is `quanto`. One has to indicate which quantization backend to use in the [`~QuantizedCacheConfig`], the default is `quanto`.
It is recommended to set `axis-key/axis-value` parameters in the cache config to `0` if you're using the `quanto` backend and to `1` if you're using the `HQQ` backend. For other config values, please use the defaults unless you're running out of memory. In that case, you may consider decreasing the residual length. It is recommended to set `axis-key/axis-value` parameters in the cache config to `0` if you're using the `quanto` backend and to `1` if you're using the `HQQ` backend. For other config values, please use the defaults unless you're running out of memory. In that case, you may consider decreasing the residual length.
<Tip warning={true}> <Tip warning={true}>
@ -308,7 +308,7 @@ Unlike other cache classes, this one can't be used directly by indicating a `cac
### Encoder-Decoder Cache ### Encoder-Decoder Cache
The [`~EncoderDecoderCache`] is a wrapper designed to handle the caching needs of encoder-decoder models. This cache type is specifically built to manage both self-attention and cross-attention caches, ensuring storage and retrieval of past key/values required for these complex models. Cool thing about Encoder-Decoder Cache is that you can set different cache types for the encoder and for the decoder, depending on your use case. Currently this cache is only supported in [Whisper](./model_doc/whisper) models but we will be adding more models soon. The [`~EncoderDecoderCache`] is a wrapper designed to handle the caching needs of encoder-decoder models. This cache type is specifically built to manage both self-attention and cross-attention caches, ensuring storage and retrieval of past key/values required for these complex models. Cool thing about Encoder-Decoder Cache is that you can set different cache types for the encoder and for the decoder, depending on your use case. Currently this cache is only supported in [Whisper](./model_doc/whisper) models but we will be adding more models soon.
In terms of usage, there is nothing special to be done and calling `generate()` or `forward()` will handle everything for you. In terms of usage, there is nothing special to be done and calling `generate()` or `forward()` will handle everything for you.
@ -379,7 +379,7 @@ Sometimes you would want to first fill-in cache object with key/values for certa
>>> model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="cuda") >>> model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="cuda")
>>> tokenizer = AutoTokenizer.from_pretrained(model_id) >>> tokenizer = AutoTokenizer.from_pretrained(model_id)
>>> # Init StaticCache with big enough max-length (1024 tokens for the below example) >>> # Init StaticCache with big enough max-length (1024 tokens for the below example)
>>> # You can also init a DynamicCache, if that suits you better >>> # You can also init a DynamicCache, if that suits you better
>>> prompt_cache = StaticCache(config=model.config, max_batch_size=1, max_cache_len=1024, device="cuda", dtype=torch.bfloat16) >>> prompt_cache = StaticCache(config=model.config, max_batch_size=1, max_cache_len=1024, device="cuda", dtype=torch.bfloat16)
@ -394,10 +394,35 @@ Sometimes you would want to first fill-in cache object with key/values for certa
>>> for prompt in prompts: >>> for prompt in prompts:
... new_inputs = tokenizer(INITIAL_PROMPT + prompt, return_tensors="pt").to("cuda") ... new_inputs = tokenizer(INITIAL_PROMPT + prompt, return_tensors="pt").to("cuda")
... past_key_values = copy.deepcopy(prompt_cache) ... past_key_values = copy.deepcopy(prompt_cache)
... outputs = model.generate(**new_inputs, past_key_values=past_key_values,max_new_tokens=20) ... outputs = model.generate(**new_inputs, past_key_values=past_key_values,max_new_tokens=20)
... response = tokenizer.batch_decode(outputs)[0] ... response = tokenizer.batch_decode(outputs)[0]
... responses.append(response) ... responses.append(response)
>>> print(responses) >>> print(responses)
['<s> You are a helpful assistant. Help me to write a blogpost about travelling.\n\nTitle: The Ultimate Guide to Travelling: Tips, Tricks, and', '<s> You are a helpful assistant. What is the capital of France?\n\nYes, the capital of France is Paris.</s>'] ['<s> You are a helpful assistant. Help me to write a blogpost about travelling.\n\nTitle: The Ultimate Guide to Travelling: Tips, Tricks, and', '<s> You are a helpful assistant. What is the capital of France?\n\nYes, the capital of France is Paris.</s>']
``` ```
## Legacy cache format
Prior to the introduction of the `Cache` object, the cache of LLMs used to be a tuple of tuples of tensors. The legacy
format has a dynamic size, growing as we generate text -- very similar to `DynamicCache`. If your project depend on
this legacy format, you can seamlessly convert it to a `DynamicCache` and back.
```python
>>> import torch
>>> from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache
>>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
>>> model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, device_map="auto")
>>> inputs = tokenizer("Hello, my name is", return_tensors="pt").to(model.device)
>>> # `return_dict_in_generate=True` is required to return the cache. `return_legacy_cache` forces the returned cache
>>> # to be of the legacy type
>>> generation_outputs = model.generate(**inputs, return_dict_in_generate=True, return_legacy_cache=True, max_new_tokens=5)
>>> # We can convert a legacy cache to a DynamicCache -- and the other way around. This is helpful if you have custom
>>> # logic to manipulate a cache in a specific format.
>>> cache = DynamicCache.from_legacy_cache(generation_outputs.past_key_values)
>>> legacy_format_cache = cache.to_legacy_cache()
```

26
src/transformers/models/bloom/modeling_bloom.py
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@ -687,14 +687,18 @@ class BloomModel(BloomPreTrainedModel):
inputs_embeds = self.word_embeddings(input_ids) inputs_embeds = self.word_embeddings(input_ids)
# kept for BC (non `Cache` `past_key_values` inputs) # kept for BC (non `Cache` `past_key_values` inputs)
use_legacy_cache = False return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache) and not self.training: if use_cache and not isinstance(past_key_values, Cache):
use_legacy_cache = True return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values) if past_key_values is None:
logger.warning_once( past_key_values = DynamicCache()
"Using `past_key_values` as a tuple is deprecated and will be removed in v4.45. " else:
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" past_key_values = DynamicCache.from_legacy_cache(past_key_values)
) logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
batch_size, seq_length, _ = inputs_embeds.shape batch_size, seq_length, _ = inputs_embeds.shape
past_length = past_key_values.get_seq_length() if past_key_values is not None else 0 past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
@ -765,9 +769,9 @@ class BloomModel(BloomPreTrainedModel):
if output_hidden_states: if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,) all_hidden_states = all_hidden_states + (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple( return tuple(

22
src/transformers/models/codegen/modeling_codegen.py
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@ -526,14 +526,18 @@ class CodeGenModel(CodeGenPreTrainedModel):
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.wte(input_ids) inputs_embeds = self.wte(input_ids)
use_legacy_cache = False # kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache): if use_cache and not isinstance(past_key_values, Cache):
use_legacy_cache = True return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values) if past_key_values is None:
if not self.training: past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once( logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.45. " "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
) )
seq_length = inputs_embeds.shape[1] seq_length = inputs_embeds.shape[1]
@ -608,9 +612,9 @@ class CodeGenModel(CodeGenPreTrainedModel):
if output_hidden_states: if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,) all_hidden_states = all_hidden_states + (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple( return tuple(

19
src/transformers/models/cohere/modeling_cohere.py
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@ -910,16 +910,19 @@ class CohereModel(CoherePreTrainedModel):
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False return_legacy_cache = False
if ( if use_cache and not isinstance(past_key_values, Cache):
use_cache and not isinstance(past_key_values, Cache) and not self.training
): # kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = True return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values) if past_key_values is None:
logger.warning_once( past_key_values = DynamicCache()
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. " else:
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" past_key_values = DynamicCache.from_legacy_cache(past_key_values)
) logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if cache_position is None: if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0

19
src/transformers/models/dbrx/modeling_dbrx.py
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@ -1059,16 +1059,19 @@ class DbrxModel(DbrxPreTrainedModel):
inputs_embeds = nn.functional.dropout(inputs_embeds, p=self.emb_pdrop, training=self.training) inputs_embeds = nn.functional.dropout(inputs_embeds, p=self.emb_pdrop, training=self.training)
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False return_legacy_cache = False
if ( if use_cache and not isinstance(past_key_values, Cache):
use_cache and not isinstance(past_key_values, Cache) and not self.training
): # kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = True return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values) if past_key_values is None:
logger.warning_once( past_key_values = DynamicCache()
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. " else:
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" past_key_values = DynamicCache.from_legacy_cache(past_key_values)
) logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if cache_position is None: if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0

24
src/transformers/models/falcon/modeling_falcon.py
View File

@ -1031,17 +1031,21 @@ class FalconModel(FalconPreTrainedModel):
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids) inputs_embeds = self.word_embeddings(input_ids)
# Compute alibi tensor: check build_alibi_tensor documentation # kept for BC (non `Cache` `past_key_values` inputs)
use_legacy_cache = False return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache): if use_cache and not isinstance(past_key_values, Cache):
use_legacy_cache = True return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values) if past_key_values is None:
if not self.training: past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once( logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.45. " "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
) )
# Compute alibi tensor: check build_alibi_tensor documentation
alibi = None alibi = None
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
batch_size, seq_length, _ = inputs_embeds.shape batch_size, seq_length, _ = inputs_embeds.shape
@ -1126,9 +1130,9 @@ class FalconModel(FalconPreTrainedModel):
if output_hidden_states: if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,) all_hidden_states = all_hidden_states + (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple( return tuple(

15
src/transformers/models/gemma/diff_gemma.py
View File

@ -476,12 +476,19 @@ class GemmaModel(LlamaModel):
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False # noqa: F841 return_legacy_cache = False # noqa: F841
if ( if use_cache and not isinstance(past_key_values, Cache):
use_cache and not isinstance(past_key_values, Cache) and not self.training
): # kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = True # noqa: F841 return_legacy_cache = True # noqa: F841
past_key_values = DynamicCache.from_legacy_cache(past_key_values) if past_key_values is None:
past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if cache_position is None: if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0

28
src/transformers/models/gemma/modeling_gemma.py
View File

@ -828,12 +828,19 @@ class GemmaModel(GemmaPreTrainedModel):
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
return_legacy_cache = False # noqa: F841 # kept for BC (non `Cache` `past_key_values` inputs)
if ( return_legacy_cache = False
use_cache and not isinstance(past_key_values, Cache) and not self.training if use_cache and not isinstance(past_key_values, Cache):
): # kept for BC (non `Cache` `past_key_values` inputs) return_legacy_cache = True
return_legacy_cache = True # noqa: F841 if past_key_values is None:
past_key_values = DynamicCache.from_legacy_cache(past_key_values) past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if cache_position is None: if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
@ -856,15 +863,6 @@ class GemmaModel(GemmaPreTrainedModel):
# See https://github.com/huggingface/transformers/pull/29402 # See https://github.com/huggingface/transformers/pull/29402
normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype) normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
hidden_states = hidden_states * normalizer hidden_states = hidden_states * normalizer
if (
use_cache and not isinstance(past_key_values, Cache) and not self.training
): # kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. "
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)"
)
# decoder layers # decoder layers
all_hidden_states = () if output_hidden_states else None all_hidden_states = () if output_hidden_states else None

27
src/transformers/models/git/modeling_git.py
View File

@ -417,14 +417,19 @@ class GitEncoder(nn.Module):
) )
use_cache = False use_cache = False
use_legacy_cache = False # kept for BC (non `Cache` `past_key_values` inputs)
if use_cache and not isinstance(past_key_values, Cache) and not self.training: return_legacy_cache = False
use_legacy_cache = True if use_cache and not isinstance(past_key_values, Cache):
past_key_values = DynamicCache.from_legacy_cache(past_key_values) return_legacy_cache = True
logger.warning_once( if past_key_values is None:
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.45. " past_key_values = DynamicCache()
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" else:
) past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
all_hidden_states = () if output_hidden_states else None all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None all_self_attentions = () if output_attentions else None
@ -463,9 +468,9 @@ class GitEncoder(nn.Module):
if output_hidden_states: if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,) all_hidden_states = all_hidden_states + (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple( return tuple(

24
src/transformers/models/gpt_neo/modeling_gpt_neo.py
View File

@ -741,14 +741,18 @@ class GPTNeoModel(GPTNeoPreTrainedModel):
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.wte(input_ids) inputs_embeds = self.wte(input_ids)
use_legacy_cache = False # kept for BC (non `Cache` `past_key_values` inputs)
if use_cache and not isinstance(past_key_values, Cache) and not self.training: return_legacy_cache = False
use_legacy_cache = True if use_cache and not isinstance(past_key_values, Cache):
past_key_values = DynamicCache.from_legacy_cache(past_key_values) return_legacy_cache = True
if not self.training: if past_key_values is None:
past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once( logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.45. " "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
) )
seq_length = inputs_embeds.shape[1] seq_length = inputs_embeds.shape[1]
@ -822,9 +826,9 @@ class GPTNeoModel(GPTNeoPreTrainedModel):
if output_hidden_states: if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,) all_hidden_states = all_hidden_states + (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple( return tuple(

22
src/transformers/models/gpt_neox/modeling_gpt_neox.py
View File

@ -943,14 +943,18 @@ class GPTNeoXModel(GPTNeoXPreTrainedModel):
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_in(input_ids) inputs_embeds = self.embed_in(input_ids)
use_legacy_cache = False # kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache): if use_cache and not isinstance(past_key_values, Cache):
use_legacy_cache = True return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values) if past_key_values is None:
if not self.training: past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once( logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.45. " "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
) )
seq_length = inputs_embeds.shape[1] seq_length = inputs_embeds.shape[1]
@ -1021,9 +1025,9 @@ class GPTNeoXModel(GPTNeoXPreTrainedModel):
if output_hidden_states: if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,) all_hidden_states = all_hidden_states + (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attentions] if v is not None) return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attentions] if v is not None)

22
src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py
View File

@ -663,14 +663,18 @@ class GPTNeoXJapaneseModel(GPTNeoXJapanesePreTrainedModel):
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_in(input_ids) inputs_embeds = self.embed_in(input_ids)
use_legacy_cache = False # kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache): if use_cache and not isinstance(past_key_values, Cache):
use_legacy_cache = True return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values) if past_key_values is None:
if not self.training: past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once( logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.45. " "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
) )
seq_length = inputs_embeds.shape[1] seq_length = inputs_embeds.shape[1]
@ -725,9 +729,9 @@ class GPTNeoXJapaneseModel(GPTNeoXJapanesePreTrainedModel):
if output_hidden_states: if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,) all_hidden_states = all_hidden_states + (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attentions] if v is not None) return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attentions] if v is not None)

22
src/transformers/models/gptj/modeling_gptj.py
View File

@ -813,14 +813,18 @@ class GPTJModel(GPTJPreTrainedModel):
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.wte(input_ids) inputs_embeds = self.wte(input_ids)
use_legacy_cache = False # kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache): if use_cache and not isinstance(past_key_values, Cache):
use_legacy_cache = True return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values) if past_key_values is None:
if not self.training: past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once( logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.45. " "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
) )
seq_length = inputs_embeds.shape[1] seq_length = inputs_embeds.shape[1]
@ -917,9 +921,9 @@ class GPTJModel(GPTJPreTrainedModel):
if output_hidden_states: if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,) all_hidden_states = all_hidden_states + (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple( return tuple(

17
src/transformers/models/granite/modeling_granite.py
View File

@ -834,14 +834,19 @@ class GraniteModel(GranitePreTrainedModel):
inputs_embeds = inputs_embeds * self.embedding_multiplier inputs_embeds = inputs_embeds * self.embedding_multiplier
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache): # kept for BC (non `Cache` `past_key_values` inputs) if use_cache and not isinstance(past_key_values, Cache):
return_legacy_cache = True return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values) if past_key_values is None:
logger.warning_once( past_key_values = DynamicCache()
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. " else:
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)" past_key_values = DynamicCache.from_legacy_cache(past_key_values)
) logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if cache_position is None: if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0

16
src/transformers/models/idefics/modeling_idefics.py
View File

@ -1239,15 +1239,19 @@ class IdeficsModel(IdeficsPreTrainedModel):
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache): if use_cache and not isinstance(past_key_values, Cache):
if not self.training:
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.45. "
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)"
)
return_legacy_cache = True return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values) if past_key_values is None:
past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
batch_size, seq_length, _ = inputs_embeds.shape batch_size, seq_length, _ = inputs_embeds.shape
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0

14
src/transformers/models/idefics2/modeling_idefics2.py
View File

@ -1345,11 +1345,19 @@ class Idefics2Model(Idefics2PreTrainedModel):
raise ValueError("You have to specify either input_ids or inputs_embeds") raise ValueError("You have to specify either input_ids or inputs_embeds")
past_seen_tokens = 0 past_seen_tokens = 0
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False return_legacy_cache = False
if use_cache: if use_cache and not isinstance(past_key_values, Cache):
if not isinstance(past_key_values, Cache): # kept for BC (non `Cache` `past_key_values` inputs) return_legacy_cache = True
return_legacy_cache = True if past_key_values is None:
past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values) past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
past_seen_tokens = past_key_values.get_seq_length() past_seen_tokens = past_key_values.get_seq_length()
if inputs_embeds is not None and input_ids is None and past_seen_tokens == 0: if inputs_embeds is not None and input_ids is None and past_seen_tokens == 0:

15
src/transformers/models/jetmoe/modeling_jetmoe.py
View File

@ -1033,12 +1033,19 @@ class JetMoeModel(JetMoePreTrainedModel):
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False return_legacy_cache = False
if ( if use_cache and not isinstance(past_key_values, Cache):
use_cache and not isinstance(past_key_values, Cache) and not self.training
): # kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = True return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values) if past_key_values is None:
past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if cache_position is None: if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0

19
src/transformers/models/llama/modeling_llama.py
View File

@ -944,16 +944,19 @@ class LlamaModel(LlamaPreTrainedModel):
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False return_legacy_cache = False
if ( if use_cache and not isinstance(past_key_values, Cache):
use_cache and not isinstance(past_key_values, Cache) and not self.training
): # kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = True return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values) if past_key_values is None:
logger.warning_once( past_key_values = DynamicCache()
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. " else:
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" past_key_values = DynamicCache.from_legacy_cache(past_key_values)
) logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if cache_position is None: if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0

17
src/transformers/models/mistral/modeling_mistral.py
View File

@ -762,14 +762,19 @@ class MistralModel(MistralPreTrainedModel):
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache) and not self.training: if use_cache and not isinstance(past_key_values, Cache):
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
return_legacy_cache = True return_legacy_cache = True
logger.warning_once( if past_key_values is None:
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. " past_key_values = DynamicCache()
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" else:
) past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if cache_position is None: if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0

27
src/transformers/models/mixtral/modeling_mixtral.py
View File

@ -1018,14 +1018,19 @@ class MixtralModel(MixtralPreTrainedModel):
) )
use_cache = False use_cache = False
use_legacy_cache = False # kept for BC (non `Cache` `past_key_values` inputs)
if use_cache and not isinstance(past_key_values, Cache) and not self.training: return_legacy_cache = False
use_legacy_cache = True if use_cache and not isinstance(past_key_values, Cache):
past_key_values = DynamicCache.from_legacy_cache(past_key_values) return_legacy_cache = True
logger.warning_once( if past_key_values is None:
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. " past_key_values = DynamicCache()
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" else:
) past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
@ -1095,9 +1100,9 @@ class MixtralModel(MixtralPreTrainedModel):
if output_hidden_states: if output_hidden_states:
all_hidden_states += (hidden_states,) all_hidden_states += (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple( return tuple(

19
src/transformers/models/olmo/modeling_olmo.py
View File

@ -866,16 +866,19 @@ class OlmoModel(OlmoPreTrainedModel):
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False return_legacy_cache = False
if ( if use_cache and not isinstance(past_key_values, Cache):
use_cache and not isinstance(past_key_values, Cache) and not self.training
): # kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = True return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values) if past_key_values is None:
logger.warning_once( past_key_values = DynamicCache()
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. " else:
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" past_key_values = DynamicCache.from_legacy_cache(past_key_values)
) logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if cache_position is None: if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0

17
src/transformers/models/olmoe/modeling_olmoe.py
View File

@ -1007,14 +1007,19 @@ class OlmoeModel(OlmoePreTrainedModel):
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache): # kept for BC (non `Cache` `past_key_values` inputs) if use_cache and not isinstance(past_key_values, Cache):
return_legacy_cache = True return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values) if past_key_values is None:
logger.warning_once( past_key_values = DynamicCache()
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. " else:
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)" past_key_values = DynamicCache.from_legacy_cache(past_key_values)
) logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if cache_position is None: if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0

27
src/transformers/models/persimmon/modeling_persimmon.py
View File

@ -685,14 +685,19 @@ class PersimmonModel(PersimmonPreTrainedModel):
) )
use_cache = False use_cache = False
use_legacy_cache = False # kept for BC (non `Cache` `past_key_values` inputs)
if use_cache and not isinstance(past_key_values, Cache) and not self.training: return_legacy_cache = False
use_legacy_cache = True if use_cache and not isinstance(past_key_values, Cache):
past_key_values = DynamicCache.from_legacy_cache(past_key_values) return_legacy_cache = True
logger.warning_once( if past_key_values is None:
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. " past_key_values = DynamicCache()
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" else:
) past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
@ -761,9 +766,9 @@ class PersimmonModel(PersimmonPreTrainedModel):
if output_hidden_states: if output_hidden_states:
all_hidden_states += (hidden_states,) all_hidden_states += (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)

28
src/transformers/models/phi/modeling_phi.py
View File

@ -976,14 +976,19 @@ class PhiModel(PhiPreTrainedModel):
) )
use_cache = False use_cache = False
use_legacy_cache = False # kept for BC (non `Cache` `past_key_values` inputs)
if use_cache and not isinstance(past_key_values, Cache) and not self.training: return_legacy_cache = False
use_legacy_cache = True if use_cache and not isinstance(past_key_values, Cache):
past_key_values = DynamicCache.from_legacy_cache(past_key_values) return_legacy_cache = True
logger.warning_once( if past_key_values is None:
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. " past_key_values = DynamicCache()
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" else:
) past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
@ -1053,9 +1058,10 @@ class PhiModel(PhiPreTrainedModel):
if output_hidden_states: if output_hidden_states:
all_hidden_states += (hidden_states,) all_hidden_states += (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPast( return BaseModelOutputWithPast(

28
src/transformers/models/phi3/modeling_phi3.py
View File

@ -1003,14 +1003,19 @@ class Phi3Model(Phi3PreTrainedModel):
) )
use_cache = False use_cache = False
use_legacy_cache = False # kept for BC (non `Cache` `past_key_values` inputs)
if use_cache and not isinstance(past_key_values, Cache) and not self.training: return_legacy_cache = False
use_legacy_cache = True if use_cache and not isinstance(past_key_values, Cache):
past_key_values = DynamicCache.from_legacy_cache(past_key_values) return_legacy_cache = True
logger.warning_once( if past_key_values is None:
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. " past_key_values = DynamicCache()
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" else:
) past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
@ -1074,9 +1079,10 @@ class Phi3Model(Phi3PreTrainedModel):
if output_hidden_states: if output_hidden_states:
all_hidden_states += (hidden_states,) all_hidden_states += (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPast( return BaseModelOutputWithPast(

27
src/transformers/models/qwen2/modeling_qwen2.py
View File

@ -915,14 +915,19 @@ class Qwen2Model(Qwen2PreTrainedModel):
) )
use_cache = False use_cache = False
use_legacy_cache = False # kept for BC (non `Cache` `past_key_values` inputs)
if use_cache and not isinstance(past_key_values, Cache) and not self.training: return_legacy_cache = False
use_legacy_cache = True if use_cache and not isinstance(past_key_values, Cache):
past_key_values = DynamicCache.from_legacy_cache(past_key_values) return_legacy_cache = True
logger.warning_once( if past_key_values is None:
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. " past_key_values = DynamicCache()
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" else:
) past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
@ -991,9 +996,9 @@ class Qwen2Model(Qwen2PreTrainedModel):
if output_hidden_states: if output_hidden_states:
all_hidden_states += (hidden_states,) all_hidden_states += (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)

27
src/transformers/models/qwen2_moe/modeling_qwen2_moe.py
View File

@ -1079,14 +1079,19 @@ class Qwen2MoeModel(Qwen2MoePreTrainedModel):
) )
use_cache = False use_cache = False
use_legacy_cache = False # kept for BC (non `Cache` `past_key_values` inputs)
if use_cache and not isinstance(past_key_values, Cache) and not self.training: return_legacy_cache = False
use_legacy_cache = True if use_cache and not isinstance(past_key_values, Cache):
past_key_values = DynamicCache.from_legacy_cache(past_key_values) return_legacy_cache = True
logger.warning_once( if past_key_values is None:
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. " past_key_values = DynamicCache()
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" else:
) past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
@ -1161,9 +1166,9 @@ class Qwen2MoeModel(Qwen2MoePreTrainedModel):
if output_hidden_states: if output_hidden_states:
all_hidden_states += (hidden_states,) all_hidden_states += (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple( return tuple(

27
src/transformers/models/stablelm/modeling_stablelm.py
View File

@ -960,14 +960,19 @@ class StableLmModel(StableLmPreTrainedModel):
) )
use_cache = False use_cache = False
use_legacy_cache = False # kept for BC (non `Cache` `past_key_values` inputs)
if use_cache and not isinstance(past_key_values, Cache) and not self.training: return_legacy_cache = False
use_legacy_cache = True if use_cache and not isinstance(past_key_values, Cache):
past_key_values = DynamicCache.from_legacy_cache(past_key_values) return_legacy_cache = True
logger.warning_once( if past_key_values is None:
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. " past_key_values = DynamicCache()
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" else:
) past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
@ -1036,9 +1041,9 @@ class StableLmModel(StableLmPreTrainedModel):
if output_hidden_states: if output_hidden_states:
all_hidden_states += (hidden_states,) all_hidden_states += (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)

27
src/transformers/models/starcoder2/modeling_starcoder2.py
View File

@ -889,14 +889,19 @@ class Starcoder2Model(Starcoder2PreTrainedModel):
) )
use_cache = False use_cache = False
use_legacy_cache = False # kept for BC (non `Cache` `past_key_values` inputs)
if use_cache and not isinstance(past_key_values, Cache) and not self.training: return_legacy_cache = False
use_legacy_cache = True if use_cache and not isinstance(past_key_values, Cache):
past_key_values = DynamicCache.from_legacy_cache(past_key_values) return_legacy_cache = True
logger.warning_once( if past_key_values is None:
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.46. " past_key_values = DynamicCache()
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)" else:
) past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if inputs_embeds is None: if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
@ -966,9 +971,9 @@ class Starcoder2Model(Starcoder2PreTrainedModel):
if output_hidden_states: if output_hidden_states:
all_hidden_states += (hidden_states,) all_hidden_states += (hidden_states,)
next_cache = None next_cache = next_decoder_cache if use_cache else None
if use_cache: if return_legacy_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache next_cache = next_cache.to_legacy_cache()
if not return_dict: if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)