Cache: don't show warning in forward passes when past_key_values is…

… None (huggingface#33541)

docs/source/en/kv_cache.md

@@ -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.
 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}>
 
@@ -308,7 +308,7 @@ Unlike other cache classes, this one can't be used directly by indicating a `cac
 
 ### 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.
 
@@ -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")
 >>> 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
 >>> 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:
 ...     new_inputs = tokenizer(INITIAL_PROMPT + prompt, return_tensors="pt").to("cuda")
 ...     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]
 ...     responses.append(response)
 
 >>> 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>']
 ```
+
+
+## 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()
+```

src/transformers/models/bloom/modeling_bloom.py

@@ -687,14 +687,18 @@ def forward(
             inputs_embeds = self.word_embeddings(input_ids)
 
         # kept for BC (non `Cache` `past_key_values` inputs)
-        use_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache) and not self.training:
-            use_legacy_cache = True
-            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            logger.warning_once(
-                "Using `past_key_values` as a tuple 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 = False
+        if use_cache and not isinstance(past_key_values, Cache):
+            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)
+                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
         past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
@@ -765,9 +769,9 @@ def forward(
         if output_hidden_states:
             all_hidden_states = all_hidden_states + (hidden_states,)
 
-        next_cache = None
-        if use_cache:
-            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
 
         if not return_dict:
             return tuple(

src/transformers/models/codegen/modeling_codegen.py

@@ -526,14 +526,18 @@ def forward(
         if inputs_embeds is None:
             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):
-            use_legacy_cache = True
-            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            if not self.training:
+            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)
                 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)"
+                    "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)"
                 )
 
         seq_length = inputs_embeds.shape[1]
@@ -608,9 +612,9 @@ def forward(
         if output_hidden_states:
             all_hidden_states = all_hidden_states + (hidden_states,)
 
-        next_cache = None
-        if use_cache:
-            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
 
         if not return_dict:
             return tuple(

src/transformers/models/cohere/modeling_cohere.py

@@ -910,16 +910,19 @@ def forward(
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
+        # kept for BC (non `Cache` `past_key_values` inputs)
         return_legacy_cache = False
-        if (
-            use_cache and not isinstance(past_key_values, Cache) and not self.training
-        ):  # kept for BC (non `Cache` `past_key_values` inputs)
+        if use_cache and not isinstance(past_key_values, Cache):
             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)"
-            )
+            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:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0

src/transformers/models/dbrx/modeling_dbrx.py

@@ -1059,16 +1059,19 @@ def forward(
 
         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
-        if (
-            use_cache and not isinstance(past_key_values, Cache) and not self.training
-        ):  # kept for BC (non `Cache` `past_key_values` inputs)
+        if use_cache and not isinstance(past_key_values, Cache):
             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)"
-            )
+            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:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0

src/transformers/models/falcon/modeling_falcon.py

@@ -1031,17 +1031,21 @@ def forward(
         if inputs_embeds is None:
             inputs_embeds = self.word_embeddings(input_ids)
 
-        # Compute alibi tensor: check build_alibi_tensor documentation
-        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):
-            use_legacy_cache = True
-            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            if not self.training:
+            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)
                 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)"
+                    "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)"
                 )
 
+        # Compute alibi tensor: check build_alibi_tensor documentation
         alibi = None
         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
@@ -1126,9 +1130,9 @@ def forward(
         if output_hidden_states:
             all_hidden_states = all_hidden_states + (hidden_states,)
 
-        next_cache = None
-        if use_cache:
-            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
 
         if not return_dict:
             return tuple(

src/transformers/models/gemma/diff_gemma.py

@@ -476,12 +476,19 @@ def forward(
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
+        # kept for BC (non `Cache` `past_key_values` inputs)
         return_legacy_cache = False  # noqa: F841
-        if (
-            use_cache and not isinstance(past_key_values, Cache) and not self.training
-        ):  # kept for BC (non `Cache` `past_key_values` inputs)
+        if use_cache and not isinstance(past_key_values, Cache):
             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:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0

src/transformers/models/gemma/modeling_gemma.py

@@ -828,12 +828,19 @@ def forward(
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
-        return_legacy_cache = False  # noqa: F841
-        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  # noqa: F841
-            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+        # kept for BC (non `Cache` `past_key_values` inputs)
+        return_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache):
+            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)
+                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:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
@@ -856,15 +863,6 @@ def forward(
         # See https://github.com/huggingface/transformers/pull/29402
         normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
         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
         all_hidden_states = () if output_hidden_states else None