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/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#streamers | .md | >>> # Despite returning the usual output, the streamer will also print the generated text to stdout.
>>> _ = model.generate(**inputs, streamer=streamer, max_new_tokens=20)
An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,
```
Streamer that stores print-ready text in a queue, to be used by a downstream application as an iterator. This is
useful for applications that benefit from acessing the generated text in a non-blocking way (e.g. in an interactive | 427_13_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#streamers | .md | useful for applications that benefit from acessing the generated text in a non-blocking way (e.g. in an interactive
Gradio demo).
<Tip warning={true}>
The API for the streamer classes is still under development and may change in the future.
</Tip>
Parameters:
tokenizer (`AutoTokenizer`):
The tokenized used to decode the tokens.
skip_prompt (`bool`, *optional*, defaults to `False`):
Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
timeout (`float`, *optional*): | 427_13_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#streamers | .md | Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
timeout (`float`, *optional*):
The timeout for the text queue. If `None`, the queue will block indefinitely. Useful to handle exceptions
in `.generate()`, when it is called in a separate thread.
decode_kwargs (`dict`, *optional*):
Additional keyword arguments to pass to the tokenizer's `decode` method.
Examples:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer | 427_13_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#streamers | .md | Examples:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
>>> from threading import Thread | 427_13_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#streamers | .md | >>> tok = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt")
>>> streamer = TextIteratorStreamer(tok) | 427_13_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#streamers | .md | >>> # Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way.
>>> generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=20)
>>> thread = Thread(target=model.generate, kwargs=generation_kwargs)
>>> thread.start()
>>> generated_text = ""
>>> for new_text in streamer:
... generated_text += new_text
>>> generated_text
'An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,'
``` | 427_13_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#streamers | .md | >>> generated_text
'An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,'
```
Streamer that stores print-ready text in a queue, to be used by a downstream application as an async iterator.
This is useful for applications that benefit from acessing the generated text asynchronously (e.g. in an
interactive Gradio demo).
<Tip warning={true}>
The API for the streamer classes is still under development and may change in the future.
</Tip>
Parameters: | 427_13_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#streamers | .md | The API for the streamer classes is still under development and may change in the future.
</Tip>
Parameters:
tokenizer (`AutoTokenizer`):
The tokenized used to decode the tokens.
skip_prompt (`bool`, *optional*, defaults to `False`):
Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
timeout (`float`, *optional*):
The timeout for the text queue. If `None`, the queue will block indefinitely. Useful to handle exceptions
in `.generate()`, when it is called in a separate thread. | 427_13_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#streamers | .md | in `.generate()`, when it is called in a separate thread.
decode_kwargs (`dict`, *optional*):
Additional keyword arguments to pass to the tokenizer's `decode` method.
Raises:
TimeoutError: If token generation time exceeds timeout value.
Examples:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, AsyncTextIteratorStreamer
>>> from threading import Thread
>>> import asyncio | 427_13_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#streamers | .md | >>> tok = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt") | 427_13_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#streamers | .md | >>> # Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way.
>>> async def main():
... # Important: AsyncTextIteratorStreamer must be initialized inside a coroutine!
... streamer = AsyncTextIteratorStreamer(tok)
... generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=20)
... thread = Thread(target=model.generate, kwargs=generation_kwargs)
... thread.start()
... generated_text = "" | 427_13_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#streamers | .md | ... thread = Thread(target=model.generate, kwargs=generation_kwargs)
... thread.start()
... generated_text = ""
... async for new_text in streamer:
... generated_text += new_text
>>> print(generated_text)
>>> asyncio.run(main())
An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,
``` | 427_13_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | Base, abstract class for all caches. The actual data structure is specific to each subclass.
- update
Base, abstract class for all caches. The actual data structure is specific to each subclass.
Config
- update
Configuration class for quantized cache settings.
Attributes:
backend (`str`, *optional*, defaults to `"quanto"`):
Backend to use when performing quantization, Can be one of [`quanto`, `HQQ`]
nbits (`Optional[int]`, *optional*, defaults to 4): | 427_14_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | nbits (`Optional[int]`, *optional*, defaults to 4):
Number of bits, can be 2 or 4 for the `quanto` backend and one of [1, 2, 3, 4, 8] for the `HQQ` backend. Defaults to 2.
axis_key (`int`, *optional*, defaults to 0):
Axis over which to perform grouping for the key tensors. Can be [0, -1] for `quanto` backend and [0, 1] for `HQQ` backend.
axis_value (`int`, *optional*, defaults to 0):
Axis over which to perform grouping for the value tensors. Can be [0, -1] for `quanto` backend and [0, 1] for `HQQ` backend. | 427_14_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | Axis over which to perform grouping for the value tensors. Can be [0, -1] for `quanto` backend and [0, 1] for `HQQ` backend.
q_group_size (`Optional[int]`, *optional*, defaults to 64):
Size of the quantization group, should be a divisor of the model's hidden dimension.
Defaults to 64.
residual_length (`Optional[int]`, *optional*, defaults to 128):
Length of the residual cache which will always be stored in original presicion.
Defaults to 128. | 427_14_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | Length of the residual cache which will always be stored in original presicion.
Defaults to 128.
compute_dtype (`torch.dtype`, *optional*, defaults to `torch.float16`):
The defualt dtype used for computations in the model. Keys and Values will be cast to this dtype after dequantization.
device (`str`, *optional*, defaults to `"cpu"`):
Device on which to perform computations, should be same as the model's device.
- validate | 427_14_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | Device on which to perform computations, should be same as the model's device.
- validate
A cache that grows dynamically as more tokens are generated. This is the default for generative models.
It stores the Key and Value states as a list of tensors, one for each layer. The expected shape for each tensor is
`[batch_size, num_heads, seq_len, head_dim]`.
Example:
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache | 427_14_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
>>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
>>> inputs = tokenizer(text="My name is Qwen2", return_tensors="pt") | 427_14_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> # Prepare a cache class and pass it to model's forward
>>> past_key_values = DynamicCache()
>>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
>>> outputs.past_key_values # access cache filled with key/values from generation
DynamicCache()
```
- update
- get_seq_length
- reorder_cache
- to_legacy_cache
- from_legacy_cache | 427_14_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | DynamicCache()
```
- update
- get_seq_length
- reorder_cache
- to_legacy_cache
- from_legacy_cache
A quantizer cache similar to what is described in the [KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache paper](https://arxiv.org/abs/2402.02750).
It allows the model to generate longer sequence length without allocating too much memory for Key and Value cache by applying quantization. | 427_14_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | The cache has two types of storage, one for original precision and one for the quantized cache. A `residual length` is set as a maximum capacity for the
original precision cache. When the length goes beyond maximum capacity, the original precision cache is discarded and moved into the quantized cache. The
quantization is done per-channel with a set `q_group_size` for both Keys and Values, in contrast to what was described in the paper. | 427_14_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | It stores Keys and Values a list of quantized tensors (tuples in case we need to store metadata), one for each layer. Additionally, it stores the Key and
Value in original precision states as a list of tensors, one for each layer. The size of each tensor
is `[batch_size, num_heads, seq_len - residual_length, head_dim]`
- update
- get_seq_length
Quantized Cache class that uses `quanto` as a backend to perform quantization. Current implementation supports `int2` and `int4` dtypes only.
Parameters: | 427_14_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | Parameters:
cache_config (`QuantizedCacheConfig`):
A configuration containing all the arguments to be used by the quantizer, including axis, qtype and group size.
Example:
```python
>>> # Run pip install quanto first if you don't have it yet
>>> from transformers import AutoTokenizer, AutoModelForCausalLM, QuantoQuantizedCache, QuantizedCacheConfig | 427_14_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
>>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
>>> inputs = tokenizer(text="My name is Qwen2", return_tensors="pt") | 427_14_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> # Prepare a cache class and pass it to model's forward
>>> cache_config = QuantizedCacheConfig(nbits=4)
>>> past_key_values = QuantoQuantizedCache(cache_config=cache_config)
>>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
>>> outputs.past_key_values # access cache filled with key/values from generation
QuantoQuantizedCache()
```
Quantized Cache class that uses `HQQ` as a backend to perform quantization. Current implementation supports `int2`, `int4`, `int8` dtypes. | 427_14_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | Parameters:
cache_config (`QuantizedCacheConfig`):
A configuration containing all the arguments to be used by the quantizer, including axis, qtype and group size.
Example:
```python
>>> # Run pip install hqq first if you don't have it yet
>>> from transformers import AutoTokenizer, AutoModelForCausalLM, HQQQuantizedCache, QuantizedCacheConfig | 427_14_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
>>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
>>> inputs = tokenizer(text="My name is Qwen2", return_tensors="pt") | 427_14_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> # Prepare a cache class and pass it to model's forward
>>> cache_config = QuantizedCacheConfig(nbits=4, axis_key=1, axis_value=1)
>>> past_key_values = HQQQuantizedCache(cache_config=cache_config)
>>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
>>> outputs.past_key_values # access cache filled with key/values from generation
HQQQuantizedCache()
```
A cache that as described in the [Attention Sinks paper](https://arxiv.org/abs/2309.17453). It allows the model to | 427_14_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | ```
A cache that as described in the [Attention Sinks paper](https://arxiv.org/abs/2309.17453). It allows the model to
generate beyond the length of its context window, without losing fluency in the conversation. As it discards past
tokens, the model will lose the ability to generate tokens that depend on the context that was discarded.
It stores the Key and Value states as a list of tensors, one for each layer. The expected shape for each tensor is
`[batch_size, num_heads, seq_len, head_dim]`. | 427_14_16 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | `[batch_size, num_heads, seq_len, head_dim]`.
Parameters:
window_length (`int`):
The length of the context window.
num_sink_tokens (`int`):
The number of sink tokens. See the original paper for more information.
Example:
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM, SinkCache | 427_14_17 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
>>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
>>> inputs = tokenizer(text="My name is Qwen2", return_tensors="pt") | 427_14_18 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> # Prepare a cache class and pass it to model's forward
>>> past_key_values = SinkCache(window_length=256, num_sink_tokens=4)
>>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
>>> outputs.past_key_values # access cache filled with key/values from generation
SinkCache()
```
- update
- get_seq_length
- reorder_cache
A drop-in replacement for DynamicCache that conserves GPU memory at the expense of more CPU memory. | 427_14_19 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | - reorder_cache
A drop-in replacement for DynamicCache that conserves GPU memory at the expense of more CPU memory.
Useful for generating from models with very long context.
In addition to the default CUDA stream, where all forward() computations happen,
this class uses another stream, the prefetch stream, which it creates itself.
Since scheduling of operations on separate streams happens independently, this class uses | 427_14_20 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | Since scheduling of operations on separate streams happens independently, this class uses
the prefetch stream to asynchronously prefetch the KV cache of layer k+1 when layer k is executing.
The movement of the layer k-1 cache to the CPU is handled by the default stream as a simple way to
ensure the eviction is scheduled after all computations on that cache are finished.
- update
- prefetch_layer
- evict_previous_layer
Static Cache class to be used with `torch.compile(model)` and `torch.export()`. | 427_14_21 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | - prefetch_layer
- evict_previous_layer
Static Cache class to be used with `torch.compile(model)` and `torch.export()`.
Parameters:
config (`PretrainedConfig`):
The configuration file defining the shape-related attributes required to initialize the static cache.
batch_size (`int`):
The batch size with which the model will be used. Note that a new instance must be instantiated if a | 427_14_22 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | batch_size (`int`):
The batch size with which the model will be used. Note that a new instance must be instantiated if a
smaller batch size is used. If you are manually setting the batch size, make sure to take into account the number of beams if you are running beam search
max_cache_len (`int`):
The maximum sequence length with which the model will be used.
device (`torch.device` or `str`):
The device on which the cache should be initialized. Should be the same as the layer. | 427_14_23 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | device (`torch.device` or `str`):
The device on which the cache should be initialized. Should be the same as the layer.
dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
The default `dtype` to use when initializing the layer.
layer_device_map(`Dict[int, Union[str, torch.device, int]]]`, `optional`):
Mapping between the layers and its device. This is required when you are manually initializing the cache and the model is splitted between differents gpus. | 427_14_24 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | You can know which layers mapped to which device by checking the associated device_map: `model.hf_device_map`.
Example:
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM, StaticCache | 427_14_25 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
>>> inputs = tokenizer(text="My name is Llama", return_tensors="pt") | 427_14_26 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> # Prepare a cache class and pass it to model's forward
>>> # Leave empty space for 10 new tokens, which can be used when calling forward iteratively 10 times to generate
>>> max_generated_length = inputs.input_ids.shape[1] + 10
>>> past_key_values = StaticCache(config=model.config, batch_size=1, max_cache_len=max_generated_length, device=model.device, dtype=model.dtype)
>>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True) | 427_14_27 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
>>> outputs.past_key_values # access cache filled with key/values from generation
StaticCache()
```
- update
- get_seq_length
- reset
Static cache class to be used with `torch.compile(model)` that offloads to the CPU or
another device.
Args:
config (`PretrainedConfig):
The configuration file defining the shape-related attributes required to initialize
the static cache.
max_batch_size (`int`): | 427_14_28 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | The configuration file defining the shape-related attributes required to initialize
the static cache.
max_batch_size (`int`):
The maximum batch size with which the model will be used.
max_cache_len (`int`):
The maximum sequence length with which the model will be used.
device (`Union[str, torch.device]`):
The device on which the cache should be initialized. Should be the same as the
layer device.
dtype (`torch.dtype`, *optional*):
The default `dtype` to use when initializing the cache. | 427_14_29 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | layer device.
dtype (`torch.dtype`, *optional*):
The default `dtype` to use when initializing the cache.
offload_device (`Union[str, torch.device]`, *optional*, defaults to `cpu`):
The device to offload to. Defaults to CPU.
layer_device_map (`Dict[int, Union[str, torch.device, int]]`, *optional*):
Mapping between the layers and its device. This is required when you are manually initializing the cache and the model is splitted between differents gpus. | 427_14_30 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | You can know which layers mapped to which device by checking the associated device_map: `model.hf_device_map`.
Attributes:
key_cache (`List[torch.Tensor]`):
Off-loaded key cache tensors. First one will be on device, where-as the others are
off-loaded.
value_cache (`List[torch.Tensor]`):
Off-loaded value cache tensors. First one will be on device, where-as the others are
off-loaded.
max_batch_size (`int`):
The maximum batch size with which this cache can be used.
max_cache_len (`int`): | 427_14_31 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | off-loaded.
max_batch_size (`int`):
The maximum batch size with which this cache can be used.
max_cache_len (`int`):
The maximum sequence length with which this cache can be used.
device (`torch.device`):
The device on which the cache is used.
offload_device (`torch.device`):
The device used to offload to.
dtype (`torch.dtype`):
The `dtype` used to initializing the cache.
Example:
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM, OffloadedStaticCache | 427_14_32 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> inputs = tokenizer(text="My name is GPT2", return_tensors="pt") | 427_14_33 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> # Prepare a cache class and pass it to model's forward
>>> # Leave empty space for 10 new tokens, which can be used when calling forward iteratively 10 times to generate
>>> max_generated_length = inputs.input_ids.shape[1] + 10
>>> past_key_values = OffloadedStaticCache(config=model.config, max_batch_size=1, max_cache_len=max_generated_length, device=model.device, dtype=model.dtype)
>>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True) | 427_14_34 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
>>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation
```
- update
- get_seq_length
- reset
Hybrid Cache class to be used with `torch.compile` for Gemma2 models that alternate between a local sliding window attention
and global attention in every other layer. Under the hood, Hybrid Cache leverages ["SlidingWindowCache"] for sliding window attention | 427_14_35 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | and ["StaticCache"] for global attention. For more information, see the documentation of each subcomponeent cache class.
Parameters:
config (`PretrainedConfig):
The configuration file defining the shape-related attributes required to initialize the static cache.
batch_size (`int`):
The batch size with which the model will be used. Note that a new instance must be instantiated if a
smaller batch size is used.
max_cache_len (`int`):
The maximum sequence length with which the model will be used. | 427_14_36 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | smaller batch size is used.
max_cache_len (`int`):
The maximum sequence length with which the model will be used.
device (`torch.device` or `str`, *optional*, defaults to `"cpu"`):
The device on which the cache should be initialized. Should be the same as the layer.
dtype (torch.dtype, *optional*, defaults to `torch.float32`):
The default `dtype` to use when initializing the layer.
layer_device_map(`Dict[int, Union[str, torch.device, int]]]`, `optional`): | 427_14_37 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | layer_device_map(`Dict[int, Union[str, torch.device, int]]]`, `optional`):
Mapping between the layers and its device. This is required when you are manually initializing the cache and the model is splitted between differents gpus.
You can know which layers mapped to which device by checking the associated device_map: `model.hf_device_map`.
Example:
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM, HybridCache | 427_14_38 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b")
>>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b")
>>> inputs = tokenizer(text="My name is Gemma", return_tensors="pt") | 427_14_39 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> # Prepare a cache class and pass it to model's forward
>>> # Leave empty space for 10 new tokens, which can be used when calling forward iteratively 10 times to generate
>>> max_generated_length = inputs.input_ids.shape[1] + 10
>>> past_key_values = HybridCache(config=model.config, batch_size=1, max_cache_len=max_generated_length, device=model.device, dtype=model.dtype)
>>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True) | 427_14_40 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
>>> outputs.past_key_values # access cache filled with key/values from generation
HybridCache()
```
- update
- get_seq_length
- reset
Sliding Window Cache class to be used with `torch.compile` for models like Mistral that support sliding window attention.
Every time when we try to update the cache, we compute the `indices` based on `cache_position >= self.config.sliding_window - 1`, | 427_14_41 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | if true(which means the cache can not hold all the old key value states and new states together because of the sliding window constraint),
we need to do a cycle shift based on `indices` to replace the oldest states by the new key value states passed in.
The `to_shift` is only true once we are above sliding_window. Thus with `sliding_window==64`:
indices = (slicing + to_shift[-1].int()-1) % self.config.sliding_window
tensor([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, | 427_14_42 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | tensor([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
55, 56, 57, 58, 59, 60, 61, 62, 63, 0])
We overwrite the cache using these, then we always write at cache_position (clamped to `sliding_window`)
Parameters:
config (`PretrainedConfig`): | 427_14_43 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | Parameters:
config (`PretrainedConfig`):
The configuration file defining the shape-related attributes required to initialize the static cache.
batch_size (`int`):
The batch size with which the model will be used. Note that a new instance must be instantiated if a
smaller batch size is used.
max_cache_len (`int`):
The maximum sequence length with which the model will be used.
device (`torch.device` or `str`):
The device on which the cache should be initialized. Should be the same as the layer. | 427_14_44 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | device (`torch.device` or `str`):
The device on which the cache should be initialized. Should be the same as the layer.
dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
The default `dtype` to use when initializing the layer.
layer_device_map(`Dict[int, Union[str, torch.device, int]]]`, `optional`):
Mapping between the layers and its device. This is required when you are manually initializing the cache and the model is splitted between differents gpus. | 427_14_45 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | You can know which layers mapped to which device by checking the associated device_map: `model.hf_device_map`.
Example:
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM, SlidingWindowCache | 427_14_46 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
>>> inputs = tokenizer(text="My name is Mistral", return_tensors="pt") | 427_14_47 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> # Prepare a cache class and pass it to model's forward
>>> # Leave empty space for 10 new tokens, which can be used when calling forward iteratively 10 times to generate
>>> max_generated_length = inputs.input_ids.shape[1] + 10
>>> past_key_values = SlidingWindowCache(config=model.config, batch_size=1, max_cache_len=max_generated_length, device=model.device, dtype=model.dtype)
>>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True) | 427_14_48 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
>>> outputs.past_key_values # access cache filled with key/values from generation
SlidingWindowCache()
```
- update
- reset
Base, abstract class for all encoder-decoder caches. Can be used to hold combinations of self-attention and
cross-attention caches.
Example:
```python
>>> from transformers import AutoProcessor, AutoModelForCausalLM, DynamicCache, EncoderDecoderCache | 427_14_49 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> model = AutoModelForCausalLM.from_pretrained("openai/whisper-small")
>>> processor = AutoProcessor.from_pretrained("openai/whisper-small")
>>> inputs = processor(audio=YOUR-AUDIO, return_tensors="pt") | 427_14_50 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> # Prepare cache classes for encoder and decoder and pass it to model's forward
>>> self_attention_cache = DynamicCache()
>>> cross_attention_cache = DynamicCache()
>>> past_key_values = EncoderDecoderCache(self_attention_cache, cross_attention_cache)
>>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
>>> outputs.past_key_values # access cache filled with key/values from generation
EncoderDecoderCache()
```
- get_seq_length
- to_legacy_cache
- from_legacy_cache
- reset | 427_14_51 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | EncoderDecoderCache()
```
- get_seq_length
- to_legacy_cache
- from_legacy_cache
- reset
- reorder_cache
Cache for mamba model which does not have attention mechanism and key value states.
Arguments:
config (`PretrainedConfig):
The configuration file defining the shape-related attributes required to initialize the static cache.
batch_size (`int`):
The batch size with which the model will be used. Note that a new instance must be instantiated if a
smaller batch size is used. | 427_14_52 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | smaller batch size is used.
dtype (`torch.dtype`, *optional*, defaults to `torch.float16`):
The default `dtype` to use when initializing the layer.
device (`torch.device` or `str`, *optional*):
The device on which the cache should be initialized. Should be the same as the layer.
Attributes:
dtype: (`torch.dtype`):
The default `dtype` used to initializing the cache.
intermediate_size: (`int`):
Model's intermediate_size taken from config.
ssm_state_size: (`int`):
Model's state_size taken from config. | 427_14_53 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | Model's intermediate_size taken from config.
ssm_state_size: (`int`):
Model's state_size taken from config.
conv_kernel_size: (`int`):
Model's convolution kernel size taken from config
conv_states: (`torch.Tensor`):
A tensor of shape `[layer_idx, batch_size, intermediate_size, conv_kernel_size]` that holds convolutional states.
ssm_states: (`torch.Tensor`):
A tensor of shape `[layer_idx, batch_size, intermediate_size, ssm_state_size]` that holds ssm states
Example:
```python | 427_14_54 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | A tensor of shape `[layer_idx, batch_size, intermediate_size, ssm_state_size]` that holds ssm states
Example:
```python
>>> from transformers import AutoTokenizer, MambaForCausalLM, MambaCache | 427_14_55 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf")
>>> inputs = tokenizer(text="My name is Mamba", return_tensors="pt") | 427_14_56 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#caches | .md | >>> inputs = tokenizer(text="My name is Mamba", return_tensors="pt")
>>> # Prepare a cache class and pass it to model's forward
>>> past_key_values = MambaCache(config=model.config, batch_size=1, device=model.device, dtype=model.dtype)
>>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
>>> outputs.past_key_values
MambaCache()
```
- update_conv_state
- update_ssm_state
- reset | 427_14_57 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | Class that holds arguments for watermark generation and should be passed into `GenerationConfig` during `generate`.
See [this paper](https://arxiv.org/abs/2306.04634) for more details on the arguments.
Accepts the following keys:
- greenlist_ratio (`float`):
Used for watermarking. The ratio of "green" tokens used to the vocabulary size. Defaults to 0.25.
- bias (`float`):
Used with watermarking. The bias added to the selected "green" tokens' logits. Defaults to 2.0.
- hashing_key (`int`): | 427_15_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | Used with watermarking. The bias added to the selected "green" tokens' logits. Defaults to 2.0.
- hashing_key (`int`):
Hashing key used for watermarking. Defaults to 15485863 (the millionth prime).
- seeding_scheme (`str`):
Algorithm to use for watermarking. Accepts values:
- "lefthash" (default): "green" tokens selection depend on the last token (Algorithm 2 from the paper)
- "selfhash": "green" tokens selection depends on the current token itself (Algorithm 3 from the paper) | 427_15_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | - "selfhash": "green" tokens selection depends on the current token itself (Algorithm 3 from the paper)
The downside of this scheme is that it considers all possible next tokens and can be slower than "lefthash".
- context_width(`int`):
The context length of previous tokens to use in seeding. Higher context length makes watermarking more robust.
- __call__
Detector for detection of watermark generated text. The detector needs to be given the exact same settings that were | 427_15_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | Detector for detection of watermark generated text. The detector needs to be given the exact same settings that were
given during text generation to replicate the watermark greenlist generation and so detect the watermark. This includes
the correct device that was used during text generation, the correct watermarking arguments and the correct tokenizer vocab size.
The code was based on the [original repo](https://github.com/jwkirchenbauer/lm-watermarking/tree/main). | 427_15_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | The code was based on the [original repo](https://github.com/jwkirchenbauer/lm-watermarking/tree/main).
See [the paper](https://arxiv.org/abs/2306.04634) for more information.
Args:
model_config (`PretrainedConfig`):
The model config that will be used to get model specific arguments used when generating.
device (`str`):
The device which was used during watermarked text generation.
watermarking_config (Union[`WatermarkingConfig`, `Dict`]): | 427_15_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | The device which was used during watermarked text generation.
watermarking_config (Union[`WatermarkingConfig`, `Dict`]):
The exact same watermarking config and arguments used when generating text.
ignore_repeated_ngrams (`bool`, *optional*, defaults to `False`):
Whether to count every unique ngram only once or not.
max_cache_size (`int`, *optional*, defaults to 128):
The max size to be used for LRU caching of seeding/sampling algorithms called for every token.
Examples:
```python | 427_15_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | The max size to be used for LRU caching of seeding/sampling algorithms called for every token.
Examples:
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM, WatermarkDetector, WatermarkingConfig | 427_15_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | >>> model_id = "openai-community/gpt2"
>>> model = AutoModelForCausalLM.from_pretrained(model_id)
>>> tok = AutoTokenizer.from_pretrained(model_id)
>>> tok.pad_token_id = tok.eos_token_id
>>> tok.padding_side = "left"
>>> inputs = tok(["This is the beginning of a long story", "Alice and Bob are"], padding=True, return_tensors="pt")
>>> input_len = inputs["input_ids"].shape[-1] | 427_15_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | >>> # first generate text with watermark and without
>>> watermarking_config = WatermarkingConfig(bias=2.5, seeding_scheme="selfhash")
>>> out_watermarked = model.generate(**inputs, watermarking_config=watermarking_config, do_sample=False, max_length=20)
>>> out = model.generate(**inputs, do_sample=False, max_length=20) | 427_15_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | >>> # now we can instantiate the detector and check the generated text
>>> detector = WatermarkDetector(model_config=model.config, device="cpu", watermarking_config=watermarking_config)
>>> detection_out_watermarked = detector(out_watermarked, return_dict=True)
>>> detection_out = detector(out, return_dict=True)
>>> detection_out_watermarked.prediction
array([ True, True]) | 427_15_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | >>> detection_out.prediction
array([False, False])
```
- __call__
This is the configuration class to store the configuration of a [`BayesianDetectorModel`]. It is used to
instantiate a Bayesian Detector model according to the specified arguments.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
watermarking_depth (`int`, *optional*):
The number of tournament layers. | 427_15_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | Args:
watermarking_depth (`int`, *optional*):
The number of tournament layers.
base_rate (`float1`, *optional*, defaults to 0.5):
Prior probability P(w) that a text is watermarked.
Bayesian classifier for watermark detection.
This detector uses Bayes' rule to compute a watermarking score, which is the sigmoid of the log of ratio of the
posterior probabilities P(watermarked|g_values) and P(unwatermarked|g_values). Please see the section on
BayesianScore in the paper for further details. | 427_15_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | BayesianScore in the paper for further details.
Paper URL: https://www.nature.com/articles/s41586-024-08025-4
Note that this detector only works with non-distortionary Tournament-based watermarking using the Bernoulli(0.5)
g-value distribution.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) | 427_15_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`BayesianDetectorConfig`]): Model configuration class with all the parameters of the model. | 427_15_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | Parameters:
config ([`BayesianDetectorConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
- forward
Class that holds arguments for watermark generation and should be passed into `GenerationConfig` during `generate`. | 427_15_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | - forward
Class that holds arguments for watermark generation and should be passed into `GenerationConfig` during `generate`.
See [this paper](https://www.nature.com/articles/s41586-024-08025-4) for more details on the arguments.
Args:
ngram_len (`int`):
Ngram length.
keys (`List[int]`):
A sequence of watermarking keys, one for each depth.
context_history_size (`int`, *optional*, defaults to 1024):
Size of the tensor to keep track of seen contexts. | 427_15_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | context_history_size (`int`, *optional*, defaults to 1024):
Size of the tensor to keep track of seen contexts.
sampling_table_seed (`int`, *optional*, defaults to 0):
Random seed to generate the sampling table.
sampling_table_size (`int`, *optional*, defaults to 65536):
Size of the sampling table.
skip_first_ngram_calls (`bool`, *optional*, defaults to `False`):
Whether to skip first ngram calls.
debug_mode (`bool`, optional, *optional*, defaults to `False`): | 427_15_16 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | Whether to skip first ngram calls.
debug_mode (`bool`, optional, *optional*, defaults to `False`):
Logits are modified to uniform one got before watermarking modification is applied. This is to test the
implementation.
Examples:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, SynthIDTextWatermarkingConfig | 427_15_17 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | >>> tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b', padding_side="left")
>>> model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b')
>>> # SynthID Text configuration
>>> watermarking_config = SynthIDTextWatermarkingConfig(
... keys=[654, 400, 836, 123, 340, 443, 597, 160, 57],
... ngram_len=5,
... ) | 427_15_18 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | >>> # Generation with watermarking
>>> tokenized_prompts = tokenizer(["Once upon a time, "], return_tensors="pt", padding=True)
>>> output_sequences = model.generate(
... **tokenized_prompts, watermarking_config=watermarking_config, do_sample=True, max_new_tokens=10
... )
>>> watermarked_text = tokenizer.batch_decode(output_sequences, skip_special_tokens=True)
```
SynthID text watermark detector class.
This class has to be initialized with the trained bayesian detector module check script | 427_15_19 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | This class has to be initialized with the trained bayesian detector module check script
in examples/synthid_text/detector_training.py for example in training/saving/loading this
detector module. The folder also showcases example use case of this detector.
Parameters:
detector_module ([`BayesianDetectorModel`]):
Bayesian detector module object initialized with parameters.
Check examples/research_projects/synthid_text/detector_training.py for usage.
logits_processor (`SynthIDTextWatermarkLogitsProcessor`): | 427_15_20 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | logits_processor (`SynthIDTextWatermarkLogitsProcessor`):
The logits processor used for watermarking.
tokenizer (`Any`):
The tokenizer used for the model.
Examples:
```python
>>> from transformers import (
... AutoTokenizer, BayesianDetectorModel, SynthIDTextWatermarkLogitsProcessor, SynthIDTextWatermarkDetector
... ) | 427_15_21 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | >>> # Load the detector. See examples/research_projects/synthid_text for training a detector.
>>> detector_model = BayesianDetectorModel.from_pretrained("joaogante/dummy_synthid_detector")
>>> logits_processor = SynthIDTextWatermarkLogitsProcessor(
... **detector_model.config.watermarking_config, device="cpu"
... )
>>> tokenizer = AutoTokenizer.from_pretrained(detector_model.config.model_name)
>>> detector = SynthIDTextWatermarkDetector(detector_model, logits_processor, tokenizer) | 427_15_22 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#watermark-utils | .md | >>> # Test whether a certain string is watermarked
>>> test_input = tokenizer(["This is a test input"], return_tensors="pt")
>>> is_watermarked = detector(test_input.input_ids)
```
- __call__ | 427_15_23 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#compile-utils | .md | Class that holds arguments relative to `torch.compile` behavior, when using automatic compilation in `generate`.
See [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) for more details on the arguments.
Args:
fullgraph (`bool`, *optional*, defaults to `True`):
If `True`, requires that the whole forward be capturable in a single graph.
dynamic (`bool` or `None`, *optional*):
Whether to try to use dynamic shape graphs. | 427_16_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#compile-utils | .md | dynamic (`bool` or `None`, *optional*):
Whether to try to use dynamic shape graphs.
backend (`str` or `Callable`, *optional*, defaults to `"inductor"`):
Backend to be used.
mode (`str`, *optional*, defaults to `"reduce-overhead"`):
Controls balance between performance and overhead.
options (`dict`, *optional*):
A dictionary of options to pass to the backend.
Examples:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, CompileConfig | 427_16_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#compile-utils | .md | >>> tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b')
>>> model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b').cuda()
>>> # Automatic compile configuration, used with static cache
>>> compile_config = CompileConfig(dynamic=True) | 427_16_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#compile-utils | .md | >>> # Automatic compile configuration, used with static cache
>>> compile_config = CompileConfig(dynamic=True)
>>> # Generation with static cache and compile config
>>> input = tokenizer.encode("Hello there, how", return_tensors="pt").cuda()
>>> output = model.generate(
... input, do_sample=False, max_new_tokens=300, cache_implementation="static", compile_config=compile_config
... )
>>> output_text = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
```
- __call__ | 427_16_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/time_series_utils.md | https://huggingface.co/docs/transformers/en/internal/time_series_utils/ | .md | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | 428_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/time_series_utils.md | https://huggingface.co/docs/transformers/en/internal/time_series_utils/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
--> | 428_0_1 |
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