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Add Cohere2 model (#35224)

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alexrs-cohere
2024-12-13 09:35:50 +01:00
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commit 64478c7631
19 changed files with 2508 additions and 9 deletions
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2
docs/source/en/_toctree.yml
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@@ -362,6 +362,8 @@
title: CodeLlama
- local: model_doc/cohere
title: Cohere
- local: model_doc/cohere2
title: Cohere2
- local: model_doc/convbert
title: ConvBERT
- local: model_doc/cpm

1
docs/source/en/index.md
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@@ -99,6 +99,7 @@ Flax), PyTorch, and/or TensorFlow.
| [CodeGen](model_doc/codegen) | ✅ | ❌ | ❌ |
| [CodeLlama](model_doc/code_llama) | ✅ | ❌ | ✅ |
| [Cohere](model_doc/cohere) | ✅ | ❌ | ❌ |
| [Cohere2](model_doc/cohere2) | ✅ | ❌ | ❌ |
| [Conditional DETR](model_doc/conditional_detr) | ✅ | ❌ | ❌ |
| [ConvBERT](model_doc/convbert) | ✅ | ✅ | ❌ |
| [ConvNeXT](model_doc/convnext) | ✅ | ✅ | ❌ |

44
docs/source/en/model_doc/cohere2.md Normal file
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@@ -0,0 +1,44 @@
# Cohere
## Usage tips
The model and tokenizer can be loaded via:
```python
# pip install transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "CohereForAI/c4ai-command-r7b-12-2024"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# Format message with the command-r chat template
messages = [{"role": "user", "content": "Hello, how are you?"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
gen_tokens = model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.3,
)
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
```
## Cohere2Config
[[autodoc]] Cohere2Config
## Cohere2Model
[[autodoc]] Cohere2Model
- forward
## Cohere2ForCausalLM
[[autodoc]] Cohere2ForCausalLM
- forward

2
docs/source/en/perf_infer_gpu_one.md
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@@ -43,6 +43,7 @@ FlashAttention-2 is currently supported for the following architectures:
* [Chameleon](https://huggingface.co/docs/transformers/model_doc/chameleon#transformers.Chameleon)
* [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPModel)
* [Cohere](https://huggingface.co/docs/transformers/model_doc/cohere#transformers.CohereModel)
* [Cohere2](https://huggingface.co/docs/transformers/model_doc/cohere2#transformers.Cohere2Model)
* [GLM](https://huggingface.co/docs/transformers/model_doc/glm#transformers.GLMModel)
* [Dbrx](https://huggingface.co/docs/transformers/model_doc/dbrx#transformers.DbrxModel)
* [DistilBert](https://huggingface.co/docs/transformers/model_doc/distilbert#transformers.DistilBertModel)
@@ -227,6 +228,7 @@ For now, Transformers supports SDPA inference and training for the following arc
* [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPModel)
* [GLM](https://huggingface.co/docs/transformers/model_doc/glm#transformers.GLMModel)
* [Cohere](https://huggingface.co/docs/transformers/model_doc/cohere#transformers.CohereModel)
* [Cohere2](https://huggingface.co/docs/transformers/model_doc/cohere2#transformers.Cohere2Model)
* [data2vec_audio](https://huggingface.co/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioModel)
* [Dbrx](https://huggingface.co/docs/transformers/model_doc/dbrx#transformers.DbrxModel)
* [DeiT](https://huggingface.co/docs/transformers/model_doc/deit#transformers.DeiTModel)

8
src/transformers/__init__.py
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@@ -305,6 +305,7 @@ _import_structure = {
"CodeGenTokenizer",
],
"models.cohere": ["CohereConfig"],
"models.cohere2": ["Cohere2Config"],
"models.conditional_detr": ["ConditionalDetrConfig"],
"models.convbert": [
"ConvBertConfig",
@@ -1787,6 +1788,7 @@ else:
]
)
_import_structure["models.cohere"].extend(["CohereForCausalLM", "CohereModel", "CoherePreTrainedModel"])
_import_structure["models.cohere2"].extend(["Cohere2ForCausalLM", "Cohere2Model", "Cohere2PreTrainedModel"])
_import_structure["models.conditional_detr"].extend(
[
"ConditionalDetrForObjectDetection",
@@ -5204,6 +5206,7 @@ if TYPE_CHECKING:
CodeGenTokenizer,
)
from .models.cohere import CohereConfig
from .models.cohere2 import Cohere2Config
from .models.conditional_detr import (
ConditionalDetrConfig,
)
@@ -6681,6 +6684,11 @@ if TYPE_CHECKING:
CohereModel,
CoherePreTrainedModel,
)
from .models.cohere2 import (
Cohere2ForCausalLM,
Cohere2Model,
Cohere2PreTrainedModel,
)
from .models.conditional_detr import (
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,

3
src/transformers/cache_utils.py
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@@ -1634,8 +1634,9 @@ class HybridCache(Cache):
self.num_key_value_heads = (
config.num_attention_heads if config.num_key_value_heads is None else config.num_key_value_heads
)
layer_switch = config.sliding_window_pattern if hasattr(config, "sliding_window_pattern") else 2 # 2 is for BC
self.is_sliding = torch.tensor(
[not bool(i % 2) for i in range(config.num_hidden_layers)], dtype=torch.bool, device=device
[bool((i + 1) % layer_switch) for i in range(config.num_hidden_layers)], dtype=torch.bool, device=device
)
self.key_cache: List[torch.Tensor] = []
self.value_cache: List[torch.Tensor] = []

1
src/transformers/models/__init__.py
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@@ -52,6 +52,7 @@ from . import (
code_llama,
codegen,
cohere,
cohere2,
conditional_detr,
convbert,
convnext,

2
src/transformers/models/auto/configuration_auto.py
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@@ -69,6 +69,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
("code_llama", "LlamaConfig"),
("codegen", "CodeGenConfig"),
("cohere", "CohereConfig"),
("cohere2", "Cohere2Config"),
("conditional_detr", "ConditionalDetrConfig"),
("convbert", "ConvBertConfig"),
("convnext", "ConvNextConfig"),
@@ -371,6 +372,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
("code_llama", "CodeLlama"),
("codegen", "CodeGen"),
("cohere", "Cohere"),
("cohere2", "Cohere2"),
("conditional_detr", "Conditional DETR"),
("convbert", "ConvBERT"),
("convnext", "ConvNeXT"),

2
src/transformers/models/auto/modeling_auto.py
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@@ -69,6 +69,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
("code_llama", "LlamaModel"),
("codegen", "CodeGenModel"),
("cohere", "CohereModel"),
("cohere2", "Cohere2Model"),
("conditional_detr", "ConditionalDetrModel"),
("convbert", "ConvBertModel"),
("convnext", "ConvNextModel"),
@@ -482,6 +483,7 @@ MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
("code_llama", "LlamaForCausalLM"),
("codegen", "CodeGenForCausalLM"),
("cohere", "CohereForCausalLM"),
("cohere2", "Cohere2ForCausalLM"),
("cpmant", "CpmAntForCausalLM"),
("ctrl", "CTRLLMHeadModel"),
("data2vec-text", "Data2VecTextForCausalLM"),

1
src/transformers/models/auto/tokenization_auto.py
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@@ -147,6 +147,7 @@ else:
),
("codegen", ("CodeGenTokenizer", "CodeGenTokenizerFast" if is_tokenizers_available() else None)),
("cohere", (None, "CohereTokenizerFast" if is_tokenizers_available() else None)),
("cohere2", (None, "CohereTokenizerFast" if is_tokenizers_available() else None)),
("convbert", ("ConvBertTokenizer", "ConvBertTokenizerFast" if is_tokenizers_available() else None)),
(
"cpm",

27
src/transformers/models/cohere2/__init__.py Normal file
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@@ -0,0 +1,27 @@
# Copyright 2024 Cohere and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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 specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_cohere2 import *
from .modeling_cohere2 import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)

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src/transformers/models/cohere2/configuration_cohere2.py Normal file
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@@ -0,0 +1,209 @@
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/cohere2/modular_cohere2.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_cohere2.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# coding=utf-8
# Copyright 2024 Cohere Inc. HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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 specific language governing permissions and
# limitations under the License.
from ...configuration_utils import PretrainedConfig
from ...modeling_rope_utils import rope_config_validation
class Cohere2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`CohereModel`]. It is used to instantiate an Cohere
model according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information. Instantiating a configuration
with the defaults will yield a similar configuration to that of the [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) model.
Args:
vocab_size (`int`, *optional*, defaults to 256000):
Vocabulary size of the Cohere model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`CohereModel`]
hidden_size (`int`, *optional*, defaults to 8192):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 22528):
Dimension of the MLP representations.
logit_scale (`float`, *optional*, defaults to 0.0625):
The scaling factor for the output logits.
num_hidden_layers (`int`, *optional*, defaults to 40):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 64):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 8192):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 5):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 255001):
End of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
sliding_window (`int`, *optional*, defaults to 4096):
Size of the sliding window attention context.
sliding_window_pattern (`int`, *optional*, defaults to 4):
Pattern for the sliding window attention.
cache_implementation (`str`, *optional*, defaults to `"hybrid"`): the cache type to be used with `generate`.
```python
>>> from transformers import Cohere2Model, Cohere2Config
>>> # Initializing a Cohere Nextmodel configuration
>>> configuration = Cohere2Config()
>>> # Initializing a model from the Cohere2 configuration
>>> model = Cohere2Model(configuration) # doctest: +SKIP
>>> # Accessing the model configuration
>>> configuration = model.config # doctest: +SKIP
```
"""
model_type = "cohere2"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=256000,
hidden_size=8192,
intermediate_size=22528,
logit_scale=0.0625,
num_hidden_layers=40,
num_attention_heads=64,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=8192,
initializer_range=0.02,
layer_norm_eps=1e-5,
use_cache=True,
pad_token_id=0,
bos_token_id=5,
eos_token_id=255001,
tie_word_embeddings=True,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
sliding_window=4096,
sliding_window_pattern=4,
cache_implementation="hybrid",
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.logit_scale = logit_scale
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.sliding_window = sliding_window
self.sliding_window_pattern = sliding_window_pattern
# Need to specify head_dim in the config so it can be used in the attention forward functions
self.head_dim = hidden_size // num_attention_heads
self.cache_implementation = cache_implementation
# Validate the correctness of rotary position embeddings parameters
rope_config_validation(self)
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
__all__ = ["Cohere2Config"]

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src/transformers/models/cohere2/modeling_cohere2.py Normal file
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src/transformers/models/cohere2/modular_cohere2.py Normal file
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@@ -0,0 +1,744 @@
# coding=utf-8
# Copyright 2024 Cohere Inc. HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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 specific language governing permissions and
# limitations under the License.
import math
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.utils.checkpoint
from ...cache_utils import Cache, HybridCache
from ...configuration_utils import PretrainedConfig
from ...modeling_outputs import (
BaseModelOutputWithPast,
)
from ...modeling_rope_utils import rope_config_validation
from ...utils import (
is_flash_attn_2_available,
logging,
)
from ..cohere.modeling_cohere import (
CohereDecoderLayer,
CohereForCausalLM,
CohereLayerNorm,
CoherePreTrainedModel,
CohereRotaryEmbedding,
apply_rotary_pos_emb,
repeat_kv,
)
from ..gemma2.modeling_gemma2 import Gemma2Model
if is_flash_attn_2_available():
from ...modeling_flash_attention_utils import _flash_attention_forward
logger = logging.get_logger(__name__)
class Cohere2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`CohereModel`]. It is used to instantiate an Cohere
model according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information. Instantiating a configuration
with the defaults will yield a similar configuration to that of the [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) model.
Args:
vocab_size (`int`, *optional*, defaults to 256000):
Vocabulary size of the Cohere model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`CohereModel`]
hidden_size (`int`, *optional*, defaults to 8192):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 22528):
Dimension of the MLP representations.
logit_scale (`float`, *optional*, defaults to 0.0625):
The scaling factor for the output logits.
num_hidden_layers (`int`, *optional*, defaults to 40):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 64):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 8192):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 5):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 255001):
End of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
sliding_window (`int`, *optional*, defaults to 4096):
Size of the sliding window attention context.
sliding_window_pattern (`int`, *optional*, defaults to 4):
Pattern for the sliding window attention.
cache_implementation (`str`, *optional*, defaults to `"hybrid"`): the cache type to be used with `generate`.
```python
>>> from transformers import Cohere2Model, Cohere2Config
>>> # Initializing a Cohere Nextmodel configuration
>>> configuration = Cohere2Config()
>>> # Initializing a model from the Cohere2 configuration
>>> model = Cohere2Model(configuration) # doctest: +SKIP
>>> # Accessing the model configuration
>>> configuration = model.config # doctest: +SKIP
```
"""
model_type = "cohere2"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=256000,
hidden_size=8192,
intermediate_size=22528,
logit_scale=0.0625,
num_hidden_layers=40,
num_attention_heads=64,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=8192,
initializer_range=0.02,
layer_norm_eps=1e-5,
use_cache=True,
pad_token_id=0,
bos_token_id=5,
eos_token_id=255001,
tie_word_embeddings=True,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
sliding_window=4096,
sliding_window_pattern=4,
cache_implementation="hybrid",
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.logit_scale = logit_scale
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.sliding_window = sliding_window
self.sliding_window_pattern = sliding_window_pattern
# Need to specify head_dim in the config so it can be used in the attention forward functions
self.head_dim = hidden_size // num_attention_heads
self.cache_implementation = cache_implementation
# Validate the correctness of rotary position embeddings parameters
rope_config_validation(self)
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
class Cohere2RotaryEmbedding(CohereRotaryEmbedding):
pass
class Cohere2LayerNorm(CohereLayerNorm):
pass
def eager_attention_forward(
config: Cohere2Config,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
mask: Optional[torch.Tensor],
**_kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
key_states = repeat_kv(key, config.num_key_value_groups)
value_states = repeat_kv(value, config.num_key_value_groups)
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) / math.sqrt(config.head_dim)
if mask is not None: # no matter the length, we just slice it
causal_mask = mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=config.attention_dropout, training=config.training)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
def flash_attention_forward(
config: Cohere2Config,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
mask: Optional[torch.Tensor],
target_dtype: torch.dtype = torch.float16,
**_kwargs,
) -> Tuple[torch.Tensor, None]:
if mask is not None:
seq_len = mask.shape[1]
query = query[:, :, :seq_len]
value = value[:, :, :seq_len]
# TODO: These transpose are quite inefficient but Flash Attention requires the layout
# [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor rotary embedding
query_states = query.transpose(1, 2)
key_states = key.transpose(1, 2)
value_states = value.transpose(1, 2)
dropout_rate = config.attention_dropout if config.training else 0.0
input_dtype = query_states.dtype
if input_dtype == torch.float32:
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
mask,
seq_len,
dropout=dropout_rate,
is_causal=config.is_causal,
sliding_window=config.sliding_window,
use_top_left_mask=config._flash_attn_uses_top_left_mask,
)
return attn_output, None
def sdpa_attention_forward(
config: Cohere2Config,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
mask: Optional[torch.Tensor],
**_kwargs,
) -> Tuple[torch.Tensor, None]:
key = repeat_kv(key, config.num_key_value_groups)
value = repeat_kv(value, config.num_key_value_groups)
causal_mask = mask
if mask is not None:
causal_mask = causal_mask[:, :, :, : key.shape[-2]]
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
if query.device.type == "cuda" and causal_mask is not None:
query = query.contiguous()
key = key.contiguous()
value = value.contiguous()
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
is_causal = True if causal_mask is None and query.shape[1] > 1 else False
attn_output = torch.nn.functional.scaled_dot_product_attention(
query,
key,
value,
attn_mask=causal_mask,
dropout_p=config.attention_dropout if config.training else 0.0,
is_causal=is_causal,
)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, None
COHERE2_ATTENTION_FUNCTION = {
"flash_attention_2": flash_attention_forward,
"eager": eager_attention_forward,
"sdpa": sdpa_attention_forward,
}
class Cohere2Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Cohere2Config, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
logger.warning_once(
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.attention_dropout = config.attention_dropout
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = config.head_dim
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
self.is_causal = True
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
self.sliding_window = (
config.sliding_window if (self.layer_idx + 1) % self.config.sliding_window_pattern != 0 else None
)
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = position_embeddings
if self.sliding_window is not None:
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
cache_kwargs = {
"sin": sin,
"cos": cos,
"sliding_window": self.sliding_window,
"cache_position": cache_position,
}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
if output_attentions and self.config._attn_implementation in ["sdpa", "flash_attention_2"]:
logger.warning_once("Setting `attention_type` to `eager` because `output_attentions=True`")
attention_type = "eager"
else:
attention_type = self.config._attn_implementation
attn_output, attn_weights = COHERE2_ATTENTION_FUNCTION[attention_type](
self, query_states, key_states, value_states, attention_mask, output_attentions=output_attentions
)
attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class Cohere2DecoderLayer(CohereDecoderLayer):
def __init__(self, config: Cohere2Config, layer_idx: int):
super().__init__(config, layer_idx)
self.self_attn = Cohere2Attention(config, layer_idx)
self.config = config
self.is_sliding = (layer_idx + 1) % self.config.sliding_window_pattern != 0
self.sliding_window = config.sliding_window
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`):
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
with `head_dim` being the embedding dimension of each attention head.
attention_mask (`torch.FloatTensor`, *optional*):
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
query_sequence_length, key_sequence_length)` if default attention is used.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence
"""
if self.is_sliding and attention_mask is not None: # efficient SDPA and no padding
# Flash-attn is a 2D tensor
if self.config._attn_implementation == "flash_attention_2":
if past_key_value is not None: # when decoding
attention_mask = attention_mask[:, -self.sliding_window :]
else:
min_dtype = torch.finfo(hidden_states.dtype).min
sliding_window_mask = torch.tril(
torch.ones_like(attention_mask, dtype=torch.bool), diagonal=-self.sliding_window
)
attention_mask = torch.where(sliding_window_mask, min_dtype, attention_mask)
if attention_mask.shape[-1] <= 1: # when decoding
attention_mask = attention_mask[:, :, :, -self.sliding_window :]
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states_attention, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
position_embeddings=position_embeddings,
attention_mask=attention_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
)
# Fully Connected
hidden_states_mlp = self.mlp(hidden_states)
# Add everything together
hidden_states = residual + hidden_states_attention + hidden_states_mlp
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
class Cohere2PreTrainedModel(CoherePreTrainedModel):
config_class = Cohere2Config
class Cohere2Model(Gemma2Model):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Cohere2DecoderLayer`]
Args:
config: Cohere2Config
"""
def __init__(self, config: Cohere2Config):
super().__init__(config)
self.norm = Cohere2LayerNorm(hidden_size=(config.hidden_size), eps=config.layer_norm_eps)
self.rotary_emb = Cohere2RotaryEmbedding(config=config)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[HybridCache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if use_cache and past_key_values is None and not self.training:
batch_size, seq_len, _ = inputs_embeds.shape
past_key_values = HybridCache(
self.config,
batch_size=batch_size,
max_cache_len=seq_len,
device=self.device,
dtype=inputs_embeds.dtype,
)
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states, position_ids)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
position_embeddings,
causal_mask,
past_key_values,
output_attentions,
use_cache,
cache_position,
)
else:
layer_outputs = decoder_layer(
hidden_states,
position_embeddings=position_embeddings,
attention_mask=causal_mask,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = past_key_values if use_cache else None
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 BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
class Cohere2ForCausalLM(CohereForCausalLM):
def __init__(self, config: Cohere2Config):
super().__init__(config)
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
cache_position=None,
position_ids=None,
use_cache=True,
num_logits_to_keep=None,
**kwargs,
):
# Overwritten: has a special cache type, `HybridCache`
# If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
# Exception 1: when passing input_embeds, input_ids may be missing entries
# Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
if past_key_values is not None:
if inputs_embeds is not None: # Exception 1
input_ids = input_ids[:, -cache_position.shape[0] :]
elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2)
input_ids = input_ids[:, cache_position]
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -input_ids.shape[1] :]
# This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s
# `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride
# during the decoding. Here, simply using `.contiguous()` is not sufficient as in the
# batch size = 1 case, `position_ids` is already contiguous but with varying stride
# which retriggers a capture.
position_ids = position_ids.clone(memory_format=torch.contiguous_format)
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and cache_position[0] == 0:
model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
else:
# The clone here is for the same reason as for `position_ids`.
model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
if (
isinstance(past_key_values, HybridCache)
and attention_mask.ndim == 2
and not self.config._attn_implementation == "flash_attention_2"
):
if model_inputs["inputs_embeds"] is not None:
batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
device = model_inputs["inputs_embeds"].device
else:
batch_size, sequence_length = model_inputs["input_ids"].shape
device = model_inputs["input_ids"].device
attention_mask = self.model._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=past_key_values.get_max_cache_shape(),
dtype=self.lm_head.weight.dtype,
device=device,
cache_position=cache_position,
batch_size=batch_size,
)
if num_logits_to_keep is not None:
model_inputs["num_logits_to_keep"] = num_logits_to_keep
model_inputs.update(
{
"position_ids": position_ids,
"cache_position": cache_position,
"past_key_values": past_key_values,
"use_cache": use_cache,
"attention_mask": attention_mask,
}
)
return model_inputs
__all__ = ["Cohere2Config", "Cohere2ForCausalLM", "Cohere2Model", "Cohere2PreTrainedModel"]

21
src/transformers/utils/dummy_pt_objects.py
View File

@@ -2237,6 +2237,27 @@ class CoherePreTrainedModel(metaclass=DummyObject):
requires_backends(self, ["torch"])
class Cohere2ForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class Cohere2Model(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class Cohere2PreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ConditionalDetrForObjectDetection(metaclass=DummyObject):
_backends = ["torch"]

20
tests/models/cohere/test_modeling_cohere.py
View File

@@ -40,6 +40,11 @@ if is_torch_available():
# Copied from transformers.tests.models.llama.LlamaModelTester with Llama->Cohere
class CohereModelTester:
config_class = CohereConfig
if is_torch_available():
model_class = CohereModel
for_causal_lm_class = CohereForCausalLM
def __init__(
self,
parent,
@@ -51,7 +56,7 @@ class CohereModelTester:
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_hidden_layers=4,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
@@ -115,7 +120,7 @@ class CohereModelTester:
# Ignore copy
def get_config(self):
return CohereConfig(
return self.config_class(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
@@ -129,13 +134,12 @@ class CohereModelTester:
is_decoder=False,
initializer_range=self.initializer_range,
pad_token_id=self.pad_token_id,
eos_token_id=self.pad_token_id,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = CohereModel(config=config)
model = self.model_class(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
@@ -155,7 +159,7 @@ class CohereModelTester:
encoder_attention_mask,
):
config.add_cross_attention = True
model = CohereModel(config)
model = self.model_class(config)
model.to(torch_device)
model.eval()
result = model(
@@ -184,7 +188,7 @@ class CohereModelTester:
encoder_hidden_states,
encoder_attention_mask,
):
model = CohereForCausalLM(config=config)
model = self.for_causal_lm_class(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
@@ -204,7 +208,7 @@ class CohereModelTester:
):
config.is_decoder = True
config.add_cross_attention = True
model = CohereForCausalLM(config=config)
model = self.for_causal_lm_class(config=config)
model.to(torch_device)
model.eval()
@@ -281,7 +285,7 @@ class CohereModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMix
)
test_headmasking = False
test_pruning = False
fx_compatible = True
fx_compatible = False
# Need to use `0.8` instead of `0.9` for `test_cpu_offload`
# This is because we are hitting edge cases with the causal_mask buffer

0
tests/models/cohere2/__init__.py Normal file
View File

347
tests/models/cohere2/test_modeling_cohere2.py Normal file
View File

@@ -0,0 +1,347 @@
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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 specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch Cohere2 model."""
import unittest
from packaging import version
from parameterized import parameterized
from pytest import mark
from transformers import AutoModelForCausalLM, AutoTokenizer, Cohere2Config, HybridCache, is_torch_available, pipeline
from transformers.generation.configuration_utils import GenerationConfig
from transformers.testing_utils import (
require_flash_attn,
require_read_token,
require_torch,
require_torch_gpu,
slow,
torch_device,
)
from ...models.cohere.test_modeling_cohere import CohereModelTest, CohereModelTester
from ...test_configuration_common import ConfigTester
if is_torch_available():
import torch
from transformers import (
Cohere2ForCausalLM,
Cohere2Model,
)
class Cohere2ModelTester(CohereModelTester):
config_class = Cohere2Config
if is_torch_available():
model_class = Cohere2Model
for_causal_lm_class = Cohere2ForCausalLM
@require_torch
class Cohere2ModelTest(CohereModelTest, unittest.TestCase):
all_model_classes = (Cohere2Model, Cohere2ForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (Cohere2ForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": Cohere2Model,
"text-generation": Cohere2ForCausalLM,
}
if is_torch_available()
else {}
)
_is_stateful = True
def setUp(self):
self.model_tester = Cohere2ModelTester(self)
self.config_tester = ConfigTester(self, config_class=Cohere2Config, hidden_size=37)
@unittest.skip("Failing because of unique cache (HybridCache)")
def test_model_outputs_equivalence(self, **kwargs):
pass
@unittest.skip("Cohere2's forcefully disables sdpa due to softcapping")
def test_sdpa_can_dispatch_non_composite_models(self):
pass
@parameterized.expand([("float16",), ("bfloat16",), ("float32",)])
@unittest.skip("Cohere2's eager attn/sdpa attn outputs are expected to be different")
def test_eager_matches_sdpa_inference(self):
pass
@unittest.skip("Cohere2's eager attn/sdpa attn outputs are expected to be different")
def test_eager_matches_sdpa_generate(self):
pass
@parameterized.expand([("random",), ("same",)])
@unittest.skip("Cohere2 has HybridCache which is not compatible with assisted decoding")
def test_assisted_decoding_matches_greedy_search(self, assistant_type):
pass
@unittest.skip("Cohere2 has HybridCache which is not compatible with assisted decoding")
def test_prompt_lookup_decoding_matches_greedy_search(self, assistant_type):
pass
@unittest.skip("Cohere2 has HybridCache which is not compatible with assisted decoding")
def test_assisted_decoding_sample(self):
pass
@unittest.skip("Cohere2 has HybridCache which is not compatible with dola decoding")
def test_dola_decoding_sample(self):
pass
@parameterized.expand([(1, False), (1, True), (4, False)])
@unittest.skip("Cohere2 has HybridCache and doesn't support old tuple format at all")
def test_new_cache_format(self, num_beams, do_sample):
pass
@unittest.skip("Cohere2 has HybridCache and doesn't support continue from past kv")
def test_generate_continue_from_past_key_values(self):
pass
@unittest.skip("Cohere2 has HybridCache and doesn't support low_memory generation")
def test_beam_search_low_memory(self):
pass
@unittest.skip("Cohere2 has HybridCache and doesn't support contrastive generation")
def test_contrastive_generate(self):
pass
@unittest.skip("Cohere2 has HybridCache and doesn't support contrastive generation")
def test_contrastive_generate_dict_outputs_use_cache(self):
pass
@unittest.skip("Cohere2 has HybridCache and doesn't support contrastive generation")
def test_contrastive_generate_low_memory(self):
pass
@unittest.skip("Cohere2 has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support.")
def test_generate_with_static_cache(self):
pass
@unittest.skip("Cohere2 has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support.")
def test_generate_from_inputs_embeds_with_static_cache(self):
pass
# overwrite because HybridCache has fixed length for key/values
def _check_attentions_for_generate(
self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
for idx, iter_attentions in enumerate(attentions):
tgt_len = min_length + idx if not use_cache else 1
src_len = min_length + idx if not use_cache else max_length
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
)
# overwrite because HybridCache has fixed length for key/values
def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config, num_beam_groups=1):
self.assertIsInstance(past_key_values, HybridCache)
# check shape key, value (batch, head, max_seq_length, head_features)
head_dim = config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads
num_key_value_heads = (
config.num_attention_heads
if getattr(config, "num_key_value_heads", None) is None
else config.num_key_value_heads
)
num_hidden_layers = config.num_hidden_layers
# we should get `max_length` in shape, not `max_length - embeds_length`
# `+1` because the test in Mixin subtracts 1 which is needed for tuple cache
static_cache_shape = (batch_size, num_key_value_heads, seq_length + 1, head_dim)
static_layers = [layer_idx for layer_idx, boolean in enumerate(past_key_values.is_sliding) if not boolean]
self.assertTrue(len(past_key_values.key_cache) == num_hidden_layers)
self.assertTrue(past_key_values.key_cache[static_layers[0]].shape == static_cache_shape)
@unittest.skip("Cohere2's eager attn/sdpa attn outputs are expected to be different")
def test_sdpa_equivalence(self):
pass
@slow
@require_torch_gpu
class Cohere2IntegrationTest(unittest.TestCase):
input_text = ["Hello I am doing", "Hi today"]
# This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
# Depending on the hardware we get different logits / generations
cuda_compute_capability_major_version = None
@classmethod
def setUpClass(cls):
if is_torch_available() and torch.cuda.is_available():
# 8 is for A100 / A10 and 7 for T4
cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
@require_read_token
@unittest.skip("Cohere2 has not been released yet")
def test_model_bf16(self):
model_id = "CohereForAI/command-r7b-12-2024"
EXPECTED_TEXTS = [
"<BOS_TOKEN>Hello I am doing a project on the 1918 flu pandemic and I am trying to find out how many",
"<PAD><PAD><BOS_TOKEN>Hi today I'm going to be talking about the history of the United States. The United States of America",
]
model = AutoModelForCausalLM.from_pretrained(
model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="eager"
).to(torch_device)
tokenizer = AutoTokenizer.from_pretrained(model_id)
inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=False)
self.assertEqual(output_text, EXPECTED_TEXTS)
@require_read_token
@unittest.skip("Cohere2 has not been released yet")
def test_model_fp16(self):
model_id = "CohereForAI/command-r7b-12-2024"
EXPECTED_TEXTS = [
"<BOS_TOKEN>Hello I am doing a project on the 1918 flu pandemic and I am trying to find out how many",
"<PAD><PAD><BOS_TOKEN>Hi today I'm going to be talking about the history of the United States. The United States of America",
]
model = AutoModelForCausalLM.from_pretrained(
model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16, attn_implementation="eager"
).to(torch_device)
tokenizer = AutoTokenizer.from_pretrained(model_id)
inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=False)
self.assertEqual(output_text, EXPECTED_TEXTS)
@require_read_token
@unittest.skip("Cohere2 has not been released yet")
def test_model_pipeline_bf16(self):
# See https://github.com/huggingface/transformers/pull/31747 -- pipeline was broken for Cohere2 before this PR
model_id = "CohereForAI/command-r7b-12-2024"
# EXPECTED_TEXTS should match the same non-pipeline test, minus the special tokens
EXPECTED_TEXTS = [
"Hello I am doing a project on the 1918 flu pandemic and I am trying to find out how many",
"Hi today I'm going to be talking about the history of the United States. The United States of America",
]
model = AutoModelForCausalLM.from_pretrained(
model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="flex_attention"
).to(torch_device)
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
output = pipe(self.input_text, max_new_tokens=20, do_sample=False, padding=True)
self.assertEqual(output[0][0]["generated_text"], EXPECTED_TEXTS[0])
self.assertEqual(output[1][0]["generated_text"], EXPECTED_TEXTS[1])
@require_read_token
@require_flash_attn
@require_torch_gpu
@mark.flash_attn_test
@slow
@unittest.skip("Cohere2 has not been released yet")
def test_model_flash_attn(self):
# See https://github.com/huggingface/transformers/issues/31953 --- flash attn was generating garbage for Gemma2, especially in long context
model_id = "CohereForAI/command-r7b-12-2024"
EXPECTED_TEXTS = [
'<BOS_TOKEN>Hello I am doing a project on the 1918 flu pandemic and I am trying to find out how many people died in the United States. I have found a few sites that say 500,000 but I am not sure if that is correct. I have also found a site that says 675,000 but I am not sure if that is correct either. I am trying to find out how many people died in the United States. I have found a few',
"<PAD><PAD><BOS_TOKEN>Hi today I'm going to be talking about the history of the United States. The United States of America is a country in North America. It is the third largest country in the world by total area and the third most populous country with over 320 million people. The United States is a federal republic consisting of 50 states and a federal district. The 48 contiguous states and the district of Columbia are in central North America between Canada and Mexico. The state of Alaska is in the"
] # fmt: skip
model = AutoModelForCausalLM.from_pretrained(
model_id, attn_implementation="flash_attention_2", torch_dtype="float16"
).to(torch_device)
tokenizer = AutoTokenizer.from_pretrained(model_id)
inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
output = model.generate(**inputs, max_new_tokens=100, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=False)
self.assertEqual(output_text, EXPECTED_TEXTS)
@slow
@require_read_token
@unittest.skip("Cohere2 has not been released yet")
def test_export_static_cache(self):
if version.parse(torch.__version__) < version.parse("2.5.0"):
self.skipTest(reason="This test requires torch >= 2.5 to run.")
from transformers.integrations.executorch import (
TorchExportableModuleWithStaticCache,
convert_and_export_with_cache,
)
tokenizer = AutoTokenizer.from_pretrained(
"CohereForAI/command-r7b-12-2024", pad_token="<PAD>", padding_side="right"
)
EXPECTED_TEXT_COMPLETION = [
"Hello I am doing a project for my school and I need to know how to make a program that will take a number",
]
max_generation_length = tokenizer(EXPECTED_TEXT_COMPLETION, return_tensors="pt", padding=True)[
"input_ids"
].shape[-1]
# Load model
device = "cpu"
dtype = torch.bfloat16
cache_implementation = "static"
attn_implementation = "sdpa"
batch_size = 1
model = AutoModelForCausalLM.from_pretrained(
"CohereForAI/command-r7b-12-2024",
device_map=device,
torch_dtype=dtype,
attn_implementation=attn_implementation,
generation_config=GenerationConfig(
use_cache=True,
cache_implementation=cache_implementation,
max_length=max_generation_length,
cache_config={
"batch_size": batch_size,
"max_cache_len": max_generation_length,
},
),
)
prompts = ["Hello I am doing"]
prompt_tokens = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
prompt_token_ids = prompt_tokens["input_ids"]
max_new_tokens = max_generation_length - prompt_token_ids.shape[-1]
# Static Cache + export
exported_program = convert_and_export_with_cache(model)
ep_generated_ids = TorchExportableModuleWithStaticCache.generate(
exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=max_new_tokens
)
ep_generated_text = tokenizer.batch_decode(ep_generated_ids, skip_special_tokens=True)
self.assertEqual(EXPECTED_TEXT_COMPLETION, ep_generated_text)

1
utils/check_config_attributes.py
View File

@@ -47,6 +47,7 @@ SPECIAL_CASES_TO_ALLOW = {
# `cache_implementation` should be in the default generation config, but we don't yet support per-model
# generation configs (TODO joao)
"Gemma2Config": ["tie_word_embeddings", "cache_implementation"],
"Cohere2Config": ["cache_implementation"],
# used to compute the property `self.chunk_length`
"EncodecConfig": ["overlap"],
# used to compute the property `self.layers_block_type`