[bugfix] interleaving sliding window for cohere2 model

docs/source/models/supported_models.md

@@ -112,7 +112,7 @@ See [this page](#generative-models) for more information on how to use generativ
     - :code:`THUDM/chatglm2-6b`, :code:`THUDM/chatglm3-6b`, etc.
     - ✅︎
     - ✅︎
-  * - :code:`CohereForCausalLM`,:code:`Cohere2ForCausalLM`
+  * - :code:`CohereForCausalLM`, :code:`Cohere2ForCausalLM`
     - Command-R
     - :code:`CohereForAI/c4ai-command-r-v01`, :code:`CohereForAI/c4ai-command-r7b-12-2024`, etc.
     - ✅︎

tests/models/test_initialization.py

@@ -1,7 +1,6 @@
 from unittest.mock import patch
 
 import pytest
-import transformers
 from transformers import PretrainedConfig
 
 from vllm import LLM
@@ -12,9 +11,6 @@
 @pytest.mark.parametrize("model_arch", HF_EXAMPLE_MODELS.get_supported_archs())
 def test_can_initialize(model_arch):
     model_info = HF_EXAMPLE_MODELS.get_hf_info(model_arch)
-    if (model_arch == "Cohere2ForCausalLM"
-            and transformers.__version__ < "4.48.0"):
-        pytest.skip(reason="Model introduced in HF >= 4.48.0")
     if not model_info.is_available_online:
         pytest.skip("Model is not available online")
 

vllm/config.py

@@ -301,7 +301,7 @@ def __init__(self,
         sliding_window = getattr(self.hf_text_config, "sliding_window", None)
         has_interleaved_attention = (sliding_window is not None) and (
             isinstance(sliding_window, list) or
-            (self.hf_text_config.model_type in ["gemma2"]))
+            (self.hf_text_config.model_type in ["gemma2", "cohere2"]))
 
         if (not self.disable_sliding_window and has_interleaved_attention):
             if envs.VLLM_ATTENTION_BACKEND == "XFORMERS":

vllm/model_executor/models/commandr.py

@@ -172,16 +172,18 @@ def __init__(
             is_neox_style=False,
         )
 
-        sliding_window = getattr(config, "sliding_window", None)
-        # Model v2 has sliding windows, v1 does not
-        self.v1 = sliding_window is None
+        # Model v2 has interleaved sliding windows, v1 does not
+        interleaved_sliding_window = getattr(config,
+                                             "interleaved_sliding_window",
+                                             None)
+        self.v1 = interleaved_sliding_window is None
 
         layer_idx = extract_layer_index(prefix)
         layer_has_sliding_window = (
             getattr(config, "sliding_window_pattern", False)
             and (layer_idx + 1) % self.config.sliding_window_pattern != 0)
 
-        self.sliding_window = (sliding_window
+        self.sliding_window = (interleaved_sliding_window
                                if layer_has_sliding_window else None)
 
         self.attn = Attention(self.num_heads,

vllm/transformers_utils/config.py

@@ -22,9 +22,9 @@
 from vllm.logger import init_logger
 # yapf conflicts with isort for this block
 # yapf: disable
-from vllm.transformers_utils.configs import (ChatGLMConfig, DbrxConfig,
-                                             EAGLEConfig, ExaoneConfig,
-                                             H2OVLChatConfig,
+from vllm.transformers_utils.configs import (ChatGLMConfig, Cohere2Config,
+                                             DbrxConfig, EAGLEConfig,
+                                             ExaoneConfig, H2OVLChatConfig,
                                              InternVLChatConfig, JAISConfig,
                                              MedusaConfig, MllamaConfig,
                                              MLPSpeculatorConfig, MPTConfig,
@@ -52,6 +52,7 @@
 
 _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
     "chatglm": ChatGLMConfig,
+    "cohere2": Cohere2Config,
     "dbrx": DbrxConfig,
     "mpt": MPTConfig,
     "RefinedWeb": RWConfig,  # For tiiuae/falcon-40b(-instruct)

vllm/transformers_utils/configs/__init__.py

@@ -1,4 +1,5 @@
 from vllm.transformers_utils.configs.chatglm import ChatGLMConfig
+from vllm.transformers_utils.configs.cohere2 import Cohere2Config
 from vllm.transformers_utils.configs.dbrx import DbrxConfig
 from vllm.transformers_utils.configs.eagle import EAGLEConfig
 from vllm.transformers_utils.configs.exaone import ExaoneConfig
@@ -22,6 +23,7 @@
 
 __all__ = [
     "ChatGLMConfig",
+    "Cohere2Config",
     "DbrxConfig",
     "MPTConfig",
     "RWConfig",

vllm/transformers_utils/configs/cohere2.py

@@ -0,0 +1,192 @@
+# ruff: noqa
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/cohere2/configuration_cohere2.py
+from transformers import PretrainedConfig
+from transformers.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"]