From 328841d00294fb8226f0368cc380350b3d671d77 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Sun, 29 Dec 2024 00:55:42 +0800 Subject: [PATCH] [bugfix] interleaving sliding window for cohere2 model (#11583) Signed-off-by: youkaichao --- docs/source/models/supported_models.md | 2 +- tests/models/test_initialization.py | 4 - vllm/config.py | 2 +- vllm/model_executor/models/commandr.py | 10 +- vllm/transformers_utils/config.py | 7 +- vllm/transformers_utils/configs/__init__.py | 2 + vllm/transformers_utils/configs/cohere2.py | 192 ++++++++++++++++++++ 7 files changed, 206 insertions(+), 13 deletions(-) create mode 100644 vllm/transformers_utils/configs/cohere2.py diff --git a/docs/source/models/supported_models.md b/docs/source/models/supported_models.md index f6e00fa71a310..e11befbb8dd30 100644 --- a/docs/source/models/supported_models.md +++ b/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. - ✅︎ diff --git a/tests/models/test_initialization.py b/tests/models/test_initialization.py index a4eea7f035c91..3b728f2744fca 100644 --- a/tests/models/test_initialization.py +++ b/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") diff --git a/vllm/config.py b/vllm/config.py index ac767bbe14be4..6ae1d4d944447 100644 --- a/vllm/config.py +++ b/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": diff --git a/vllm/model_executor/models/commandr.py b/vllm/model_executor/models/commandr.py index c846e42f1b0c3..d22d1f3171463 100644 --- a/vllm/model_executor/models/commandr.py +++ b/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, diff --git a/vllm/transformers_utils/config.py b/vllm/transformers_utils/config.py index 4529cf27ef565..58417980e7b47 100644 --- a/vllm/transformers_utils/config.py +++ b/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) diff --git a/vllm/transformers_utils/configs/__init__.py b/vllm/transformers_utils/configs/__init__.py index c24433cd436b4..a41a35c88b3a1 100644 --- a/vllm/transformers_utils/configs/__init__.py +++ b/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", diff --git a/vllm/transformers_utils/configs/cohere2.py b/vllm/transformers_utils/configs/cohere2.py new file mode 100644 index 0000000000000..1509330fc2179 --- /dev/null +++ b/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"]