[Model] Support Solar Model (#8386)

Co-authored-by: Michael Goin <michael@neuralmagic.com>

docs/source/models/supported_models.rst

@@ -179,6 +179,10 @@ Decoder-only Language Models
     - Starcoder2
     - :code:`bigcode/starcoder2-3b`, :code:`bigcode/starcoder2-7b`, :code:`bigcode/starcoder2-15b`, etc.
     -
+  * - :code:`SolarForCausalLM`
+    - EXAONE-3
+    - :code:`upstage/solar-pro-preview-instruct`, etc.
+    -
   * - :code:`XverseForCausalLM`
     - Xverse
     - :code:`xverse/XVERSE-7B-Chat`, :code:`xverse/XVERSE-13B-Chat`, :code:`xverse/XVERSE-65B-Chat`, etc.

vllm/model_executor/models/__init__.py

@@ -60,6 +60,7 @@ _GENERATION_MODELS = {
     "StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"),
     "StableLmForCausalLM": ("stablelm", "StablelmForCausalLM"),
     "Starcoder2ForCausalLM": ("starcoder2", "Starcoder2ForCausalLM"),
+    "SolarForCausalLM": ("solar", "SolarForCausalLM"),
     "ArcticForCausalLM": ("arctic", "ArcticForCausalLM"),
     "XverseForCausalLM": ("xverse", "XverseForCausalLM"),
     "Phi3SmallForCausalLM": ("phi3_small", "Phi3SmallForCausalLM"),

vllm/transformers_utils/config.py

@@ -24,7 +24,7 @@ from vllm.transformers_utils.configs import (ChatGLMConfig, DbrxConfig,
                                              JAISConfig, MedusaConfig,
                                              MLPSpeculatorConfig, MPTConfig,
                                              NemotronConfig, RWConfig,
-                                             UltravoxConfig)
+                                             SolarConfig, UltravoxConfig)
 # yapf: enable
 from vllm.transformers_utils.utils import check_gguf_file
 
@@ -50,6 +50,7 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
     "exaone": ExaoneConfig,
     "internvl_chat": InternVLChatConfig,
     "nemotron": NemotronConfig,
+    "solar": SolarConfig,
     "ultravox": UltravoxConfig,
     # Granite can be removed from here once we have upgraded to
     # transformers 4.45+

vllm/transformers_utils/configs/__init__.py

@@ -13,6 +13,7 @@ from vllm.transformers_utils.configs.medusa import MedusaConfig
 from vllm.transformers_utils.configs.mlp_speculator import MLPSpeculatorConfig
 from vllm.transformers_utils.configs.mpt import MPTConfig
 from vllm.transformers_utils.configs.nemotron import NemotronConfig
+from vllm.transformers_utils.configs.solar import SolarConfig
 from vllm.transformers_utils.configs.ultravox import UltravoxConfig
 
 __all__ = [
@@ -27,6 +28,7 @@ __all__ = [
     "ExaoneConfig",
     "MLPSpeculatorConfig",
     "NemotronConfig",
+    "SolarConfig",
     "UltravoxConfig",
     # Granite can be removed from here once we have upgraded to
     # transformers 4.45+

vllm/transformers_utils/configs/solar.py

+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+"""Solar model configuration"""
+
+from transformers import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class SolarConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store
+    the configuration of a [`SolarModel`].
+    It is used to instantiate an LLaMA model
+    according to the specified arguments,
+    defining the model architecture.
+    Instantiating a configuration with the
+    defaults will yield a similar
+    configuration to that of the LLaMA-7B.
+    Configuration objects inherit from [`PretrainedConfig`]
+    and can be used to control the model outputs.
+    Read the documentation from [`PretrainedConfig`] for more information.
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the LLaMA model.
+            Defines the number of different tokens
+            that can be represented by the `inputs_ids`
+            passed when calling [`SolarModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            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 2048):
+            The maximum sequence length that this model might ever be used with.
+            Solar 1 supports up to 2048 tokens,
+            Solar 2 up to 4096, CodeSolar up to 16384.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of
+            the truncated_normal_initializer for initializing
+            all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        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*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank
+            used during pretraining.
+            Please refer to [this
+            document](https://huggingface.co/docs/
+            transformers/main/
+            perf_train_gpu_many#tensor-parallelism)
+             to understand more about it. This value is
+            necessary to ensure exact reproducibility
+            of the pretraining results.
+            Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            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.
+            Currently supports two scaling
+            strategies: linear and dynamic.
+            Their scaling factor must be a float greater than 1.
+            The expected format is
+            `{"type": strategy name, "factor": scaling factor}`.
+            When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum.
+            See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/
+            dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking
+            API changes in future versions.
+        attention_bias (`bool`, *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.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in up_proj, down_proj and gate_proj
+            layers in the MLP layers.
+        sliding_window (`int`, *optional*, defaults to 2047):
+            Sliding window attention window size. If not specified,
+            will default to `2047`.
+    ```python
+    >>> from transformers import SolarModel, SolarConfig
+    >>> # Initializing a Solar-pro style configuration
+    >>> configuration = SolarConfig()
+    >>> # Initializing a model from the Solar-pro style configuration
+    >>> model = SolarModel(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "solar"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        sliding_window=2047,
+        bskcn_1=None,
+        bskcn_2=None,
+        bskcn_3=None,
+        bskcn_4=None,
+        bskcn_tv=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        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.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.mlp_bias = mlp_bias
+        self.sliding_window = sliding_window
+        self.bskcn_1 = bskcn_1 if bskcn_1 is not None else [12, 20, 32, 44]
+        self.bskcn_2 = bskcn_2 if bskcn_2 is not None else [20, 32]
+        self.bskcn_3 = bskcn_3 if bskcn_3 is not None else [16, 24, 36, 48]
+        self.bskcn_4 = bskcn_4 if bskcn_4 is not None else [28, 40]
+        self.bskcn_tv = bskcn_tv if bskcn_tv is not None else [0.9, 0.8]
+
+        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,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if (not isinstance(self.rope_scaling, dict)
+                or len(self.rope_scaling) != 2):
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with two fields,"
+                " `type` and `factor`, "
+                f"got {self.rope_scaling}")
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in [
+                "linear",
+                "dynamic",
+        ]:
+            raise ValueError(f"`rope_scaling`'s type field must be one of "
+                             f"['linear', 'dynamic'], got {rope_scaling_type}")
+        if (rope_scaling_factor is None
+                or not isinstance(rope_scaling_factor, float)
+                or rope_scaling_factor <= 1.0):
+            raise ValueError(
+                f"`rope_scaling`'s factor field must be a float > 1,"
+                f" got {rope_scaling_factor}")