Support starcoder2 architecture (#3089)

README.md

@@ -78,6 +78,7 @@ vLLM seamlessly supports many Hugging Face models, including the following archi
 - Qwen (`Qwen/Qwen-7B`, `Qwen/Qwen-7B-Chat`, etc.)
 - Qwen2 (`Qwen/Qwen2-7B-beta`, `Qwen/Qwen-7B-Chat-beta`, etc.)
 - StableLM(`stabilityai/stablelm-3b-4e1t`, `stabilityai/stablelm-base-alpha-7b-v2`, etc.)
+- Starcoder2(`bigcode/starcoder2-3b`, `bigcode/starcoder2-7b`, `bigcode/starcoder2-15b`, etc.)
 - Yi (`01-ai/Yi-6B`, `01-ai/Yi-34B`, etc.)
 
 Install vLLM with pip or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source):

tests/models/test_models.py

@@ -19,6 +19,7 @@ MODELS = [
     "microsoft/phi-2",
     "stabilityai/stablelm-3b-4e1t",
     "allenai/OLMo-1B",
+    "bigcode/starcoder2-3b",
 ]
 
 

vllm/model_executor/models/__init__.py

@@ -45,6 +45,7 @@ _MODELS = {
     "RWForCausalLM": ("falcon", "FalconForCausalLM"),
     "StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"),
     "StableLmForCausalLM": ("stablelm", "StablelmForCausalLM"),
+    "Starcoder2ForCausalLM": ("starcoder2", "Starcoder2ForCausalLM"),
 }
 
 # Models not supported by ROCm.

vllm/model_executor/models/starcoder2.py

+# coding=utf-8
+# Copyright 2024 BigCode 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.
+""" PyTorch Starcoder2 model."""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               LinearMethodBase,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding, ParallelLMHead, DEFAULT_VOCAB_PADDING_SIZE)
+from vllm.model_executor.parallel_utils.parallel_state import get_tensor_model_parallel_world_size
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+try:
+    from transformers import Starcoder2Config
+except ImportError:
+    # fallback to PretrainedConfig
+    # NOTE: Please install transformers from source or use transformers>=4.39.0
+    from transformers import PretrainedConfig as Starcoder2Config
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class Starcoder2Attention(nn.Module):
+
+    def __init__(self,
+                 config: Starcoder2Config,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.config = config
+
+        self.hidden_size = config.hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = config.num_attention_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = config.num_key_value_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = self.hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = config.rope_theta
+        self.max_position_embeddings = config.max_position_embeddings
+        self.use_bias = config.use_bias
+        self.sliding_window = config.sliding_window
+
+        self.qkv_proj = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=self.use_bias,
+            linear_method=linear_method,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=self.use_bias,
+            linear_method=linear_method,
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=self.max_position_embeddings,
+            base=int(self.rope_theta),
+            is_neox_style=True,
+        )
+        self.attn = PagedAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            sliding_window=self.sliding_window,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Starcoder2MLP(nn.Module):
+
+    def __init__(self,
+                 config: Starcoder2Config,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.c_fc = ColumnParallelLinear(
+            config.hidden_size,
+            config.intermediate_size,
+            bias=config.use_bias,
+            linear_method=linear_method,
+        )
+        self.c_proj = RowParallelLinear(
+            config.intermediate_size,
+            config.hidden_size,
+            bias=config.use_bias,
+            linear_method=linear_method,
+        )
+        self.act = get_act_fn(config.hidden_act,
+                              intermediate_size=config.intermediate_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.c_fc(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.c_proj(hidden_states)
+        return hidden_states
+
+
+class Starcoder2DecoderLayer(nn.Module):
+
+    def __init__(self,
+                 config: Starcoder2Config,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = Starcoder2Attention(config,
+                                             linear_method=linear_method)
+        self.mlp = Starcoder2MLP(config, linear_method=linear_method)
+        self.input_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.norm_epsilon)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
+                                                     eps=config.norm_epsilon)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class Starcoder2Model(nn.Module):
+
+    def __init__(self,
+                 config: Starcoder2Config,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        # TODO: consider padding_idx (currently removed)
+        self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
+                                                   config.hidden_size)
+        self.layers = nn.ModuleList([
+            Starcoder2DecoderLayer(config, linear_method=linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states = layer(positions, hidden_states, kv_caches[i],
+                                  input_metadata)
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+class Starcoder2ForCausalLM(nn.Module):
+
+    def __init__(self,
+                 config: Starcoder2Config,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.config = config
+        self.model = Starcoder2Model(config, linear_method=linear_method)
+        self.vocab_size = config.vocab_size
+        self.unpadded_vocab_size = config.vocab_size
+        if config.tie_word_embeddings:
+            self.lm_head_weight = self.model.embed_tokens.weight
+        else:
+            self.unpadded_vocab_size = config.vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+            )
+            self.lm_head_weight = self.lm_head.weight
+        self.sampler = Sampler(self.unpadded_vocab_size, config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: Optional[torch.Tensor],
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if self.config.tie_word_embeddings and "lm_head.weight" in name:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)

vllm/transformers_utils/config.py

@@ -9,6 +9,7 @@ _CONFIG_REGISTRY = {
     "mpt": MPTConfig,
     "RefinedWeb": RWConfig,  # For tiiuae/falcon-40b(-instruct)
     "RefinedWebModel": RWConfig,  # For tiiuae/falcon-7b(-instruct)
+    "starcoder2": Starcoder2Config,
 }
 
 
@@ -16,6 +17,15 @@ def get_config(model: str,
                trust_remote_code: bool,
                revision: Optional[str] = None,
                code_revision: Optional[str] = None) -> PretrainedConfig:
+    # FIXME(woosuk): This is a temporary fix for StarCoder2.
+    # Remove this when the model is supported by HuggingFace transformers.
+    if "bigcode" in model and "starcoder2" in model:
+        config_class = _CONFIG_REGISTRY["starcoder2"]
+        config = config_class.from_pretrained(model,
+                                              revision=revision,
+                                              code_revision=code_revision)
+        return config
+
     try:
         config = AutoConfig.from_pretrained(
             model,

vllm/transformers_utils/configs/__init__.py

@@ -4,9 +4,11 @@ from vllm.transformers_utils.configs.mpt import MPTConfig
 # tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
 # `FalconConfig` class from the official HuggingFace transformers library.
 from vllm.transformers_utils.configs.falcon import RWConfig
+from vllm.transformers_utils.configs.starcoder2 import Starcoder2Config
 
 __all__ = [
     "ChatGLMConfig",
     "MPTConfig",
     "RWConfig",
+    "Starcoder2Config",
 ]

vllm/transformers_utils/configs/starcoder2.py

+from transformers import PretrainedConfig
+
+
+class Starcoder2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Starcoder2Model`]. It is used to instantiate a
+    Starcoder2 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 [bigcode/starcoder2-7b_16k](https://huggingface.co/bigcode/starcoder2-7b_16k) model.
+
+
+    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 49152):
+            Vocabulary size of the Starcoder2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Starcoder2Model`]
+        hidden_size (`int`, *optional*, defaults to 3072):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 12288):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 30):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 24):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 2):
+            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 `8`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with. Starcoder2's sliding window attention
+            allows sequence of up to 4096*32 tokens.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        norm_epsilon (`float`, *optional*, defaults to 1e-05):
+            Epsilon value for the layer norm
+        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`.
+        bos_token_id (`int`, *optional*, defaults to 50256):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 50256):
+            The id of the "end-of-sequence" token.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If not specified, will default to `None` (no sliding window).
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        residual_dropout (`float`, *optional*, defaults to 0.0):
+            Residual connection dropout value.
+        embedding_dropout (`float`, *optional*, defaults to 0.0):
+            Embedding dropout.
+        use_bias (`bool`, *optional*, defaults to `True`):
+            Whether to use bias term on linear layers of the model.
+
+
+    ```python
+    >>> from transformers import Starcoder2Model, Starcoder2Config
+
+    >>> # Initializing a Starcoder2 7B style configuration
+    >>> configuration = Starcoder2Config()
+
+    >>> # Initializing a model from the Starcoder2 7B style configuration
+    >>> model = Starcoder2Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "starcoder2"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=49152,
+        hidden_size=3072,
+        intermediate_size=12288,
+        num_hidden_layers=30,
+        num_attention_heads=24,
+        num_key_value_heads=2,
+        hidden_act="gelu_pytorch_tanh",
+        max_position_embeddings=4096,
+        initializer_range=0.018042,
+        norm_epsilon=1e-5,
+        use_cache=True,
+        bos_token_id=50256,
+        eos_token_id=50256,
+        rope_theta=10000.0,
+        sliding_window=None,
+        attention_dropout=0.0,
+        residual_dropout=0.0,
+        embedding_dropout=0.0,
+        use_bias=True,
+        **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
+        self.sliding_window = sliding_window
+        self.use_bias = use_bias
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.norm_epsilon = norm_epsilon
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+        self.residual_dropout = residual_dropout
+        self.embedding_dropout = embedding_dropout
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        if self.architectures is None:
+            self.architectures = ['Starcoder2ForCausalLM']