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Unverified Commit 317b29de authored by Philipp Moritz's avatar Philipp Moritz Committed by GitHub
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Remove Yi model definition, please use `LlamaForCausalLM` instead (#2854) 


Co-authored-by: default avatarRoy <jasonailu87@gmail.com>
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docs/source/models/supported_models.rst
View file @ 317b29de
...@@ -51,8 +51,8 @@ Alongside each architecture, we include some popular models that use it. ...@@ -51,8 +51,8 @@ Alongside each architecture, we include some popular models that use it.
- InternLM2 - InternLM2
- :code:`internlm/internlm2-7b`, :code:`internlm/internlm2-chat-7b`, etc. - :code:`internlm/internlm2-7b`, :code:`internlm/internlm2-chat-7b`, etc.
* - :code:`LlamaForCausalLM` * - :code:`LlamaForCausalLM`
- LLaMA, LLaMA-2, Vicuna, Alpaca, Koala, Guanaco - LLaMA, LLaMA-2, Vicuna, Alpaca, Yi
- :code:`meta-llama/Llama-2-13b-hf`, :code:`meta-llama/Llama-2-70b-hf`, :code:`openlm-research/open_llama_13b`, :code:`lmsys/vicuna-13b-v1.3`, :code:`young-geng/koala`, etc. - :code:`meta-llama/Llama-2-13b-hf`, :code:`meta-llama/Llama-2-70b-hf`, :code:`openlm-research/open_llama_13b`, :code:`lmsys/vicuna-13b-v1.3`, :code:`01-ai/Yi-6B`, :code:`01-ai/Yi-34B`, etc.
* - :code:`MistralForCausalLM` * - :code:`MistralForCausalLM`
- Mistral, Mistral-Instruct - Mistral, Mistral-Instruct
- :code:`mistralai/Mistral-7B-v0.1`, :code:`mistralai/Mistral-7B-Instruct-v0.1`, etc. - :code:`mistralai/Mistral-7B-v0.1`, :code:`mistralai/Mistral-7B-Instruct-v0.1`, etc.
...@@ -77,9 +77,6 @@ Alongside each architecture, we include some popular models that use it. ...@@ -77,9 +77,6 @@ Alongside each architecture, we include some popular models that use it.
* - :code:`StableLMEpochForCausalLM` * - :code:`StableLMEpochForCausalLM`
- StableLM - StableLM
- :code:`stabilityai/stablelm-3b-4e1t/` , :code:`stabilityai/stablelm-base-alpha-7b-v2`, etc. - :code:`stabilityai/stablelm-3b-4e1t/` , :code:`stabilityai/stablelm-base-alpha-7b-v2`, etc.
* - :code:`YiForCausalLM`
- Yi
- :code:`01-ai/Yi-6B`, :code:`01-ai/Yi-34B`, etc.
If your model uses one of the above model architectures, you can seamlessly run your model with vLLM. If your model uses one of the above model architectures, you can seamlessly run your model with vLLM.
Otherwise, please refer to :ref:`Adding a New Model <adding_a_new_model>` for instructions on how to implement support for your model. Otherwise, please refer to :ref:`Adding a New Model <adding_a_new_model>` for instructions on how to implement support for your model.
......
vllm/model_executor/models/yi.py deleted 100644 → 0
View file @ a463c333
# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
# Copyright 2023 The vLLM team.
# 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.
"""Inference-only Yi model (https://01.ai) compatible with HuggingFace weights."""
from typing import Any, Dict, List, Optional, Tuple
import torch
from torch import nn
from vllm.transformers_utils.configs.yi import YiConfig
from vllm.model_executor.input_metadata import InputMetadata
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.attention import PagedAttention
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (LinearMethodBase,
MergedColumnParallelLinear,
QKVParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import Sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
VocabParallelEmbedding, ParallelLMHead)
from vllm.model_executor.parallel_utils.parallel_state import (
get_tensor_model_parallel_world_size)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.model_executor.weight_utils import (default_weight_loader,
hf_model_weights_iterator)
from vllm.sequence import SamplerOutput
KVCache = Tuple[torch.Tensor, torch.Tensor]
class YiMLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
linear_method: Optional[LinearMethodBase] = None,
) -> None:
super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(
hidden_size, [intermediate_size] * 2,
bias=False,
linear_method=linear_method)
self.down_proj = RowParallelLinear(intermediate_size,
hidden_size,
bias=False,
linear_method=linear_method)
if hidden_act != "silu":
raise ValueError(f"Unsupported activation: {hidden_act}. "
"Only silu is supported for now.")
self.act_fn = SiluAndMul()
def forward(self, x):
gate_up, _ = self.gate_up_proj(x)
x = self.act_fn(gate_up)
x, _ = self.down_proj(x)
return x
class YiAttention(nn.Module):
def __init__(
self,
hidden_size: int,
num_heads: int,
num_kv_heads: int,
rope_theta: float = 10000,
rope_scaling: Optional[Dict[str, Any]] = None,
max_position_embeddings: int = 8192,
linear_method: Optional[LinearMethodBase] = None,
) -> None:
super().__init__()
self.hidden_size = hidden_size
tp_size = get_tensor_model_parallel_world_size()
self.total_num_heads = num_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.total_num_kv_heads = num_kv_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 = 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 = rope_theta
self.max_position_embeddings = max_position_embeddings
self.qkv_proj = QKVParallelLinear(
hidden_size,
self.head_dim,
self.total_num_heads,
self.total_num_kv_heads,
bias=False,
linear_method=linear_method,
)
self.o_proj = RowParallelLinear(
self.total_num_heads * self.head_dim,
hidden_size,
bias=False,
linear_method=linear_method,
)
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.head_dim,
max_position=max_position_embeddings,
base=self.rope_theta,
rope_scaling=rope_scaling,
)
self.attn = PagedAttention(self.num_heads,
self.head_dim,
self.scaling,
num_kv_heads=self.num_kv_heads)
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 YiDecoderLayer(nn.Module):
def __init__(
self,
config: YiConfig,
linear_method: Optional[LinearMethodBase] = None,
) -> None:
super().__init__()
self.hidden_size = config.hidden_size
rope_theta = getattr(config, "rope_theta", 10000)
rope_scaling = getattr(config, "rope_scaling", None)
max_position_embeddings = getattr(config, "max_position_embeddings",
8192)
self.self_attn = YiAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
num_kv_heads=config.num_key_value_heads,
rope_theta=rope_theta,
rope_scaling=rope_scaling,
max_position_embeddings=max_position_embeddings,
linear_method=linear_method,
)
self.mlp = YiMLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
linear_method=linear_method,
)
self.ln1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.ln2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
kv_cache: KVCache,
input_metadata: InputMetadata,
residual: Optional[torch.Tensor],
) -> Tuple[torch.Tensor, torch.Tensor]:
# Self Attention
if residual is None:
residual = hidden_states
hidden_states = self.ln1(hidden_states)
else:
hidden_states, residual = self.ln1(hidden_states, residual)
hidden_states = self.self_attn(
positions=positions,
hidden_states=hidden_states,
kv_cache=kv_cache,
input_metadata=input_metadata,
)
# Fully Connected
hidden_states, residual = self.ln2(hidden_states, residual)
hidden_states = self.mlp(hidden_states)
return hidden_states, residual
class YiModel(nn.Module):
def __init__(
self,
config: YiConfig,
linear_method: Optional[LinearMethodBase] = None,
) -> None:
super().__init__()
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = VocabParallelEmbedding(
config.vocab_size,
config.hidden_size,
)
self.layers = nn.ModuleList([
YiDecoderLayer(config, linear_method)
for _ in range(config.num_hidden_layers)
])
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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)
residual = None
for i in range(len(self.layers)):
layer = self.layers[i]
hidden_states, residual = layer(
positions,
hidden_states,
kv_caches[i],
input_metadata,
residual,
)
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
class YiForCausalLM(nn.Module):
def __init__(
self,
config: YiConfig,
linear_method: Optional[LinearMethodBase] = None,
) -> None:
super().__init__()
self.config = config
self.linear_method = linear_method
self.model = YiModel(config, linear_method)
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
self.sampler = Sampler(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: 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"),
("gate_up_proj", "gate_proj", 0),
("gate_up_proj", "up_proj", 1),
]
params_dict = dict(self.named_parameters())
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)
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
vllm/transformers_utils/config.py
View file @ 317b29de
...@@ -12,7 +12,6 @@ _CONFIG_REGISTRY = { ...@@ -12,7 +12,6 @@ _CONFIG_REGISTRY = {
"qwen": QWenConfig, "qwen": QWenConfig,
"RefinedWeb": RWConfig, # For tiiuae/falcon-40b(-instruct) "RefinedWeb": RWConfig, # For tiiuae/falcon-40b(-instruct)
"RefinedWebModel": RWConfig, # For tiiuae/falcon-7b(-instruct) "RefinedWebModel": RWConfig, # For tiiuae/falcon-7b(-instruct)
"yi": YiConfig,
} }
......
vllm/transformers_utils/configs/__init__.py
View file @ 317b29de
...@@ -7,7 +7,6 @@ from vllm.transformers_utils.configs.qwen import QWenConfig ...@@ -7,7 +7,6 @@ from vllm.transformers_utils.configs.qwen import QWenConfig
# tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the # tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
# `FalconConfig` class from the official HuggingFace transformers library. # `FalconConfig` class from the official HuggingFace transformers library.
from vllm.transformers_utils.configs.falcon import RWConfig from vllm.transformers_utils.configs.falcon import RWConfig
from vllm.transformers_utils.configs.yi import YiConfig
__all__ = [ __all__ = [
"AquilaConfig", "AquilaConfig",
...@@ -16,5 +15,4 @@ __all__ = [ ...@@ -16,5 +15,4 @@ __all__ = [
"MPTConfig", "MPTConfig",
"QWenConfig", "QWenConfig",
"RWConfig", "RWConfig",
"YiConfig",
] ]
vllm/transformers_utils/configs/yi.py deleted 100644 → 0
View file @ a463c333
""" Yi model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
Yi_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class YiConfig(PretrainedConfig):
r"""
Reference:
https://huggingface.co/01-ai/Yi-6B/blob/main/configuration_yi.py
"""
model_type = "Yi"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=64000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=4,
hidden_act="silu",
max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
output_attentions=False,
rope_theta=5000000.0,
**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.use_cache = use_cache
self.output_attentions = output_attentions
self.rope_theta = rope_theta
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,
)
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