mirror of https://github.com/vllm-project/vllm
Add StableLM3B model (#2372)
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Hyunsung Lee
GitHub
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e1957c6ebd
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@ -77,6 +77,7 @@ vLLM seamlessly supports many Hugging Face models, including the following archi
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- OPT (`facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc.)
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- Phi (`microsoft/phi-1_5`, `microsoft/phi-2`, etc.)
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- Qwen (`Qwen/Qwen-7B`, `Qwen/Qwen-7B-Chat`, etc.)
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- StableLM(`stabilityai/stablelm-3b-4e1t`, `stabilityai/stablelm-base-alpha-7b-v2`, etc.)
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- Yi (`01-ai/Yi-6B`, `01-ai/Yi-34B`, etc.)
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Install vLLM with pip or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source):
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@ -68,6 +68,9 @@ Alongside each architecture, we include some popular models that use it.
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* - :code:`QWenLMHeadModel`
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- Qwen
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- :code:`Qwen/Qwen-7B`, :code:`Qwen/Qwen-7B-Chat`, etc.
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* - :code:`StableLMEpochForCausalLM`
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- StableLM
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- :code:`stabilityai/stablelm-3b-4e1t/` , :code:`stabilityai/stablelm-base-alpha-7b-v2`, etc.
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* - :code:`YiForCausalLM`
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- Yi
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- :code:`01-ai/Yi-6B`, :code:`01-ai/Yi-34B`, etc.
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@ -5,18 +5,11 @@ Run `pytest tests/models/test_models.py --forked`.
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import pytest
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MODELS = [
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"facebook/opt-125m",
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"meta-llama/Llama-2-7b-hf",
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"mistralai/Mistral-7B-v0.1",
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"Deci/DeciLM-7b",
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"tiiuae/falcon-7b",
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"gpt2",
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"bigcode/tiny_starcoder_py",
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"EleutherAI/gpt-j-6b",
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"EleutherAI/pythia-70m",
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"bigscience/bloom-560m",
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"mosaicml/mpt-7b",
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"microsoft/phi-2",
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"facebook/opt-125m", "meta-llama/Llama-2-7b-hf",
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"mistralai/Mistral-7B-v0.1", "Deci/DeciLM-7b", "tiiuae/falcon-7b", "gpt2",
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"bigcode/tiny_starcoder_py", "EleutherAI/gpt-j-6b",
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"EleutherAI/pythia-70m", "bigscience/bloom-560m", "mosaicml/mpt-7b",
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"microsoft/phi-2", "stabilityai/stablelm-3b-4e1t"
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]
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@ -36,7 +36,8 @@ _MODELS = {
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"PhiForCausalLM": ("phi", "PhiForCausalLM"),
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"QWenLMHeadModel": ("qwen", "QWenLMHeadModel"),
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"RWForCausalLM": ("falcon", "FalconForCausalLM"),
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"YiForCausalLM": ("yi", "YiForCausalLM"),
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"StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"),
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"YiForCausalLM": ("yi", "YiForCausalLM")
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}
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# Models not supported by ROCm.
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@ -0,0 +1,299 @@
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# coding=utf-8
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# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# This code is based off the following work:
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# https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/modeling_stablelm_epoch.py
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# https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/config.json
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"""Inference-only StabeLM (https://github.com/Stability-AI/StableLM) model compatible with HuggingFace weights."""
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from typing import List, Optional, Tuple
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.model_executor.input_metadata import InputMetadata
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.attention import PagedAttention
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from vllm.model_executor.layers.linear import (LinearMethodBase,
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding, ParallelLMHead)
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from vllm.model_executor.parallel_utils.parallel_state import (
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get_tensor_model_parallel_world_size)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.model_executor.weight_utils import (default_weight_loader,
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hf_model_weights_iterator)
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from vllm.sequence import SamplerOutput
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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class StablelmMLP(nn.Module):
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def __init__(self,
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config: PretrainedConfig,
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linear_method: Optional[LinearMethodBase] = None) -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_up_proj = MergedColumnParallelLinear(
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config.hidden_size, [config.intermediate_size] * 2,
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bias=False,
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linear_method=linear_method)
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self.down_proj = RowParallelLinear(config.intermediate_size,
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config.hidden_size,
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bias=False)
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self.act_fn = SiluAndMul()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class StablelmAttention(nn.Module):
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def __init__(self,
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config: PretrainedConfig,
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linear_method: Optional[LinearMethodBase] = None) -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_heads
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_key_value_heads = config.num_key_value_heads
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if self.total_num_key_value_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_key_value_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_key_value_heads == 0
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self.num_key_value_heads = max(
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1, self.total_num_key_value_heads // tp_size)
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self.head_dim = self.hidden_size // self.total_num_heads
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self.max_position_embeddings = config.max_position_embeddings
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self.rotary_ndims = int(self.head_dim * self.config.rope_pct)
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self.scaling = self.head_dim**-0.5
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_key_value_heads * self.head_dim
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if (self.head_dim * self.num_heads) != self.hidden_size:
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raise ValueError(
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f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
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f" and `num_heads`: {self.num_heads}).")
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self.qkv_proj = QKVParallelLinear(self.hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_key_value_heads,
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bias=False,
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linear_method=linear_method)
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self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
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self.hidden_size,
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bias=False,
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linear_method=linear_method)
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self.rotary_ndims = int(self.head_dim * self.config.rope_pct)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.rotary_ndims,
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max_position=self.config.max_position_embeddings,
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base=self.config.rope_theta,
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)
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self.attn = PagedAttention(self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_key_value_heads)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: KVCache,
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input_metadata: InputMetadata,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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k_cache, v_cache = kv_cache
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attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
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output, _ = self.o_proj(attn_output)
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return output
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class StablelmDecoderLayer(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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linear_method: Optional[LinearMethodBase] = None,
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) -> None:
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super().__init__()
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self.self_attn = StablelmAttention(config)
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self.mlp = StablelmMLP(config, linear_method)
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self.input_layernorm = nn.LayerNorm(config.hidden_size,
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eps=config.norm_eps)
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self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
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eps=config.norm_eps)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: KVCache,
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input_metadata: InputMetadata,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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kv_cache=kv_cache,
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input_metadata=input_metadata,
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)
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hidden_states = residual + hidden_states
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# Fully Connected
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states, residual
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class StableLMEpochModel(nn.Module):
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def __init__(self,
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config: PretrainedConfig,
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linear_method: Optional[LinearMethodBase] = None) -> None:
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super().__init__()
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# self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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)
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self.layers = nn.ModuleList([
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StablelmDecoderLayer(config, linear_method)
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for _ in range(config.num_hidden_layers)
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])
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self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: List[KVCache],
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input_metadata: InputMetadata,
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) -> torch.Tensor:
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hidden_states = self.embed_tokens(input_ids)
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for i in range(len(self.layers)):
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layer = self.layers[i]
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hidden_states, residual = layer(
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positions,
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hidden_states,
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kv_caches[i],
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input_metadata,
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)
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hidden_states = self.norm(hidden_states)
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return hidden_states
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class StablelmForCausalLM(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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linear_method: Optional[LinearMethodBase] = None,
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) -> None:
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super().__init__()
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self.config = config
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self.linear_method = linear_method
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self.model = StableLMEpochModel(config, linear_method)
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self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
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self.sampler = Sampler(config.vocab_size)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: List[KVCache],
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input_metadata: InputMetadata,
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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, kv_caches,
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input_metadata)
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return hidden_states
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def sample(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(self.lm_head.weight, hidden_states,
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sampling_metadata)
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return next_tokens
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def load_weights(self,
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model_name_or_path: str,
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cache_dir: Optional[str] = None,
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load_format: str = "auto",
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revision: Optional[str] = None):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in hf_model_weights_iterator(
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model_name_or_path, cache_dir, load_format, revision):
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if "rotary_emb.inv_freq" in name:
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continue
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if ("rotary_emb.cos_cached" in name
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or "rotary_emb.sin_cached" in name):
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# Models trained using ColossalAI may include these tensors in
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# the checkpoint. Skip them.
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continue
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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