Add StableLM model. (#301)
This commit is contained in:
Jani Monoses
293
python/sglang/srt/models/stablelm.py
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293
python/sglang/srt/models/stablelm.py
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# This code is based on:
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# https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/stablelm.py
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"""Inference-only StableLM-2 (https://huggingface.co/stabilityai/stablelm-2-1_6b)
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model compatible with HuggingFace weights."""
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from typing import 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 sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.managers.router.model_runner import InputMetadata
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.linear import (
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LinearMethodBase,
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding,
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ParallelLMHead,
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)
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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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)
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from vllm.model_executor.weight_utils import (
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default_weight_loader,
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hf_model_weights_iterator,
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)
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class StablelmMLP(nn.Module):
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def __init__(
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self, config: PretrainedConfig, 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.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,
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[config.intermediate_size] * 2,
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bias=False,
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linear_method=linear_method,
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)
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self.down_proj = RowParallelLinear(
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config.intermediate_size, config.hidden_size, bias=False
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)
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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__(
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self,
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config: PretrainedConfig,
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layer_id: int = 0,
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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.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(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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rope_pct = getattr(
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config, "rope_pct", getattr(config, "partial_rotary_factor", 1)
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)
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self.rotary_ndims = int(self.head_dim * 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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self.qkv_bias = getattr(config, "use_qkv_bias", False)
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if (self.head_dim * self.num_heads * tp_size) != self.hidden_size:
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raise ValueError(
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f"hidden_size must be divisible by num_heads "
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f"(got `hidden_size`: {self.hidden_size}"
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f" and `num_heads`: {self.num_heads})."
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)
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self.qkv_proj = QKVParallelLinear(
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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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self.qkv_bias,
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linear_method=linear_method,
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)
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self.o_proj = RowParallelLinear(
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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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)
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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 = RadixAttention(
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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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layer_id=layer_id,
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)
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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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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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attn_output = self.attn(q, k, v, 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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layer_id: int = 0,
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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, layer_id=layer_id)
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self.mlp = StablelmMLP(config, linear_method)
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norm_eps = getattr(config, "norm_eps", getattr(config, "layer_norm_eps", 1e-05))
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self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=norm_eps)
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self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=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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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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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__(
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self, config: PretrainedConfig, linear_method: Optional[LinearMethodBase] = None
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) -> None:
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super().__init__()
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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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[
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StablelmDecoderLayer(config, i, linear_method)
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for i in range(config.num_hidden_layers)
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]
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)
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norm_eps = getattr(config, "norm_eps", getattr(config, "layer_norm_eps", 1e-05))
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self.norm = nn.LayerNorm(config.hidden_size, eps=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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input_metadata: InputMetadata,
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input_embeds: torch.Tensor = None,
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) -> torch.Tensor:
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if input_embeds is None:
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hidden_states = self.embed_tokens(input_ids)
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else:
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hidden_states = input_embeds
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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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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.logits_processor = LogitsProcessor(config)
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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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input_metadata: InputMetadata,
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input_embeds: torch.Tensor = None,
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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
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return self.logits_processor(
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input_ids, hidden_states, self.lm_head.weight, input_metadata
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)
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def load_weights(
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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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):
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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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):
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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 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", default_weight_loader)
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weight_loader(param, loaded_weight)
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EntryClass = StableLmForCausalLM
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