Add OLMo model (#1676)
This commit is contained in:
Jani Monoses
352
python/sglang/srt/models/olmo.py
Executable file
352
python/sglang/srt/models/olmo.py
Executable file
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"""
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Copyright 2023-2024 SGLang Team
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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# Adapted from
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# https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/olmo.py#L1
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"""Inference-only OLMo model compatible with HuggingFace weights."""
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from typing import Iterable, List, Optional, Tuple
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import torch
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from torch import nn
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from transformers import OlmoConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.linear import (
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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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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from sglang.srt.layers.activation import SiluAndMul
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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class OlmoAttention(nn.Module):
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"""
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This is the attention block where the output is computed as
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``Attention(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))``
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(plus another skip connection).
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"""
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def __init__(
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self,
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config: OlmoConfig,
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layer_id: int = 0,
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quant_config: Optional[QuantizationConfig] = None,
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):
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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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tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_heads
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assert self.hidden_size % self.total_num_heads == 0
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assert self.total_num_heads % tensor_model_parallel_world_size == 0
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self.num_heads = self.total_num_heads // tensor_model_parallel_world_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.rope_theta = config.rope_theta
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self.clip_qkv = config.clip_qkv
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# Attention input projection. Projects x -> (q, k, v)
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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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bias=config.attention_bias,
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)
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# Rotary embeddings.
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=self.max_position_embeddings,
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base=self.rope_theta,
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)
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self.scaling = self.head_dim**-0.5
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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_heads,
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layer_id=layer_id,
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)
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# Attention output projection.
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self.o_proj = RowParallelLinear(
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self.hidden_size,
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self.hidden_size,
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bias=config.attention_bias,
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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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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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if self.clip_qkv is not None:
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qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
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q, k, v = qkv.chunk(chunks=3, 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, forward_batch)
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output, _ = self.o_proj(attn_output)
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return output
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class OlmoMLP(nn.Module):
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"""
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This is the MLP block where the output is computed as
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``MLP(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))``
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(plus another skip connection).
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"""
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def __init__(
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self,
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config: OlmoConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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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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# Feed-forward input projection.
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self.gate_up_proj = MergedColumnParallelLinear(
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self.hidden_size,
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[self.intermediate_size] * 2,
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bias=False,
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quant_config=quant_config,
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)
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# Activation function.
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self.act_fn = SiluAndMul()
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# Feed-forward output projection.
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self.down_proj = RowParallelLinear(
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self.intermediate_size,
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self.hidden_size,
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bias=False,
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quant_config=quant_config,
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)
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def forward(
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self,
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x: torch.Tensor,
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) -> 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 OlmoDecoderLayer(nn.Module):
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"""
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This is a typical transformer block where the output is
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computed as ``MLP(LN(x + Attention(LN(x))))``
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(plus another skip connection).
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"""
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def __init__(
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self,
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config: OlmoConfig,
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layer_id: int = 0,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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# Attention block.
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self.self_attn = OlmoAttention(config, layer_id, quant_config)
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# MLP block.
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self.mlp = OlmoMLP(config, quant_config)
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# LayerNorm
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self.input_layernorm = nn.LayerNorm(
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config.hidden_size, elementwise_affine=False, bias=False
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)
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self.post_attention_layernorm = nn.LayerNorm(
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config.hidden_size, elementwise_affine=False, bias=False
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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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forward_batch: ForwardBatch,
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) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
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# Attention block.
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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(positions, hidden_states, forward_batch)
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hidden_states = hidden_states + residual
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# MLP block.
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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
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class OlmoModel(nn.Module):
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def __init__(
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self, config: OlmoConfig, quant_config: Optional[QuantizationConfig] = None
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):
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super().__init__()
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self.config = config
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size, config.hidden_size
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)
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self.layers = nn.ModuleList(
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[
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OlmoDecoderLayer(config, layer_idx, quant_config)
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for layer_idx in range(config.num_hidden_layers)
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]
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)
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self.norm = nn.LayerNorm(
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config.hidden_size, elementwise_affine=False, bias=False
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)
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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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forward_batch: ForwardBatch,
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input_embeds: torch.Tensor = None,
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) -> torch.Tensor:
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"""
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:param input_ids: A tensor of shape `(batch_size, seq_len)`.
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"""
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# Get embeddings of input.
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# shape: (batch_size, seq_len, d_model)
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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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# Apply blocks one-by-one.
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for layer_idx, decoder_layer in enumerate(self.layers):
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# shape: (batch_size, seq_len, d_model)
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hidden_states = decoder_layer(
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positions,
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hidden_states,
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forward_batch,
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)
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# Apply final layer norm.
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# shape: (batch_size, seq_len or 1, d_model)
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hidden_states = self.norm(hidden_states)
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return hidden_states
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class OlmoForCausalLM(nn.Module):
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"""
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Extremely barebones HF model wrapper.
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"""
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def __init__(
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self,
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config: OlmoConfig,
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cache_config=None,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.config = config
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self.model = OlmoModel(config, quant_config)
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if config.tie_word_embeddings:
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self.lm_head = self.model.embed_tokens
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else:
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self.unpadded_vocab_size = config.vocab_size
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self.lm_head = ParallelLMHead(
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self.unpadded_vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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quant_config=quant_config,
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)
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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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forward_batch: ForwardBatch,
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input_embeds: torch.Tensor = None,
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) -> torch.Tensor:
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hidden_states = self.model(
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input_ids=input_ids,
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positions=positions,
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forward_batch=forward_batch,
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input_embeds=input_embeds,
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)
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return self.logits_processor(
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input_ids, hidden_states, self.lm_head.weight, forward_batch
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)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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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(remove_duplicate=False))
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for name, loaded_weight in weights:
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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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# With tie_word_embeddings, we can skip lm_head.weight
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# The weight might appear unnecessarily in the files if the model is
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# processed with quantization, LoRA, fine-tuning, etc.
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if self.config.tie_word_embeddings and "lm_head.weight" in name:
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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 = OlmoForCausalLM
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