model: support starcoder2 (#10609)
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Praneth Paruchuri
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python/sglang/srt/models/starcoder2.py
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357
python/sglang/srt/models/starcoder2.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2024 BigCode and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/starcoder2.py
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""" PyTorch Starcoder2 model."""
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from collections.abc import Iterable
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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 Starcoder2Config
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from sglang.srt.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from sglang.srt.layers.activation import get_act_fn
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from sglang.srt.layers.linear import (
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ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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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.layers.rotary_embedding import get_rope
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from sglang.srt.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE,
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.utils import add_prefix, make_layers
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class Starcoder2Attention(nn.Module):
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def __init__(
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self,
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config: Starcoder2Config,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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layer_id: int = 0,
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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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tp_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.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = config.num_key_value_heads
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if self.total_num_kv_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_kv_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_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = self.hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = config.rope_theta
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self.max_position_embeddings = config.max_position_embeddings
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self.use_bias = config.use_bias
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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_kv_heads,
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bias=self.use_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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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=self.use_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj",
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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.head_dim,
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max_position=self.max_position_embeddings,
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base=int(self.rope_theta),
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is_neox_style=True,
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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_kv_heads,
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layer_id=layer_id,
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quant_config=quant_config,
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prefix=f"{prefix}.attn",
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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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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, forward_batch)
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output, _ = self.o_proj(attn_output)
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return output
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class Starcoder2MLP(nn.Module):
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def __init__(
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self,
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config: Starcoder2Config,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.c_fc = ColumnParallelLinear(
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config.hidden_size,
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config.intermediate_size,
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bias=config.use_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.c_fc",
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)
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self.c_proj = RowParallelLinear(
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config.intermediate_size,
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config.hidden_size,
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bias=config.use_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.c_proj",
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)
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self.act = get_act_fn(config.hidden_act)
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def forward(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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hidden_states, _ = self.c_fc(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states, _ = self.c_proj(hidden_states)
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return hidden_states
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class Starcoder2DecoderLayer(nn.Module):
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def __init__(
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self,
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config: Starcoder2Config,
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layer_id: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = Starcoder2Attention(
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config=config,
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layer_id=layer_id,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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)
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self.mlp = Starcoder2MLP(
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config, quant_config=quant_config, prefix=f"{prefix}.mlp"
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)
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self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_epsilon)
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self.post_attention_layernorm = nn.LayerNorm(
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config.hidden_size, eps=config.norm_epsilon
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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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# 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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forward_batch=forward_batch,
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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
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class Starcoder2Model(nn.Module):
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def __init__(
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self,
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config: Starcoder2Config,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.config = config
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self.vocab_size = config.vocab_size
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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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quant_config=quant_config,
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prefix=f"{prefix}.embed_tokens",
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)
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pp_group = get_pp_group()
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pp_size = pp_group.world_size
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pp_rank = pp_group.rank
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self.start_layer = pp_rank * config.num_hidden_layers // pp_size
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self.end_layer = (pp_rank + 1) * config.num_hidden_layers // pp_size
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self.layers = make_layers(
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config.num_hidden_layers,
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lambda idx, prefix: Starcoder2DecoderLayer(
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config=config, quant_config=quant_config, layer_id=idx, prefix=prefix
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),
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prefix=f"{prefix}.layers",
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)
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self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_epsilon)
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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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inputs_embeds: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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if inputs_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 = inputs_embeds
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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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hidden_states = 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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hidden_states = self.norm(hidden_states)
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return hidden_states
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class Starcoder2ForCausalLM(nn.Module):
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def __init__(
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self,
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config: Starcoder2Config,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.config = config
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self.model = Starcoder2Model(
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config, quant_config, prefix=add_prefix("model", prefix)
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)
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self.vocab_size = config.vocab_size
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self.unpadded_vocab_size = config.vocab_size
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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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padding_size=DEFAULT_VOCAB_PADDING_SIZE,
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quant_config=quant_config,
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prefix=f"{prefix}.lm_head",
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)
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self.logits_processor = LogitsProcessor(config=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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inputs_embeds: Optional[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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inputs_embeds=inputs_embeds,
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)
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return self.logits_processor(
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input_ids, hidden_states, self.lm_head, 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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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freqs" in name:
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continue
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is_stacked = False
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name in name:
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name = name.replace(weight_name, param_name)
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param = params_dict[name]
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weight_loader = getattr(
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param, "weight_loader", default_weight_loader
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)
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weight_loader(param, loaded_weight, shard_id)
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is_stacked = True
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break
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if is_stacked:
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continue
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param = params_dict.get(name)
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if param is None:
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continue
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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 = Starcoder2ForCausalLM
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