436 lines
16 KiB
Python
436 lines
16 KiB
Python
# Copyright 2023-2025 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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#
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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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# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/nemotron_nas.py
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"""Inference-only deci model compatible with HuggingFace weights."""
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from typing import Iterable, Optional, Tuple, Type, Union
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import torch
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from torch import nn
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from transformers import LlamaConfig
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from sglang.srt.distributed import get_pp_group
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
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from sglang.srt.layers.pooler import Pooler, PoolingType
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from sglang.srt.layers.quantization import QuantizationConfig
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from sglang.srt.layers.utils import PPMissingLayer
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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, PPProxyTensors
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from sglang.srt.model_loader.weight_utils import (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from sglang.srt.models.llama import LlamaAttention, LlamaMLP
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from sglang.srt.utils import add_prefix, make_layers
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from sglang.utils import logger
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def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int:
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# DeciLM-specific code
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intermediate_size = int(2 * ffn_mult * n_embd / 3)
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return _find_multiple(intermediate_size, 256)
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def _find_multiple(n: int, k: int) -> int:
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# DeciLM-specific code
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if n % k == 0:
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return n
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return n + k - (n % k)
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class DeciLMDecoderLayer(nn.Module):
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def __init__(
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self,
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config: LlamaConfig,
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layer_idx: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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block_config = config.block_configs[layer_idx]
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self._is_no_op_attention = block_config.attention.no_op
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self._is_no_op_ffn = block_config.ffn.no_op
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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if rope_scaling is not None and getattr(
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config, "original_max_position_embeddings", None
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):
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rope_scaling["original_max_position_embeddings"] = (
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config.original_max_position_embeddings
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)
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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# Support abacusai/Smaug-72B-v0.1 with attention_bias
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# Support internlm/internlm-7b with bias
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rope_is_neox_style = getattr(config, "rope_is_neox_style", True)
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attention_bias = getattr(config, "attention_bias", False) or getattr(
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config, "bias", False
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)
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# support internlm/internlm3-8b with qkv_bias
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if hasattr(config, "qkv_bias"):
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attention_bias = config.qkv_bias
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if not self._is_no_op_attention:
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num_kv_heads = (
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config.num_attention_heads // block_config.attention.n_heads_in_group
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)
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self.self_attn = LlamaAttention(
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config=config,
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=num_kv_heads,
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layer_id=layer_idx,
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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rope_is_neox_style=rope_is_neox_style,
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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prefix=add_prefix("self_attn", prefix),
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bias=attention_bias,
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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if not self._is_no_op_ffn:
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ffn_mult = block_config.ffn.ffn_mult
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intermediate_size = _ffn_mult_to_intermediate_size(
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ffn_mult, config.hidden_size
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)
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self.mlp = LlamaMLP(
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hidden_size=self.hidden_size,
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intermediate_size=intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=add_prefix("mlp", prefix),
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)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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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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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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if self._is_no_op_attention:
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pass
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else:
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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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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# Fully Connected
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if not self._is_no_op_ffn:
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hidden_states, residual = self.post_attention_layernorm(
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hidden_states, residual
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)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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class DeciModel(nn.Module):
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def __init__(
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self,
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*,
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config: LlamaConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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layer_type: Type[DeciLMDecoderLayer] = DeciLMDecoderLayer,
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):
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super().__init__()
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lora_config = None
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self.config = config
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self.quant_config = quant_config
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self.padding_idx = config.pad_token_id
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lora_vocab = (
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(lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
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if lora_config
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else 0
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)
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vocab_size = config.vocab_size + lora_vocab
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if get_pp_group().is_first_rank:
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self.embed_tokens = VocabParallelEmbedding(
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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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else:
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self.embed_tokens = PPMissingLayer()
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def get_layer(idx: int, prefix: str):
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return layer_type(
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config,
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layer_idx=idx,
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quant_config=quant_config,
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prefix=prefix,
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)
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self.layers, self.start_layer, self.end_layer = make_layers(
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config.num_hidden_layers,
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get_layer,
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pp_rank=get_pp_group().rank_in_group,
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pp_size=get_pp_group().world_size,
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prefix=add_prefix("layers", prefix),
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)
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if get_pp_group().is_last_rank:
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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else:
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self.norm = PPMissingLayer(return_tuple=True)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: Optional[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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pp_proxy_tensors: Optional[PPProxyTensors] = None,
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) -> Union[torch.Tensor, PPProxyTensors]:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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residual = None
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else:
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assert pp_proxy_tensors is not None
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hidden_states = pp_proxy_tensors["hidden_states"]
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residual = pp_proxy_tensors["residual"]
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kv_cache_index = 0
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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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if not layer._is_no_op_attention:
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hidden_states, residual = layer(
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positions, hidden_states, forward_batch, residual
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)
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kv_cache_index += 1
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else:
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hidden_states, residual = layer(
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positions, hidden_states, forward_batch, residual
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)
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if not get_pp_group().is_last_rank:
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return PPProxyTensors(
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{"hidden_states": hidden_states, "residual": residual}
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)
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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class DeciLMForCausalLM(nn.Module):
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packed_modules_mapping = {
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"qkv_proj": ["q_proj", "k_proj", "v_proj"],
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"gate_up_proj": ["gate_proj", "up_proj"],
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}
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# LoRA specific attributes
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supported_lora_modules = [
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"qkv_proj",
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"o_proj",
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"gate_up_proj",
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"down_proj",
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"embed_tokens",
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"lm_head",
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]
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embedding_modules = {
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"embed_tokens": "input_embeddings",
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"lm_head": "output_embeddings",
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}
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embedding_padding_modules = ["lm_head"]
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# Mistral/Llama models can also be loaded with --load-format mistral
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# from consolidated.safetensors checkpoints
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mistral_mapping = {
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"layers": "model.layers",
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"attention": "self_attn",
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"wq": "q_proj",
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"wk": "k_proj",
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"wv": "v_proj",
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"wo": "o_proj",
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"attention_norm": "input_layernorm",
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"feed_forward": "mlp",
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"w1": "gate_proj",
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"w2": "down_proj",
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"w3": "up_proj",
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"ffn_norm": "post_attention_layernorm",
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"tok_embeddings": "model.embed_tokens",
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"output": "lm_head",
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"norm": "model.norm",
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}
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def __init__(
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self,
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*,
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config: LlamaConfig,
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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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lora_config = None
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self.config = config
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self.lora_config = lora_config
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self.model = self._init_model(
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config=config, quant_config=quant_config, prefix=add_prefix("model", prefix)
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)
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if self.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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if lora_config:
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self.unpadded_vocab_size += lora_config.lora_extra_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=(
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DEFAULT_VOCAB_PADDING_SIZE
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# We need bigger padding if using lora for kernel
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# compatibility
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if not lora_config
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else lora_config.lora_vocab_padding_size
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),
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quant_config=quant_config,
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prefix=add_prefix("lm_head", prefix),
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)
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self.logits_processor = LogitsProcessor(config)
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self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
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def _init_model(
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self,
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config: LlamaConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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return DeciModel(config=config, quant_config=quant_config, prefix=prefix)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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@torch.no_grad()
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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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get_embedding: bool = False,
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pp_proxy_tensors: Optional[PPProxyTensors] = None,
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) -> LogitsProcessorOutput:
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hidden_states = self.model(
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input_ids,
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positions,
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forward_batch,
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inputs_embeds,
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pp_proxy_tensors=pp_proxy_tensors,
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)
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if get_pp_group().is_last_rank:
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if not get_embedding:
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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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else:
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return self.pooler(hidden_states, forward_batch)
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else:
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return hidden_states
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> 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 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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if self.config.tie_word_embeddings and "lm_head.weight" in name:
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continue
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if self.model.quant_config is not None and (
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scale_name := self.model.quant_config.get_cache_scale(name)
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):
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# Loading kv cache quantization scales
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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loaded_weight = (
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loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
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)
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weight_loader(param, loaded_weight)
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continue
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if "scale" in name:
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name = maybe_remap_kv_scale_name(name, params_dict)
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if name is None:
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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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if 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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if name in params_dict.keys():
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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)
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else:
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logger.warning(f"Parameter {name} not found in params_dict")
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EntryClass = [DeciLMForCausalLM]
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