model: support solar (#8189)
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Praneth Paruchuri
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python/sglang/srt/models/solar.py
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507
python/sglang/srt/models/solar.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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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI 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/solar.py
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from collections.abc import Iterable
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from typing import Any, List, Optional, Tuple, Union
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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.python.sglang.srt.distributed.parallel_state import (
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get_tensor_model_parallel_rank,
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)
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from sglang.python.sglang.srt.utils import add_prefix, make_layers
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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 SiluAndMul
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.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 sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
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from sglang.srt.layers.quantization 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.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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kv_cache_scales_loader,
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)
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class SolarMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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bias: bool = False,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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input_size=hidden_size,
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output_sizes=[intermediate_size] * 2,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.down_proj = RowParallelLinear(
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input_size=intermediate_size,
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output_size=hidden_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.down_proj",
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)
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if hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now."
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)
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self.act_fn = SiluAndMul()
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def forward(self, x):
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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 SolarAttention(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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quant_config: Optional[QuantizationConfig] = None,
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bias: bool = False,
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prefix: str = "",
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layer_id: int = 0,
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_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 = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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assert self.total_num_kv_heads % tp_size == 0
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else:
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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 = getattr(config, "head_dim", None)
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if self.head_dim is None:
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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 = rope_theta
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self.max_position_embeddings = max_position_embeddings
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self.qkv_proj = QKVParallelLinear(
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hidden_size=hidden_size,
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head_size=self.head_dim,
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total_num_heads=self.total_num_heads,
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total_num_kv_heads=self.total_num_kv_heads,
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bias=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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input_size=self.total_num_heads * self.head_dim,
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output_size=hidden_size,
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bias=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=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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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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forward_batch: ForwardBatch,
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hidden_states: torch.Tensor,
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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=forward_batch)
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output, _ = self.o_proj(attn_output)
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return output
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class SolarDecoderLayer(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,
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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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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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attention_bias = getattr(config, "attention_bias", False) or getattr(
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config, "bias", False
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)
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self.self_attn = SolarAttention(
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config=config,
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layer_id=layer_id,
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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=getattr(
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config, "num_key_value_heads", config.num_attention_heads
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),
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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bias=attention_bias,
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prefix=f"{prefix}.self_attn",
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)
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self.mlp = SolarMLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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bias=getattr(config, "mlp_bias", False),
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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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 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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hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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class SolarModel(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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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.org_vocab_size = config.vocab_size
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self.pp_group = get_pp_group()
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if self.pp_group.is_first_rank:
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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=add_prefix("embed_tokens", prefix),
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)
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else:
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self.embed_tokens = PPMissingLayer()
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda idx, prefix: SolarDecoderLayer(
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config=config,
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quant_config=quant_config,
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layer_id=idx,
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prefix=prefix,
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),
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prefix=f"{prefix}.layers",
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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()
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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, Tuple[torch.Tensor, List[torch.Tensor]], PPProxyTensors]:
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if self.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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# Depth up-scaling mechanism: caches hidden states and residuals from intermediate layers and interpolates them with the states of later layers.
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# `bskcn` stands for "backbone skip connection".
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bskcn_h_1 = None
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bskcn_h_2 = None
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bskcn_r_1 = None
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bskcn_r_2 = None
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bskcn_tv = self.config.bskcn_tv[0] if self.training else self.config.bskcn_tv[1]
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for i in range(self.start_layer, self.end_layer):
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if i in self.config.bskcn_1:
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bskcn_h_1 = hidden_states.clone()
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bskcn_r_1 = residual.clone() if residual is not None else None
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if i in self.config.bskcn_2:
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bskcn_h_2 = hidden_states.clone()
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bskcn_r_2 = residual.clone() if residual is not None else None
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if i in self.config.bskcn_3:
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hidden_states = bskcn_h_1 * bskcn_tv + hidden_states * (1 - bskcn_tv)
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if bskcn_r_1 is not None and residual is not None:
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residual = bskcn_r_1 * bskcn_tv + residual * (1 - bskcn_tv)
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if i in self.config.bskcn_4:
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hidden_states = bskcn_h_2 * bskcn_tv + hidden_states * (1 - bskcn_tv)
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if bskcn_r_2 is not None and residual is not None:
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residual = bskcn_r_2 * bskcn_tv + residual * (1 - bskcn_tv)
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layer = self.layers[i]
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hidden_states, residual = layer(
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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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residual=residual,
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)
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if not self.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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def load_kv_cache_scales(self, quantization_param_path: str) -> None:
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tp_size = get_tensor_model_parallel_world_size()
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tp_rank = get_tensor_model_parallel_rank()
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for layer_idx, scaling_factor in kv_cache_scales_loader(
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quantization_param_path,
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tp_rank,
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tp_size,
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self.config.num_hidden_layers,
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self.config.__class__.model_type,
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):
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if not isinstance(self.layers[layer_idx], nn.Identity):
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layer_self_attn = self.layers[layer_idx].self_attn
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if hasattr(layer_self_attn.attn, "k_scale"):
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layer_self_attn.attn.k_scale = scaling_factor
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layer_self_attn.attn.v_scale = scaling_factor
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else:
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raise RuntimeError(
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"Self attention has no KV cache scaling " "factor attribute!"
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)
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class SolarForCausalLM(nn.Module):
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packed_modules_mapping = {
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"qkv_proj": [
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("q_proj", "q"),
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("k_proj", "k"),
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("v_proj", "v"),
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],
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"gate_up_proj": [
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("gate_proj", 0),
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("up_proj", 1),
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],
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}
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default_bitsandbytes_target_modules = [
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".gate_proj.",
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".down_proj.",
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".up_proj.",
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".q_proj.",
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".k_proj.",
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".v_proj.",
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".o_proj.",
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]
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column_parallel_weights_modules = [".down_proj.", ".o_proj."]
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bitsandbytes_stacked_params_mapping = {
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".q_proj": (".qkv_proj", 0),
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".k_proj": (".qkv_proj", 1),
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".v_proj": (".qkv_proj", 2),
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".gate_proj": (".gate_up_proj", 0),
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".up_proj": (".gate_up_proj", 1),
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}
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def __init__(
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self,
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config: PretrainedConfig,
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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.pp_group = get_pp_group()
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self.config = config
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self.quant_config = quant_config
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self.model = SolarModel(
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config=config,
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quant_config=self.quant_config,
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prefix=add_prefix("model", prefix),
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)
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if self.pp_group.is_last_rank:
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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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)
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if config.tie_word_embeddings and self.pp_group.is_first_rank:
|
||||
self.lm_head.weight = self.model.embed_tokens.weight
|
||||
|
||||
logit_scale = getattr(config, "logit_scale", 1.0)
|
||||
self.logits_processor = LogitsProcessor(
|
||||
self.unpadded_vocab_size, config.vocab_size, logit_scale
|
||||
)
|
||||
else:
|
||||
self.lm_head = PPMissingLayer()
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
inputs_embeds: Optional[torch.Tensor] = None,
|
||||
) -> Union[torch.Tensor, LogitsProcessorOutput]:
|
||||
hidden_states = self.model(
|
||||
input_ids=input_ids,
|
||||
positions=positions,
|
||||
forward_batch=forward_batch,
|
||||
inputs_embeds=inputs_embeds,
|
||||
)
|
||||
|
||||
if self.pp_group().is_last_rank:
|
||||
logits = self.logits_processor(self.lm_head, hidden_states, forward_batch)
|
||||
return logits
|
||||
|
||||
return hidden_states
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
|
||||
|
||||
params_dict = dict(self.named_parameters())
|
||||
for name, loaded_weight in weights:
|
||||
|
||||
is_packed = False
|
||||
for packed_name, sources in self.packed_modules_mapping.items():
|
||||
for src_name, shard_id in sources:
|
||||
if src_name in name:
|
||||
|
||||
model_param_name = name.replace(src_name, packed_name)
|
||||
|
||||
if model_param_name in params_dict:
|
||||
param = params_dict[model_param_name]
|
||||
weight_loader = getattr(
|
||||
param, "weight_loader", default_weight_loader
|
||||
)
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
is_packed = True
|
||||
break
|
||||
if is_packed:
|
||||
break
|
||||
|
||||
if is_packed:
|
||||
continue
|
||||
|
||||
if name in params_dict:
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
|
||||
|
||||
EntryClass = SolarForCausalLM
|
||||
Reference in New Issue
Block a user