440 lines
15 KiB
Python
440 lines
15 KiB
Python
# coding=utf-8
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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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"""Inference-only BaiChuan model compatible with HuggingFace weights."""
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import math
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from typing import Iterable, Optional, Tuple
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from sglang.srt.distributed import (
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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)
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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
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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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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
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def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
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closest_power_of_2 = 2 ** math.floor(math.log2(total_num_heads))
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base = torch.tensor(
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2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))),
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dtype=torch.float32,
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)
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powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
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slopes = torch.pow(base, powers)
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if closest_power_of_2 != total_num_heads:
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extra_base = torch.tensor(
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2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))),
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dtype=torch.float32,
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)
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num_remaining_heads = min(
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closest_power_of_2, total_num_heads - closest_power_of_2
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)
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extra_powers = torch.arange(
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start=1, end=1 + 2 * num_remaining_heads, step=2, dtype=torch.int32
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)
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slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
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return slopes
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class BaiChuanMLP(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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prefix: str = "",
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):
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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quant_config=quant_config,
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prefix=add_prefix("gate_up_proj", prefix),
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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prefix=add_prefix("down_proj", prefix),
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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 BaiChuanAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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position_embedding: str,
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rope_theta: float = 10000,
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max_position_embeddings: int = 8192,
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quant_config: Optional[QuantizationConfig] = None,
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layer_id: int = 0,
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prefix: str = "",
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):
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super().__init__()
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self.hidden_size = hidden_size
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tensor_model_parallel_world_size = get_tensor_model_parallel_world_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 % tensor_model_parallel_world_size == 0
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self.num_heads = self.total_num_heads // tensor_model_parallel_world_size
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self.head_dim = hidden_size // self.total_num_heads
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self.postion_embedding = position_embedding
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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.total_num_kv_heads = self.num_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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# pylint: disable=invalid-name
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self.W_pack = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_heads,
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bias=False,
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quant_config=quant_config,
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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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hidden_size,
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bias=False,
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quant_config=quant_config,
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)
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# Create the alibi slopes and slice them.
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if self.postion_embedding == "ALIBI":
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tp_rank = get_tensor_model_parallel_rank()
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head_start = tp_rank * self.num_heads
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head_end = (tp_rank + 1) * self.num_heads
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alibi_slopes = _get_alibi_slopes(self.total_num_heads)
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alibi_slopes = alibi_slopes[head_start:head_end].tolist()
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scaling = self.head_dim**-0.5
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self.attn = RadixAttention(
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self.num_heads,
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self.head_dim,
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scaling,
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num_kv_heads=self.num_kv_heads,
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layer_id=layer_id,
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prefix=add_prefix("attn", prefix),
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)
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else:
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=self.max_position_embeddings,
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base=self.rope_theta,
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)
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self.scaling = self.head_dim**-0.5
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self.attn = RadixAttention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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layer_id=layer_id,
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prefix=add_prefix("attn", prefix),
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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.W_pack(hidden_states)
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q, k, v = qkv.chunk(chunks=3, dim=-1)
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if self.postion_embedding != "ALIBI":
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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 BaiChuanDecoderLayer(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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position_embedding: str,
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layer_id: int = 0,
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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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rope_theta = getattr(config, "rope_theta", 10000)
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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self.self_attn = BaiChuanAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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position_embedding=position_embedding,
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rope_theta=rope_theta,
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layer_id=layer_id,
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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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)
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self.mlp = BaiChuanMLP(
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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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prefix=add_prefix("mlp", prefix),
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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 BaiChuanModel(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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position_embedding: str,
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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.padding_idx = config.pad_token_id
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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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)
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self.layers = nn.ModuleList(
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[
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BaiChuanDecoderLayer(
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config,
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layer_id=i,
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position_embedding=position_embedding,
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quant_config=quant_config,
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prefix=add_prefix(f"layers.{i}", prefix),
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)
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for i in range(config.num_hidden_layers)
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]
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)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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hidden_states = self.embed_tokens(input_ids)
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residual = None
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for i in range(len(self.layers)):
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layer = self.layers[i]
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hidden_states, residual = layer(
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positions,
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hidden_states,
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forward_batch,
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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 BaiChuanBaseForCausalLM(nn.Module):
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packed_modules_mapping = {
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"W_pack": ["W_pack"],
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"gate_up_proj": [
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"gate_proj",
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"up_proj",
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],
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}
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# LoRA specific attributes
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supported_lora_modules = [
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"W_pack",
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"o_proj",
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"gate_up_proj",
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"down_proj",
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]
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embedding_modules = {}
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embedding_padding_modules = []
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def __init__(
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self,
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config: PretrainedConfig,
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position_embedding: str,
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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.quant_config = quant_config
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self.model = BaiChuanModel(
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config, position_embedding, 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.lm_head = ParallelLMHead(
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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("lm_head", prefix),
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)
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self.logits_processor = LogitsProcessor(config)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, forward_batch)
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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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("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 name == "lm_head.weight":
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# Unlike Baichuan, Baichuan2 normalizes the head weights.
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# Refer to:
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# https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/84603cde5ebffb6084e476cfaeceaf0b8b91fe54/modeling_baichuan.py#L508
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# Distinguish between Baichuan and Baichuan2 by checking the
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# vocab size. This is suggested by
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# https://github.com/vllm-project/vllm/pull/1022#discussion_r1325652704
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is_baichuan2 = self.config.vocab_size == 125696
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if is_baichuan2:
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loaded_weight = torch.nn.functional.normalize(loaded_weight)
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
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"""Baichuan 13B and Baichuan2 7B/13B."""
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def __init__(
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self,
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config,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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if config.hidden_size == 4096: # baichuan2 7b
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super().__init__(config, "ROPE", quant_config, prefix=prefix)
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else: # baichuan 13b, baichuan2 13b
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super().__init__(config, "ALIBI", quant_config, prefix=prefix)
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EntryClass = [BaichuanForCausalLM]
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