316 lines
12 KiB
Python
316 lines
12 KiB
Python
# Copyright (c) 2023, Albert Gu, Tri Dao.
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import math
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from functools import partial
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import json
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import os
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import copy
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from collections import namedtuple
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import torch
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import torch.nn as nn
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from mamba_ssm.models.config_mamba import MambaConfig
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from mamba_ssm.modules.mamba_simple import Mamba
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from mamba_ssm.modules.mamba2 import Mamba2
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from mamba_ssm.modules.mha import MHA
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from mamba_ssm.modules.mlp import GatedMLP
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from mamba_ssm.modules.block import Block
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from mamba_ssm.utils.generation import GenerationMixin
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from mamba_ssm.utils.hf import load_config_hf, load_state_dict_hf
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try:
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from mamba_ssm.ops.triton.layer_norm import RMSNorm, layer_norm_fn, rms_norm_fn
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except ImportError:
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RMSNorm, layer_norm_fn, rms_norm_fn = None, None, None
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# 通过create_block 创建多个处理块
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def create_block(
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d_model,
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d_intermediate,
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ssm_cfg=None,
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attn_layer_idx=None,
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attn_cfg=None,
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norm_epsilon=1e-5,
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rms_norm=False,
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residual_in_fp32=False,
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fused_add_norm=False,
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layer_idx=None,
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device=None,
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dtype=None,
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):
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if ssm_cfg is None:
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ssm_cfg = {}
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if attn_layer_idx is None:
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attn_layer_idx = []
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if attn_cfg is None:
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attn_cfg = {}
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factory_kwargs = {"device": device, "dtype": dtype}
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if layer_idx not in attn_layer_idx:
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# Create a copy of the config to modify
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ssm_cfg = copy.deepcopy(ssm_cfg) if ssm_cfg is not None else {}
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ssm_layer = ssm_cfg.pop("layer", "Mamba1")
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if ssm_layer not in ["Mamba1", "Mamba2"]:
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raise ValueError(f"Invalid ssm_layer: {ssm_layer}, only support Mamba1 and Mamba2")
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mixer_cls = partial(
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Mamba2 if ssm_layer == "Mamba2" else Mamba,
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layer_idx=layer_idx,
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**ssm_cfg,
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**factory_kwargs
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)
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else:
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mixer_cls = partial(MHA, layer_idx=layer_idx, **attn_cfg, **factory_kwargs)
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norm_cls = partial(
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nn.LayerNorm if not rms_norm else RMSNorm, eps=norm_epsilon, **factory_kwargs
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)
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if d_intermediate == 0:
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mlp_cls = nn.Identity
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else:
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mlp_cls = partial(
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GatedMLP, hidden_features=d_intermediate, out_features=d_model, **factory_kwargs
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)
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block = Block(
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d_model,
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mixer_cls,
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mlp_cls,
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norm_cls=norm_cls,
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fused_add_norm=fused_add_norm,
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residual_in_fp32=residual_in_fp32,
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)
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block.layer_idx = layer_idx
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return block
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# https://github.com/huggingface/transformers/blob/c28d04e9e252a1a099944e325685f14d242ecdcd/src/transformers/models/gpt2/modeling_gpt2.py#L454
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def _init_weights(
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module,
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n_layer,
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initializer_range=0.02, # Now only used for embedding layer.
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rescale_prenorm_residual=True,
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n_residuals_per_layer=1, # Change to 2 if we have MLP
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):
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if isinstance(module, nn.Linear):
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if module.bias is not None:
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if not getattr(module.bias, "_no_reinit", False):
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nn.init.zeros_(module.bias)
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elif isinstance(module, nn.Embedding):
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nn.init.normal_(module.weight, std=initializer_range)
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if rescale_prenorm_residual:
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# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
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# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
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# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
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# > -- GPT-2 :: https://openai.com/blog/better-language-models/
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#
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# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
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for name, p in module.named_parameters():
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if name in ["out_proj.weight", "fc2.weight"]:
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# Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
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# Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
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# We need to reinit p since this code could be called multiple times
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# Having just p *= scale would repeatedly scale it down
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nn.init.kaiming_uniform_(p, a=math.sqrt(5))
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with torch.no_grad():
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p /= math.sqrt(n_residuals_per_layer * n_layer)
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class MixerModel(nn.Module):
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def __init__(
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self,
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d_model: int,
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n_layer: int,
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d_intermediate: int,
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vocab_size: int,
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ssm_cfg=None,
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attn_layer_idx=None,
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attn_cfg=None,
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norm_epsilon: float = 1e-5,
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rms_norm: bool = False,
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initializer_cfg=None,
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fused_add_norm=False,
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residual_in_fp32=False,
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device=None,
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dtype=None,
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) -> None:
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factory_kwargs = {"device": device, "dtype": dtype}
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super().__init__()
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self.residual_in_fp32 = residual_in_fp32
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# 获取输入token的Embedding，在 MixModule中；
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self.embedding = nn.Embedding(vocab_size, d_model, **factory_kwargs)
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# We change the order of residual and layer norm:
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# Instead of LN -> Attn / MLP -> Add, we do:
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# Add -> LN -> Attn / MLP / Mixer, returning both the residual branch (output of Add) and
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# the main branch (output of MLP / Mixer). The model definition is unchanged.
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# This is for performance reason: we can fuse add + layer_norm.
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self.fused_add_norm = fused_add_norm
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if self.fused_add_norm:
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if layer_norm_fn is None or rms_norm_fn is None:
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raise ImportError("Failed to import Triton LayerNorm / RMSNorm kernels")
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# ModuleList管理模型中的Mamba block块（Mamba block堆叠），调用create_block函数
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self.layers = nn.ModuleList(
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[
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create_block(
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d_model,
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d_intermediate=d_intermediate,
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ssm_cfg=ssm_cfg,
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attn_layer_idx=attn_layer_idx,
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attn_cfg=attn_cfg,
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norm_epsilon=norm_epsilon,
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rms_norm=rms_norm,
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residual_in_fp32=residual_in_fp32,
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fused_add_norm=fused_add_norm,
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layer_idx=i,
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**factory_kwargs,
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)
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#循环决定输出block个数，n_layer是超参数，在config中配置
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for i in range(n_layer)
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]
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)
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self.norm_f = (nn.LayerNorm if not rms_norm else RMSNorm)(
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d_model, eps=norm_epsilon, **factory_kwargs
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)
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self.apply(
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partial(
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_init_weights,
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n_layer=n_layer,
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**(initializer_cfg if initializer_cfg is not None else {}),
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n_residuals_per_layer=1 if d_intermediate == 0 else 2, # 2 if we have MLP
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)
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)
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def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
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return {
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i: layer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype, **kwargs)
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for i, layer in enumerate(self.layers)
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}
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#前向传播：输入序列通过嵌入层，依次通过每个块处理，最后应用规范化层
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def forward(self, input_ids, inference_params=None, **mixer_kwargs):
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#hidden_states 并非隐藏态，而是在Mamba块对输入的Embedding处理中的一个中间状态
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#每个Mamba块有hidden_states的输入和输出
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hidden_states = self.embedding(input_ids)
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residual = None
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for layer in self.layers:
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hidden_states, residual = layer(
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hidden_states, residual, inference_params=inference_params
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)
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if not self.fused_add_norm:
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residual = (hidden_states + residual) if residual is not None else hidden_states
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hidden_states = self.norm_f(residual.to(dtype=self.norm_f.weight.dtype))
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else:
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# Set prenorm=False here since we don't need the residual
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hidden_states = layer_norm_fn(
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hidden_states,
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self.norm_f.weight,
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self.norm_f.bias,
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eps=self.norm_f.eps,
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residual=residual,
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prenorm=False,
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residual_in_fp32=self.residual_in_fp32,
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is_rms_norm=isinstance(self.norm_f, RMSNorm)
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)
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return hidden_states
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class MambaLMHeadModel(nn.Module, GenerationMixin):
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def __init__(
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self,
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config: MambaConfig,
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initializer_cfg=None,
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device=None,
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dtype=None,
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) -> None:
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self.config = config
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d_model = config.d_model
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n_layer = config.n_layer
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d_intermediate = config.d_intermediate
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vocab_size = config.vocab_size
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ssm_cfg = config.ssm_cfg
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attn_layer_idx = config.attn_layer_idx
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attn_cfg = config.attn_cfg
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rms_norm = config.rms_norm
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residual_in_fp32 = config.residual_in_fp32
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fused_add_norm = config.fused_add_norm
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pad_vocab_size_multiple = config.pad_vocab_size_multiple
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factory_kwargs = {"device": device, "dtype": dtype}
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super().__init__()
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if vocab_size % pad_vocab_size_multiple != 0:
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vocab_size += pad_vocab_size_multiple - (vocab_size % pad_vocab_size_multiple)
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self.backbone = MixerModel(
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d_model=d_model,
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n_layer=n_layer,
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d_intermediate=d_intermediate,
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vocab_size=vocab_size,
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ssm_cfg=ssm_cfg,
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attn_layer_idx=attn_layer_idx,
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attn_cfg=attn_cfg,
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rms_norm=rms_norm,
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initializer_cfg=initializer_cfg,
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fused_add_norm=fused_add_norm,
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residual_in_fp32=residual_in_fp32,
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**factory_kwargs,
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)
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self.lm_head = nn.Linear(d_model, vocab_size, bias=False, **factory_kwargs)
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# Initialize weights and apply final processing
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self.apply(
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partial(
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_init_weights,
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n_layer=n_layer,
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**(initializer_cfg if initializer_cfg is not None else {}),
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)
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)
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self.tie_weights()
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#tie_weights 绑定预训练权重的函数：lm_head 和 embedding的权重，这里将语言模型头（lm_head）的权重
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#设置为主干网络self.backbone中词嵌入层的权重；使用于小型数据集
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def tie_weights(self):
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if self.config.tie_embeddings:
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self.lm_head.weight = self.backbone.embedding.weight
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def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
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return self.backbone.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype, **kwargs)
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def forward(self, input_ids, position_ids=None, inference_params=None, num_last_tokens=0, **mixer_kwargs):
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"""
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"position_ids" is just to be compatible with Transformer generation. We don't use it.
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num_last_tokens: if > 0, only return the logits for the last n tokens
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"""
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hidden_states = self.backbone(input_ids, inference_params=inference_params, **mixer_kwargs)
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if num_last_tokens > 0:
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hidden_states = hidden_states[:, -num_last_tokens:]
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lm_logits = self.lm_head(hidden_states)
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CausalLMOutput = namedtuple("CausalLMOutput", ["logits"])
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return CausalLMOutput(logits=lm_logits)
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# 加载预训练权重函数
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@classmethod
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def from_pretrained(cls, pretrained_model_name, device=None, dtype=None, **kwargs):
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config_data = load_config_hf(pretrained_model_name)
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config = MambaConfig(**config_data)
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model = cls(config, device=device, dtype=dtype, **kwargs)
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model.load_state_dict(load_state_dict_hf(pretrained_model_name, device=device, dtype=dtype))
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return model
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# 保存预训练权重函数
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def save_pretrained(self, save_directory):
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"""
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Minimal implementation of save_pretrained for MambaLMHeadModel.
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Save the model and its configuration file to a directory.
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"""
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# Ensure save_directory exists
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os.makedirs(save_directory, exist_ok=True)
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# Save the model's state_dict
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model_path = os.path.join(save_directory, 'pytorch_model.bin')
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torch.save(self.state_dict(), model_path)
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# Save the configuration of the model
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config_path = os.path.join(save_directory, 'config.json')
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with open(config_path, 'w') as f:
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json.dump(self.config.__dict__, f, indent=4)
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