# Copyright (c) 2023, Albert Gu, Tri Dao. import math from functools import partial import json import os import copy from collections import namedtuple import torch import torch.nn as nn from mamba_ssm.models.config_mamba import MambaConfig from mamba_ssm.modules.mamba_simple import Mamba from mamba_ssm.modules.mamba2 import Mamba2 from mamba_ssm.modules.mha import MHA from mamba_ssm.modules.mlp import GatedMLP from mamba_ssm.modules.block import Block from mamba_ssm.utils.generation import GenerationMixin from mamba_ssm.utils.hf import load_config_hf, load_state_dict_hf try: from mamba_ssm.ops.triton.layer_norm import RMSNorm, layer_norm_fn, rms_norm_fn except ImportError: RMSNorm, layer_norm_fn, rms_norm_fn = None, None, None def create_block( d_model, d_intermediate, ssm_cfg=None, attn_layer_idx=None, attn_cfg=None, norm_epsilon=1e-5, rms_norm=False, residual_in_fp32=False, fused_add_norm=False, layer_idx=None, device=None, dtype=None, ): if ssm_cfg is None: ssm_cfg = {} if attn_layer_idx is None: attn_layer_idx = [] if attn_cfg is None: attn_cfg = {} factory_kwargs = {"device": device, "dtype": dtype} if layer_idx not in attn_layer_idx: # Create a copy of the config to modify ssm_cfg = copy.deepcopy(ssm_cfg) if ssm_cfg is not None else {} ssm_layer = ssm_cfg.pop("layer", "Mamba1") if ssm_layer not in ["Mamba1", "Mamba2"]: raise ValueError(f"Invalid ssm_layer: {ssm_layer}, only support Mamba1 and Mamba2") mixer_cls = partial( Mamba2 if ssm_layer == "Mamba2" else Mamba, layer_idx=layer_idx, **ssm_cfg, **factory_kwargs ) else: mixer_cls = partial(MHA, layer_idx=layer_idx, **attn_cfg, **factory_kwargs) norm_cls = partial( nn.LayerNorm if not rms_norm else RMSNorm, eps=norm_epsilon, **factory_kwargs ) if d_intermediate == 0: mlp_cls = nn.Identity else: mlp_cls = partial( GatedMLP, hidden_features=d_intermediate, out_features=d_model, **factory_kwargs ) block = Block( d_model, mixer_cls, mlp_cls, norm_cls=norm_cls, fused_add_norm=fused_add_norm, residual_in_fp32=residual_in_fp32, ) block.layer_idx = layer_idx return block # https://github.com/huggingface/transformers/blob/c28d04e9e252a1a099944e325685f14d242ecdcd/src/transformers/models/gpt2/modeling_gpt2.py#L454 def _init_weights( module, n_layer, initializer_range=0.02, # Now only used for embedding layer. rescale_prenorm_residual=True, n_residuals_per_layer=1, # Change to 2 if we have MLP ): if isinstance(module, nn.Linear): if module.bias is not None: if not getattr(module.bias, "_no_reinit", False): nn.init.zeros_(module.bias) elif isinstance(module, nn.Embedding): nn.init.normal_(module.weight, std=initializer_range) if rescale_prenorm_residual: # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. # > -- GPT-2 :: https://openai.com/blog/better-language-models/ # # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py for name, p in module.named_parameters(): if name in ["out_proj.weight", "fc2.weight"]: # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) # We need to reinit p since this code could be called multiple times # Having just p *= scale would repeatedly scale it down nn.init.kaiming_uniform_(p, a=math.sqrt(5)) with torch.no_grad(): p /= math.sqrt(n_residuals_per_layer * n_layer) class MixerModel(nn.Module): def __init__( self, d_model: int, n_layer: int, d_intermediate: int, vocab_size: int, ssm_cfg=None, attn_layer_idx=None, attn_cfg=None, norm_epsilon: float = 1e-5, rms_norm: bool = False, initializer_cfg=None, fused_add_norm=False, residual_in_fp32=False, device=None, dtype=None, ) -> None: factory_kwargs = {"device": device, "dtype": dtype} super().__init__() self.residual_in_fp32 = residual_in_fp32 self.embedding = nn.Embedding(vocab_size, d_model, **factory_kwargs) # We change the order of residual and layer norm: # Instead of LN -> Attn / MLP -> Add, we do: # Add -> LN -> Attn / MLP / Mixer, returning both the residual branch (output of Add) and # the main branch (output of MLP / Mixer). The model definition is unchanged. # This is for performance reason: we can fuse add + layer_norm. self.fused_add_norm = fused_add_norm if self.fused_add_norm: if layer_norm_fn is None or rms_norm_fn is None: raise ImportError("Failed to import Triton LayerNorm / RMSNorm kernels") self.layers = nn.ModuleList( [ create_block( d_model, d_intermediate=d_intermediate, ssm_cfg=ssm_cfg, attn_layer_idx=attn_layer_idx, attn_cfg=attn_cfg, norm_epsilon=norm_epsilon, rms_norm=rms_norm, residual_in_fp32=residual_in_fp32, fused_add_norm=fused_add_norm, layer_idx=i, **factory_kwargs, ) for i in range(n_layer) ] ) self.norm_f = (nn.LayerNorm if not rms_norm else RMSNorm)( d_model, eps=norm_epsilon, **factory_kwargs ) self.apply( partial( _init_weights, n_layer=n_layer, **(initializer_cfg if initializer_cfg is not None else {}), n_residuals_per_layer=1 if d_intermediate == 0 else 2, # 2 if we have MLP ) ) def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs): return { i: layer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype, **kwargs) for i, layer in enumerate(self.layers) } def forward(self, input_ids, inference_params=None, **mixer_kwargs): hidden_states = self.embedding(input_ids) residual = None for layer in self.layers: hidden_states, residual = layer( hidden_states, residual, inference_params=inference_params, **mixer_kwargs ) if not self.fused_add_norm: residual = (hidden_states + residual) if residual is not None else hidden_states hidden_states = self.norm_f(residual.to(dtype=self.norm_f.weight.dtype)) else: # Set prenorm=False here since we don't need the residual hidden_states = layer_norm_fn( hidden_states, self.norm_f.weight, self.norm_f.bias, eps=self.norm_f.eps, residual=residual, prenorm=False, residual_in_fp32=self.residual_in_fp32, is_rms_norm=isinstance(self.norm_f, RMSNorm) ) return hidden_states class MambaLMHeadModel(nn.Module, GenerationMixin): def __init__( self, config: MambaConfig, initializer_cfg=None, device=None, dtype=None, ) -> None: self.config = config d_model = config.d_model n_layer = config.n_layer d_intermediate = config.d_intermediate vocab_size = config.vocab_size ssm_cfg = config.ssm_cfg attn_layer_idx = config.attn_layer_idx attn_cfg = config.attn_cfg rms_norm = config.rms_norm residual_in_fp32 = config.residual_in_fp32 fused_add_norm = config.fused_add_norm pad_vocab_size_multiple = config.pad_vocab_size_multiple factory_kwargs = {"device": device, "dtype": dtype} super().__init__() if vocab_size % pad_vocab_size_multiple != 0: vocab_size += pad_vocab_size_multiple - (vocab_size % pad_vocab_size_multiple) self.backbone = MixerModel( d_model=d_model, n_layer=n_layer, d_intermediate=d_intermediate, vocab_size=vocab_size, ssm_cfg=ssm_cfg, attn_layer_idx=attn_layer_idx, attn_cfg=attn_cfg, rms_norm=rms_norm, initializer_cfg=initializer_cfg, fused_add_norm=fused_add_norm, residual_in_fp32=residual_in_fp32, **factory_kwargs, ) self.lm_head = nn.Linear(d_model, vocab_size, bias=False, **factory_kwargs) # Initialize weights and apply final processing self.apply( partial( _init_weights, n_layer=n_layer, **(initializer_cfg if initializer_cfg is not None else {}), ) ) self.tie_weights() def tie_weights(self): if self.config.tie_embeddings: self.lm_head.weight = self.backbone.embedding.weight def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs): return self.backbone.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype, **kwargs) def forward(self, input_ids, position_ids=None, inference_params=None, num_last_tokens=0, **mixer_kwargs): """ "position_ids" is just to be compatible with Transformer generation. We don't use it. num_last_tokens: if > 0, only return the logits for the last n tokens """ hidden_states = self.backbone(input_ids, inference_params=inference_params, **mixer_kwargs) if num_last_tokens > 0: hidden_states = hidden_states[:, -num_last_tokens:] lm_logits = self.lm_head(hidden_states) CausalLMOutput = namedtuple("CausalLMOutput", ["logits"]) return CausalLMOutput(logits=lm_logits) @classmethod def from_pretrained(cls, pretrained_model_name, device=None, dtype=None, **kwargs): config_data = load_config_hf(pretrained_model_name) config = MambaConfig(**config_data) model = cls(config, device=device, dtype=dtype, **kwargs) model.load_state_dict(load_state_dict_hf(pretrained_model_name, device=device, dtype=dtype)) return model def save_pretrained(self, save_directory): """ Minimal implementation of save_pretrained for MambaLMHeadModel. Save the model and its configuration file to a directory. """ # Ensure save_directory exists os.makedirs(save_directory, exist_ok=True) # Save the model's state_dict model_path = os.path.join(save_directory, 'pytorch_model.bin') torch.save(self.state_dict(), model_path) # Save the configuration of the model config_path = os.path.join(save_directory, 'config.json') with open(config_path, 'w') as f: json.dump(self.config.__dict__, f, indent=4)