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# 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)
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