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from dataclasses import dataclass, field
from enum import Enum
from typing import Optional, Tuple
import torch
from torch import nn
from torch.nn import functional as F
from causal_conv1d.causal_conv1d_varlen import causal_conv1d_varlen_states
from apps.mamba.component.causal_conv1d_compilable import (
causal_conv1d_fn,
causal_conv1d_update,
)
from apps.mamba.component.ssm_compilable import mamba_chunk_scan_combined
from mamba_ssm.ops.triton.selective_state_update import selective_state_update
from lingua.transformer import InitStdFactor, RMSNorm
from lingua.probe import log_stats
@dataclass
class InitArgs:
dt_max: float = 0.1
dt_min: float = 0.001
dt_init_floor: float = 1e-4
A_init_min: float = 1
A_init_max: float = 16
@dataclass
class BaseMambaArgs:
dim: int = 512
n_layers: int = 8
n_heads: int = 8
state_dim: int = 128
n_groups: int = 1
conv_size: Optional[int] = None
dt_bias: bool = False
D_has_head_dim: bool = False
learnable_init_states: bool = False
ssm_chunk_size: int = 256
vocab_size: int = -1
ffn_dim_multiplier: Optional[float] = None
multiple_of: int = 256
"""
Enforces that the SwiGLU hidden layer size is a multiple
of large power of 2.
"""
norm_eps: float = 1e-5
init_use_depth: bool = False
init_base_std: Optional[float] = None
init_std_factor: str = "disabled"
init_args: InitArgs = field(default_factory=InitArgs)
seed: int = 42
class SSM(nn.Module):
def __init__(
self,
dim: int,
hidden_dim: int,
multiple_of: int,
ffn_dim_multiplier: Optional[float],
state_dim: int,
n_heads: int,
n_groups: int,
conv_size: Optional[int],
dt_bias: bool,
D_has_head_dim: Optional[bool],
learnable_init_states: bool,
dt_limit: Tuple[float, float] = (0.0, float("inf")),
# Fused kernel and sharding options
chunk_size=256,
):
super().__init__()
self.dim = dim
hidden_dim = int(2 * hidden_dim / 3)
if ffn_dim_multiplier is not None:
hidden_dim = int(ffn_dim_multiplier * hidden_dim)
self.hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
assert (
self.hidden_dim % n_heads == 0
), f"Hidden dim must be divisible by n_heads: {self.hidden_dim} % {n_heads} != 0"
self.state_dim = state_dim
self.head_dim = self.hidden_dim // n_heads
self.n_heads = n_heads
self.n_groups = n_groups
self.dt_limit = dt_limit
self.chunk_size = chunk_size
# Order: [z, x, B, C, dt]
d_in_proj = (
2 * self.hidden_dim + 2 * self.n_groups * self.state_dim + self.n_heads
)
self.in_proj = nn.Linear(dim, d_in_proj, bias=False)
self.conv_size = conv_size
self.conv_dim = None
if conv_size is not None:
self.conv_dim = self.hidden_dim + 2 * self.n_groups * self.state_dim
assert (self.conv_dim % 8 == 0) and (
conv_size in [2, 3, 4]
), f"Causal conv1d only supports conv_size in [2, 3, 4] and hidden_dim/head_dim % 8 == 0, got {self.conv_dim} and {conv_size}"
self.conv_dim = self.hidden_dim + 2 * self.n_groups * self.state_dim
self.conv_weight = nn.Parameter(torch.empty((self.conv_dim, conv_size)))
self.learnable_init_states = learnable_init_states
if learnable_init_states:
self.init_states = nn.Parameter(
torch.zeros(n_heads, self.head_dim, state_dim)
)
self.dt_bias = None
if dt_bias:
self.dt_bias = nn.Parameter(torch.empty(n_heads))
self.A_log = nn.Parameter(torch.empty(n_heads))
if D_has_head_dim is None:
self.D = None
elif D_has_head_dim:
self.D = nn.Parameter(torch.ones(n_heads, self.head_dim))
else:
self.D = nn.Parameter(torch.ones(n_heads))
self.out_proj = nn.Linear(self.hidden_dim, self.dim, bias=False)
self.ssm_norm = RMSNorm(self.hidden_dim, eps=1e-5)
self.dt_limit_kwargs = (
{} if self.dt_limit == (0.0, float("inf")) else dict(dt_limit=self.dt_limit)
)
def forward(
self,
x: torch.Tensor,
tok_idx: Optional[torch.Tensor] = None,
cu_seqlens: Optional[torch.Tensor] = None,
ssm_impl: str = "ssm",
) -> torch.Tensor:
bsz, seq_len, _ = x.shape
zxbcdt = self.in_proj(x)
# Causal conv1d path
if self.conv_size is not None:
z, xBC, dt = torch.split(
zxbcdt,
[
self.hidden_dim,
self.hidden_dim + 2 * self.n_groups * self.state_dim,
self.n_heads,
],
dim=-1,
)
conv1d = log_stats(self.conv_weight, "conv1d.w")
xBC = log_stats(xBC, "conv1d.in")
if ssm_impl == "ssm": # For training
if hasattr(self, "cache"):
conv_varlen_states = causal_conv1d_varlen_states(
xBC.squeeze(0),
cu_seqlens,
state_len=self.cache.conv_cache.shape[-1],
)
self.cache.conv_cache.copy_(conv_varlen_states)
xBC = causal_conv1d_fn(
x=xBC.transpose(1, 2),
weight=conv1d,
bias=None,
activation="silu",
seq_idx=tok_idx,
).transpose(1, 2)
elif ssm_impl == "ssm_update": # For generation only
xBC = causal_conv1d_update(
x=xBC.squeeze(0),
conv_state=self.cache.conv_cache,
weight=self.conv_weight,
bias=None,
activation="silu",
).unsqueeze(0)
else:
raise NotImplementedError(
f"SSM implementation {ssm_impl} not supported"
)
xBC = log_stats(xBC, "conv1d.out")
x, B, C = torch.split(
xBC,
[
self.hidden_dim,
self.n_groups * self.state_dim,
self.n_groups * self.state_dim,
],
dim=-1,
)
else:
z, x, B, C, dt = torch.split(
zxbcdt,
[
self.hidden_dim,
self.hidden_dim,
self.n_groups * self.state_dim,
self.n_groups * self.state_dim,
self.n_heads,
],
dim=-1,
)
initial_states = None
if self.learnable_init_states:
initial_states = self.init_states.expand(bsz, -1, -1, -1)
x = x.view(
bsz, seq_len, self.n_heads, self.head_dim
) # (bsz, seq_len, n_heads, head_dim)
A_log = log_stats(self.A_log, "A_log")
A = -torch.exp(A_log.float())
B = B.view(
bsz, seq_len, self.n_groups, self.state_dim
) # (bsz, seq_len, ngroups, state_dim)
C = C.view(
bsz, seq_len, self.n_groups, self.state_dim
) # (bsz, seq_len, ngroups, state_dim)
A, B, C = log_stats(A, "A"), log_stats(B, "B"), log_stats(C, "C") # For probing
if ssm_impl == "ssm": # For training
y = mamba_chunk_scan_combined(
x,
dt,
A,
B,
C,
dt_bias=self.dt_bias,
dt_softplus=True,
chunk_size=self.chunk_size,
D=self.D,
z=None,
seq_idx=tok_idx,
cu_seqlens=cu_seqlens,
initial_states=initial_states,
**self.dt_limit_kwargs,
) # (bsz, seq_len, n_heads, head_dim)
if hasattr(self, "cache"):
y, varlen_states = y
self.cache.state_cache.copy_(varlen_states)
elif ssm_impl == "ssm_update": # For generation only
x = x.squeeze(0)
A = A[..., None, None].expand(self.n_heads, self.head_dim, self.state_dim)
dt = dt.permute(1, 2, 0).expand(seq_len, self.n_heads, self.head_dim)
D = self.D
if D is not None and D.dim() == 1:
D = D.unsqueeze(1).expand(self.n_heads, self.head_dim)
B, C = B.squeeze(0), C.squeeze(0)
y = selective_state_update(
self.cache.state_cache,
x,
dt,
A,
B,
C,
D,
z=None,
dt_bias=(
torch.zeros(self.n_heads, self.head_dim).to(x)
if self.dt_bias is None
else self.dt_bias.unsqueeze(1).expand(self.n_heads, self.head_dim)
),
dt_softplus=True,
).unsqueeze(0)
else:
raise NotImplementedError(f"SSM implementation {ssm_impl} not supported")
y = y.view(bsz, seq_len, self.hidden_dim)
# Could be different activation function, including None, Mamba people post_norm here also (sometime norm(z)*y or norm(z*y))
y = log_stats(y, "ssm_out")
# y = self.ssm_norm(y)
y = self.ssm_norm(y * F.silu(z))
out = self.out_proj(y)
return out
def reset_parameters(self, init_std, factor, init_args: InitArgs):
# Linear layers
in_init_std = init_std or (self.dim ** (-0.5))
out_init_std = init_std or (self.hidden_dim ** (-0.5))
out_init_std = out_init_std / factor
nn.init.trunc_normal_(
self.in_proj.weight,
mean=0.0,
std=in_init_std,
a=-3 * in_init_std,
b=3 * in_init_std,
)
nn.init.trunc_normal_(
self.out_proj.weight,
mean=0.0,
std=out_init_std,
a=-3 * out_init_std,
b=3 * out_init_std,
)
# SSM
if self.dt_bias is not None:
self.dt_bias.uniform_(init_args.dt_min, init_args.dt_max)
self.dt_bias.clamp_(min=init_args.dt_init_floor)
# Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
self.dt_bias.data = self.dt_bias.data + torch.log(
-torch.expm1(-self.dt_bias.data)
)
if self.conv_size is not None:
conv_std = init_std or (self.conv_size ** (-0.5))
nn.init.trunc_normal_(
self.conv_weight,
mean=0.0,
std=conv_std,
a=-3 * conv_std,
b=3 * conv_std,
)
if self.learnable_init_states:
self.init_states.zero_()
# Initialize A
self.A_log.uniform_(init_args.A_init_min, init_args.A_init_max)
self.A_log.log_()
self.D.data.fill_(1.0)
self.ssm_norm.reset_parameters()
class MambaBlock(nn.Module):
def __init__(self, args: BaseMambaArgs):
super().__init__()
self.ssm_norm = RMSNorm(args.dim, args.norm_eps)
self.ssm = SSM(
dim=args.dim,
hidden_dim=3 * args.dim,
multiple_of=args.multiple_of,
ffn_dim_multiplier=args.ffn_dim_multiplier,
state_dim=args.state_dim,
n_heads=args.n_heads,
n_groups=args.n_groups,
conv_size=args.conv_size,
dt_bias=args.dt_bias,
D_has_head_dim=args.D_has_head_dim,
learnable_init_states=args.learnable_init_states,
chunk_size=args.ssm_chunk_size,
)
def forward(
self,
x: torch.Tensor,
tok_idx: Optional[torch.Tensor],
cu_seqlens: Optional[torch.Tensor],
ssm_impl: str = "ssm",
) -> torch.Tensor:
x = x + self.ssm(
self.ssm_norm(x), tok_idx=tok_idx, cu_seqlens=cu_seqlens, ssm_impl=ssm_impl
)
return x
def init_weights(self, init_std=None, factor=1.0, init_args: InitArgs = InitArgs()):
self.ssm_norm.reset_parameters()
self.ssm.reset_parameters(init_std, factor, init_args)
class BaseMamba(nn.Module):
def __init__(self, args: BaseMambaArgs):
super().__init__()
self.model_dim = args.dim
self.init_base_std = args.init_base_std
self.init_args = args.init_args
self.init_std_factor = InitStdFactor(args.init_std_factor)
self.layers = nn.ModuleList()
for _ in range(args.n_layers):
self.layers.append(MambaBlock(args))
def forward(
self,
h: torch.Tensor,
tok_idx: Optional[torch.Tensor],
cu_seqlens: Optional[torch.Tensor],
ssm_impl: str = "ssm",
) -> torch.Tensor:
for layer in self.layers:
h = layer(h, tok_idx=tok_idx, cu_seqlens=cu_seqlens, ssm_impl=ssm_impl)
return h
def reset_parameters(self):
pass
def init_weights(self):
self.reset_parameters()
for depth, layer in enumerate(self.layers):
factor = {
InitStdFactor.CURRENT_DEPTH: (2 * (depth + 1)) ** 0.5,
InitStdFactor.GLOBAL_DEPTH: (2 * (len(self.layers) + 1)) ** 0.5,
InitStdFactor.DIM_RATIO: self.model_dim / 4096,
InitStdFactor.DISABLED: 1.0,
}[self.init_std_factor]
layer.init_weights(self.init_base_std, factor)
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