mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-26 02:33:23 +08:00
313 lines
11 KiB
Python
313 lines
11 KiB
Python
import math
|
|
from dataclasses import dataclass, field
|
|
from typing import Tuple, Union
|
|
import mlx.core as mx
|
|
import mlx.nn as nn
|
|
|
|
from .base import BaseModelArgs
|
|
from .cache import MambaCache
|
|
|
|
@dataclass
|
|
class ModelArgs(BaseModelArgs):
|
|
num_heads: int
|
|
head_dim: int
|
|
vocab_size: int
|
|
hidden_size: int
|
|
state_size: int
|
|
num_hidden_layers: int
|
|
layer_norm_epsilon: float
|
|
expand: int
|
|
conv_kernel: int
|
|
n_groups: int
|
|
use_bias: bool
|
|
use_conv_bias: bool
|
|
initializer_range: float
|
|
residual_in_fp32: bool
|
|
time_step_min: float
|
|
time_step_max: float
|
|
time_step_floor: float
|
|
rescale_prenorm_residual: bool
|
|
rms_norm: bool
|
|
chunk_size: int
|
|
tie_word_embeddings: bool
|
|
intermediate_size: int = None
|
|
use_cache: bool = True
|
|
time_step_limit: Tuple[float, float] = field(default_factory=lambda: (0.0, float("inf")))
|
|
time_step_rank: Union[int, str] = "auto"
|
|
model_type: str = "mamba2"
|
|
|
|
def __post_init__(self):
|
|
if not hasattr(self, "intermediate_size"):
|
|
self.intermediate_size = int(self.expand * self.hidden_size)
|
|
if not hasattr(self, "head_dim"):
|
|
self.head_dim = self.hidden_size // self.num_heads
|
|
if self.time_step_rank == "auto":
|
|
self.time_step_rank = math.ceil(self.hidden_size / 16)
|
|
|
|
|
|
class MambaRMSNormGated(nn.Module):
|
|
def __init__(self, hidden_size, eps=1e-6):
|
|
super().__init__()
|
|
self.weight = mx.ones((hidden_size,))
|
|
self.variance_epsilon = eps
|
|
|
|
def __call__(self, hidden_states, gate=None):
|
|
if gate is not None:
|
|
hidden_states = hidden_states * nn.silu(gate)
|
|
variance = mx.mean(hidden_states ** 2, axis=-1, keepdims=True)
|
|
hidden_states = hidden_states * mx.rsqrt(variance + self.variance_epsilon)
|
|
return self.weight * hidden_states
|
|
|
|
|
|
def silu(x):
|
|
return x * mx.sigmoid(x)
|
|
|
|
|
|
def ssd(x, A, B, C, chunk_size):
|
|
batch, seqlen, nheads, dim = x.shape
|
|
|
|
B = mx.expand_dims(B, axis=2)
|
|
C = mx.expand_dims(C, axis=2)
|
|
|
|
state = mx.zeros((batch, nheads, dim, B.shape[-1]))
|
|
outputs = []
|
|
|
|
for i in range(0, seqlen, chunk_size):
|
|
chunk = slice(i, min(i + chunk_size, seqlen))
|
|
dA = mx.exp(mx.expand_dims(A[chunk], axis=0))
|
|
|
|
x_chunk = x[:, chunk] # [batch, chunk_size, nheads, dim]
|
|
x_chunk = mx.transpose(x_chunk, [0, 2, 3, 1]) # [batch, nheads, dim, chunk_size]
|
|
B_chunk = B[:, chunk] # [batch, chunk_size, state_size]
|
|
dBx = mx.matmul(x_chunk, B_chunk) # [batch, nheads, dim, state_size]
|
|
|
|
state = state * mx.expand_dims(dA, axis=-1) + dBx
|
|
|
|
C_chunk = C[:, chunk] # [batch, chunk_size, state_size]
|
|
y = mx.matmul(state, mx.transpose(C_chunk, [0, 2, 1])) # [batch, nheads, dim, chunk_size]
|
|
y = mx.transpose(y, [0, 3, 1, 2]) # [batch, chunk_size, nheads, dim]
|
|
outputs.append(y)
|
|
|
|
return mx.concatenate(outputs, axis=1), state
|
|
|
|
|
|
class DepthWiseConv1d(nn.Module):
|
|
def __init__(self, channels, kernel_size, bias=True, padding=0):
|
|
super().__init__()
|
|
self.channels = channels
|
|
self.kernel_size = kernel_size
|
|
self.padding = padding
|
|
self.weight = mx.random.normal((channels, kernel_size, 1))
|
|
self.bias = mx.zeros((channels,)) if bias else None
|
|
|
|
def __call__(self, x, cache=None):
|
|
B, L, C = x.shape
|
|
_, K, _ = self.weight.shape
|
|
|
|
if cache is not None:
|
|
x = mx.concatenate([cache, x], axis=1)
|
|
else:
|
|
x = mx.pad(x, [(0, 0), (K - 1, 0), (0, 0)])
|
|
|
|
y = mx.conv_general(x, self.weight, groups=C)
|
|
y = y + self.bias
|
|
return y, x[:, -K + 1:, :]
|
|
|
|
|
|
class Mamba2Block(nn.Module):
|
|
def __init__(self, args: ModelArgs):
|
|
super().__init__()
|
|
self.args = args
|
|
|
|
# Calculate dimensions
|
|
self.d_model = args.hidden_size
|
|
self.d_state = args.state_size
|
|
self.d_conv = args.conv_kernel
|
|
self.expand = args.expand
|
|
if args.intermediate_size == None:
|
|
self.d_inner = int(self.expand * self.d_model)
|
|
else:
|
|
self.d_inner = args.intermediate_size
|
|
self.n_groups = args.n_groups
|
|
self.n_heads = args.num_heads
|
|
self.d_head = self.d_inner // self.n_heads
|
|
|
|
# Input projection
|
|
d_in_proj = 2 * self.d_inner + 2 * self.n_groups * self.d_state + self.n_heads
|
|
self.in_proj = nn.Linear(self.d_model, d_in_proj, bias=args.use_bias)
|
|
|
|
# Convolution
|
|
conv_dim = self.d_inner + 2 * self.n_groups * self.d_state
|
|
self.conv1d = DepthWiseConv1d(
|
|
channels=conv_dim,
|
|
kernel_size=self.d_conv,
|
|
bias=args.use_conv_bias
|
|
)
|
|
|
|
# SSM parameters
|
|
self.dt_bias = mx.random.normal((self.n_heads,)) * args.initializer_range
|
|
self.A_log = mx.random.normal((self.n_heads,)) * args.initializer_range
|
|
self.D = mx.random.normal((self.n_heads,)) * args.initializer_range
|
|
|
|
# Output projection
|
|
self.norm = MambaRMSNormGated(self.d_inner, eps=args.layer_norm_epsilon)
|
|
self.out_proj = nn.Linear(self.d_inner, self.d_model, bias=args.use_bias)
|
|
|
|
if args.rescale_prenorm_residual:
|
|
layer_scale = math.sqrt(1.0 / args.num_hidden_layers)
|
|
self.out_proj.weight = self.out_proj.weight * layer_scale
|
|
|
|
def __call__(self, u: mx.array, cache=None):
|
|
batch_size, seq_len, _ = u.shape
|
|
|
|
# Project input
|
|
proj = self.in_proj(u) # [batch, seq_len, d_in_proj]
|
|
|
|
# Calculate split indices and slice tensors
|
|
z = proj[..., :self.d_inner]
|
|
x_conv = proj[..., self.d_inner:self.d_inner + (self.d_inner + 2 * self.n_groups * self.d_state)]
|
|
dt = proj[..., -self.n_heads:]
|
|
|
|
# Process time steps
|
|
dt = nn.softplus(dt + self.dt_bias)
|
|
dt = mx.clip(dt, self.args.time_step_min, self.args.time_step_max)
|
|
dt = mx.maximum(dt, self.args.time_step_floor)
|
|
|
|
# Convolution and activation
|
|
x_conv, conv_state = self.conv1d(x_conv, cache[0] if cache else None)
|
|
if cache is not None:
|
|
cache[0] = conv_state
|
|
x_conv = silu(x_conv)
|
|
|
|
# Split conv output
|
|
x = x_conv[..., :self.d_inner]
|
|
B = x_conv[..., self.d_inner:self.d_inner + self.d_state]
|
|
C = x_conv[..., -self.d_state:]
|
|
|
|
# Reshape x for SSM
|
|
x = mx.reshape(x, (batch_size, seq_len, self.n_heads, self.d_head))
|
|
|
|
# Process B and C without reshaping heads
|
|
B = mx.expand_dims(B, axis=2) # [batch, seq_len, 1, d_state]
|
|
B = mx.broadcast_to(B, (batch_size, seq_len, self.n_heads, self.d_state))
|
|
|
|
C = mx.expand_dims(C, axis=2) # [batch, seq_len, 1, d_state]
|
|
C = mx.broadcast_to(C, (batch_size, seq_len, self.n_heads, self.d_state))
|
|
|
|
# Initialize or get previous state
|
|
if cache and cache[1] is not None:
|
|
prev_state = cache[1]
|
|
else:
|
|
prev_state = mx.zeros((batch_size, self.n_heads, self.d_head, self.d_state))
|
|
|
|
# Compute dA
|
|
dA = -mx.exp(self.A_log) # [n_heads]
|
|
dt = mx.reshape(dt, (batch_size, seq_len, self.n_heads)) # Ensure correct shape
|
|
dA = mx.exp(mx.expand_dims(dt * mx.expand_dims(dA, 0), -1)) # [batch, seq_len, n_heads, 1]
|
|
dA = mx.expand_dims(dA, -1) # [batch, seq_len, n_heads, 1, 1]
|
|
|
|
# Process sequence
|
|
next_state = prev_state
|
|
outputs = []
|
|
|
|
for t in range(seq_len):
|
|
# Get current step tensors
|
|
xt = x[:, t] # [batch, n_heads, d_head]
|
|
Bt = B[:, t] # [batch, n_heads, d_state]
|
|
Ct = C[:, t] # [batch, n_heads, d_state]
|
|
dAt = dA[:, t] # [batch, n_heads, 1, 1]
|
|
|
|
# Update state
|
|
next_state = (
|
|
next_state * dAt + # Broadcasting: [batch, n_heads, d_head, d_state] * [batch, n_heads, 1, 1]
|
|
mx.matmul(
|
|
mx.expand_dims(xt, -1), # [batch, n_heads, d_head, 1]
|
|
mx.expand_dims(Bt, -2) # [batch, n_heads, 1, d_state]
|
|
)
|
|
)
|
|
|
|
# Compute output
|
|
yt = mx.matmul(
|
|
next_state, # [batch, n_heads, d_head, d_state]
|
|
mx.expand_dims(Ct, -1) # [batch, n_heads, d_state, 1]
|
|
)
|
|
yt = mx.squeeze(yt, -1) # [batch, n_heads, d_head]
|
|
yt = yt + xt * mx.expand_dims(self.D, -1)
|
|
|
|
# Reshape and normalize
|
|
yt = mx.reshape(yt, (batch_size, 1, self.d_inner))
|
|
yt = self.norm(yt, z[:, t:t+1])
|
|
outputs.append(self.out_proj(yt))
|
|
|
|
# Update cache
|
|
if cache is not None:
|
|
cache[1] = next_state
|
|
|
|
return mx.concatenate(outputs, axis=1)
|
|
|
|
|
|
class ResidualBlock(nn.Module):
|
|
def __init__(self, args: ModelArgs):
|
|
super().__init__()
|
|
self.residual_in_fp32 = args.residual_in_fp32
|
|
self.mixer = Mamba2Block(args)
|
|
self.norm = nn.RMSNorm(args.hidden_size)
|
|
|
|
def __call__(self, x: mx.array, cache):
|
|
if self.residual_in_fp32:
|
|
x = x.astype(mx.float32)
|
|
normed = self.norm(x)
|
|
output = self.mixer(normed, cache)
|
|
return output + x
|
|
|
|
class Mamba2(nn.Module):
|
|
def __init__(self, args: ModelArgs):
|
|
super().__init__()
|
|
self.args = args
|
|
self.embeddings = nn.Embedding(args.vocab_size, args.hidden_size)
|
|
self.layers = [ResidualBlock(args) for _ in range(args.num_hidden_layers)]
|
|
self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
|
|
|
|
def __call__(self, x: mx.array, cache):
|
|
x = self.embeddings(x)
|
|
if cache is None:
|
|
cache = [None] * len(self.layers)
|
|
|
|
hidden = x
|
|
for layer, c in zip(self.layers, cache):
|
|
hidden = layer(hidden, c)
|
|
return self.norm_f(hidden)
|
|
|
|
|
|
class Model(nn.Module):
|
|
def __init__(self, args: ModelArgs):
|
|
super().__init__()
|
|
self.args = args
|
|
self.model_type = args.model_type
|
|
self.backbone = Mamba2(args)
|
|
|
|
if not args.tie_word_embeddings:
|
|
self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
|
|
|
|
def __call__(self, inputs: mx.array, cache=None):
|
|
hidden = self.backbone(inputs, cache)
|
|
|
|
if self.args.tie_word_embeddings:
|
|
logits = self.backbone.embeddings.as_linear(hidden)
|
|
else:
|
|
logits = self.lm_head(hidden)
|
|
|
|
return logits
|
|
|
|
def sanitize(self, weights):
|
|
for k, v in weights.items():
|
|
if "conv1d.weight" in k and v.shape[-1] != 1:
|
|
weights[k] = v.moveaxis(2, 1)
|
|
return weights
|
|
|
|
def make_cache(self):
|
|
return [MambaCache() for _ in range(len(self.layers))]
|
|
|
|
@property
|
|
def layers(self):
|
|
return self.backbone.layers |