mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-26 10:41:18 +08:00
316 lines
11 KiB
Python
316 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 Mamba2Cache
|
|
|
|
|
|
@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
|
|
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)
|
|
|
|
|
|
def silu(x):
|
|
return x * mx.sigmoid(x)
|
|
|
|
|
|
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):
|
|
# Fuse operations where possible
|
|
if gate is not None:
|
|
hidden_states = hidden_states * nn.silu(gate)
|
|
# Compute variance in fp32 for better numerical stability
|
|
hidden_states_fp32 = hidden_states.astype(mx.float32)
|
|
variance = mx.mean(hidden_states_fp32 * hidden_states_fp32, axis=-1, keepdims=True)
|
|
hidden_states = hidden_states * mx.rsqrt(variance + self.variance_epsilon)
|
|
return self.weight * hidden_states
|
|
|
|
|
|
def ssd_optimized(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)
|
|
|
|
output = mx.zeros((batch, seqlen, nheads, dim))
|
|
state = mx.zeros((batch, nheads, dim, B.shape[-1]))
|
|
|
|
for i in range(0, seqlen, chunk_size):
|
|
chunk = slice(i, min(i + chunk_size, seqlen))
|
|
chunk_size_actual = min(chunk_size, seqlen - i)
|
|
|
|
dA = mx.exp(mx.expand_dims(A[chunk], axis=0))
|
|
x_chunk = mx.transpose(x[:, chunk], [0, 2, 3, 1])
|
|
dBx = mx.matmul(x_chunk, B[:, chunk])
|
|
state = state * mx.expand_dims(dA, axis=-1) + dBx
|
|
y = mx.matmul(state, mx.transpose(C[:, chunk], [0, 2, 1]))
|
|
output[:, i:i+chunk_size_actual] = mx.transpose(y, [0, 3, 1, 2])
|
|
|
|
return output, state
|
|
|
|
|
|
def update_conv_cache(x: mx.array, cache, kernel_size: int) -> Tuple[mx.array, mx.array]:
|
|
"""Update convolution cache for sequential processing."""
|
|
B, L, C = x.shape
|
|
|
|
if cache is None:
|
|
# Initialize cache with zeros
|
|
cache = mx.zeros((B, kernel_size - 1, C))
|
|
|
|
# Concatenate cache with current input
|
|
x_with_cache = mx.concatenate([cache, x], axis=1)
|
|
|
|
# Update cache with the last (kernel_size - 1) elements
|
|
new_cache = x_with_cache[:, -kernel_size+1:] if x_with_cache.shape[1] >= kernel_size else x_with_cache
|
|
|
|
return x_with_cache, new_cache
|
|
|
|
|
|
class Mamba2Block(nn.Module):
|
|
def __init__(self, args: ModelArgs):
|
|
super().__init__()
|
|
self.args = args
|
|
|
|
self.intermediate_size = int(args.expand * args.hidden_size)
|
|
self.state_size = args.state_size
|
|
self.num_heads = args.num_heads
|
|
self.head_dim = args.head_dim
|
|
self.ssm_state_size = args.state_size
|
|
self.n_groups = args.n_groups
|
|
self.conv_kernel = args.conv_kernel
|
|
|
|
self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size
|
|
projection_size = self.intermediate_size + self.conv_dim + self.num_heads
|
|
|
|
self.in_proj = nn.Linear(args.hidden_size, projection_size, bias=args.use_bias)
|
|
|
|
# Using built-in Conv1d instead of custom DepthwiseConv1d
|
|
self.conv1d = nn.Conv1d(
|
|
in_channels=self.conv_dim,
|
|
out_channels=self.conv_dim,
|
|
kernel_size=args.conv_kernel,
|
|
groups=self.conv_dim, # For depthwise convolution
|
|
padding=0, # We'll handle padding manually with the cache
|
|
bias=args.use_conv_bias
|
|
)
|
|
|
|
self.dt_bias = mx.random.normal((args.num_heads,)) * args.initializer_range
|
|
self.A_log = mx.random.normal((args.num_heads,)) * args.initializer_range
|
|
self.D = mx.random.normal((args.num_heads,)) * args.initializer_range
|
|
|
|
self.norm = MambaRMSNormGated(args.intermediate_size, eps=args.layer_norm_epsilon)
|
|
self.out_proj = nn.Linear(args.intermediate_size, args.hidden_size, 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):
|
|
batch_size, seq_len, _ = u.shape
|
|
|
|
projected = self.in_proj(u)
|
|
d_conv = self.conv_dim
|
|
|
|
z = projected[..., :self.intermediate_size]
|
|
xBC = projected[..., self.intermediate_size:self.intermediate_size + d_conv]
|
|
dt = projected[..., -self.num_heads:]
|
|
|
|
dt = mx.clip(
|
|
nn.softplus(dt + self.dt_bias),
|
|
self.args.time_step_min,
|
|
self.args.time_step_max
|
|
)
|
|
dt = mx.maximum(dt, self.args.time_step_floor)
|
|
|
|
# Handle convolution with separate cache update
|
|
if cache is not None:
|
|
# Update cache and get padded input
|
|
xBC_padded, new_cache = update_conv_cache(xBC, cache.conv_states, self.conv_kernel)
|
|
cache.conv_states = new_cache
|
|
|
|
# Prepare input for conv1d: [B, L, C] -> [B, C, L]
|
|
xBC_conv = mx.transpose(xBC_padded, [0, 2, 1])
|
|
|
|
# Apply convolution
|
|
xBC = self.conv1d(xBC_conv)
|
|
|
|
# Transform back: [B, C, L] -> [B, L, C]
|
|
xBC = mx.transpose(xBC, [0, 2, 1])
|
|
|
|
# Take only the relevant part corresponding to input length
|
|
xBC = xBC[:, :seq_len]
|
|
else:
|
|
# For training, use regular convolution with padding
|
|
xBC = mx.transpose(xBC, [0, 2, 1])
|
|
xBC = self.conv1d(xBC)
|
|
xBC = mx.transpose(xBC, [0, 2, 1])
|
|
|
|
xBC = silu(xBC)
|
|
|
|
x = xBC[..., :self.intermediate_size]
|
|
BC = xBC[..., self.intermediate_size:]
|
|
B = BC[..., :self.state_size]
|
|
C = BC[..., self.state_size:]
|
|
|
|
x = mx.reshape(x, (-1, seq_len, self.num_heads, self.intermediate_size // self.num_heads))
|
|
|
|
A = -mx.exp(self.A_log)
|
|
D_expanded = mx.expand_dims(self.D, -1)
|
|
|
|
if cache is not None and cache.ssm_state is None:
|
|
cache.ssm_state = mx.zeros((
|
|
batch_size,
|
|
self.num_heads,
|
|
self.intermediate_size // self.num_heads,
|
|
self.state_size
|
|
))
|
|
|
|
if cache is not None:
|
|
output = mx.zeros((batch_size, seq_len, self.args.hidden_size))
|
|
|
|
for pos in range(seq_len):
|
|
x_t = x[:, pos:pos+1]
|
|
|
|
dA = mx.exp(dt[:, pos:pos+1] * mx.expand_dims(A, 0))
|
|
dA = mx.expand_dims(mx.expand_dims(dA, -1), -1)
|
|
|
|
x_expanded = mx.expand_dims(x_t, axis=3)
|
|
dBx = mx.matmul(x_expanded, mx.expand_dims(B[:, pos:pos+1], axis=2))
|
|
|
|
cache.ssm_state = cache.ssm_state * dA + dBx
|
|
|
|
y = mx.matmul(cache.ssm_state, mx.expand_dims(C[:, pos:pos+1], axis=3))
|
|
y = mx.squeeze(y, axis=-1)
|
|
y = y + x_t * D_expanded
|
|
|
|
y = mx.reshape(y, (batch_size, 1, -1))
|
|
y = self.norm(y + z[:, pos:pos+1])
|
|
y = self.out_proj(y)
|
|
|
|
if self.args.residual_in_fp32:
|
|
y = y.astype(mx.float32)
|
|
|
|
output = output.at[:, pos:pos+1].set(y)
|
|
else:
|
|
y, ssm_state = ssd_optimized(
|
|
x * mx.expand_dims(dt, -1),
|
|
-mx.exp(self.A_log) * dt,
|
|
B, C,
|
|
self.args.chunk_size
|
|
)
|
|
|
|
y = mx.reshape(
|
|
y + x * mx.expand_dims(self.D, -1),
|
|
(batch_size, seq_len, -1)
|
|
)
|
|
|
|
y = self.norm(y + z)
|
|
output = self.out_proj(y)
|
|
|
|
if self.args.residual_in_fp32:
|
|
output = output.astype(mx.float32)
|
|
|
|
return output
|
|
|
|
|
|
class ResidualBlock(nn.Module):
|
|
def __init__(self, args: ModelArgs):
|
|
super().__init__()
|
|
self.mixer = Mamba2Block(args)
|
|
self.norm = nn.RMSNorm(args.hidden_size)
|
|
|
|
def __call__(self, x: mx.array, cache):
|
|
return self.mixer(self.norm(x), cache) + 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)
|
|
for layer, c in zip(self.layers, cache):
|
|
x = layer(x, c)
|
|
return self.norm_f(x)
|
|
|
|
|
|
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):
|
|
B, T = inputs.shape
|
|
|
|
x = self.backbone(inputs, cache)
|
|
|
|
if self.args.tie_word_embeddings:
|
|
logits = self.backbone.embeddings.as_linear(x)
|
|
else:
|
|
logits = self.lm_head(x)
|
|
|
|
return logits
|
|
|
|
def make_cache(self, batch_size=1):
|
|
return [Mamba2Cache() for _ in range(len(self.layers))]
|
|
|
|
def sanitize(self, weights):
|
|
for k, v in weights.items():
|
|
if "conv1d.weight" in k and v.ndim == 3:
|
|
weights[k] = v.moveaxis(2, 1)
|
|
return weights
|
|
|
|
@property
|
|
def layers(self):
|
|
return self.backbone.layers |