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generation works! trying training now

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Goekdeniz-Guelmez
2024-10-22 18:25:59 +02:00
parent c1634ce81b
commit b9c57cd429
3 changed files with 537 additions and 327 deletions
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142
llms/mlx_lm/models/mamba2 copy.py
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@@ -1,14 +1,11 @@
# Copyright © 2024 Apple Inc.
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
# python -m mlx_lm.generate --model rokyang/mamba2-130m-hf --prompt "hello how are you."
from .base import BaseModelArgs
from .cache import MambaCache
@dataclass
class ModelArgs(BaseModelArgs):
@@ -24,7 +21,7 @@ class ModelArgs(BaseModelArgs):
n_groups: int
use_bias: bool
use_conv_bias: bool
initializer_range: float
initializer_range: float
residual_in_fp32: bool
time_step_min: float
time_step_max: float
@@ -47,21 +44,6 @@ class ModelArgs(BaseModelArgs):
self.time_step_rank = math.ceil(self.hidden_size / 16)
class Mamba2Cache:
def __init__(self):
self.cache = [None, None]
def __setitem__(self, idx, value):
self.cache[idx] = value
def __getitem__(self, idx):
return self.cache[idx]
@property
def state(self):
return self.cache
class MambaRMSNormGated(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
super().__init__()
@@ -74,7 +56,7 @@ class MambaRMSNormGated(nn.Module):
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
class DepthWiseConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, bias=True, groups=None, padding=0):
@@ -109,9 +91,9 @@ class DepthWiseConv1d(nn.Module):
y = y + self.bias
return y, x[:, -K + 1 :, :]
class Mamba2Mixer(nn.Module):
class Mamba2Block(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.args = args
@@ -124,35 +106,36 @@ class Mamba2Mixer(nn.Module):
self.head_dim = args.hidden_size // args.num_heads
self.n_groups = args.n_groups
self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.state_size
self.conv1d = DepthWiseConv1d(
in_channels=self.conv_dim,
out_channels=self.conv_dim,
bias=args.use_conv_bias,
kernel_size=args.conv_kernel,
groups=self.conv_dim,
padding=args.conv_kernel - 1
)
projection_size = self.intermediate_size + self.conv_dim + self.num_heads
# projection_size = 2 * args.intermediate_size + 2 * args.n_groups * args.state_size + args.num_heads
projection_size = 2 * args.intermediate_size + 2 * args.state_size + args.num_heads
self.in_proj = nn.Linear(
self.hidden_size,
args.hidden_size,
projection_size,
bias=args.use_bias
)
self.A_log = mx.zeros(self.num_heads)
self.D = mx.ones(self.num_heads)
self.dt_bias = mx.zeros(self.num_heads)
# self.conv_dim = args.intermediate_size + 2 * args.n_groups * args.state_size
self.conv_dim = args.intermediate_size + 2 * args.state_size
self.conv1d = DepthWiseConv1d(
in_channels=self.conv_dim,
out_channels=self.conv_dim,
kernel_size=args.conv_kernel,
bias=args.use_conv_bias,
groups=self.conv_dim,
padding=args.conv_kernel - 1
)
self.norm = MambaRMSNormGated(self.intermediate_size, eps=args.layer_norm_epsilon)
self.A_log = mx.zeros(args.num_heads)
self.D = mx.ones((args.num_heads,))
self.dt_bias = mx.zeros(args.num_heads)
self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias)
self.out_proj = nn.Linear(args.intermediate_size, args.hidden_size, bias=args.use_bias)
self.norm = MambaRMSNormGated(args.intermediate_size, eps=args.layer_norm_epsilon)
def ssm_step(self, x, state, dt_proj):
def ssm_step(self, x, state, dt):
A = -mx.exp(self.A_log)
D = self.D
delta = nn.softplus(dt_proj + self.dt_bias)
dt = nn.softplus(dt + self.dt_bias)
B, C = mx.split(x, indices_or_sections=[self.state_size * self.n_groups], axis=-1)
@@ -160,13 +143,13 @@ class Mamba2Mixer(nn.Module):
B = B.reshape(batch_size, self.n_groups, self.state_size)
C = C.reshape(batch_size, -1, self.state_size)
delta = delta.reshape(batch_size, self.num_heads, 1)
dt = dt.reshape(batch_size, self.num_heads, 1)
A = A.reshape(1, self.num_heads, 1)
if state is None:
new_state = delta * B
new_state = dt * B
else:
new_state = delta * (B + state * mx.exp(delta * A))
new_state = dt * (B + state * mx.exp(dt * A))
y = mx.sum(new_state[:, :, None, :] * C[:, None, :, :], axis=(-1, -2))
y = y + D * x[:, :self.num_heads]
@@ -180,26 +163,31 @@ class Mamba2Mixer(nn.Module):
outputs = []
for t in range(T):
xt = x[:, t, :]
xz = self.in_proj(xt)
zxbcdt = self.in_proj(xt)
x_t, z_t, dt_proj = mx.split(
xz,
indices_or_sections=[self.conv_dim, self.conv_dim + self.intermediate_size],
z, xBC, dt = mx.split(
zxbcdt,
# indices_or_sections=[self.conv_dim, self.conv_dim + self.intermediate_size],
indices_or_sections=[
self.intermediate_size,
self.intermediate_size + 2 * self.state_size,
self.num_heads
],
axis=-1
)
conv_out, cache[0] = self.conv1d(mx.expand_dims(x_t, 1), cache[0])
x_t = conv_out.squeeze(1)
x_t = nn.silu(x_t)
y_t, cache[1] = self.ssm_step(x_t, cache[1], dt_proj)
z_t = nn.silu(z_t)
# Use the new DepthWiseConv1d with caching
conv_out, cache[0] = self.conv1d(mx.expand_dims(z, 1), cache[0])
z = conv_out.squeeze(1)
z = nn.silu(z)
y_t, cache[1] = self.ssm_step(z, cache[1], dt)
xBC = nn.silu(xBC)
# Element-wise multiplication
output_t = y_t[:, :, None] * z_t[:, None, :]
output_t = y_t[:, :, None] * xBC[:, None, :]
# Sum across the second dimension to match the intermediate_size
output_t = self.norm(output_t)
output_t = output_t.sum(axis=1)
output_t = self.out_proj(output_t)
outputs.append(output_t)
@@ -207,10 +195,10 @@ class Mamba2Mixer(nn.Module):
return output
class Mamba2Block(nn.Module):
class ResidualBlock(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.mixer = Mamba2Mixer(args)
self.mixer = Mamba2Block(args)
self.norm = nn.RMSNorm(args.hidden_size)
def __call__(self, x: mx.array, cache):
@@ -222,24 +210,16 @@ class Mamba2(nn.Module):
super().__init__()
self.args = args
self.embeddings = nn.Embedding(args.vocab_size, args.hidden_size)
self.layers = [Mamba2Block(args) for idx in range(args.num_hidden_layers)]
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,
inputs: mx.array,
cache=None
):
hidden_states = self.embeddings(inputs)
def __call__(self, x: mx.array, cache):
x = self.embeddings(x)
if cache is None:
cache = Mamba2Cache(len(self.layers))
for i, layer in enumerate(self.layers):
hidden_states = layer(hidden_states, cache[i])
hidden_states = self.norm_f(hidden_states)
return hidden_states
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):
@@ -247,7 +227,10 @@ class Model(nn.Module):
super().__init__()
self.args = args
self.model_type = args.model_type
self.backbone = Mamba2(args)
# self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
if not args.tie_word_embeddings:
self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
@@ -261,19 +244,16 @@ class Model(nn.Module):
else:
logits = self.lm_head(x)
print(logits)
print(logits.shape)
return logits
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
def make_cache(self, batch_size: int = 1):
return [Mamba2Cache() for _ in range(len(self.layers))]
def make_cache(self):
return [MambaCache() for _ in range(len(self.layers))]
@property
def layers(self):