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adding debug statements (somehiw generating only goes through the fist MambaMixer block pass)

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Goekdeniz-Guelmez 2024-10-16 21:09:30 +02:00
parent 00ba27fe6c
commit 8073cb486c
2 changed files with 163 additions and 151 deletions
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219
llms/mlx_lm/models/mamba2-other.py
View File

@ -2,7 +2,7 @@
import math
from dataclasses import dataclass, field
from typing import Optional, Tuple, Union
from typing import Tuple, Union
import mlx.core as mx
import mlx.nn as nn
@ -48,14 +48,18 @@ class ModelArgs(BaseModelArgs):
class Mamba2Cache:
def __init__(self, num_layers):
self.cache = [[None, None] for _ in range(num_layers)]
def __init__(self):
self.cache = [None, None]
def __setitem__(self, idx, value):
self.cache[idx] = value
def __getitem__(self, idx):
return self.cache[idx]
def __setitem__(self, idx, value):
self.cache[idx] = value
@property
def state(self):
return self.cache
class MambaRMSNormGated(nn.Module):
@ -71,6 +75,40 @@ class MambaRMSNormGated(nn.Module):
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):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.padding = padding
self.groups = groups if groups is not None else in_channels
# Ensure in_channels and out_channels are the same for depthwise conv
assert in_channels == out_channels, "In and out channels must be the same for depthwise convolution"
# Ensure groups is equal to in_channels for depthwise conv
assert self.groups == in_channels, "Groups must be equal to in_channels for depthwise convolution"
# Initialize weight with shape (out_channels, kernel_size, 1)
self.weight = mx.random.normal((out_channels, kernel_size, 1))
self.bias = mx.zeros((out_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=self.groups)
if self.bias is not None:
y = y + self.bias
return y, x[:, -K + 1 :, :]
class Mamba2Mixer(nn.Module):
def __init__(self, args: ModelArgs):
@ -82,11 +120,11 @@ class Mamba2Mixer(nn.Module):
self.hidden_size = args.hidden_size
self.state_size = args.state_size
self.num_heads = args.num_heads
self.head_dim = args.head_dim
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 = nn.Conv1d(
self.conv1d = DepthWiseConv1d(
in_channels=self.conv_dim,
out_channels=self.conv_dim,
bias=args.use_conv_bias,
@ -102,7 +140,6 @@ class Mamba2Mixer(nn.Module):
bias=args.use_bias
)
self.act = nn.SiLU()
self.dt_bias = mx.ones((self.num_heads,))
self.A_log = mx.log(mx.arange(1, self.num_heads + 1))
self.D = mx.ones((self.num_heads,))
@ -111,105 +148,84 @@ class Mamba2Mixer(nn.Module):
self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias)
def ssm_step(self, x, dt, state):
B, L, C = x.shape
print(f"x shape: {x.shape}")
projected_states = self.in_proj(x)
print(f"deltaBC shape: {projected_states.shape}")
d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 * self.n_groups * self.state_size - self.num_heads) // 2
gate = projected_states[:, :, 2*d_mlp:2*d_mlp+self.intermediate_size]
conv_state = projected_states[:, :, 2*d_mlp+self.intermediate_size:2*d_mlp+self.intermediate_size+self.conv_dim]
time_step = projected_states[:, :, -self.num_heads:]
print(f"conv_state shape before reshape: {conv_state.shape}")
print(f"self.conv_dim: {self.conv_dim}")
def ssm_step(self, x, state, dt_proj):
A = -mx.exp(self.A_log)
D = self.D
delta = nn.softplus(dt_proj + self.dt_bias)
# Reshape and handle the case where L=1
conv_state = conv_state.reshape(B, self.conv_dim, L)
if L == 1:
# If sequence length is 1, we need to pad to apply convolution
conv_state = mx.pad(conv_state, ((0, 0), (0, 0), (0, self.conv_kernel_size - 1)))
B, C = mx.split(x, indices_or_sections=[self.state_size * self.n_groups], axis=-1)
conv_out = self.conv1d(conv_state)
B = B.reshape(-1, self.n_groups, self.state_size)
C = C.reshape(-1, self.n_groups, self.state_size)
# If we padded, we need to remove the padding
if L == 1:
conv_out = conv_out[:, :, :L]
# Reshape back to (B, L, C)
conv_out = conv_out.transpose(0, 2, 1)
x_and_conv_out, B, C = mx.split(
conv_out,
[self.intermediate_size, self.n_groups * self.state_size],
axis=-1
)
dt = nn.softplus(time_step + self.dt_bias)
dt = mx.clip(dt, self.args.time_step_min, self.args.time_step_max)
B = B.reshape(-1, self.num_heads, self.head_dim, self.state_size)
C = C.reshape(-1, self.num_heads, self.head_dim, self.state_size)
dA = mx.exp(dt[:, :, None, None] * A[None, :, None, None])
dB = dt[:, :, None, None] * B
new_state = state * dA + x_and_conv_out[:, :, None, None] * dB
y = mx.sum(new_state * C, axis=-1)
y = y + C[None, :, None] * x_and_conv_out
y = self.norm(y.reshape(-1, self.intermediate_size), gate)
output = self.out_proj(y)
return output, new_state
def __call__(
self,
x: mx.array,
cache = None
):
B, L, _ = x.shape
if cache[0] is not None: # Using cached state
conv_state, ssm_state = cache
x = x[:, -1:]
output, new_ssm_state = self.ssm_step(x, None, ssm_state)
cache[1] = new_ssm_state # Update SSM state in cache
if state is None:
new_state = mx.expand_dims(delta, -1) * B
else:
conv_state, ssm_state = None, None
outputs = []
for t in range(L):
x = x[:, t:t+1]
output, ssm_state = self.ssm_step(x, None, ssm_state)
outputs.append(output)
output = mx.concatenate(outputs, axis=1)
cache[1] = ssm_state # Store final SSM state in cache
# Update conv state in cache
new_conv_state = x[:, -self.conv_kernel_size:]
cache[0] = new_conv_state
new_state = mx.expand_dims(delta, -1) * (B + state * mx.exp(mx.expand_dims(delta, -1) * A))
y = mx.sum(new_state * C, axis=-1)
y = y + D * x[:, :self.num_heads]
return y, new_state
def __call__(self, x, cache):
B, T, D = x.shape
if cache is None:
cache = [None, None]
outputs = []
for t in range(T):
xt = x[:, t, :]
xz = 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],
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)
# Print shapes for debugging
print(f"y_t shape: {y_t.shape}")
print(f"z_t shape: {z_t.shape}")
# Reshape y_t to (B, num_heads, head_dim)
y_t_reshaped = y_t.reshape(B, self.num_heads, -1)
# Reshape z_t to (B, num_heads, intermediate_size // num_heads)
z_t_reshaped = z_t.reshape(B, self.num_heads, -1)
print(f"y_t_reshaped shape: {y_t_reshaped.shape}")
print(f"z_t_reshaped shape: {z_t_reshaped.shape}")
# Element-wise multiplication (broadcasting across the last dimension)
output_t = y_t_reshaped * z_t_reshaped
# Reshape to match the expected input of out_proj
output_t = output_t.reshape(B, -1)
print(f"output_t shape before out_proj: {output_t.shape}")
print(f"out_proj weight shape: {self.out_proj.weight.shape}")
output_t = self.out_proj(output_t)
outputs.append(output_t)
output = mx.stack(outputs, axis=1)
return output
class Mamba2Block(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.args = args
self.residual_in_fp32 = args.residual_in_fp32
self.norm = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
self.mixer = Mamba2Mixer(args)
self.norm = nn.RMSNorm(args.hidden_size)
def __call__(
self,
inputs: mx.array,
cache=None,
):
h = self.mixer(self.norm(inputs), cache=cache)
r = inputs + h
return r
def __call__(self, x: mx.array, cache):
return self.mixer(self.norm(x), cache) + x
class Mamba2(nn.Module):
@ -246,11 +262,7 @@ class Model(nn.Module):
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
):
def __call__(self, inputs: mx.array, cache=None):
B, T = inputs.shape
x = self.backbone(inputs, cache)
@ -259,17 +271,18 @@ class Model(nn.Module):
logits = self.backbone.embeddings.as_linear(x)
else:
logits = self.lm_head(x)
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(len(self.backbone.layers))
return [Mamba2Cache() for _ in range(len(self.layers))]
@property
def layers(self):
return self.backbone.layers

95
llms/mlx_lm/models/mamba2.py
View File

@ -6,37 +6,37 @@ 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."
@dataclass
class ModelArgs(BaseModelArgs):
model_type: str = "mamba2"
num_heads: int = 128
head_dim: int = 64
vocab_size: int = 32768
hidden_size: int = 4096
state_size: int = 128
num_hidden_layers: int = 64
layer_norm_epsilon: float = 1e-5
expand: int = 2
conv_kernel: int = 4
n_groups: int = 8
use_bias: bool = False
use_conv_bias: bool = True
initializer_range: float = 0.1
residual_in_fp32: bool = True
time_step_rank: Union[int, str] = "auto"
time_step_min: float = 0.001
time_step_max: float = 0.1
time_step_floor: float = 1e-4
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
use_cache: bool
rms_norm: bool
chunk_size: int
tie_word_embeddings: bool
time_step_limit: Tuple[float, float] = field(default_factory=lambda: (0.0, float("inf")))
rescale_prenorm_residual: bool = False
use_cache: bool = True
rms_norm: bool = True
chunk_size: int = 256
tie_word_embeddings: bool = False
time_step_rank: Union[int, str] = "auto"
model_type: str = "mamba2"
def __post_init__(self):
if not hasattr(self, "intermediate_size"):
@ -149,26 +149,35 @@ class Mamba2Mixer(nn.Module):
self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias)
def ssm_step(self, x, state, dt_proj):
print(f"ssm_step input shapes - x: {x.shape}, dt_proj: {dt_proj.shape}")
A = -mx.exp(self.A_log)
D = self.D
delta = nn.softplus(dt_proj + self.dt_bias)
B, C = mx.split(x, indices_or_sections=[self.state_size * self.n_groups], axis=-1)
print(f"ssm_step split shapes - B: {B.shape}, C: {C.shape}")
B = B.reshape(-1, self.n_groups, self.state_size)
C = C.reshape(-1, self.n_groups, self.state_size)
print(f"After reshape - B: {B.shape}, C: {C.shape}")
delta = delta.reshape(-1, self.num_heads, 1)
A = A.reshape(1, self.num_heads, 1)
if state is None:
new_state = mx.expand_dims(delta, -1) * B
new_state = delta * B
else:
new_state = mx.expand_dims(delta, -1) * (B + state * mx.exp(mx.expand_dims(delta, -1) * A))
new_state = delta * (B + state * mx.exp(delta * A))
print(f"Before final computation - new_state: {new_state.shape}, C: {C.shape}")
y = mx.sum(new_state * C, axis=-1)
y = y + D * x[:, :self.num_heads]
print(f"ssm_step output shape - y: {y.shape}")
return y, new_state
def __call__(self, x, cache):
B, T, D = x.shape
print(f"__call__ input shape - x: {x.shape}")
if cache is None:
cache = [None, None]
@ -176,47 +185,37 @@ class Mamba2Mixer(nn.Module):
for t in range(T):
xt = x[:, t, :]
xz = self.in_proj(xt)
print(f"After in_proj shape - xz: {xz.shape}")
x_t, z_t, dt_proj = mx.split(
xz,
indices_or_sections=[self.conv_dim, self.conv_dim + self.intermediate_size],
axis=-1
)
print(f"After split shapes - x_t: {x_t.shape}, z_t: {z_t.shape}, dt_proj: {dt_proj.shape}")
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)
print(f"Before ssm_step shape - x_t: {x_t.shape}")
y_t, cache[1] = self.ssm_step(x_t, cache[1], dt_proj)
z_t = nn.silu(z_t)
print(f"After ssm_step shapes - y_t: {y_t.shape}, z_t: {z_t.shape}")
# Print shapes for debugging
print(f"y_t shape: {y_t.shape}")
print(f"z_t shape: {z_t.shape}")
print(f"self.num_heads: {self.num_heads}")
print(f"self.intermediate_size: {self.intermediate_size}")
print(f"self.head_dim: {self.head_dim}")
# Flexible reshaping
y_t_reshaped = y_t.reshape(B, -1, 1)
z_t_reshaped = z_t.reshape(B, y_t_reshaped.shape[1], -1)
# Print reshaped shapes
print(f"y_t_reshaped shape: {y_t_reshaped.shape}")
print(f"z_t_reshaped shape: {z_t_reshaped.shape}")
# Element-wise multiplication
output_t = y_t_reshaped * z_t_reshaped
output_t = y_t[:, :, None] * z_t[:, None, :]
print(f"After multiplication shape - output_t: {output_t.shape}")
# Reshape to match the expected input of out_proj
output_t = output_t.reshape(B, self.intermediate_size)
print(f"output_t shape before out_proj: {output_t.shape}")
print(f"out_proj weight shape: {self.out_proj.weight.shape}")
# Sum across the second dimension to match the intermediate_size
output_t = output_t.sum(axis=1)
print(f"After sum shape - output_t: {output_t.shape}")
output_t = self.out_proj(output_t)
print(f"After out_proj shape - output_t: {output_t.shape}")
outputs.append(output_t)
output = mx.stack(outputs, axis=1)
print(f"Final output shape: {output.shape}")
return output