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@ -338,3 +338,30 @@ class MambaCache(_BaseCache):
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@state.setter
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def state(self, v):
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self.cache = v
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class Mamba2Cache:
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def __init__(self, num_layers):
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self.conv_states = [None] * num_layers
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self.ssm_states = [None] * num_layers
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self.seqlen_offset = 0
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def __getitem__(self, idx):
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return (self.conv_states[idx], self.ssm_states[idx])
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def __setitem__(self, idx, value):
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self.conv_states[idx], self.ssm_states[idx] = value
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@property
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def state(self):
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return {
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'conv_states': self.conv_states,
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'ssm_states': self.ssm_states,
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'seqlen_offset': self.seqlen_offset
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}
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@state.setter
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def state(self, v):
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self.conv_states = v['conv_states']
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self.ssm_states = v['ssm_states']
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self.seqlen_offset = v['seqlen_offset']
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@ -6,41 +6,37 @@ from typing import Tuple, Union
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import mlx.core as mx
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import mlx.nn as nn
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from .base import BaseModelArgs
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# python -m mlx_lm.generate --model rokyang/mamba2-130m-hf --prompt "hello how are you."
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@dataclass
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class ModelArgs(BaseModelArgs):
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model_type: str = "mamba2"
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num_heads: int = 128
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head_dim: int = 64
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vocab_size: int = 32768
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hidden_size: int = 4096
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state_size: int = 128
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num_hidden_layers: int = 64
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layer_norm_epsilon: float = 1e-5
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pad_token_id: int = 1
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bos_token_id: int = 0
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eos_token_id: int = 2
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expand: int = 2
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conv_kernel: int = 4
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n_groups: int = 8
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use_bias: bool = False
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use_conv_bias: bool = True
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hidden_act: str = "silu"
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initializer_range: float = 0.1
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residual_in_fp32: bool = True
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time_step_rank: Union[int, str] = "auto"
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time_step_min: float = 0.001
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time_step_max: float = 0.1
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time_step_floor: float = 1e-4
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num_heads: int
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head_dim: int
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vocab_size: int
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hidden_size: int
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state_size: int
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num_hidden_layers: int
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layer_norm_epsilon: float
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expand: int
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conv_kernel: int
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n_groups: int
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use_bias: bool
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use_conv_bias: bool
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initializer_range: float
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residual_in_fp32: bool
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time_step_min: float
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time_step_max: float
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time_step_floor: float
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rescale_prenorm_residual: bool
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use_cache: bool
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rms_norm: bool
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chunk_size: int
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tie_word_embeddings: bool
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time_step_limit: Tuple[float, float] = field(default_factory=lambda: (0.0, float("inf")))
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rescale_prenorm_residual: bool = False
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use_cache: bool = True
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rms_norm: bool = True
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chunk_size: int = 256
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tie_word_embeddings: bool = False
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time_step_rank: Union[int, str] = "auto"
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model_type: str = "mamba2"
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def __post_init__(self):
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if not hasattr(self, "intermediate_size"):
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@ -79,15 +75,24 @@ class MambaRMSNormGated(nn.Module):
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hidden_states = hidden_states * mx.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states
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class DepthWiseConv1d(nn.Module):
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def __init__(self, channels, kernel_size, bias=True, groups=1, padding=0):
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def __init__(self, in_channels, out_channels, kernel_size, bias=True, groups=None, padding=0):
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super().__init__()
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self.channels = channels
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.kernel_size = kernel_size
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self.padding = padding
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self.groups = groups
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self.weight = mx.random.normal((self.channels, kernel_size, 1))
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self.bias = mx.zeros((channels,)) if bias else None
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self.groups = groups if groups is not None else in_channels
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# Ensure in_channels and out_channels are the same for depthwise conv
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assert in_channels == out_channels, "In and out channels must be the same for depthwise convolution"
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# Ensure groups is equal to in_channels for depthwise conv
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assert self.groups == in_channels, "Groups must be equal to in_channels for depthwise convolution"
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# Initialize weight with shape (out_channels, kernel_size, 1)
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self.weight = mx.random.normal((out_channels, kernel_size, 1))
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self.bias = mx.zeros((out_channels,)) if bias else None
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def __call__(self, x, cache=None):
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B, L, C = x.shape
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@ -116,16 +121,17 @@ class Mamba2Mixer(nn.Module):
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self.hidden_size = args.hidden_size
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self.state_size = args.state_size
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self.num_heads = args.num_heads
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self.head_dim = args.head_dim
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self.head_dim = args.hidden_size // args.num_heads
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self.n_groups = args.n_groups
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self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.state_size
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self.conv1d = DepthWiseConv1d(
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channels=self.conv_dim,
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kernel_size=self.conv_kernel_size,
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bias=self.args.use_conv_bias,
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in_channels=self.conv_dim,
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out_channels=self.conv_dim,
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bias=args.use_conv_bias,
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kernel_size=args.conv_kernel,
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groups=self.conv_dim,
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padding=self.conv_kernel_size - 1,
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padding=args.conv_kernel - 1
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)
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projection_size = self.intermediate_size + self.conv_dim + self.num_heads
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@ -135,33 +141,35 @@ class Mamba2Mixer(nn.Module):
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bias=args.use_bias
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)
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self.act = nn.SiLU()
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self.dt_bias = mx.ones((self.num_heads,))
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self.A_log = mx.log(mx.arange(1, self.num_heads + 1))
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self.D = mx.ones((self.num_heads,))
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self.A_log = mx.zeros(self.num_heads)
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self.D = mx.ones(self.num_heads)
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self.dt_bias = mx.zeros(self.num_heads)
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self.norm = MambaRMSNormGated(self.intermediate_size, eps=args.layer_norm_epsilon)
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self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias)
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def ssm_step(self, x, state=None):
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def ssm_step(self, x, state, dt_proj):
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A = -mx.exp(self.A_log)
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D = self.D
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deltaBC = self.x_proj(x)
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delta, B, C = mx.split(
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deltaBC,
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indices_or_sections=[
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self.time_step_rank,
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self.time_step_rank + self.ssm_state_size,
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],
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axis=-1,
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)
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delta = nn.softplus(self.dt_proj(delta))
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new_state = mx.expand_dims(delta * x, -1) * mx.expand_dims(B, 1)
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if state is not None:
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new_state += state * mx.exp(mx.expand_dims(delta, -1) * A)
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y = (new_state @ mx.expand_dims(C, -1)).squeeze(2)
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y = y + D * x
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delta = nn.softplus(dt_proj + self.dt_bias)
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B, C = mx.split(x, indices_or_sections=[self.state_size * self.n_groups], axis=-1)
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batch_size = B.shape[0]
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B = B.reshape(batch_size, self.n_groups, self.state_size)
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C = C.reshape(batch_size, -1, self.state_size)
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delta = delta.reshape(batch_size, self.num_heads, 1)
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A = A.reshape(1, self.num_heads, 1)
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if state is None:
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new_state = delta * B
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else:
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new_state = delta * (B + state * mx.exp(delta * A))
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y = mx.sum(new_state[:, :, None, :] * C[:, None, :, :], axis=(-1, -2))
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y = y + D * x[:, :self.num_heads]
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return y, new_state
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def __call__(self, x, cache):
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@ -173,15 +181,28 @@ class Mamba2Mixer(nn.Module):
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for t in range(T):
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xt = x[:, t, :]
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xz = self.in_proj(xt)
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x_t, z_t = xz.split(indices_or_sections=2, axis=1)
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x_t, z_t, dt_proj = mx.split(
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xz,
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indices_or_sections=[self.conv_dim, self.conv_dim + self.intermediate_size],
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axis=-1
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)
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conv_out, cache[0] = self.conv1d(mx.expand_dims(x_t, 1), cache[0])
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x_t = conv_out.squeeze(1)
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x_t = nn.silu(x_t)
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y_t, cache[1] = self.ssm_step(x_t, cache[1])
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y_t, cache[1] = self.ssm_step(x_t, cache[1], dt_proj)
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z_t = nn.silu(z_t)
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output_t = y_t * z_t
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# Element-wise multiplication
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output_t = y_t[:, :, None] * z_t[:, None, :]
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# Sum across the second dimension to match the intermediate_size
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output_t = output_t.sum(axis=1)
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output_t = self.out_proj(output_t)
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outputs.append(output_t)
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output = mx.stack(outputs, axis=1)
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return output
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@ -240,6 +261,9 @@ class Model(nn.Module):
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else:
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logits = self.lm_head(x)
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print(logits)
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print(logits.shape)
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return logits
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def sanitize(self, weights):
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@ -2,11 +2,13 @@
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import math
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from dataclasses import dataclass, field
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from typing import Tuple, Union
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from typing import Tuple, Union, Optional
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import mlx.core as mx
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import mlx.nn as nn
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import mlx.core as mx
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from .base import BaseModelArgs
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from .cache import Mamba2Cache
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# python -m mlx_lm.generate --model rokyang/mamba2-130m-hf --prompt "hello how are you."
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@ -46,22 +48,6 @@ class ModelArgs(BaseModelArgs):
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if self.time_step_rank == "auto":
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self.time_step_rank = math.ceil(self.hidden_size / 16)
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class Mamba2Cache:
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def __init__(self):
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self.cache = [None, None]
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def __setitem__(self, idx, value):
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self.cache[idx] = value
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def __getitem__(self, idx):
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return self.cache[idx]
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@property
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def state(self):
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return self.cache
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class MambaRMSNormGated(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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super().__init__()
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@ -75,6 +61,7 @@ class MambaRMSNormGated(nn.Module):
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hidden_states = hidden_states * mx.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states
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class DepthWiseConv1d(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size, bias=True, groups=None, padding=0):
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super().__init__()
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@ -111,27 +98,22 @@ class DepthWiseConv1d(nn.Module):
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class Mamba2Mixer(nn.Module):
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def __init__(self, args: ModelArgs):
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def __init__(self, args, layer_idx):
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super().__init__()
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self.args = args
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self.intermediate_size = args.intermediate_size
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self.time_step_rank = args.time_step_rank
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self.conv_kernel_size = args.conv_kernel
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self.layer_idx = layer_idx
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self.hidden_size = args.hidden_size
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self.state_size = args.state_size
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self.intermediate_size = args.intermediate_size
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self.num_heads = args.num_heads
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self.head_dim = args.hidden_size // args.num_heads
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self.head_dim = args.head_dim
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self.ssm_state_size = args.state_size
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self.n_groups = args.n_groups
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self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.state_size
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self.conv1d = DepthWiseConv1d(
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in_channels=self.conv_dim,
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out_channels=self.conv_dim,
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bias=args.use_conv_bias,
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kernel_size=args.conv_kernel,
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groups=self.conv_dim,
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padding=args.conv_kernel - 1
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)
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self.conv_kernel_size = args.conv_kernel
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self.use_conv_bias = args.use_conv_bias
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self.use_bias = args.use_bias
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self.time_step_min = args.time_step_min
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self.time_step_max = args.time_step_max
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self.chunk_size = args.chunk_size
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self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size
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projection_size = self.intermediate_size + self.conv_dim + self.num_heads
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self.in_proj = nn.Linear(
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@ -139,91 +121,151 @@ class Mamba2Mixer(nn.Module):
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projection_size,
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bias=args.use_bias
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)
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self.dt_bias = mx.ones((self.num_heads,))
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self.A_log = mx.log(mx.arange(1, self.num_heads + 1))
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self.D = mx.ones((self.num_heads,))
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self.norm = MambaRMSNormGated(self.intermediate_size, eps=args.layer_norm_epsilon)
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self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias)
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def ssm_step(self, x, state, dt_proj):
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print(f"ssm_step input shapes - x: {x.shape}, dt_proj: {dt_proj.shape}")
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A = -mx.exp(self.A_log)
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D = self.D
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delta = nn.softplus(dt_proj + self.dt_bias)
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B, C = mx.split(x, indices_or_sections=[self.state_size * self.n_groups], axis=-1)
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print(f"ssm_step split shapes - B: {B.shape}, C: {C.shape}")
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batch_size = B.shape[0]
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B = B.reshape(batch_size, self.n_groups, self.state_size)
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C = C.reshape(batch_size, -1, self.state_size)
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print(f"After reshape - B: {B.shape}, C: {C.shape}")
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delta = delta.reshape(batch_size, self.num_heads, 1)
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A = A.reshape(1, self.num_heads, 1)
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if state is None:
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new_state = delta * B
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else:
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new_state = delta * (B + state * mx.exp(delta * A))
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print(f"Before final computation - new_state: {new_state.shape}, C: {C.shape}")
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y = mx.sum(new_state[:, :, None, :] * C[:, None, :, :], axis=(-1, -2))
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y = y + D * x[:, :self.num_heads]
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print(f"ssm_step output shape - y: {y.shape}")
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return y, new_state
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def __call__(self, x, cache):
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B, T, D = x.shape
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print(f"__call__ input shape - x: {x.shape}")
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if cache is None:
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cache = [None, None]
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outputs = []
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for t in range(T):
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xt = x[:, t, :]
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xz = self.in_proj(xt)
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print(f"After in_proj shape - xz: {xz.shape}")
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x_t, z_t, dt_proj = mx.split(
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xz,
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indices_or_sections=[self.conv_dim, self.conv_dim + self.intermediate_size],
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axis=-1
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self.conv1d = nn.Conv1d(
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self.conv_dim,
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self.conv_dim,
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self.conv_kernel_size,
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groups=self.conv_dim,
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bias=self.use_conv_bias
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)
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self.act = nn.SiLU()
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self.norm = MambaRMSNormGated(self.intermediate_size, eps=args.layer_norm_epsilon)
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self.out_proj = nn.Linear(
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self.intermediate_size,
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self.hidden_size,
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bias=self.use_bias
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)
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print(f"After split shapes - x_t: {x_t.shape}, z_t: {z_t.shape}, dt_proj: {dt_proj.shape}")
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conv_out, cache[0] = self.conv1d(mx.expand_dims(x_t, 1), cache[0])
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x_t = conv_out.squeeze(1)
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x_t = nn.silu(x_t)
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print(f"Before ssm_step shape - x_t: {x_t.shape}")
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y_t, cache[1] = self.ssm_step(x_t, cache[1], dt_proj)
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z_t = nn.silu(z_t)
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print(f"After ssm_step shapes - y_t: {y_t.shape}, z_t: {z_t.shape}")
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self.A_log = mx.zeros(self.num_heads)
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self.D = mx.ones(self.num_heads)
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self.dt_bias = mx.zeros(self.num_heads)
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# Element-wise multiplication
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output_t = y_t[:, :, None] * z_t[:, None, :]
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print(f"After multiplication shape - output_t: {output_t.shape}")
|
||||
def __call__(self, input_states, cache):
|
||||
batch_size, seq_len, _ = input_states.shape
|
||||
dtype = input_states.dtype
|
||||
|
||||
# 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}")
|
||||
projected_states = self.in_proj(input_states)
|
||||
|
||||
output_t = self.out_proj(output_t)
|
||||
print(f"After out_proj shape - output_t: {output_t.shape}")
|
||||
outputs.append(output_t)
|
||||
# Calculate the sizes of each split
|
||||
total_size = projected_states.shape[-1]
|
||||
remaining_size = total_size - self.intermediate_size - self.conv_dim - self.num_heads
|
||||
d_mlp = remaining_size // 2
|
||||
sizes = [
|
||||
d_mlp,
|
||||
d_mlp,
|
||||
self.intermediate_size,
|
||||
self.conv_dim,
|
||||
self.num_heads
|
||||
]
|
||||
|
||||
output = mx.stack(outputs, axis=1)
|
||||
print(f"Final output shape: {output.shape}")
|
||||
return output
|
||||
# Perform the split operation
|
||||
split_result = mx.split(projected_states, sizes, axis=-1)
|
||||
|
||||
# Print debug information
|
||||
print(f"Number of split parts: {len(split_result)}")
|
||||
print(f"Shapes of split parts: {[part.shape for part in split_result]}")
|
||||
|
||||
# Flexibly handle the split result
|
||||
_, _, _, gate, hidden_states, dt = split_result
|
||||
|
||||
if cache is not None:
|
||||
conv_state = cache.conv_states[self.layer_idx]
|
||||
if conv_state is None:
|
||||
# Initialize conv_state if it's None
|
||||
conv_state = mx.zeros((batch_size, 1, self.conv_kernel_size, hidden_states.shape[-1]))
|
||||
|
||||
conv_state = mx.roll(conv_state, -1, -2) # Roll along the kernel dimension
|
||||
|
||||
# Reshape hidden_states to match conv_state dimensions
|
||||
hidden_states_reshaped = hidden_states[:, None, None, :]
|
||||
|
||||
conv_state = mx.concat([conv_state[:, :, :-1, :], hidden_states_reshaped], axis=-2)
|
||||
cache.conv_states[self.layer_idx] = conv_state
|
||||
|
||||
# Adjust the convolution operation
|
||||
hidden_states = mx.sum(conv_state * self.conv1d.weight[:, :, None, :], axis=(-2, -1))
|
||||
|
||||
if self.use_conv_bias:
|
||||
hidden_states += self.conv1d.bias
|
||||
hidden_states = self.act(hidden_states)[:, None, :]
|
||||
else:
|
||||
hidden_states = hidden_states.transpose(0, 2, 1)
|
||||
hidden_states = self.act(self.conv1d(hidden_states)).transpose(0, 2, 1)
|
||||
|
||||
hidden_states, B, C = mx.split(hidden_states, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], axis=-1)
|
||||
|
||||
A = -mx.exp(self.A_log.astype(mx.float32))
|
||||
dt = nn.softplus(dt + self.dt_bias)
|
||||
dt = mx.clip(dt, self.time_step_min, self.time_step_max)
|
||||
|
||||
hidden_states = hidden_states.reshape(batch_size, seq_len, -1, self.head_dim).astype(mx.float32)
|
||||
B = B.reshape(batch_size, seq_len, -1, self.ssm_state_size).astype(mx.float32)
|
||||
C = C.reshape(batch_size, seq_len, -1, self.ssm_state_size).astype(mx.float32)
|
||||
|
||||
B = mx.repeat(B, repeats=self.num_heads // self.n_groups, axis=2)
|
||||
C = mx.repeat(C, repeats=self.num_heads // self.n_groups, axis=2)
|
||||
|
||||
if cache is not None and cache.seqlen_offset > 0:
|
||||
ssm_state = cache.ssm_states[self.layer_idx]
|
||||
dA = mx.exp(dt[:, None, :, None] * A[None, :, None, None])
|
||||
dB = dt[:, None, :, None] * B
|
||||
dBx = dB * hidden_states[:, :, :, None]
|
||||
ssm_state = ssm_state * dA + dBx
|
||||
cache.ssm_states[self.layer_idx] = ssm_state
|
||||
|
||||
y = mx.sum(ssm_state * C[:, None, :, :], axis=-1)
|
||||
D = self.D[None, :, None].expand(self.D.shape[0], self.head_dim)
|
||||
y = y + hidden_states * D
|
||||
|
||||
y = y.reshape(batch_size, -1)[:, None, :]
|
||||
else:
|
||||
# Implement chunked computation here (simplified version)
|
||||
pad_size = self.chunk_size - (seq_len % self.chunk_size)
|
||||
hidden_states_padded = mx.pad(hidden_states, [(0, 0), (0, pad_size), (0, 0), (0, 0)])
|
||||
B_padded = mx.pad(B, [(0, 0), (0, pad_size), (0, 0), (0, 0)])
|
||||
C_padded = mx.pad(C, [(0, 0), (0, pad_size), (0, 0), (0, 0)])
|
||||
|
||||
chunks = seq_len // self.chunk_size + (1 if pad_size > 0 else 0)
|
||||
y_list = []
|
||||
ssm_state = mx.zeros((batch_size, self.num_heads, self.head_dim, self.ssm_state_size))
|
||||
|
||||
for i in range(chunks):
|
||||
chunk_start = i * self.chunk_size
|
||||
chunk_end = (i + 1) * self.chunk_size
|
||||
chunk_h = hidden_states_padded[:, chunk_start:chunk_end]
|
||||
chunk_B = B_padded[:, chunk_start:chunk_end]
|
||||
chunk_C = C_padded[:, chunk_start:chunk_end]
|
||||
|
||||
chunk_dt = dt[:, chunk_start:chunk_end]
|
||||
dA = mx.exp(chunk_dt[:, :, None, None] * A[None, None, :, None])
|
||||
dB = chunk_dt[:, :, None, None] * chunk_B
|
||||
dBx = dB * chunk_h[:, :, :, None]
|
||||
|
||||
chunk_y = mx.zeros_like(chunk_h)
|
||||
for j in range(self.chunk_size):
|
||||
ssm_state = ssm_state * dA[:, j] + dBx[:, j]
|
||||
chunk_y[:, j] = mx.sum(ssm_state * chunk_C[:, j], axis=-1)
|
||||
|
||||
y_list.append(chunk_y)
|
||||
|
||||
y = mx.concat(y_list, axis=1)
|
||||
if pad_size > 0:
|
||||
y = y[:, :seq_len]
|
||||
|
||||
D = self.D[None, :, None].expand(self.D.shape[0], self.head_dim)
|
||||
y = y + hidden_states * D
|
||||
y = y.reshape(batch_size, seq_len, -1)
|
||||
|
||||
y = self.norm(y, gate)
|
||||
contextualized_states = self.out_proj(y.astype(dtype))
|
||||
|
||||
return contextualized_states
|
||||
|
||||
|
||||
class Mamba2Block(nn.Module):
|
||||
def __init__(self, args: ModelArgs):
|
||||
def __init__(self, args: ModelArgs, layer_idx: int):
|
||||
super().__init__()
|
||||
self.mixer = Mamba2Mixer(args)
|
||||
self.mixer = Mamba2Mixer(args, layer_idx)
|
||||
self.norm = nn.RMSNorm(args.hidden_size)
|
||||
|
||||
def __call__(self, x: mx.array, cache):
|
||||
@ -235,7 +277,7 @@ 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 = [Mamba2Block(args, idx) for idx in range(args.num_hidden_layers)]
|
||||
self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
|
||||
|
||||
def __call__(
|
||||
@ -274,6 +316,9 @@ class Model(nn.Module):
|
||||
else:
|
||||
logits = self.lm_head(x)
|
||||
|
||||
print(logits)
|
||||
print(logits.shape)
|
||||
|
||||
return logits
|
||||
|
||||
def sanitize(self, weights):
|
||||
@ -282,8 +327,8 @@ class Model(nn.Module):
|
||||
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 [Mamba2Cache(self.args.num_hidden_layers) for _ in range(len(self.layers))]
|
||||
|
||||
@property
|
||||
def layers(self):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user