mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-26 18:51:18 +08:00
261 lines
8.7 KiB
Python
261 lines
8.7 KiB
Python
import math
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from dataclasses import dataclass, field
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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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from .cache import MambaCache
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@dataclass
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class ModelArgs(BaseModelArgs):
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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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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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self.intermediate_size = int(self.expand * self.hidden_size)
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if not hasattr(self, "head_dim"):
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self.head_dim = self.hidden_size // self.num_heads
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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 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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self.weight = mx.ones((hidden_size,))
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self.variance_epsilon = eps
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def __call__(self, hidden_states, gate=None):
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if gate is not None:
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hidden_states = hidden_states * nn.silu(gate)
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variance = mx.mean(hidden_states ** 2, axis=-1, keepdims=True)
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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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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 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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_, K, _ = self.weight.shape
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if cache is not None:
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x = mx.concatenate([cache, x], axis=1)
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else:
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x = mx.pad(x, [(0, 0), (K - 1, 0), (0, 0)])
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y = mx.conv_general(x, self.weight, groups=self.groups)
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if self.bias is not None:
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y = y + self.bias
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return y, x[:, -K + 1 :, :]
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class Mamba2Block(nn.Module):
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def __init__(self, args: ModelArgs):
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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.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.hidden_size // args.num_heads
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self.n_groups = args.n_groups
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# projection_size = 2 * args.intermediate_size + 2 * args.n_groups * args.state_size + args.num_heads
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projection_size = 2 * args.intermediate_size + 2 * args.state_size + args.num_heads
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self.in_proj = nn.Linear(
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args.hidden_size,
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projection_size,
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bias=args.use_bias
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)
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# self.conv_dim = args.intermediate_size + 2 * args.n_groups * args.state_size
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self.conv_dim = args.intermediate_size + 2 * args.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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kernel_size=args.conv_kernel,
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bias=args.use_conv_bias,
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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.A_log = mx.zeros(args.num_heads)
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self.D = mx.ones((args.num_heads,))
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self.dt_bias = mx.zeros(args.num_heads)
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self.out_proj = nn.Linear(args.intermediate_size, args.hidden_size, bias=args.use_bias)
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self.norm = MambaRMSNormGated(args.intermediate_size, eps=args.layer_norm_epsilon)
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def ssm_step(self, x, state, dt):
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A = -mx.exp(self.A_log)
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D = self.D
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dt = nn.softplus(dt + 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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dt = dt.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 = dt * B
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else:
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new_state = dt * (B + state * mx.exp(dt * 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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B, T, D = 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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zxbcdt = self.in_proj(xt)
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z, xBC, dt = mx.split(
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zxbcdt,
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# indices_or_sections=[self.conv_dim, self.conv_dim + self.intermediate_size],
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indices_or_sections=[
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self.intermediate_size,
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self.intermediate_size + 2 * self.state_size,
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self.num_heads
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],
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axis=-1
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)
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# Use the new DepthWiseConv1d with caching
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conv_out, cache[0] = self.conv1d(mx.expand_dims(z, 1), cache[0])
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z = conv_out.squeeze(1)
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z = nn.silu(z)
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y_t, cache[1] = self.ssm_step(z, cache[1], dt)
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xBC = nn.silu(xBC)
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# Element-wise multiplication
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output_t = y_t[:, :, None] * xBC[:, None, :]
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output_t = self.norm(output_t)
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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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class ResidualBlock(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.mixer = Mamba2Block(args)
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self.norm = nn.RMSNorm(args.hidden_size)
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def __call__(self, x: mx.array, cache):
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return self.mixer(self.norm(x), cache) + x
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class Mamba2(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.args = args
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self.embeddings = nn.Embedding(args.vocab_size, args.hidden_size)
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self.layers = [ResidualBlock(args) for _ in range(args.num_hidden_layers)]
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self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
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def __call__(self, x: mx.array, cache):
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x = self.embeddings(x)
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if cache is None:
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cache = [None] * len(self.layers)
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for layer, c in zip(self.layers, cache):
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x = layer(x, c)
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return self.norm_f(x)
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class Model(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.args = args
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self.model_type = args.model_type
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self.backbone = Mamba2(args)
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# self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
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if not args.tie_word_embeddings:
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self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
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def __call__(self, inputs: mx.array, cache=None):
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B, T = inputs.shape
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x = self.backbone(inputs, cache)
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if self.args.tie_word_embeddings:
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logits = self.backbone.embeddings.as_linear(x)
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else:
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logits = self.lm_head(x)
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return logits
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def sanitize(self, weights):
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for k, v in weights.items():
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if "conv1d.weight" in k and v.ndim == 3:
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weights[k] = v.moveaxis(2, 1)
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return weights
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def make_cache(self):
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return [MambaCache() for _ in range(len(self.layers))]
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@property
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def layers(self):
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return self.backbone.layers
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