mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-12-16 02:08:55 +08:00
updates
This commit is contained in:
Goekdeniz-Guelmez
@@ -1,275 +1,7 @@
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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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import mlx.core as mx
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import mlx.nn as nn
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from typing import Optional, Tuple, Union
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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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# ------
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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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import mlx.core as mx
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import mlx.nn as nn
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@@ -296,130 +28,79 @@ class ModelArgs(BaseModelArgs):
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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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use_cache: bool = True
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intermediate_size: int = None
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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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self.intermediate_size = int(self.expand * self.hidden_size) # E*D = ED
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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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def selective_scan(x, A, B, C, chunk_size):
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"""
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Selective scan implementation for training.
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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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Arguments
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x: (batch, seqlen, n_heads, d_head)
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A: (batch, seqlen, n_heads)
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B: (batch, seqlen, n_heads, d_state)
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C: (batch, seqlen, n_heads, d_state)
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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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Return
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y: (batch, seqlen, n_heads, d_head)
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"""
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assert x.shape[1] % chunk_size == 0
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def silu(x):
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return x * mx.sigmoid(x)
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def ssd(x, A, B, C, chunk_size):
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# Replace einsum operations with explicit reshape and matrix multiply
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batch, seqlen, nheads, dim = x.shape
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B = mx.expand_dims(B, axis=2)
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C = mx.expand_dims(C, axis=2)
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# Reshape into chunks
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def chunk_reshape(m):
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shape = list(m.shape)
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shape[1:2] = [shape[1] // chunk_size, chunk_size]
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return m.reshape(shape)
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state = mx.zeros((batch, nheads, dim, B.shape[-1]))
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outputs = []
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x, A, B, C = map(chunk_reshape, (x, A, B, C))
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A = mx.transpose(A, [0, 3, 1, 2])
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for i in range(0, seqlen, chunk_size):
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chunk = slice(i, min(i + chunk_size, seqlen))
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dA = mx.exp(mx.expand_dims(A[chunk], axis=0))
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# Replace einsum with explicit operations
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x_chunk = x[:, chunk] # [batch, chunk_size, nheads, dim]
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x_chunk = mx.transpose(x_chunk, [0, 2, 3, 1]) # [batch, nheads, dim, chunk_size]
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B_chunk = B[:, chunk] # [batch, chunk_size, state_size]
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dBx = mx.matmul(x_chunk, B_chunk) # [batch, nheads, dim, state_size]
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state = state * mx.expand_dims(dA, axis=-1) + dBx
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# Replace einsum with explicit operations
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C_chunk = C[:, chunk] # [batch, chunk_size, state_size]
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y = mx.matmul(state, mx.transpose(C_chunk, [0, 2, 1])) # [batch, nheads, dim, chunk_size]
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y = mx.transpose(y, [0, 3, 1, 2]) # [batch, chunk_size, nheads, dim]
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outputs.append(y)
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# Compute cumulative sums
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A_cumsum = mx.cumsum(A, axis=-1)
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return mx.concatenate(outputs, axis=1), state
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# Process chunks
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L = mx.exp(selective_cumsum(A))
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Y_diag = mx.einsum('bclhn,bcshn,bhcls,bcshp->bclhp', C, B, L, x)
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decay_states = mx.exp(A_cumsum[..., -1:] - A_cumsum)
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states = mx.einsum('bclhn,bhcl,bclhp->bchpn', B, decay_states, x)
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initial_states = mx.zeros_like(states[:, :1])
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states = mx.concatenate([initial_states, states], axis=1)
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decay_chunk = mx.exp(selective_cumsum(mx.pad(A_cumsum[..., -1], ((0,0), (0,0), (1,0)))))
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new_states = mx.einsum('bhzc,bchpn->bzhpn', decay_chunk, states)
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states = new_states[:, :-1]
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state_decay_out = mx.exp(A_cumsum)
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Y_off = mx.einsum('bclhn,bchpn,bhcl->bclhp', C, states, state_decay_out)
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Y = (Y_diag + Y_off).reshape((-1, x.shape[1] * chunk_size, *Y_diag.shape[-2:]))
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return Y
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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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assert in_channels == out_channels, "In and out channels must be same for depthwise convolution"
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assert self.groups == in_channels, "Groups must be equal to in_channels for depthwise convolution"
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self.weight = mx.random.normal((in_channels, 1, kernel_size))
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self.bias = mx.zeros((out_channels,)) if bias else None
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def __call__(self, x: mx.array, cache=None) -> mx.array:
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B, L, C = x.shape
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K = self.kernel_size
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assert C == self.in_channels, f"Input channels {C} doesn't match expected {self.in_channels}"
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if cache is not None and 'conv_states' in cache:
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conv_states = cache['conv_states']
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if conv_states is not None:
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assert conv_states.shape[0] == B, "Cache batch size mismatch"
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assert conv_states.shape[2] == C, "Cache channel count mismatch"
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x = mx.concatenate([conv_states, x], axis=1)
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# Process each channel independently
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outputs = []
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for c in range(C):
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x_c = x[:, :, c]
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x_c = mx.expand_dims(x_c, axis=1)
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w_c = self.weight[c]
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if w_c.ndim == 2:
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w_c = mx.expand_dims(w_c, axis=0)
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elif w_c.ndim == 1:
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w_c = mx.expand_dims(mx.expand_dims(w_c, axis=0), axis=0)
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# Apply convolution
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y_c = mx.conv_general(
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x_c,
|
||||
w_c,
|
||||
stride=1,
|
||||
padding=0
|
||||
)
|
||||
|
||||
if self.bias is not None:
|
||||
y_c = y_c + self.bias[c]
|
||||
|
||||
outputs.append(mx.squeeze(y_c, axis=1))
|
||||
|
||||
y = mx.stack(outputs, axis=-1)
|
||||
|
||||
# Update cache
|
||||
if cache is not None:
|
||||
cache['conv_states'] = x[:, -K+1:, :] if x.shape[1] >= K else x
|
||||
|
||||
return y
|
||||
def selective_cumsum(x: mx.array) -> mx.array:
|
||||
"""Stable selective cumulative sum calculation."""
|
||||
T = x.shape[-1]
|
||||
x = mx.repeat(x[..., None], T, axis=-1)
|
||||
mask = mx.tril(mx.ones((T, T)), k=-1)
|
||||
x = x * mask
|
||||
x_cumsum = mx.cumsum(x, axis=-2)
|
||||
mask = mx.tril(mx.ones((T, T)), k=0)
|
||||
return mx.where(mask, x_cumsum, float('-inf'))
|
||||
|
||||
|
||||
class Mamba2Block(nn.Module):
|
||||
@@ -427,165 +108,250 @@ class Mamba2Block(nn.Module):
|
||||
super().__init__()
|
||||
self.args = args
|
||||
|
||||
d_in_proj = 2 * args.intermediate_size + 2 * args.state_size + args.num_heads
|
||||
self.in_proj = nn.Linear(args.hidden_size, d_in_proj, bias=args.use_bias)
|
||||
# Project input to get various components [z, x, B, C, dt]
|
||||
projection_size = (2 * args.intermediate_size + 2 * args.n_groups * args.state_size + args.num_heads)
|
||||
self.in_proj = nn.Linear(
|
||||
args.hidden_size,
|
||||
projection_size,
|
||||
bias=args.use_bias
|
||||
)
|
||||
|
||||
conv_dim = args.intermediate_size + 2 * args.state_size
|
||||
self.conv1d = DepthWiseConv1d(
|
||||
# Convolution layer
|
||||
conv_dim = args.intermediate_size + 2 * args.n_groups * args.state_size
|
||||
self.conv1d = nn.Conv1d(
|
||||
in_channels=conv_dim,
|
||||
out_channels=conv_dim,
|
||||
kernel_size=args.conv_kernel,
|
||||
groups=conv_dim,
|
||||
bias=args.use_conv_bias,
|
||||
padding=args.conv_kernel - 1
|
||||
padding=args.conv_kernel - 1,
|
||||
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
|
||||
# SSM parameters
|
||||
self.dt_bias = mx.zeros(args.num_heads)
|
||||
self.A_log = mx.zeros(args.num_heads)
|
||||
self.D = mx.ones(args.num_heads)
|
||||
|
||||
self.norm = MambaRMSNormGated(args.intermediate_size, eps=args.layer_norm_epsilon)
|
||||
# Output projections
|
||||
self.norm = nn.RMSNorm(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=None) -> mx.array:
|
||||
# return self.forward_training(x) if x.shape[1] > 1 else self.forward_inference(x, cache)
|
||||
|
||||
def __call__(self, x: mx.array, cache=None):
|
||||
if cache is not None:
|
||||
return self.step(x, cache)
|
||||
|
||||
# Regular forward pass code remains the same...
|
||||
d_model = self.args.intermediate_size
|
||||
d_state = self.args.state_size
|
||||
n_heads = self.args.num_heads
|
||||
# def forward_training(self, u: mx.array) -> mx.array:
|
||||
# # Reset cache during training
|
||||
# self.cache = None
|
||||
|
||||
A = -mx.exp(self.A_log)
|
||||
zxbcdt = self.in_proj(x)
|
||||
|
||||
splits = [d_model, d_model + 2 * d_state, n_heads]
|
||||
z = zxbcdt[:, :, :splits[0]]
|
||||
xBC = zxbcdt[:, :, splits[0]:splits[0] + splits[1]]
|
||||
dt = zxbcdt[:, :, -splits[2]:]
|
||||
# # Input projection and splitting
|
||||
# zxbcdt = self.in_proj(u)
|
||||
# z, xBC, dt = mx.split(
|
||||
# zxbcdt,
|
||||
# [
|
||||
# self.args.hidden_size,
|
||||
# self.args.hidden_size + 2 * self.args.state_size
|
||||
# ],
|
||||
# axis=-1
|
||||
# )
|
||||
|
||||
# # Time step processing
|
||||
# dt = mx.clip(
|
||||
# nn.softplus(dt + self.dt_bias),
|
||||
# self.args.time_step_min,
|
||||
# self.args.time_step_max
|
||||
# )
|
||||
|
||||
# # Convolution processing
|
||||
# xBC_t = mx.transpose(xBC, [0, 2, 1])
|
||||
# conv_out = self.conv1d(xBC_t)
|
||||
# xBC = mx.transpose(conv_out, [0, 2, 1])[:, :u.shape[1]]
|
||||
# xBC = mx.sigmoid(xBC) * xBC # SiLU
|
||||
|
||||
# # Split states
|
||||
# x, B, C = mx.split(
|
||||
# xBC,
|
||||
# [self.args.hidden_size, self.args.state_size],
|
||||
# axis=-1
|
||||
# )
|
||||
|
||||
# # Reshape for selective scan
|
||||
# x = x.reshape((-1, x.shape[1], self.args.num_heads, self.args.head_dim))
|
||||
# A = -mx.exp(self.A_log)
|
||||
|
||||
# # Apply selective scan
|
||||
# y = selective_scan(
|
||||
# x * dt[..., None],
|
||||
# A * dt,
|
||||
# B[..., None, :],
|
||||
# C[..., None, :],
|
||||
# self.args.chunk_size
|
||||
# )
|
||||
|
||||
# # Output processing
|
||||
# y = y + x * self.D[None, None, :, None]
|
||||
# y = y.reshape((-1, y.shape[1], self.args.hidden_size))
|
||||
# y = self.norm(y, z)
|
||||
# y = self.out_proj(y)
|
||||
|
||||
# return y
|
||||
|
||||
# def forward_inference(self, u: mx.array, cache=None) -> mx.array:
|
||||
# """
|
||||
# u: (B, 1, D)
|
||||
# cache: (h_cache, conv_cache)
|
||||
# """
|
||||
# """Single token processing during inference."""
|
||||
# assert u.shape[1] == 1, "Inference mode expects single token"
|
||||
|
||||
# batch_size = u.shape[0]
|
||||
# # Use provided cache or create new one
|
||||
# self.cache = cache if cache is not None else Mamba2Cache.get_cache(self.args, batch_size, None)
|
||||
|
||||
# # Project input
|
||||
# zxbcdt = self.in_proj(u.squeeze(1)) # (B, 2D)
|
||||
# d_mlp = (zxbcdt.shape[-1] - 2 * self.args.hidden_size - 2 * self.args.n_groups * self.args.state_size - self.args.num_heads) // 2
|
||||
|
||||
# # (1, 768) (1, 0) (1, 0) (1, 256) (1, 0) (1, 3328)
|
||||
# y0, z0, x0, z, xBC, dt = mx.split(
|
||||
# zxbcdt,
|
||||
# [
|
||||
# d_mlp,
|
||||
# d_mlp,
|
||||
# self.args.hidden_size,
|
||||
# self.args.hidden_size + 2 * self.args.n_groups * self.args.state_size,
|
||||
# self.args.num_heads
|
||||
# ],
|
||||
# axis=-1
|
||||
# )
|
||||
|
||||
# # Update convolution state and apply
|
||||
# conv_state = self.cache.update_conv_state(xBC)
|
||||
# xBC = mx.sum(conv_state[:, :, -1] * mx.transpose(self.conv1d.weight, [1, 0, 2]), axis=-1) # (B, D) (4, 1792)
|
||||
|
||||
# if self.args.use_conv_bias:
|
||||
# xBC = xBC + self.conv1d.bias
|
||||
|
||||
# xBC = mx.sigmoid(xBC) * xBC # SiLU (4, 1792)
|
||||
|
||||
# # Split states and ensure proper shapes
|
||||
# a0, x, B, C = mx.split(
|
||||
# xBC, # (4, 1792)
|
||||
# [
|
||||
# self.args.hidden_size,
|
||||
# self.args.n_groups * self.args.state_size,
|
||||
# self.args.n_groups * self.args.state_size
|
||||
# ],
|
||||
# axis=-1
|
||||
# )
|
||||
|
||||
# # SSM step with explicit shapes
|
||||
# A = -mx.exp(self.A_log) # (num_heads) (24,)
|
||||
# print(A.shape) # (24,)
|
||||
# print(dt.shape) # (1, 3328)
|
||||
# dA = mx.exp(dt * A[None, :]) # Shape: (batch_size, num_heads) <------- her eis the error
|
||||
|
||||
# # Reshape x considering intermediate size
|
||||
# # x shape should be (batch_size * num_heads, head_dim)
|
||||
# x = mx.reshape(x, (batch_size, self.args.num_heads, -1))
|
||||
# assert x.shape[-1] == self.args.head_dim, f"Head dimension mismatch: {x.shape[-1]} vs {self.args.head_dim}"
|
||||
|
||||
# B = mx.reshape(B, (batch_size, -1)) # Should be (batch_size, state_size)
|
||||
# C = mx.reshape(C, (batch_size, -1)) # Should be (batch_size, state_size)
|
||||
|
||||
# # Compute dBx with explicit shapes
|
||||
# dBx = mx.einsum('bh,bs,bhd->bhds', dt, B, x)
|
||||
|
||||
# ssm_state = self.cache.update_ssm_state(dA, dBx)
|
||||
|
||||
# y = mx.einsum('bhds,bs->bhd', ssm_state, C)
|
||||
# y = y + x * self.D[None, :, None]
|
||||
# y = mx.reshape(y, (batch_size, self.args.hidden_size))
|
||||
|
||||
# # Output processing
|
||||
# y = self.norm(y, z)
|
||||
|
||||
# if d_mlp > 0:
|
||||
# y = mx.cat([nn.silu(z0) * x0, y], axis=-1)
|
||||
|
||||
# y = self.out_proj(y)
|
||||
|
||||
# return mx.expand_dims(y, 1)
|
||||
|
||||
assert u.shape[1] == 1, "Inference mode expects single token"
|
||||
|
||||
batch_size = u.shape[0]
|
||||
# Use provided cache or create new one
|
||||
self.cache = cache if cache is not None else Mamba2Cache.get_cache(self.args, batch_size, None)
|
||||
|
||||
# Project input
|
||||
zxbcdt = self.in_proj(u.squeeze(1)) # (B, projection_size)
|
||||
|
||||
# Calculate splits based on model dimensions
|
||||
d_mlp = self.args.intermediate_size
|
||||
d_state = self.args.state_size * self.args.n_groups
|
||||
|
||||
# Split the projection into its components
|
||||
splits = [
|
||||
d_mlp, # y0
|
||||
d_mlp, # z0
|
||||
self.args.hidden_size, # x0
|
||||
self.args.hidden_size, # z
|
||||
d_state * 2, # xBC (includes both B and C)
|
||||
self.args.num_heads # dt
|
||||
]
|
||||
|
||||
y0, z0, x0, z, xBC, dt = mx.split(zxbcdt, splits[:-1], axis=-1)
|
||||
|
||||
# Update convolution state and apply
|
||||
conv_state = self.cache.update_conv_state(xBC)
|
||||
xBC = mx.sum(conv_state[:, :, -1] * mx.transpose(self.conv1d.weight, [1, 0, 2]), axis=-1)
|
||||
|
||||
if self.args.use_conv_bias:
|
||||
xBC = xBC + self.conv1d.bias
|
||||
|
||||
xBC = mx.sigmoid(xBC) * xBC # SiLU
|
||||
|
||||
# Split states and reshape
|
||||
x, BC = mx.split(xBC, [self.args.intermediate_size], axis=-1)
|
||||
B, C = mx.split(BC, [d_state], axis=-1)
|
||||
|
||||
# Reshape for SSM computation
|
||||
x = mx.reshape(x, (batch_size, self.args.num_heads, -1)) # (B, H, head_dim)
|
||||
B = mx.reshape(B, (batch_size, self.args.num_heads, -1)) # (B, H, state_per_head)
|
||||
C = mx.reshape(C, (batch_size, self.args.num_heads, -1)) # (B, H, state_per_head)
|
||||
|
||||
# Process dt to match expected shape
|
||||
dt = mx.reshape(dt, (batch_size, self.args.num_heads)) # (B, H)
|
||||
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)
|
||||
|
||||
xBC = silu(self.conv1d(xBC))
|
||||
|
||||
x = xBC[:, :, :d_model]
|
||||
B = xBC[:, :, d_model:d_model + d_state]
|
||||
C = xBC[:, :, -d_state:]
|
||||
|
||||
b, l, hp = x.shape
|
||||
h = self.args.num_heads
|
||||
p = hp // h
|
||||
x = mx.reshape(x, (b, l, h, p))
|
||||
|
||||
y, ssm_state = ssd(x * mx.expand_dims(dt, -1), A * dt, B, C, self.args.chunk_size)
|
||||
y = y + x * mx.expand_dims(self.D, -1)
|
||||
y = mx.reshape(y, (b, l, h * p))
|
||||
|
||||
y = self.norm(y + z)
|
||||
|
||||
# SSM step
|
||||
A = -mx.exp(self.A_log) # (H,)
|
||||
dA = mx.exp(dt * A[None, :]) # (B, H)
|
||||
|
||||
# Compute dBx
|
||||
dBx = mx.einsum('bh,bhs,bhd->bhds', dt, B, x)
|
||||
|
||||
# Update SSM state and compute output
|
||||
ssm_state = self.cache.update_ssm_state(dA, dBx)
|
||||
y = mx.einsum('bhds,bhs->bhd', ssm_state, C)
|
||||
y = y + x * self.D[None, :, None]
|
||||
|
||||
# Reshape output
|
||||
y = mx.reshape(y, (batch_size, self.args.hidden_size))
|
||||
|
||||
# Final output processing
|
||||
y = self.norm(y, z)
|
||||
|
||||
if d_mlp > 0:
|
||||
y = mx.concat([nn.silu(z0) * x0, y], axis=-1)
|
||||
|
||||
y = self.out_proj(y)
|
||||
|
||||
if self.args.residual_in_fp32:
|
||||
y = y.astype(mx.float32)
|
||||
|
||||
return y
|
||||
|
||||
def step(self, u: mx.array, cache):
|
||||
batch_size = u.shape[0]
|
||||
seq_len = u.shape[1]
|
||||
outputs = []
|
||||
|
||||
# Initialize cache if needed
|
||||
if cache.conv_states is None:
|
||||
conv_dim = self.args.intermediate_size + 2 * self.args.state_size
|
||||
cache.conv_states = mx.zeros((
|
||||
batch_size,
|
||||
self.args.conv_kernel - 1,
|
||||
conv_dim
|
||||
))
|
||||
|
||||
if cache.ssm_state is None:
|
||||
cache.ssm_state = mx.zeros((
|
||||
batch_size,
|
||||
self.args.num_heads,
|
||||
self.args.head_dim,
|
||||
self.args.state_size
|
||||
))
|
||||
|
||||
for pos in range(seq_len):
|
||||
u_t = u[:, pos:pos+1, :]
|
||||
zxbcdt = self.in_proj(u_t)
|
||||
|
||||
d_model = self.args.intermediate_size
|
||||
d_state = self.args.state_size
|
||||
n_heads = self.args.num_heads
|
||||
|
||||
z = zxbcdt[:, :, :d_model]
|
||||
xBC = zxbcdt[:, :, d_model:d_model + 2*d_state + d_model]
|
||||
dt = zxbcdt[:, :, -(n_heads):]
|
||||
|
||||
dt = mx.reshape(dt, (batch_size, n_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)
|
||||
|
||||
# Create a temporary cache dictionary for the convolution
|
||||
conv_cache = {'conv_states': cache.conv_states}
|
||||
xBC = self.conv1d(xBC, cache=conv_cache)
|
||||
cache.conv_states = conv_cache['conv_states']
|
||||
|
||||
xBC = silu(xBC)
|
||||
|
||||
x = xBC[:, :, :d_model]
|
||||
B = xBC[:, :, d_model:d_model + d_state]
|
||||
C = xBC[:, :, -d_state:]
|
||||
|
||||
x = mx.reshape(x, (batch_size, 1, n_heads, self.args.head_dim))
|
||||
x = mx.squeeze(x, axis=1)
|
||||
|
||||
B = mx.reshape(B, (batch_size, 1, d_state))
|
||||
B = mx.broadcast_to(B, (batch_size, n_heads, d_state))
|
||||
B = mx.expand_dims(B, axis=2)
|
||||
|
||||
C = mx.reshape(C, (batch_size, 1, d_state))
|
||||
C = mx.broadcast_to(C, (batch_size, n_heads, d_state))
|
||||
C = mx.expand_dims(C, axis=3)
|
||||
|
||||
A = -mx.exp(self.A_log)
|
||||
dA = mx.exp(dt * mx.expand_dims(A, 0))
|
||||
dA = mx.expand_dims(mx.expand_dims(dA, -1), -1)
|
||||
|
||||
x = mx.expand_dims(x, axis=3)
|
||||
dBx = mx.matmul(x, B)
|
||||
|
||||
cache.ssm_state = cache.ssm_state * dA + dBx
|
||||
|
||||
y = mx.matmul(cache.ssm_state, C)
|
||||
y = mx.squeeze(y, axis=-1)
|
||||
|
||||
y = y + x[:, :, :, 0] * mx.expand_dims(self.D, -1)
|
||||
|
||||
y = mx.reshape(y, (batch_size, 1, n_heads * self.args.head_dim))
|
||||
y = self.norm(y + z)
|
||||
y = self.out_proj(y)
|
||||
|
||||
if self.args.residual_in_fp32:
|
||||
y = y.astype(mx.float32)
|
||||
|
||||
outputs.append(y)
|
||||
|
||||
return mx.concatenate(outputs, axis=1)
|
||||
|
||||
return mx.expand_dims(y, 1) # (B, 1, D)
|
||||
|
||||
|
||||
class ResidualBlock(nn.Module):
|
||||
@@ -594,11 +360,12 @@ class ResidualBlock(nn.Module):
|
||||
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
|
||||
def __call__(self, x: mx.array, cache=None) -> mx.array:
|
||||
# x : (B, L, D)
|
||||
return self.mixer(self.norm(x), cache) + x # (B, L, D)
|
||||
|
||||
|
||||
class Mamba2(nn.Module):
|
||||
class Mamba2Model(nn.Module):
|
||||
def __init__(self, args: ModelArgs):
|
||||
super().__init__()
|
||||
self.args = args
|
||||
@@ -606,12 +373,15 @@ class Mamba2(nn.Module):
|
||||
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):
|
||||
def __call__(self, x: mx.array, cache=None) -> mx.array:
|
||||
# x : (B, L)
|
||||
x = self.embeddings(x)
|
||||
# x : (B, L, D)
|
||||
if cache is None:
|
||||
cache = [None] * len(self.layers)
|
||||
for layer, c in zip(self.layers, cache):
|
||||
x = layer(x, c)
|
||||
|
||||
for layer, layer_cache in zip(self.layers, cache):
|
||||
x = layer(x, layer_cache)
|
||||
return self.norm_f(x)
|
||||
|
||||
|
||||
@@ -619,14 +389,13 @@ class Model(nn.Module):
|
||||
def __init__(self, args: ModelArgs):
|
||||
super().__init__()
|
||||
self.args = args
|
||||
self.model_type = args.model_type
|
||||
self.backbone = Mamba2Model(args)
|
||||
|
||||
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):
|
||||
def __call__(self, inputs: mx.array, cache=None) -> mx.array:
|
||||
# inputs : (B, L)
|
||||
B, T = inputs.shape
|
||||
|
||||
x = self.backbone(inputs, cache)
|
||||
@@ -637,24 +406,19 @@ class Model(nn.Module):
|
||||
logits = self.lm_head(x)
|
||||
|
||||
return logits
|
||||
|
||||
def make_cache(self):
|
||||
return [Mamba2Cache() for _ in range(len(self.layers))]
|
||||
|
||||
def make_cache(self, batch_size=1):
|
||||
return [Mamba2Cache(
|
||||
batch_size=batch_size,
|
||||
hidden_size=self.args.hidden_size,
|
||||
state_size=self.args.state_size,
|
||||
conv_kernel=self.args.conv_kernel,
|
||||
num_heads=self.args.num_heads,
|
||||
head_dim=self.args.head_dim
|
||||
) for _ in range(len(self.backbone.layers))]
|
||||
|
||||
def sanitize(self, weights):
|
||||
sanitized = {}
|
||||
for k, v in weights.items():
|
||||
if "conv1d.weight" in k:
|
||||
# Ensure weights are in correct shape (channels, 1, kernel_size)
|
||||
if v.ndim == 2:
|
||||
v = mx.expand_dims(v, axis=1)
|
||||
elif v.ndim == 1:
|
||||
v = mx.expand_dims(mx.expand_dims(v, axis=0), axis=0)
|
||||
sanitized[k] = v
|
||||
else:
|
||||
sanitized[k] = v
|
||||
return sanitized
|
||||
|
||||
@property
|
||||
def layers(self):
|
||||
return self.backbone.layers
|
||||
if "conv1d.weight" in k and v.ndim == 3:
|
||||
weights[k] = v.moveaxis(2, 1)
|
||||
return weights
|
||||
Reference in New Issue
Block a user