mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-26 02:33:23 +08:00
301 lines
9.3 KiB
Python
301 lines
9.3 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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model_type: str
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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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chunk_size: int
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tie_word_embeddings: bool
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time_step_limit: Tuple[float, float]
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time_step_rank: Union[int, str]
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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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norm_before_gate: bool = True
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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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def ssd_forward_attn(
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x: mx.array,
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dt: mx.array,
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A: mx.array,
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B: mx.array,
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C: mx.array,
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D: mx.array,
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dt_bias: mx.array,
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dt_min: float,
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dt_max: float,
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prev_state=None,
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) -> Tuple[mx.array, mx.array]:
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b, l, h, dh = x.shape
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_, _, g, _ = B.shape
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# Process dt
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if dt_bias is not None:
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dt = dt + dt_bias.reshape(1, 1, -1)
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dt = nn.softplus(dt)
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dt = mx.clip(dt, a_min=dt_min, a_max=dt_max)
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# Reshape tensors
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B_reshaped = mx.swapaxes(mx.swapaxes(B, 1, 3), 1, 2)
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C_reshaped = mx.swapaxes(C, 1, 2)
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# Compute CB
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CB = C_reshaped @ B_reshaped
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CB = mx.repeat(CB, repeats=h // g, axis=1)
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# Compute decay terms
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dtA = dt * A.reshape(1, 1, -1)
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dtA = mx.swapaxes(dtA, 1, 2)
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decay = mx.exp(segsum(dtA))
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# Create attention matrix
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surrogate_attention_matrix = mx.tril(CB * decay, 0)
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# Apply attention
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dtx = dt.reshape(b, l, h, 1) * x
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y = surrogate_attention_matrix @ dtx.swapaxes(1, 2)
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y = mx.swapaxes(y, 1, 2)
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# Compute next state
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decay_last = decay[:, :, -1, :].reshape(b, h, l).swapaxes(1, 2).reshape(b, l, h, 1)
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B_for_state = mx.repeat(B_reshaped, h // g, axis=1).swapaxes(2, 3)
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dtxdecay = dtx * decay_last
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dtxdecay = dtxdecay.swapaxes(1, 2).swapaxes(2, 3)
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# Calculate new state contribution
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new_state_contribution = dtxdecay @ B_for_state
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# Initialize or update state
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if prev_state is not None:
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decayed_prev_state = prev_state * decay[:, :, -1, :].reshape(b, h, 1, 1)
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next_state = decayed_prev_state + new_state_contribution
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else:
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next_state = new_state_contribution
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# Add skip connection if D is provided
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if D is not None:
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y += x * D.reshape(1, 1, h, 1)
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# Reshape output
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y = y.reshape(b, l, h * dh)
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return y, next_state
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def segsum(x):
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# x shape: [b, h, l]
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b, h, l = x.shape
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indices = mx.arange(l)
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mask = indices[:, None] >= indices[None, :] # [l, l] lower triangular mask
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# Expand x for broadcasting
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x_expanded = x.reshape(b, h, l, 1) # [b, h, l, 1]
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# Apply mask and sum
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masked_x = x_expanded * mask.reshape(1, 1, l, l) # [b, h, l, l]
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x_segsum = mx.sum(masked_x, axis=2, keepdims=True) # [b, h, 1, l]
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return x_segsum
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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.d_model = args.hidden_size
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self.d_state = args.state_size
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self.d_conv = args.conv_kernel
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self.expand = args.expand
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self.d_inner = int(self.expand * self.d_model)
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self.n_groups = args.n_groups
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self.n_heads = args.num_heads
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self.d_head = self.d_inner // self.n_heads
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self.chunk_size = args.chunk_size
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d_in_proj = 2 * self.d_inner + 2 * self.n_groups * self.d_state + self.n_heads
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self.in_proj = nn.Linear(self.d_model, d_in_proj, bias=args.use_bias)
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self.dt_bias = mx.random.normal((self.n_heads,)) * args.initializer_range
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self.A_log = mx.random.normal((self.n_heads,)) * args.initializer_range
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self.D = mx.random.normal((self.n_heads,)) * args.initializer_range
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conv_channels = self.d_inner + 2 * self.n_groups * self.d_state
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self.conv1d = nn.Conv1d(
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in_channels=conv_channels,
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out_channels=conv_channels,
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kernel_size=self.d_conv,
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groups=conv_channels,
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padding=self.d_conv - 1,
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)
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self.norm = nn.RMSNorm(self.d_inner, eps=args.layer_norm_epsilon)
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self.out_proj = nn.Linear(self.d_inner, self.d_model, bias=args.use_bias)
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def __call__(self, u: mx.array, cache=None):
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batch_size, seq_len, _ = u.shape
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if cache is None:
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cache = [None, None]
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else:
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conv_state, ssm_state = cache
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zxBCdt = self.in_proj(u)
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z, xBC, dt = mx.split(
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zxBCdt,
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[self.d_inner, 2 * self.d_inner + 2 * self.n_groups * self.d_state],
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axis=-1
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)
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# Handle convolution with caching
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xBC = mx.swapaxes(xBC, 1, 2) # [B, L, C] -> [B, C, L]
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if conv_state is not None and seq_len > 0:
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# Concatenate cached state with current input
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xBC_with_cache = mx.concatenate([conv_state, xBC], axis=2)
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elif seq_len > 0:
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# For the first call, pad with zeros
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padding = mx.zeros((batch_size, xBC.shape[1], self.d_conv - 1))
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xBC_with_cache = mx.concatenate([padding, xBC], axis=2)
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else:
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xBC_with_cache = conv_state if conv_state is not None else mx.zeros((batch_size, xBC.shape[1], 0))
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# Save state for next iteration
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if seq_len > 0:
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next_conv_state = xBC_with_cache[:, :, -(self.d_conv - 1):]
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else:
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next_conv_state = conv_state
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# Apply regular convolution using nn.Conv1d
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if seq_len > 0:
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# Use the standard Conv1d module for the actual computation
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xBC_conv = self.conv1d(xBC_with_cache)
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xBC = xBC_conv[:, :, -seq_len:] # Take only the relevant output positions
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xBC = mx.swapaxes(xBC, 1, 2) # [B, C, L] -> [B, L, C]
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xBC = xBC * mx.sigmoid(xBC)
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else:
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# Handle empty sequence case
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xBC = mx.swapaxes(xBC, 1, 2) # [B, C, L] -> [B, L, C]
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x, B, C = mx.split(
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xBC,
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[self.d_inner, self.d_inner + self.d_state * self.n_groups],
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axis=-1
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)
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x = mx.reshape(x, (batch_size, seq_len, self.n_heads, self.d_head))
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B = mx.reshape(B, (batch_size, seq_len, self.n_groups, -1))
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C = mx.reshape(C, (batch_size, seq_len, self.n_groups, -1))
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y, next_ssm_state = ssd_forward_attn(
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x=x,
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dt=dt,
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A=-mx.exp(self.A_log),
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B=B,
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C=C,
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D=self.D,
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dt_bias=self.dt_bias,
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dt_min=self.args.time_step_min,
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dt_max=self.args.time_step_max,
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prev_state=ssm_state
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)
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if self.args.norm_before_gate:
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y = self.norm(y)
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y = y * nn.silu(z)
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else:
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y = y * nn.silu(z)
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y = self.norm(y)
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y = self.out_proj(y)
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cache[0] = next_conv_state
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cache[1] = next_ssm_state
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return y
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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.residual_in_fp32 = args.residual_in_fp32
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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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if self.residual_in_fp32:
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x = x.astype(mx.float32)
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normed = self.norm(x)
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output = self.mixer(normed, cache)
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return output + 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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hidden = x
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for layer, c in zip(self.layers, cache):
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hidden = layer(hidden, c)
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return self.norm_f(hidden)
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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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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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hidden = self.backbone(inputs, cache)
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if self.args.tie_word_embeddings:
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logits = self.backbone.embeddings.as_linear(hidden)
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else:
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logits = self.lm_head(hidden)
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return logits
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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 |