mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 09:21:18 +08:00
Awni Hannun
GitHub
parent
1e05aef344
commit
20e221f7f7
505
llms/mlx_lm/models/recurrent_gemma.py
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505
llms/mlx_lm/models/recurrent_gemma.py
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import math
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from dataclasses import dataclass
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from typing import List, Literal, Optional
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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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@dataclass
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class ModelArgs(BaseModelArgs):
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model_type: str
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hidden_size: int
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attention_bias: bool
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conv1d_width: int
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embeddings_scale_by_sqrt_dim: bool
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hidden_size: int
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intermediate_size: int
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logits_soft_cap: float
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num_attention_heads: int
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num_hidden_layers: int
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num_key_value_heads: int
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rms_norm_eps: float
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rope_theta: float
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attention_window_size: int
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vocab_size: int
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_block_types: List[str]
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def create_window_causal_mask(N: int, window_size: int):
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inds = mx.arange(N)
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linds = inds[:, None]
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rinds = inds[None]
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mask = (linds < rinds) | (linds > rinds + window_size)
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return mask * -1e9
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class RecurrentCache:
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def __init__(self):
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self._cache = (None, None)
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def __getitem__(self, idx):
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return self._cache[idx]
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def update(self, conv_state, recurrent_state):
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self._cache = (conv_state, recurrent_state)
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class WindowKVCache:
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def __init__(self, window_size):
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self.keys = None
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self.values = None
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self.offset = 0
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self.window_size = window_size
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def update_and_fetch(self, keys, values):
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# TODO consider using rotating buffer here
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# especially for very long generations
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def _update(x, v):
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t = x.shape[2] - self.window_size
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if t > 0:
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x = x[..., t:, :]
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return mx.concatenate([x, v], axis=2)
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self.offset += keys.shape[2]
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if self.keys is None:
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self.keys = keys
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self.values = values
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else:
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self.keys = _update(self.keys, keys)
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self.values = _update(self.values, values)
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return self.keys, self.values
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class RMSNorm(nn.Module):
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def __init__(self, dims: int, eps: float = 1e-5):
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super().__init__()
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self.weight = mx.ones((dims,))
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self.eps = eps
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def __call__(self, x):
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return mx.fast.rms_norm(x, 1.0 + self.weight, self.eps)
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def rnn_scan(x, a, h0):
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assert x.ndim == 3
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assert a.shape == x.shape[-a.ndim :]
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assert a.dtype == x.dtype
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if x.shape[1] == 1:
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# Using scan in sampling mode.
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if h0 is None:
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return x, x[:, 0]
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else:
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y = a * h0[:, None] + x
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return y, y[:, -1]
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else:
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# Using scan in linear mode.
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if h0 is not None:
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h_t = h0
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else:
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B, _, D = x.shape
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h_t = mx.zeros((B, D), dtype=x.dtype)
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y = mx.zeros_like(x)
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for t in range(x.shape[1]):
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h_t = a[:, t] * h_t + x[:, t]
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y[:, t] = h_t
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return y, h_t
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class Conv1d(nn.Module):
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def __init__(
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self,
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channels: int,
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kernel_size: int,
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):
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super().__init__()
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self.weight = mx.zeros((kernel_size, channels))
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self.bias = mx.zeros((channels,))
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def __call__(self, x, cache=None):
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w = self.weight.T[..., None]
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kw, groups = self.weight.shape
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if cache is not None:
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l = []
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# Pad the cache if needed
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if cache.shape[1] < kw - 1:
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l.append(
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mx.zeros(
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(x.shape[0], kw - 1 - cache.shape[1], groups), dtype=x.dtype
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)
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)
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l.extend([cache, x])
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x = mx.concatenate(l, axis=1)
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y = (x * w.swapaxes(0, 2)).sum(axis=1, keepdims=True)
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else:
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y = mx.conv_general(x, w, padding=([kw - 1], [0]), groups=groups)
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# The cache is always kw - 1
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cache = x[:, max(x.shape[1] - kw + 1, 0) :, :]
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y = y + self.bias
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return y, cache
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class RGLRU(nn.Module):
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"""A Real-Gated Linear Recurrent Unit (RG-LRU) layer."""
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def __init__(
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self,
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width: int,
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num_heads: int,
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):
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super().__init__()
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self.width = width
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self.num_heads = num_heads
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self.head_dim = self.width // self.num_heads
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self.recurrent_param = mx.zeros((self.width,))
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self.input_gate_weight = mx.zeros(
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(self.num_heads, self.head_dim, self.head_dim),
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)
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self.input_gate_bias = mx.zeros((self.num_heads, self.head_dim))
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self.recurrent_gate_weight = mx.zeros(
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(self.num_heads, self.head_dim, self.head_dim),
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)
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self.recurrent_gate_bias = mx.zeros((self.num_heads, self.head_dim))
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def __call__(
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self,
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x: mx.array,
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cache=None,
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):
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B, L, _ = x.shape
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def apply_block_linear(h, w, b):
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h = h.reshape((B, L, self.num_heads, self.head_dim))
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h = (h.swapaxes(1, 2) @ w).swapaxes(1, 2) + b
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return mx.sigmoid(h.flatten(2, 3))
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# Gates for x and a.
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gate_x = apply_block_linear(x, self.input_gate_weight, self.input_gate_bias)
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gate_a = apply_block_linear(
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x, self.recurrent_gate_weight, self.recurrent_gate_bias
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)
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# Compute the parameter `A` of the recurrence.
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log_a = -8.0 * gate_a * nn.softplus(self.recurrent_param)
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a = mx.exp(log_a)
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a_square = mx.exp(2 * log_a)
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# Gate the input.
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gated_x = x * gate_x
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# Apply gamma normalization to the input.
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multiplier = mx.sqrt(1 - a_square)
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normalized_x = gated_x * multiplier.astype(x.dtype)
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y, last_h = rnn_scan(
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x=normalized_x,
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a=a,
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h0=cache,
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)
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return y, last_h
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class RecurrentBlock(nn.Module):
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def __init__(
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self,
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width: int,
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num_heads: int,
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lru_width: int = None,
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conv1d_temporal_width: int = 4,
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):
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super().__init__()
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self.width = width
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self.num_heads = num_heads
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self.lru_width = lru_width or width
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self.conv1d_temporal_width = conv1d_temporal_width
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self.linear_y = nn.Linear(width, self.lru_width)
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self.linear_x = nn.Linear(width, self.lru_width)
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self.linear_out = nn.Linear(self.lru_width, width)
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self.conv_1d = Conv1d(
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channels=self.lru_width,
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kernel_size=self.conv1d_temporal_width,
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)
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self.rg_lru = RGLRU(
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width=self.lru_width,
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num_heads=self.num_heads,
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)
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def __call__(
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self,
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x: mx.array,
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cache=None,
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mask=None,
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):
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# y branch.
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y = self.linear_y(x)
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y = nn.gelu_approx(y)
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# x branch.
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x = self.linear_x(x)
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if cache is None:
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conv_state, recurrent_state = (None, None)
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else:
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conv_state, recurrent_state = cache[0], cache[1]
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x, conv_state = self.conv_1d(
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x=x,
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cache=conv_state,
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)
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x, recurrent_state = self.rg_lru(
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x=x,
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cache=recurrent_state,
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)
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if cache is not None:
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cache.update(conv_state, recurrent_state)
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x = x * y
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x = self.linear_out(x)
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return x
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class LocalAttentionBlock(nn.Module):
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def __init__(
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self,
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width: int,
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num_heads: int,
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window_size: int,
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):
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super().__init__()
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self.width = width
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self.num_heads = num_heads
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self.window_size = window_size
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self.scale = (width // num_heads) ** (-0.5)
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self.head_dim = self.width // self.num_heads
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self.q_proj = nn.Linear(self.width, self.width, bias=False)
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self.k_proj = nn.Linear(self.width, self.head_dim, bias=False)
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self.v_proj = nn.Linear(self.width, self.head_dim, bias=False)
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self.o_proj = nn.Linear(self.width, self.width, bias=True)
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self.rope = nn.RoPE(
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self.head_dim // 2,
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traditional=False,
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)
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def __call__(
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self,
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x: mx.array,
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cache=None,
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mask=None,
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):
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B, L, D = x.shape
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queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
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queries = queries.reshape(B, L, self.num_heads, -1).transpose(0, 2, 1, 3)
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keys = keys.reshape(B, L, 1, -1).transpose(0, 2, 1, 3)
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values = values.reshape(B, L, 1, -1).transpose(0, 2, 1, 3)
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if cache is not None:
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queries = self.rope(queries, offset=cache.offset)
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keys = self.rope(keys, offset=cache.offset)
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keys, values = cache.update_and_fetch(keys, values)
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else:
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queries = self.rope(queries)
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keys = self.rope(keys)
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output = mx.fast.scaled_dot_product_attention(
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queries, keys, values, scale=self.scale, mask=mask
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)
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output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
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return self.o_proj(output)
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class MLPBlock(nn.Module):
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def __init__(self, width: int, expanded_width: int):
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super().__init__()
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self.up_proj = nn.Linear(width, expanded_width // 2)
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self.gate_proj = nn.Linear(width, expanded_width // 2)
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self.down_proj = nn.Linear(expanded_width // 2, width)
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def __call__(self, x: mx.array):
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gate = self.gate_proj(x)
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x = self.up_proj(x)
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return self.down_proj(nn.gelu_approx(gate) * x)
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class ResidualBlock(nn.Module):
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def __init__(
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self,
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width: int,
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mlp_expanded_width: int,
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num_heads: int,
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attention_window_size: int,
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temporal_block_type: str,
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lru_width: Optional[int] = None,
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conv1d_temporal_width: int = 4,
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):
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"""Initializes the residual block.
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Args:
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width: The width of the block.
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mlp_expanded_width: The width of the expansion inside the MLP block.
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num_heads: The number of heads for the Attention or the RG-LRU.
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attention_window_size: The window size for the local attention block.
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temporal_block_type: Either "recurrent" or "attention", specifying the
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type of recurrent block to use.
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lru_width: The width of the RG-LRU if different from `width`.
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conv1d_temporal_width: The width of the temporal convolution.
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"""
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super().__init__()
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self.width = width
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self.mlp_expanded_width = mlp_expanded_width
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self.num_heads = num_heads
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self.attention_window_size = attention_window_size
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self.temporal_block_type = temporal_block_type
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self.lru_width = lru_width
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self.conv1d_temporal_width = conv1d_temporal_width
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self.temporal_pre_norm = RMSNorm(width)
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if self.temporal_block_type == "recurrent":
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self.temporal_block = RecurrentBlock(
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width=self.width,
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num_heads=self.num_heads,
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lru_width=self.lru_width,
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conv1d_temporal_width=self.conv1d_temporal_width,
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)
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else:
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self.temporal_block = LocalAttentionBlock(
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width=self.width,
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num_heads=self.num_heads,
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window_size=self.attention_window_size,
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)
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self.channel_pre_norm = RMSNorm(width)
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self.mlp_block = MLPBlock(
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width=self.width,
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expanded_width=self.mlp_expanded_width,
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)
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def __call__(
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self,
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x: mx.array,
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cache=None,
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mask=None,
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):
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raw_x = x
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inputs_normalized = self.temporal_pre_norm(raw_x)
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x = self.temporal_block(inputs_normalized, cache=cache, mask=mask)
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residual = x + raw_x
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x = self.channel_pre_norm(residual)
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x = self.mlp_block(x)
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x = x + residual
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return x
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class Griffin(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.embed_tokens = nn.Embedding(
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config.vocab_size,
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config.hidden_size,
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)
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self.scale_by_sqrt_dim = config.embeddings_scale_by_sqrt_dim
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block_types = config._block_types
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self.layers = [
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ResidualBlock(
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width=config.hidden_size,
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mlp_expanded_width=config.intermediate_size,
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num_heads=config.num_attention_heads,
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attention_window_size=config.attention_window_size,
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temporal_block_type=block_types[i % len(block_types)],
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lru_width=None,
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)
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for i in range(config.num_hidden_layers)
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]
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self.final_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def __call__(
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self,
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tokens,
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cache=None,
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):
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x = self.embed_tokens(tokens)
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if self.scale_by_sqrt_dim:
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x = x * math.sqrt(x.shape[-1])
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mask = None
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if x.shape[1] > 1:
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mask = create_window_causal_mask(
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x.shape[1], self.config.attention_window_size
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)
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mask = mask.astype(x.dtype)
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for i, block in enumerate(self.layers):
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x = block(x, mask=mask, cache=cache[i])
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x = self.final_norm(x)
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logits = self.embed_tokens.as_linear(x)
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c = self.config.logits_soft_cap
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if c:
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logits = mx.tanh(logits / c) * c
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return logits
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class Model(nn.Module):
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def __init__(self, config):
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self.args = config
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self.model = Griffin(config)
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def __call__(self, tokens: mx.array, cache=None) -> mx.array:
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"""
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Args:
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tokens: Sequence of input tokens.
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"""
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return self.model(tokens, cache=cache)
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@property
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def layers(self):
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return self.model.layers
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def sanitize(self, weights):
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# Remove unused precomputed rotary freqs
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for k, v in weights.items():
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if "conv_1d.weight" in k and v.ndim == 3:
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weights[k] = v.squeeze(1).T
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return weights
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|
||||
def make_cache(self):
|
||||
cache = []
|
||||
for layer in self.layers:
|
||||
if layer.temporal_block_type == "recurrent":
|
||||
cache.append(RecurrentCache())
|
||||
else:
|
||||
cache.append(WindowKVCache(self.args.attention_window_size))
|
||||
return cache
|
||||
@ -181,12 +181,15 @@ def generate_step(
|
||||
)
|
||||
|
||||
y = prompt
|
||||
kv_heads = (
|
||||
[model.n_kv_heads] * len(model.layers)
|
||||
if isinstance(model.n_kv_heads, int)
|
||||
else model.n_kv_heads
|
||||
)
|
||||
cache = [KVCache(model.head_dim, n) for n in kv_heads]
|
||||
if hasattr(model, "make_cache"):
|
||||
cache = model.make_cache()
|
||||
else:
|
||||
kv_heads = (
|
||||
[model.n_kv_heads] * len(model.layers)
|
||||
if isinstance(model.n_kv_heads, int)
|
||||
else model.n_kv_heads
|
||||
)
|
||||
cache = [KVCache(model.head_dim, n) for n in kv_heads]
|
||||
|
||||
repetition_context = prompt.tolist()
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user