mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-25 01:41:19 +08:00
214 lines
6.5 KiB
Python
214 lines
6.5 KiB
Python
import inspect
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import math
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from dataclasses import dataclass
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from typing import Tuple
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import mlx.core as mx
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import mlx.nn as nn
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import numpy as np
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from .layers import LayerNorm
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@dataclass
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class ModelArgs:
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model_type: str
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max_sequence_length: int = 2048
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num_vocab: int = 51200
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model_dim: int = 2560
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num_heads: int = 32
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num_layers: int = 32
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rotary_dim: int = 32
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num_experts_per_tok: int = 2
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num_local_experts: int = 4
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@classmethod
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def from_dict(cls, params):
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return cls(
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**{
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k: v
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for k, v in params.items()
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if k in inspect.signature(cls).parameters
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}
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)
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class RoPEAttention(nn.Module):
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def __init__(self, dims: int, num_heads: int, rotary_dim: int):
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super().__init__()
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self.num_heads = num_heads
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self.rope = nn.RoPE(rotary_dim, traditional=False)
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self.Wqkv = nn.Linear(dims, 3 * dims)
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self.out_proj = nn.Linear(dims, dims)
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def __call__(self, x, mask=None, cache=None):
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qkv = self.Wqkv(x)
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queries, keys, values = mx.split(qkv, 3, axis=-1)
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# Extract some shapes
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num_heads = self.num_heads
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B, L, D = queries.shape
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# Prepare the queries, keys and values for the attention computation
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queries = queries.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
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keys = keys.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
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values = values.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
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# Add RoPE to the queries and keys and combine them with the cache
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if cache is not None:
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key_cache, value_cache = cache
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queries = self.rope(queries, offset=key_cache.shape[2])
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keys = self.rope(keys, offset=key_cache.shape[2])
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keys = mx.concatenate([key_cache, keys], axis=2)
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values = mx.concatenate([value_cache, values], axis=2)
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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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queries = queries.astype(mx.float32)
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keys = keys.astype(mx.float32)
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# Finally perform the attention computation
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scale = math.sqrt(1 / queries.shape[-1])
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scores = (queries * scale) @ keys.transpose(0, 1, 3, 2)
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if mask is not None:
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scores = scores + mask
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scores = mx.softmax(scores, axis=-1).astype(values.dtype)
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values_hat = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
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return self.out_proj(values_hat), (keys, values)
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class MLP(nn.Module):
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def __init__(self, dim, hidden_dim):
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super().__init__()
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self.fc1 = nn.Linear(dim, hidden_dim)
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self.fc2 = nn.Linear(hidden_dim, dim)
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self.act = nn.GELU(approx="precise")
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def __call__(self, x) -> mx.array:
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return self.fc2(self.act(self.fc1(x)))
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class MOE(nn.Module):
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def __init__(self, args: ModelArgs, dim: int, hidden_dim: int):
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super().__init__()
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self.dim = dim
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self.hidden_dim = hidden_dim
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self.num_experts = args.num_local_experts
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self.num_experts_per_tok = args.num_experts_per_tok
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self.mlp = [MLP(self.dim, self.hidden_dim) for _ in range(self.num_experts)]
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self.gate = nn.Linear(args.model_dim, self.num_experts, bias=False)
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def __call__(self, x: mx.array) -> mx.array:
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ne = self.num_experts_per_tok
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orig_shape = x.shape
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x = x.reshape(-1, x.shape[-1])
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gates = self.gate(x)
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inds = mx.stop_gradient(mx.argpartition(-gates, kth=ne, axis=-1))[:, :ne]
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scores = mx.softmax(
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mx.take_along_axis(gates, inds, axis=-1).astype(mx.float32),
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axis=-1,
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).astype(gates.dtype)
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if self.training:
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ys = []
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y = mx.zeros((x.shape[0], ne, x.shape[-1]))
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for e, expert in enumerate(self.mlp):
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idx1, idx2 = map(mx.array, np.where(inds == e))
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if idx1.size == 0:
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continue
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y[idx1, idx2] = expert(x[idx1])
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y = (y * scores[..., None]).sum(axis=1)
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else:
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y = []
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for xt, st, it in zip(x, scores, inds.tolist()):
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yt = mx.concatenate([self.mlp[e](xt)[:, None] for e in it], axis=-1)
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yt = (yt * st).sum(axis=-1)
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y.append(yt[None, :])
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y = mx.concatenate(y)
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return y.reshape(orig_shape)
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class ParallelBlock(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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dims = config.model_dim
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mlp_dims = dims * 4
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self.mixer = RoPEAttention(dims, config.num_heads, config.rotary_dim)
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self.ln = LayerNorm(dims)
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self.moe = MOE(config, dims, mlp_dims)
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def __call__(self, x, mask, cache):
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h = self.ln(x)
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attn_h, cache = self.mixer(h, mask, cache)
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ff_h = self.moe(h)
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return attn_h + ff_h + x, cache
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class TransformerDecoder(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.embd = Embd(config)
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self.h = [ParallelBlock(config) for i in range(config.num_layers)]
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def __call__(self, x, mask, cache):
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x = self.embd(x)
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if cache is None:
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cache = [None] * len(self.h)
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for e, layer in enumerate(self.h):
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x, cache[e] = layer(x, mask, cache[e])
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return x, cache
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class Embd(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.wte = nn.Embedding(config.num_vocab, config.model_dim)
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def __call__(self, x):
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return self.wte(x)
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class OutputHead(nn.Module):
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def __init__(self, config: ModelArgs) -> None:
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super().__init__()
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self.ln = LayerNorm(config.model_dim)
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self.linear = nn.Linear(config.model_dim, config.num_vocab)
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def __call__(self, inputs):
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return self.linear(self.ln(inputs))
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class Model(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.model_type = config.model_type
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self.transformer = TransformerDecoder(config)
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self.lm_head = OutputHead(config)
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def __call__(
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self,
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x: mx.array,
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mask: mx.array = None,
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cache: mx.array = None,
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) -> Tuple[mx.array, mx.array]:
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mask = None
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if x.shape[1] > 1:
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mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
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mask = mask.astype(x.dtype)
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y, cache = self.transformer(x, mask, cache)
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return self.lm_head(y), cache
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@property
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def layers(self):
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return self.transformer.h
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