mirror of
https://github.com/ml-explore/mlx-examples.git
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359 lines
11 KiB
Python
359 lines
11 KiB
Python
# Copyright © 2023 Apple Inc.
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import glob
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import inspect
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import json
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import math
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple, Union
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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 huggingface_hub import snapshot_download
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from transformers import AutoTokenizer
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@dataclass
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class ModelArgs:
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hidden_size: int
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num_hidden_layers: int
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intermediate_size: int
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num_attention_heads: int
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rms_norm_eps: float
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vocab_size: int
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num_key_value_heads: int = None
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rope_theta: float = 10000
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rope_traditional: bool = False
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model_type: str = None
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rope_scaling: Optional[Dict[str, Union[float, str]]] = None
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def __post_init__(self):
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if self.num_key_value_heads is None:
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self.num_key_value_heads = self.num_attention_heads
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if self.rope_scaling:
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required_keys = {"factor", "type"}
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if not all(key in self.rope_scaling for key in required_keys):
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raise ValueError(f"rope_scaling must contain keys {required_keys}")
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if self.rope_scaling["type"] != "linear":
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raise ValueError("rope_scaling 'type' currently only supports 'linear'")
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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 LoRALinear(nn.Module):
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@staticmethod
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def from_linear(linear: nn.Linear, rank: int = 8):
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# TODO remove when input_dims and output_dims are attributes
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# on linear and quantized linear
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output_dims, input_dims = linear.weight.shape
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if isinstance(linear, nn.QuantizedLinear):
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input_dims *= 32 // linear.bits
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lora_lin = LoRALinear(input_dims, output_dims, rank)
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lora_lin.linear = linear
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return lora_lin
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def to_linear(self):
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linear = self.linear
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bias = "bias" in linear
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weight = linear.weight
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is_quantized = isinstance(linear, nn.QuantizedLinear)
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# Use the same type as the linear weight if not quantized
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dtype = weight.dtype
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if is_quantized:
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dtype = mx.float16
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weight = mx.dequantize(
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weight,
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linear.scales,
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linear.biases,
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linear.group_size,
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linear.bits,
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)
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output_dims, input_dims = weight.shape
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fused_linear = nn.Linear(input_dims, output_dims, bias=bias)
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lora_b = (self.scale * self.lora_b.T).astype(dtype)
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lora_a = self.lora_a.T.astype(dtype)
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fused_linear.weight = weight + lora_b @ lora_a
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if bias:
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fused_linear.bias = linear.bias
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if is_quantized:
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fused_linear = nn.QuantizedLinear.from_linear(
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fused_linear,
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linear.group_size,
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linear.bits,
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)
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return fused_linear
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def __init__(
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self,
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input_dims: int,
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output_dims: int,
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lora_rank: int = 8,
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bias: bool = False,
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scale: float = 20.0,
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):
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super().__init__()
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# Regular linear layer weights
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self.linear = nn.Linear(input_dims, output_dims, bias=bias)
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# Scale for low-rank update
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self.scale = scale
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# Low rank lora weights
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scale = 1 / math.sqrt(input_dims)
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self.lora_a = mx.random.uniform(
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low=-scale,
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high=scale,
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shape=(input_dims, lora_rank),
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)
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self.lora_b = mx.zeros(shape=(lora_rank, output_dims))
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def __call__(self, x):
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dtype = self.linear.weight.dtype
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if isinstance(self.linear, nn.QuantizedLinear):
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dtype = self.linear.scales.dtype
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y = self.linear(x.astype(dtype))
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z = (x @ self.lora_a) @ self.lora_b
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return y + self.scale * z
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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 _norm(self, x):
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return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.eps)
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def __call__(self, x):
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output = self._norm(x.astype(mx.float32)).astype(x.dtype)
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return self.weight * output
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class Attention(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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dim = args.hidden_size
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self.n_heads = n_heads = args.num_attention_heads
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self.n_kv_heads = n_kv_heads = args.num_key_value_heads
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self.repeats = n_heads // n_kv_heads
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head_dim = args.hidden_size // n_heads
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self.scale = head_dim**-0.5
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self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False)
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self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
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self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
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self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
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rope_scale = (
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1 / args.rope_scaling["factor"]
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if args.rope_scaling is not None and args.rope_scaling["type"] == "linear"
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else 1
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)
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self.rope = nn.RoPE(
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head_dim,
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traditional=args.rope_traditional,
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base=args.rope_theta,
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scale=rope_scale,
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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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mask: Optional[mx.array] = None,
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cache: Optional[Tuple[mx.array, mx.array]] = None,
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) -> mx.array:
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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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# Prepare the queries, keys and values for the attention computation
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queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
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keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
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values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
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def repeat(a):
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a = mx.concatenate([mx.expand_dims(a, 2)] * self.repeats, axis=2)
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return a.reshape([B, self.n_heads, L, -1])
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if self.repeats > 1:
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keys, values = map(repeat, (keys, values))
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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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scores = (queries * self.scale) @ keys.transpose(0, 1, 3, 2)
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if mask is not None:
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scores += mask
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scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
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output = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
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return self.o_proj(output), (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.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
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self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
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self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
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def __call__(self, x) -> mx.array:
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return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
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class TransformerBlock(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.num_attention_heads = args.num_attention_heads
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self.hidden_size = args.hidden_size
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self.self_attn = Attention(args)
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self.mlp = MLP(args.hidden_size, args.intermediate_size)
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self.input_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
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self.args = args
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def __call__(
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self,
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x: mx.array,
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mask: Optional[mx.array] = None,
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cache: Optional[Tuple[mx.array, mx.array]] = None,
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) -> mx.array:
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r, cache = self.self_attn(self.input_layernorm(x), mask, cache)
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h = x + r
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r = self.mlp(self.post_attention_layernorm(h))
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out = h + r
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return out, cache
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class LlamaModel(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.vocab_size = args.vocab_size
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self.num_hidden_layers = args.num_hidden_layers
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assert self.vocab_size > 0
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self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
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self.layers = [
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TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
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]
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self.norm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
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def __call__(
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self,
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inputs: mx.array,
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cache=None,
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):
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h = self.embed_tokens(inputs)
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mask = None
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if h.shape[1] > 1:
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mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
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mask = mask.astype(h.dtype)
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if cache is None:
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cache = [None] * len(self.layers)
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for e, layer in enumerate(self.layers):
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h, cache[e] = layer(h, mask, cache[e])
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return self.norm(h), cache
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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.model = LlamaModel(args)
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self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
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def __call__(
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self,
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inputs: mx.array,
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cache=None,
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):
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out, cache = self.model(inputs, cache)
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return self.lm_head(out), cache
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def load(path_or_hf_repo: str):
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# If the path exists, it will try to load model form it
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# otherwise download and cache from the hf_repo and cache
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model_path = Path(path_or_hf_repo)
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if not model_path.exists():
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model_path = Path(
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snapshot_download(
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repo_id=path_or_hf_repo,
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allow_patterns=["*.json", "*.safetensors", "tokenizer.model"],
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)
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)
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with open(model_path / "config.json", "r") as f:
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config = json.loads(f.read())
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quantization = config.get("quantization", None)
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model_args = ModelArgs.from_dict(config)
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weight_files = glob.glob(str(model_path / "*.safetensors"))
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if len(weight_files) == 0:
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raise FileNotFoundError("No safetensors found in {}".format(model_path))
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weights = {}
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for wf in weight_files:
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weights.update(mx.load(wf).items())
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model = Model(model_args)
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if quantization is not None:
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nn.QuantizedLinear.quantize_module(
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model,
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**quantization,
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linear_class_predicate=lambda m: isinstance(m, nn.Linear)
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and m.weight.shape[0] != 8,
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)
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model.load_weights(list(weights.items()))
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mx.eval(model.parameters())
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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return model, tokenizer, config
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def generate(prompt: mx.array, model: Model, temp: float = 0.0):
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def sample(logits):
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if temp == 0:
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return mx.argmax(logits, axis=-1)
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else:
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return mx.random.categorical(logits * (1 / temp))
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y = prompt
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cache = None
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while True:
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logits, cache = model(y[None], cache=cache)
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logits = logits[:, -1, :]
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y = sample(logits)
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yield y
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