mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 01:17:28 +08:00
2146bcd7ee
* more async eval * quantize embedding / update quantize api * more updates for quantize * update for quantize embeddings * update sd quant API * update sdxl quants * error for datasets < batch_size * async * fix config loading * fix quant * fix tests * fix req * remove lm head if tie weights is true * fix test
277 lines
8.4 KiB
Python
277 lines
8.4 KiB
Python
# Copyright © 2023 Apple Inc.
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import argparse
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import json
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import time
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from dataclasses import dataclass
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from pathlib import Path
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from typing import List, Optional, Tuple
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import mlx.core as mx
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import mlx.nn as nn
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from mlx.utils import tree_unflatten
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from sentencepiece import SentencePieceProcessor
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@dataclass
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class ModelArgs:
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dim: int
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n_layers: int
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head_dim: int
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hidden_dim: int
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n_heads: int
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n_kv_heads: int
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norm_eps: float
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vocab_size: int
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rope_theta: float = 10000
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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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self.args = args
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self.n_heads: int = args.n_heads
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self.n_kv_heads: int = args.n_kv_heads
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self.repeats = self.n_heads // self.n_kv_heads
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self.scale = self.args.head_dim**-0.5
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self.wq = nn.Linear(args.dim, args.n_heads * args.head_dim, bias=False)
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self.wk = nn.Linear(args.dim, args.n_kv_heads * args.head_dim, bias=False)
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self.wv = nn.Linear(args.dim, args.n_kv_heads * args.head_dim, bias=False)
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self.wo = nn.Linear(args.n_heads * args.head_dim, args.dim, bias=False)
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self.rope = nn.RoPE(args.head_dim, traditional=True, base=args.rope_theta)
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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.wq(x), self.wk(x), self.wv(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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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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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.wo(output), (keys, values)
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class FeedForward(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.w1 = nn.Linear(args.dim, args.hidden_dim, bias=False)
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self.w2 = nn.Linear(args.hidden_dim, args.dim, bias=False)
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self.w3 = nn.Linear(args.dim, args.hidden_dim, bias=False)
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def __call__(self, x) -> mx.array:
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return self.w2(nn.silu(self.w1(x)) * self.w3(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.n_heads = args.n_heads
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self.dim = args.dim
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self.attention = Attention(args)
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self.feed_forward = FeedForward(args=args)
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self.attention_norm = nn.RMSNorm(args.dim, eps=args.norm_eps)
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self.ffn_norm = nn.RMSNorm(args.dim, eps=args.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.attention(self.attention_norm(x), mask, cache)
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h = x + r
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r = self.feed_forward(self.ffn_norm(h))
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out = h + r
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return out, cache
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class Mistral(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.n_layers = args.n_layers
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assert self.vocab_size > 0
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self.tok_embeddings = nn.Embedding(args.vocab_size, args.dim)
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self.layers = [TransformerBlock(args=args) for _ in range(args.n_layers)]
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self.norm = nn.RMSNorm(args.dim, eps=args.norm_eps)
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self.output = nn.Linear(args.dim, 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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h = self.tok_embeddings(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.output(self.norm(h)), cache
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class Tokenizer:
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def __init__(self, model_path: str):
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assert Path(model_path).exists(), model_path
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self._model = SentencePieceProcessor(model_file=model_path)
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self._sep = "▁"
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assert self._model.vocab_size() == self._model.get_piece_size()
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@property
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def eos_id(self) -> int:
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return self._model.eos_id()
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@property
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def pad_id(self) -> int:
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return self._model.pad_id()
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def encode(self, s: str) -> List[int]:
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return [self._model.bos_id(), *self._model.encode(s)]
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def decode(self, t: List[int]) -> str:
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out = self._model.decode(t)
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if t and self._model.id_to_piece(t[0])[0] == self._sep:
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return " " + out
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return out
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def load_model(folder: str):
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model_path = Path(folder)
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tokenizer = Tokenizer(str(model_path / "tokenizer.model"))
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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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config.pop("sliding_window", None)
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config.pop("model_type", None)
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quantization = config.pop("quantization", None)
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model_args = ModelArgs(**config)
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weights = mx.load(str(model_path / "weights.npz"))
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weights = tree_unflatten(list(weights.items()))
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model = Mistral(model_args)
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if quantization is not None:
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nn.quantize(model, **quantization)
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model.update(weights)
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mx.eval(model.parameters())
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return model, tokenizer
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def generate(prompt: mx.array, model: Mistral, temp: Optional[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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logits, cache = model(prompt[None])
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y = sample(logits[:, -1, :])
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yield y
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while True:
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logits, cache = model(y[:, None], cache)
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y = sample(logits.squeeze(1))
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yield y
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Mistral inference script")
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parser.add_argument(
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"--model-path",
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type=str,
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default="mlx_model",
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help="The path to the model weights and tokenizer",
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)
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parser.add_argument(
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"--prompt",
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help="The message to be processed by the model",
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default="In the beginning the Universe was created.",
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)
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parser.add_argument(
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"--max-tokens",
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"-m",
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type=int,
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default=100,
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help="Maximum number of tokens to generate",
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)
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parser.add_argument(
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"--temp",
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help="The sampling temperature.",
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type=float,
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default=0.0,
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)
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parser.add_argument(
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"--tokens-per-eval",
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help="The batch size of tokens to generate.",
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type=int,
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default=10,
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)
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parser.add_argument("--seed", type=int, default=0, help="The PRNG seed")
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args = parser.parse_args()
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mx.random.seed(args.seed)
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print("[INFO] Loading model from disk.")
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model, tokenizer = load_model(args.model_path)
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print("[INFO] Starting generation...")
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tic = time.time()
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print(args.prompt, end="", flush=True)
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prompt = mx.array(tokenizer.encode(args.prompt))
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tokens = []
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for token, ntoks in zip(generate(prompt, model, args.temp), range(args.max_tokens)):
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tokens.append(token)
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if ntoks == 0:
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mx.eval(tokens)
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toc = time.time()
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prompt_tps = prompt.size / (toc - tic)
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tic = time.time()
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if (len(tokens) % args.tokens_per_eval) == 0:
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mx.eval(tokens)
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s = tokenizer.decode([t.item() for t in tokens])
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print(s, end="", flush=True)
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tokens = []
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mx.eval(tokens)
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s = tokenizer.decode([t.item() for t in tokens])
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print(s, flush=True)
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print("------")
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generation_tps = ntoks / (time.time() - tic)
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print(
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f"Tokens per second: prompt {prompt_tps:.3f}, "
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f"generation {generation_tps:.3f}"
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)
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