mixtral.py

# Copyright © 2023 Apple Inc.

import argparse
import glob
import json
from dataclasses import dataclass
from pathlib import Path
from typing import List, Optional, Tuple

import mlx.core as mx
import mlx.nn as nn
from mlx.utils import tree_map, tree_unflatten
from sentencepiece import SentencePieceProcessor


@dataclass
class ModelArgs:
    dim: int
    n_layers: int
    head_dim: int
    hidden_dim: int
    n_heads: int
    n_kv_heads: int
    norm_eps: float
    vocab_size: int
    moe: dict = None


class RMSNorm(nn.Module):
    def __init__(self, dims: int, eps: float = 1e-5):
        super().__init__()
        self.weight = mx.ones((dims,))
        self.eps = eps

    def _norm(self, x):
        return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.eps)

    def __call__(self, x):
        output = self._norm(x.astype(mx.float32)).astype(x.dtype)
        return self.weight * output


class Attention(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.args = args

        self.n_heads: int = args.n_heads
        self.n_kv_heads: int = args.n_kv_heads

        self.repeats = self.n_heads // self.n_kv_heads

        self.scale = self.args.head_dim**-0.5

        self.wq = nn.Linear(args.dim, args.n_heads * args.head_dim, bias=False)
        self.wk = nn.Linear(args.dim, args.n_kv_heads * args.head_dim, bias=False)
        self.wv = nn.Linear(args.dim, args.n_kv_heads * args.head_dim, bias=False)
        self.wo = nn.Linear(args.n_heads * args.head_dim, args.dim, bias=False)
        self.rope = nn.RoPE(args.head_dim, traditional=True, base=1000000)

    def __call__(
        self,
        x: mx.array,
        mask: Optional[mx.array] = None,
        cache: Optional[Tuple[mx.array, mx.array]] = None,
    ) -> mx.array:
        B, L, D = x.shape

        queries, keys, values = self.wq(x), self.wk(x), self.wv(x)

        # Prepare the queries, keys and values for the attention computation
        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)

        keys = mx.repeat(keys, self.repeats, axis=1)
        values = mx.repeat(values, self.repeats, axis=1)

        if cache is not None:
            key_cache, value_cache = cache
            queries = self.rope(queries, offset=key_cache.shape[2])
            keys = self.rope(keys, offset=key_cache.shape[2])
            keys = mx.concatenate([key_cache, keys], axis=2)
            values = mx.concatenate([value_cache, values], axis=2)
        else:
            queries = self.rope(queries)
            keys = self.rope(keys)

        scores = (queries * self.scale) @ keys.transpose(0, 1, 3, 2)
        if mask is not None:
            scores += mask
        scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
        output = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
        return self.wo(output), (keys, values)


class FeedForward(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()

        self.w1 = nn.Linear(args.dim, args.hidden_dim, bias=False)
        self.w2 = nn.Linear(args.hidden_dim, args.dim, bias=False)
        self.w3 = nn.Linear(args.dim, args.hidden_dim, bias=False)

    def __call__(self, x) -> mx.array:
        return self.w2(nn.silu(self.w1(x)) * self.w3(x))


class MOEFeedForward(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()

        self.num_experts = args.moe["num_experts"]
        self.num_experts_per_tok = args.moe["num_experts_per_tok"]
        self.experts = [FeedForward(args) for _ in range(self.num_experts)]
        self.gate = nn.Linear(args.dim, self.num_experts, bias=False)

    def __call__(self, x) -> mx.array:
        ne = self.num_experts_per_tok
        orig_shape = x.shape
        x = x.reshape(-1, x.shape[-1])

        gates = self.gate(x)
        inds = mx.argpartition(-gates, kth=ne, axis=-1)[:, :ne]
        scores = mx.softmax(
            mx.take_along_axis(gates, inds, axis=-1).astype(mx.float32),
            axis=-1,
        ).astype(gates.dtype)

        y = []
        for xt, st, it in zip(x, scores, inds.tolist()):
            yt = mx.concatenate([self.experts[e](xt)[:, None] for e in it], axis=-1)
            yt = (yt * st).sum(axis=-1)
            y.append(yt[None, :])
        y = mx.concatenate(y)

        return y.reshape(orig_shape)


class MOETransformerBlock(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.n_heads = args.n_heads
        self.dim = args.dim
        self.attention = Attention(args)
        self.feed_forward = MOEFeedForward(args=args)
        self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
        self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
        self.args = args

    def __call__(
        self,
        x: mx.array,
        mask: Optional[mx.array] = None,
        cache: Optional[Tuple[mx.array, mx.array]] = None,
    ) -> mx.array:
        r, cache = self.attention(self.attention_norm(x), mask, cache)
        h = x + r
        r = self.feed_forward(self.ffn_norm(h))
        out = h + r
        return out, cache


class Mixtral(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.args = args
        self.vocab_size = args.vocab_size
        self.n_layers = args.n_layers
        assert self.vocab_size > 0
        self.tok_embeddings = nn.Embedding(args.vocab_size, args.dim)
        self.layers = [MOETransformerBlock(args=args) for _ in range(args.n_layers)]
        self.norm = RMSNorm(args.dim, eps=args.norm_eps)
        self.output = nn.Linear(args.dim, args.vocab_size, bias=False)

    def __call__(
        self,
        inputs: mx.array,
        cache=None,
    ):
        h = self.tok_embeddings(inputs)

        mask = None
        T = h.shape[1]
        if T > 1:
            mask = nn.MultiHeadAttention.create_additive_causal_mask(T)
            mask = mask.astype(h.dtype)

        if cache is None:
            cache = [None] * len(self.layers)

        for e, layer in enumerate(self.layers):
            h, cache[e] = layer(h, mask, cache[e])

        return self.output(self.norm(h[:, T - 1 : T, :])), cache


class Tokenizer:
    def __init__(self, model_path: str):
        assert Path(model_path).exists(), model_path
        self._model = SentencePieceProcessor(model_file=model_path)
        self._sep = "▁"
        assert self._model.vocab_size() == self._model.get_piece_size()

    @property
    def eos_id(self) -> int:
        return self._model.eos_id()

    @property
    def pad_id(self) -> int:
        return self._model.pad_id()

    def encode(self, s: str) -> List[int]:
        return [self._model.bos_id(), *self._model.encode(s)]

    def decode(self, t: List[int]) -> str:
        out = self._model.decode(t)
        if t and self._model.id_to_piece(t[0])[0] == self._sep:
            return " " + out
        return out


def load_model(folder: str):
    model_path = Path(folder)
    tokenizer = Tokenizer(str(model_path / "tokenizer.model"))
    with open(model_path / "config.json", "r") as f:
        config = json.loads(f.read())
        config.pop("model_type", None)
        quantization = config.pop("quantization", None)
        model_args = ModelArgs(**config)
    weight_files = glob.glob(str(model_path / "weights.*.npz"))
    weights = {}
    for wf in weight_files:
        weights.update(mx.load(wf).items())
    weights = tree_unflatten(list(weights.items()))
    model = Mixtral(model_args)
    if quantization is not None:
        # TODO: Quantize gate matrices when < 32 tiles supported
        quantization["linear_class_predicate"] = (
            lambda m: isinstance(m, nn.Linear) and m.weight.shape[0] != 8
        )
        nn.QuantizedLinear.quantize_module(model, **quantization)

    model.update(weights)
    return model, tokenizer


def generate(prompt: mx.array, model: Mixtral, temp: Optional[float] = 0.0):
    def sample(logits):
        if temp == 0:
            return mx.argmax(logits, axis=-1)
        else:
            return mx.random.categorical(logits * (1 / temp))

    logits, cache = model(prompt[None])
    y = sample(logits[:, -1, :])
    yield y

    while True:
        logits, cache = model(y[:, None], cache)
        y = sample(logits.squeeze(1))
        yield y


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Mixtral inference script")
    parser.add_argument(
        "--model-path",
        type=str,
        default="mlx_model",
        help="The path to the model weights, tokenizer, and config",
    )
    parser.add_argument(
        "--prompt",
        help="The message to be processed by the model",
        default="In the beginning the Universe was created.",
    )
    parser.add_argument(
        "--max-tokens",
        "-m",
        type=int,
        default=100,
        help="Maximum number of tokens to generate",
    )
    parser.add_argument(
        "--temp",
        help="The sampling temperature.",
        type=float,
        default=0.0,
    )
    parser.add_argument("--seed", type=int, default=0, help="The PRNG seed")

    args = parser.parse_args()

    mx.random.seed(args.seed)
    print("[INFO] Loading model from disk.")
    model, tokenizer = load_model(args.model_path)

    print("[INFO] Starting generation...")

    print(args.prompt, end="", flush=True)
    prompt = mx.array(tokenizer.encode(args.prompt))
    tokens = []
    for token, _ in zip(generate(prompt, model, args.temp), range(args.max_tokens)):
        tokens.append(token)

        if (len(tokens) % 10) == 0:
            mx.eval(tokens)
            eos_index = next(
                (i for i, t in enumerate(tokens) if t.item() == tokenizer.eos_id), None
            )
            if eos_index is not None:
                tokens = tokens[:eos_index]
            s = tokenizer.decode([t.item() for t in tokens])
            print(s, end="", flush=True)
            tokens = []
            if eos_index is not None:
                break

    mx.eval(tokens)
    s = tokenizer.decode([t.item() for t in tokens])
    print(s, flush=True)