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Add llms subdir + update README (#145)
* add llms subdir + update README * nits * use same pre-commit as mlx * update readmes a bit * format
This commit is contained in:
Awni Hannun
1
llms/mistral/.gitignore
vendored
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1
llms/mistral/.gitignore
vendored
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mistral-7B-v0.1/
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50
llms/mistral/README.md
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50
llms/mistral/README.md
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# Mistral
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An example of generating text with Mistral using MLX.
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Mistral 7B is one of the top large language models in its size class. It is
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also fully open source with a permissive license[^1].
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### Setup
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Install the dependencies:
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```
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pip install -r requirements.txt
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```
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Next, download the model and tokenizer:
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```
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curl -O https://files.mistral-7b-v0-1.mistral.ai/mistral-7B-v0.1.tar
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tar -xf mistral-7B-v0.1.tar
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```
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Then, convert the weights with:
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```
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python convert.py
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```
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The conversion script will save the converted weights in the same location.
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> [!TIP]
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> Alternatively, you can also download a few converted checkpoints from the
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> [MLX Community](https://huggingface.co/mlx-community) organization on Hugging
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> Face and skip the conversion step.
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### Run
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Once you've converted the weights to MLX format, you can generate text with
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the Mistral model:
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```
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python mistral.py --prompt "It is a truth universally acknowledged," --temp 0
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```
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Run `python mistral.py --help` for more details.
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[^1]: Refer to the [blog post](https://mistral.ai/news/announcing-mistral-7b/)
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and [github repository](https://github.com/mistralai/mistral-src) for more
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details.
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32
llms/mistral/convert.py
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32
llms/mistral/convert.py
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# Copyright © 2023 Apple Inc.
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import argparse
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import json
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from pathlib import Path
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import numpy as np
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import torch
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Convert Mistral weights to MLX.")
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parser.add_argument(
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"--model_path",
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type=str,
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default="mistral-7B-v0.1/",
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help="The path to the Mistral model. The MLX weights will also be saved there.",
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)
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args = parser.parse_args()
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model_path = Path(args.model_path)
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state = torch.load(str(model_path / "consolidated.00.pth"))
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np.savez(
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str(model_path / "weights.npz"),
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**{k: v.to(torch.float16).numpy() for k, v in state.items()}
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)
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# Save config.json with model_type
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with open(model_path / "params.json", "r") as f:
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config = json.loads(f.read())
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config["model_type"] = "mistral"
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with open(model_path / "config.json", "w") as f:
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json.dump(config, f, indent=4)
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282
llms/mistral/mistral.py
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282
llms/mistral/mistral.py
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# Copyright © 2023 Apple Inc.
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import argparse
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import json
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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_map, 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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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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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)
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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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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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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.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 = RMSNorm(args.dim, eps=args.norm_eps)
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self.ffn_norm = 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 = 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, dtype=mx.float16):
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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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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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weights = tree_map(lambda p: p.astype(dtype), weights)
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model = Mistral(model_args)
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model.update(weights)
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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="mistral-7B-v0.1",
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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=1.0,
|
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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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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, _ in zip(generate(prompt, model, args.temp), range(args.max_tokens)):
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tokens.append(token)
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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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|
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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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4
llms/mistral/requirements.txt
Normal file
4
llms/mistral/requirements.txt
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@@ -0,0 +1,4 @@
|
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mlx
|
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sentencepiece
|
||||
torch
|
||||
numpy
|
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117
llms/mistral/test.py
Normal file
117
llms/mistral/test.py
Normal file
@@ -0,0 +1,117 @@
|
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# Copyright © 2023 Apple Inc.
|
||||
|
||||
import unittest
|
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|
||||
import mistral
|
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import mlx.core as mx
|
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from mlx.utils import tree_map
|
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|
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|
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class TestMistral(unittest.TestCase):
|
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def test_model(self):
|
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vocab_size = 100
|
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L = 32
|
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args = mistral.ModelArgs(
|
||||
dim=128,
|
||||
n_layers=2,
|
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head_dim=32,
|
||||
hidden_dim=256,
|
||||
n_heads=4,
|
||||
n_kv_heads=4,
|
||||
norm_eps=1e-3,
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vocab_size=vocab_size,
|
||||
)
|
||||
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model = mistral.Mistral(args)
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inputs = mx.random.randint(0, vocab_size, (L,))
|
||||
logits, cache = model(inputs[None])
|
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self.assertEqual(logits.shape, [1, L, vocab_size])
|
||||
self.assertEqual(logits.dtype, mx.float32)
|
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self.assertEqual(len(cache), args.n_layers)
|
||||
|
||||
params = tree_map(lambda p: p.astype(mx.float16), model.parameters())
|
||||
model.update(params)
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||||
logits, _ = model(inputs[None])
|
||||
self.assertEqual(logits.dtype, mx.float16)
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def test_generate(self):
|
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model, tokenizer = mistral.load_model("mistral-7B-v0.1")
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prompt = mx.array(tokenizer.encode("This is a test"))
|
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tokens = [t for t, _ in zip(mistral.generate(prompt, model), range(30))]
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||||
mx.eval(tokens)
|
||||
tokens = [t.item() for t in tokens]
|
||||
expected = [
|
||||
302,
|
||||
272,
|
||||
11843,
|
||||
11837,
|
||||
1587,
|
||||
28723,
|
||||
851,
|
||||
349,
|
||||
865,
|
||||
264,
|
||||
1369,
|
||||
28723,
|
||||
13,
|
||||
13,
|
||||
3381,
|
||||
456,
|
||||
654,
|
||||
264,
|
||||
1353,
|
||||
11843,
|
||||
28725,
|
||||
368,
|
||||
682,
|
||||
347,
|
||||
2240,
|
||||
767,
|
||||
298,
|
||||
511,
|
||||
28723,
|
||||
13,
|
||||
]
|
||||
self.assertEqual(tokens, expected)
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||||
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||||
def benchmark(self):
|
||||
import time
|
||||
|
||||
model, tokenizer = mistral.load_model("mistral-7B-v0.1")
|
||||
prompt = mx.random.randint(0, model.vocab_size, (128,))
|
||||
|
||||
# warmup
|
||||
for _ in range(2):
|
||||
generator = mistral.generate(prompt, model)
|
||||
mx.eval(next(generator))
|
||||
|
||||
tic = time.time()
|
||||
its = 5
|
||||
for _ in range(its):
|
||||
generator = mistral.generate(prompt, model)
|
||||
mx.eval(next(generator))
|
||||
toc = time.time()
|
||||
tps = its * prompt.size / (toc - tic)
|
||||
print(f"Prompt processing: {tps:.2f} tokens per second")
|
||||
|
||||
# warmup
|
||||
for _ in range(2):
|
||||
tokens = [t for t, _ in zip(mistral.generate(prompt, model), range(101))]
|
||||
mx.eval(tokens)
|
||||
|
||||
time_total = 0.0
|
||||
its = 2
|
||||
for _ in range(its):
|
||||
generator = mistral.generate(prompt, model)
|
||||
mx.eval(next(generator))
|
||||
tic = time.time()
|
||||
tokens = [t for t, _ in zip(generator, range(100))]
|
||||
mx.eval(tokens)
|
||||
time_total += time.time() - tic
|
||||
|
||||
tps = len(tokens) * its / time_total
|
||||
print(f"Token generation: {tps:.3f} tokens per second")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user