mistral

mistral/.gitignore

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+mistral-7B-v0.1/

mistral/README.md

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+# Mistral 
+
+An example of generating text with Mistral using MLX.
+
+Mistral 7B is one of the top large language models in its size class. It is also fully open source with a permissive license[^1].
+
+### Setup
+
+Install the dependencies:
+
+```
+pip install -r requirements.txt
+```
+
+Next, download the model and tokenizer.
+
+```
+curl -O https://files.mistral-7b-v0-1.mistral.ai/mistral-7B-v0.1.tar
+tar -xf mistral-7B-v0.1.tar
+```
+
+Then, convert the weights with:
+
+```
+python convert.py <path_to_torch_weights> mlx_mistral_weights.npz
+```
+
+### Run
+
+Once you've converted the weights to MLX format, you can interact with the
+Mistral model:
+
+```
+python mistral.py mlx_mistral.npz tokenizer.model "hello"
+```
+
+Run `python mistral.py --help` for more details.
+
+[^1]: Refer to the [blog post](https://mistral.ai/news/announcing-mistral-7b/) and [github repository](https://github.com/mistralai/mistral-src) for more details.

mistral/convert.py

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+# Copyright © 2023 Apple Inc.
+
+import argparse
+import numpy as np
+import torch
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Convert Mistral weights to MLX.")
+    parser.add_argument(
+        "--torch_model",
+        type=str,
+        default="mistral-7B-v0.1/consolidated.00.pth",
+        help="The path to the torch model weights",
+    )
+    parser.add_argument(
+        "--mlx_model",
+        type=str,
+        default="mistral-7B-v0.1/mlx_mistral_7b.npz",
+        help="The path to store the mlx model weights",
+    )
+    args = parser.parse_args()
+
+    state = torch.load(args.torch_model)
+    np.savez(
+        args.mlx_model, **{k: v.to(torch.float16).numpy() for k, v in state.items()}
+    )

mistral/mistral.py

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+# Copyright © 2023 Apple Inc.
+
+import argparse
+from dataclasses import dataclass
+import json
+from pathlib import Path
+from typing import Optional, Tuple, List
+from sentencepiece import SentencePieceProcessor
+
+import mlx.core as mx
+import mlx.nn as nn
+from mlx.utils import tree_map, tree_unflatten
+
+
+@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
+
+
+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)
+
+    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)
+
+        def repeat(a):
+            a = mx.concatenate([mx.expand_dims(a, 2)] * self.repeats, axis=2)
+            return a.reshape([B, self.n_heads, L, -1])
+
+        keys, values = map(repeat, (keys, values))
+
+        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)
+
+        #        queries = queries.reshape(B, self.n_kv_heads, self.repeats, L, -1)
+        #        scores = (queries * self.scale) @ mx.expand_dims(keys.transpose(0, 1, 3, 2), 2)
+        #        if mask is not None:
+        #            scores += mask
+        #        scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
+        #        output = (scores @ mx.expand_dims(values, 2)).reshape(B, self.n_heads, L, -1)
+        #        output = output.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 TransformerBlock(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 = FeedForward(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 Mistral(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 = [TransformerBlock(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
+        if h.shape[1] > 1:
+            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
+            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)), 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, dtype=mx.float16):
+    model_path = Path(folder)
+    tokenizer = Tokenizer(str(model_path / "tokenizer.model"))
+    with open(model_path / "params.json", "r") as f:
+        config = json.loads(f.read())
+        config.pop("sliding_window")
+        model_args = ModelArgs(**config)
+    weights = mx.load(str(model_path / "mlx_mistral_7b.npz"))
+    weights = tree_unflatten(list(weights.items()))
+    weights = tree_map(lambda p: p.astype(dtype), weights)
+    model = Mistral(model_args)
+    model.update(weights)
+    return model, tokenizer
+
+
+def generate(prompt: mx.array, model: Mistral, 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="Mistral inference script")
+    parser.add_argument(
+        "--model_path",
+        type=str,
+        default="mistral-7B-v0.1",
+        help="The path to the model weights and tokenizer",
+    )
+    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("--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), range(args.max_tokens)):
+        tokens.append(token)
+
+        if (len(tokens) % 10) == 0:
+            mx.eval(tokens)
+            s = tokenizer.decode([t.item() for t in tokens])
+            print(s, end="", flush=True)
+            tokens = []
+
+    mx.eval(tokens)
+    s = tokenizer.decode([t.item() for t in tokens])
+    print(s, flush=True)
+    print("------")

mistral/requirements.txt

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+mlx
+sentencepiece
+torch

mistral/test.py

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+# Copyright © 2023 Apple Inc.
+
+import unittest
+
+import mlx.core as mx
+from mlx.utils import tree_map
+
+import mistral
+
+
+class TestMistral(unittest.TestCase):
+    def test_model(self):
+        vocab_size = 100
+        L = 32
+        args = mistral.ModelArgs(
+            dim=128,
+            n_layers=2,
+            head_dim=32,
+            hidden_dim=256,
+            n_heads=4,
+            n_kv_heads=4,
+            norm_eps=1e-3,
+            vocab_size=vocab_size,
+        )
+
+        model = mistral.Mistral(args)
+        inputs = mx.random.randint(0, vocab_size, (L,))
+        logits, cache = model(inputs[None])
+        self.assertEqual(logits.shape, [1, L, vocab_size])
+        self.assertEqual(logits.dtype, mx.float32)
+        self.assertEqual(len(cache), args.n_layers)
+
+        params = tree_map(lambda p: p.astype(mx.float16), model.parameters())
+        model.update(params)
+        logits, _ = model(inputs[None])
+        self.assertEqual(logits.dtype, mx.float16)
+
+    def test_generate(self):
+        model, tokenizer = mistral.load_model("mistral-7B-v0.1")
+        prompt = mx.array(tokenizer.encode("This is a test"))
+        tokens = [t for t, _ in zip(mistral.generate(prompt, model), range(30))]
+        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)
+
+    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()