Phi-2

phi2/.gitignore

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+weights.npz

phi2/README.md

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+# Phi-2
+
+Phi-2 is a 2.7B parameter language model released by Microsoft with
+performance that rivals much larger models.[^1] It was trained on a mixture of
+GPT-4 outputs and clean web text.
+
+Phi-2 efficiently runs on Apple silicon devices with 8GB of memory in 16-bit
+precision.
+
+## Setup 
+
+Download and convert the model:
+
+```sh 
+python convert.py
+```
+
+This will make the `weights.npz` file which MLX can read.
+
+## Generate 
+
+To generate text with the default prompt:
+
+```sh
+python phi2.py
+```
+
+Should give the output:
+
+```
+Answer: Mathematics is like a lighthouse that guides us through the darkness of
+uncertainty. Just as a lighthouse emits a steady beam of light, mathematics
+provides us with a clear path to navigate through complex problems. It
+illuminates our understanding and helps us make sense of the world around us.
+
+Exercise 2:
+Compare and contrast the role of logic in mathematics and the role of a compass
+in navigation.
+
+Answer: Logic in mathematics is like a compass in navigation. It helps
+```
+
+To use your own prompt:
+
+```sh
+python phi2.py --prompt <your prompt here> --max_tokens <max_tokens_to_generate>
+```
+
+To see a list of options run:
+
+```sh
+python phi2.py --help
+```
+
+[^1]: For more details on the model see the [blog post](
+https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/)
+and the [Hugging Face repo](https://huggingface.co/microsoft/phi-2)

phi2/convert.py

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+from transformers import AutoModelForCausalLM
+import numpy as np
+
+def replace_key(key: str) -> str:
+    if "wte.weight" in key:
+        key = "wte.weight"
+
+    if ".mlp" in key:
+        key = key.replace(".mlp", "")
+    return key
+
+
+def convert():
+    model = AutoModelForCausalLM.from_pretrained(
+        "microsoft/phi-2", torch_dtype="auto", trust_remote_code=True
+    )
+    state_dict = model.state_dict()
+    weights = {replace_key(k): v.numpy() for k, v in state_dict.items()}
+    np.savez("weights.npz", **weights)
+
+
+if __name__ == "__main__":
+    convert()

phi2/phi2.py

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+import argparse
+from typing import Optional
+from dataclasses import dataclass
+from mlx.utils import tree_unflatten
+from transformers import AutoTokenizer
+
+import mlx.core as mx
+import mlx.nn as nn
+import math
+
+
+@dataclass
+class ModelArgs:
+    max_sequence_length: int = 2048
+    num_vocab: int = 51200
+    model_dim: int = 2560
+    num_heads: int = 32
+    num_layers: int = 32
+    rotary_dim: int = 32
+
+
+class LayerNorm(nn.LayerNorm):
+    def __call__(self, x: mx.array) -> mx.array:
+        return super().__call__(x.astype(mx.float32)).astype(x.dtype)
+
+
+class RoPEAttention(nn.Module):
+    def __init__(self, dims: int, num_heads: int, rotary_dim: int):
+        super().__init__()
+
+        self.num_heads = num_heads
+
+        self.rope = nn.RoPE(rotary_dim, traditional=False)
+        self.Wqkv = nn.Linear(dims, 3 * dims)
+        self.out_proj = nn.Linear(dims, dims)
+
+    def __call__(self, x, mask=None, cache=None):
+        qkv = self.Wqkv(x)
+        queries, keys, values = mx.split(qkv, 3, axis=-1)
+
+        # Extract some shapes
+        num_heads = self.num_heads
+        B, L, D = queries.shape
+
+        # Prepare the queries, keys and values for the attention computation
+        queries = queries.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
+        keys = keys.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
+
+        # Add RoPE to the queries and keys and combine them with the cache
+        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)
+
+        queries = queries.astype(mx.float32)
+        keys = keys.astype(mx.float32)
+
+        # Finally perform the attention computation
+        scale = math.sqrt(1 / queries.shape[-1])
+        scores = (queries * scale) @ keys.transpose(0, 1, 3, 2)
+        if mask is not None:
+            scores = scores + mask
+
+        scores = mx.softmax(scores, axis=-1).astype(values.dtype)
+        values_hat = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
+
+        return self.out_proj(values_hat), (keys, values)
+
+
+class ParallelBlock(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        dims = config.model_dim
+        mlp_dims = dims * 4
+        self.mixer = RoPEAttention(dims, config.num_heads, config.rotary_dim)
+        self.ln = LayerNorm(dims)
+        self.fc1 = nn.Linear(dims, mlp_dims)
+        self.fc2 = nn.Linear(mlp_dims, dims)
+        self.act = nn.GELU(approx="precise")
+
+    def __call__(self, x, mask, cache):
+        h = self.ln(x)
+        attn_h, cache = self.mixer(h, mask, cache)
+        ff_h = self.fc2(self.act(self.fc1(h)))
+        return attn_h + ff_h + x, cache
+
+
+class TransformerDecoder(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.h = [ParallelBlock(config) for i in range(config.num_layers)]
+
+    def __call__(self, x, mask, cache):
+        if cache is None:
+            cache = [None] * len(self.h)
+
+        for e, layer in enumerate(self.h):
+            x, cache[e] = layer(x, mask, cache[e])
+        return x, cache
+
+
+class OutputHead(nn.Module):
+    def __init__(self, config: ModelArgs) -> None:
+        self.ln = LayerNorm(config.model_dim)
+        self.linear = nn.Linear(config.model_dim, config.num_vocab)
+
+    def __call__(self, inputs):
+        return self.linear(self.ln(inputs))
+
+
+class Phi2(nn.Module):
+    def __init__(self, config: ModelArgs):
+        self.wte = nn.Embedding(config.num_vocab, config.model_dim)
+        self.transformer = TransformerDecoder(config)
+        self.lm_head = OutputHead(config)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        mask: mx.array = None,
+        cache: mx.array = None,
+    ) -> tuple[mx.array, mx.array]:
+        x = self.wte(inputs)
+
+        mask = None
+        if x.shape[1] > 1:
+            mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
+            mask = mask.astype(x.dtype)
+
+        y, cache = self.transformer(x, mask, cache)
+        return self.lm_head(y), cache
+
+
+def generate(prompt: mx.array, model: Phi2, 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)
+    y = sample(logits[:, -1, :])
+    yield y
+
+    while True:
+        logits, cache = model(y[:, None], cache=cache)
+        y = sample(logits.squeeze(1))
+        yield y
+
+
+def load_model():
+    model = Phi2(ModelArgs())
+    weights = mx.load("weights.npz")
+    model.update(tree_unflatten(list(weights.items())))
+    tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
+    return model, tokenizer
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Phi-2 inference script")
+    parser.add_argument(
+        "--prompt",
+        help="The message to be processed by the model",
+        default="Write a detailed analogy between mathematics and a lighthouse.",
+    )
+    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)
+
+    model, tokenizer = load_model()
+
+    prompt = tokenizer(
+        args.prompt,
+        return_tensors="np",
+        return_attention_mask=False,
+    )["input_ids"]
+
+    prompt = mx.array(prompt)
+
+    print("[INFO] Generating with Phi-2...", flush=True)
+    print(args.prompt, end="", flush=True)
+
+    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)

phi2/requirements.txt

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+einops
+mlx
+numpy
+transformers