mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 17:31:18 +08:00
refactor(qwen): moving qwen into mlx-lm (#312)
* refactor(qwen): moving qwen into mlx-lm * chore: update doc * chore: fix type hint * add qwen model support in convert * chore: fix doc * chore: only load model in quantize_model * chore: make the convert script only copy tokenizer files instead of load it and save * chore: update docstring * chore: remove unnecessary try catch * chore: clean up for tokenizer and update transformers 4.37 * nits in README --------- Co-authored-by: Awni Hannun <awni@apple.com>
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@ -102,11 +102,26 @@ Here are a few examples of Hugging Face models that work with this example:
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- [01-ai/Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat)
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- [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
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- [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
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- [Qwen/Qwen-7B](https://huggingface.co/Qwen/Qwen-7B)
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Most
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[Mistral](https://huggingface.co/models?library=transformers,safetensors&other=mistral&sort=trending),
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[Llama](https://huggingface.co/models?library=transformers,safetensors&other=llama&sort=trending),
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[Phi-2](https://huggingface.co/models?library=transformers,safetensors&other=phi&sort=trending)
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[Phi-2](https://huggingface.co/models?library=transformers,safetensors&other=phi&sort=trending),
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and
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[Mixtral](https://huggingface.co/models?library=transformers,safetensors&other=mixtral&sort=trending)
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style models should work out of the box.
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For
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[Qwen](https://huggingface.co/models?library=transformers,safetensors&other=qwen&sort=trending)
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style models, you must enable the `trust_remote_code` option and specify the
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`eos_token`. This ensures the tokenizer works correctly. You can do this by
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passing `--trust-remote-code` and `--eos-token "<|endoftext|>"` in the command
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line, or by setting these options in the Python API:
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```python
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model, tokenizer = load(
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"qwen/Qwen-7B",
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tokenizer_config={"eos_token": "<|endoftext|>", "trust_remote_code": True},
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)
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```
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@ -21,6 +21,17 @@ def setup_arg_parser():
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default="mlx_model",
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help="The path to the local model directory or Hugging Face repo.",
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)
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parser.add_argument(
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"--trust-remote-code",
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action="store_true",
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help="Enable trusting remote code for tokenizer",
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)
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parser.add_argument(
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"--eos-token",
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type=str,
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default=None,
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help="End of sequence token for tokenizer",
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)
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parser.add_argument(
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"--prompt", default=DEFAULT_PROMPT, help="Message to be processed by the model"
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)
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@ -40,7 +51,13 @@ def setup_arg_parser():
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def main(args):
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mx.random.seed(args.seed)
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model, tokenizer = load(args.model)
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# Building tokenizer_config
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tokenizer_config = {"trust_remote_code": True if args.trust_remote_code else None}
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if args.eos_token is not None:
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tokenizer_config["eos_token"] = args.eos_token
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model, tokenizer = load(args.model, tokenizer_config=tokenizer_config)
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print("=" * 10)
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print("Prompt:", args.prompt)
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prompt = tokenizer.encode(args.prompt)
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@ -1,16 +1,14 @@
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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 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 transformers import AutoTokenizer
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from .base import BaseModelArgs
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@dataclass
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class ModelArgs:
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class ModelArgs(BaseModelArgs):
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hidden_size: int = 2048
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num_attention_heads: int = 16
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num_hidden_layers: int = 24
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@ -20,6 +18,11 @@ class ModelArgs:
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intermediate_size: int = 11008
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no_bias: bool = True
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vocab_size: int = 151936
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num_key_value_heads = None
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def __post_init__(self):
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if self.num_key_value_heads is None:
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self.num_key_value_heads = self.num_attention_heads
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class RMSNorm(nn.Module):
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@ -95,7 +98,7 @@ class MLP(nn.Module):
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args.hidden_size, args.intermediate_size // 2, bias=not args.no_bias
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)
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self.w2 = nn.Linear(
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args.intermediate_size // 2, args.hidden_size, bias=not args.no_bias
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args.hidden_size, args.intermediate_size // 2, bias=not args.no_bias
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)
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self.c_proj = nn.Linear(
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args.intermediate_size // 2, args.hidden_size, bias=not args.no_bias
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@ -128,17 +131,12 @@ class TransformerBlock(nn.Module):
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return x, cache
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class Qwen(nn.Module):
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class QwenModel(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.embed_dim = args.hidden_size
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self.wte = nn.Embedding(args.vocab_size, args.hidden_size)
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self.h = [TransformerBlock(args) for _ in range(args.num_hidden_layers)]
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self.ln_f = RMSNorm(self.embed_dim, eps=args.layer_norm_epsilon)
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self.lm_head = nn.Linear(self.embed_dim, args.vocab_size, bias=False)
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self.ln_f = RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
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def __call__(self, inputs, mask=None, cache=None):
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x = self.wte(inputs)
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@ -156,123 +154,22 @@ class Qwen(nn.Module):
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x, cache[e] = layer(x, mask, cache[e])
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x = self.ln_f(x[:, T - 1 : T, :])
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return self.lm_head(x), cache
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return x, cache
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def generate(prompt: mx.array, model: Qwen, temp: 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)
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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=cache)
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y = sample(logits.squeeze(1))
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yield y
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def load_model(model_path: str, tokenizer_path: str = "Qwen/Qwen-1_8B"):
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model_args = ModelArgs()
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model_path = Path(model_path)
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with open(model_path / "config.json", "r") as f:
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config = json.load(f)
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model_args.vocab_size = config["vocab_size"]
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model_args.hidden_size = config["hidden_size"]
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model_args.num_attention_heads = config["num_attention_heads"]
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model_args.num_hidden_layers = config["num_hidden_layers"]
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model_args.kv_channels = config["kv_channels"]
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model_args.max_position_embeddings = config["max_position_embeddings"]
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model_args.layer_norm_epsilon = config["layer_norm_epsilon"]
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model_args.intermediate_size = config["intermediate_size"]
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model_args.no_bias = config["no_bias"]
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model = Qwen(model_args)
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weights = mx.load(str(model_path / "weights.npz"))
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if quantization := config.get("quantization", False):
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nn.QuantizedLinear.quantize_module(model, **quantization)
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model.update(tree_unflatten(list(weights.items())))
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tokenizer = AutoTokenizer.from_pretrained(
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tokenizer_path, trust_remote_code=True, eos_token="<|endoftext|>"
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)
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return model, tokenizer
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Qwen 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 config",
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)
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parser.add_argument(
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"--tokenizer",
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help="The tokenizer to be used, defaults to Qwen/Qwen-1_8B",
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default="Qwen/Qwen-1_8B",
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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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# The example from the official huggingface repo of Qwen
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default="蒙古国的首都是乌兰巴托（Ulaanbaatar）\n冰岛的首都是雷克雅未克（Reykjavik）\n埃塞俄比亚的首都是",
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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("--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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model, tokenizer = load_model(args.model_path, args.tokenizer)
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prompt = tokenizer(
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args.prompt,
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return_tensors="np",
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return_attention_mask=False,
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)["input_ids"]
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prompt = mx.array(prompt)
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print(args.prompt, end="", flush=True)
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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) % 10) == 0:
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mx.eval(tokens)
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eos_index = next(
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(i for i, t in enumerate(tokens) if t.item() == tokenizer.eos_token_id),
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None,
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class Model(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.transformer = QwenModel(config)
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self.lm_head = nn.Linear(
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config.hidden_size, config.vocab_size, bias=not config.no_bias
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)
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if eos_index is not None:
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tokens = tokens[:eos_index]
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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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if eos_index is not None:
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break
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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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def __call__(
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self,
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x: mx.array,
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mask: mx.array = None,
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cache: mx.array = None,
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) -> Tuple[mx.array, mx.array]:
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y, cache = self.transformer(x, mask, cache)
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return self.lm_head(y), cache
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@ -1,4 +1,4 @@
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mlx
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numpy
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transformers
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transformers>=4.37.0
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protobuf
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@ -10,8 +10,7 @@ from huggingface_hub import snapshot_download
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from transformers import AutoTokenizer, PreTrainedTokenizer
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# Local imports
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from .models import llama, mixtral, phi2
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from .models.base import BaseModelArgs
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from .models import llama, mixtral, phi2, qwen
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# Constants
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MODEL_MAPPING = {
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@ -19,6 +18,7 @@ MODEL_MAPPING = {
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"mistral": llama, # mistral is compatible with llama
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"mixtral": mixtral,
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"phi": phi2,
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"qwen": qwen,
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}
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linear_class_predicate = (
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@ -64,7 +64,13 @@ def get_model_path(path_or_hf_repo: str) -> Path:
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model_path = Path(
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snapshot_download(
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repo_id=path_or_hf_repo,
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allow_patterns=["*.json", "*.safetensors", "*.py", "tokenizer.model"],
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allow_patterns=[
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"*.json",
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"*.safetensors",
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"*.py",
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"tokenizer.model",
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"*.tiktoken",
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],
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)
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)
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return model_path
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@ -196,15 +202,18 @@ def load_model(model_path: Path) -> nn.Module:
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return model
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def load(path_or_hf_repo: str) -> Tuple[nn.Module, PreTrainedTokenizer]:
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def load(
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path_or_hf_repo: str, tokenizer_config={}
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) -> Tuple[nn.Module, PreTrainedTokenizer]:
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"""
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Load the model from a given path or a huggingface repository.
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Args:
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path_or_hf_repo (str): The path or the huggingface repository to load the model from.
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model_path (Path): The path or the huggingface repository to load the model from.
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tokenizer_config (dict, optional): Configuration parameters specifically for the tokenizer.
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Defaults to an empty dictionary.
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Returns:
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Tuple[nn.Module, PreTrainedTokenizer]: The loaded model and tokenizer.
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nn.Module: The loaded model.
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Raises:
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FileNotFoundError: If config file or safetensors are not found.
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@ -213,5 +222,5 @@ def load(path_or_hf_repo: str) -> Tuple[nn.Module, PreTrainedTokenizer]:
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model_path = get_model_path(path_or_hf_repo)
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model = load_model(model_path)
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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tokenizer = AutoTokenizer.from_pretrained(model_path, **tokenizer_config)
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return model, tokenizer
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@ -1,45 +0,0 @@
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# Qwen
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Qwen (通义千问) are a family of language models developed by Alibaba Cloud.[^1]
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The architecture of the Qwen models is similar to Llama except for the bias in
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the attention layers.
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## Setup
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First download and convert the model with:
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```sh
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python convert.py
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```
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To generate a 4-bit quantized model, use ``-q``. For a full list of options:
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The script downloads the model from Hugging Face. The default model is
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`Qwen/Qwen-1_8B`. Check out the [Hugging Face
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page](https://huggingface.co/Qwen) to see a list of available models.
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By default, the conversion script will make the directory `mlx_model` and save
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the converted `weights.npz` and `config.json` there.
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## Generate
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To generate text with the default prompt:
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```sh
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python qwen.py
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```
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If you change the model, make sure to pass the corresponding tokenizer. E.g.,
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for Qwen 7B use:
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```
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python qwen.py --tokenizer Qwen/Qwen-7B
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```
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To see a list of options, run:
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```sh
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python qwen.py --help
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```
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[^1]: For more details on the model see the official repo of [Qwen](https://github.com/QwenLM/Qwen) and the [Hugging Face](https://huggingface.co/Qwen).
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@ -1,115 +0,0 @@
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import argparse
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import copy
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import json
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from pathlib import Path
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import mlx.core as mx
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import mlx.nn as nn
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import numpy as np
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import torch
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from mlx.utils import tree_flatten, tree_map, tree_unflatten
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from qwen import ModelArgs, Qwen
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from transformers import AutoModelForCausalLM
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def replace_key(key: str) -> str:
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if key.startswith("transformer."):
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# remove transformer prefix
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key = key.replace("transformer.", "")
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return key
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def quantize(weights, config, args):
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quantized_config = copy.deepcopy(config)
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# Load the model:
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model_args = ModelArgs()
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model_args.vocab_size = config["vocab_size"]
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model_args.hidden_size = config["hidden_size"]
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model_args.num_attention_heads = config["num_attention_heads"]
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model_args.num_hidden_layers = config["num_hidden_layers"]
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model_args.kv_channels = config["kv_channels"]
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model_args.max_position_embeddings = config["max_position_embeddings"]
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model_args.layer_norm_epsilon = config["layer_norm_epsilon"]
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model_args.intermediate_size = config["intermediate_size"]
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model_args.no_bias = config["no_bias"]
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model = Qwen(model_args)
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weights = tree_map(mx.array, weights)
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model.update(tree_unflatten(list(weights.items())))
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# Quantize the model:
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nn.QuantizedLinear.quantize_module(model, args.q_group_size, args.q_bits)
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# Update the config:
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quantized_config["quantization"] = {
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"group_size": args.q_group_size,
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"bits": args.q_bits,
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}
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quantized_weights = dict(tree_flatten(model.parameters()))
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return quantized_weights, quantized_config
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def convert(args):
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mlx_path = Path(args.mlx_path)
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mlx_path.mkdir(parents=True, exist_ok=True)
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model = AutoModelForCausalLM.from_pretrained(
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args.model, trust_remote_code=True, torch_dtype=torch.float16
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)
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state_dict = model.state_dict()
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weights = {
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replace_key(k): (
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v.numpy() if v.dtype != torch.bfloat16 else v.to(torch.float32).numpy()
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)
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for k, v in state_dict.items()
|
||||
}
|
||||
config = model.config.to_dict()
|
||||
|
||||
if args.quantize:
|
||||
print("[INFO] Quantizing")
|
||||
weights, config = quantize(weights, config, args)
|
||||
|
||||
np.savez(str(mlx_path / "weights.npz"), **weights)
|
||||
|
||||
# write config
|
||||
with open(mlx_path / "config.json", "w") as f:
|
||||
json.dump(config, f, indent=4)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Convert Qwen model to npz")
|
||||
|
||||
parser.add_argument(
|
||||
"--model",
|
||||
help="The huggingface model to be converted",
|
||||
default="Qwen/Qwen-1_8B",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--mlx-path",
|
||||
type=str,
|
||||
default="mlx_model",
|
||||
help="The path to save the MLX model.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-q",
|
||||
"--quantize",
|
||||
help="Generate a quantized model.",
|
||||
action="store_true",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--q-group-size",
|
||||
help="Group size for quantization.",
|
||||
type=int,
|
||||
default=64,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--q-bits",
|
||||
help="Bits per weight for quantization.",
|
||||
type=int,
|
||||
default=4,
|
||||
)
|
||||
args = parser.parse_args()
|
||||
convert(args)
|
||||
@ -1,7 +0,0 @@
|
||||
einops
|
||||
mlx
|
||||
numpy
|
||||
transformers>=4.35
|
||||
transformers_stream_generator>=0.0.4
|
||||
torch
|
||||
tiktoken
|
||||
Loading…
Reference in New Issue
Block a user