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refactor(qwen): moving qwen into mlx-lm (#312)

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* 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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17
llms/README.md
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@ -102,11 +102,26 @@ Here are a few examples of Hugging Face models that work with this example:
- [01-ai/Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat)
- [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
- [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
- [Qwen/Qwen-7B](https://huggingface.co/Qwen/Qwen-7B)
Most
[Mistral](https://huggingface.co/models?library=transformers,safetensors&other=mistral&sort=trending),
[Llama](https://huggingface.co/models?library=transformers,safetensors&other=llama&sort=trending),
[Phi-2](https://huggingface.co/models?library=transformers,safetensors&other=phi&sort=trending)
[Phi-2](https://huggingface.co/models?library=transformers,safetensors&other=phi&sort=trending),
and
[Mixtral](https://huggingface.co/models?library=transformers,safetensors&other=mixtral&sort=trending)
style models should work out of the box.
For
[Qwen](https://huggingface.co/models?library=transformers,safetensors&other=qwen&sort=trending)
style models, you must enable the `trust_remote_code` option and specify the
`eos_token`. This ensures the tokenizer works correctly. You can do this by
passing `--trust-remote-code` and `--eos-token "<|endoftext|>"` in the command
line, or by setting these options in the Python API:
```python
model, tokenizer = load(
"qwen/Qwen-7B",
tokenizer_config={"eos_token": "<|endoftext|>", "trust_remote_code": True},
)
```

19
llms/mlx_lm/generate.py
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@ -21,6 +21,17 @@ def setup_arg_parser():
default="mlx_model",
help="The path to the local model directory or Hugging Face repo.",
)
parser.add_argument(
"--trust-remote-code",
action="store_true",
help="Enable trusting remote code for tokenizer",
)
parser.add_argument(
"--eos-token",
type=str,
default=None,
help="End of sequence token for tokenizer",
)
parser.add_argument(
"--prompt", default=DEFAULT_PROMPT, help="Message to be processed by the model"
)
@ -40,7 +51,13 @@ def setup_arg_parser():
def main(args):
mx.random.seed(args.seed)
model, tokenizer = load(args.model)
# Building tokenizer_config
tokenizer_config = {"trust_remote_code": True if args.trust_remote_code else None}
if args.eos_token is not None:
tokenizer_config["eos_token"] = args.eos_token
model, tokenizer = load(args.model, tokenizer_config=tokenizer_config)
print("=" * 10)
print("Prompt:", args.prompt)
prompt = tokenizer.encode(args.prompt)

159
llms/qwen/qwen.py → llms/mlx_lm/models/qwen.py
View File

@ -1,16 +1,14 @@
import argparse
import json
from dataclasses import dataclass
from pathlib import Path
from typing import Tuple
import mlx.core as mx
import mlx.nn as nn
from mlx.utils import tree_unflatten
from transformers import AutoTokenizer
from .base import BaseModelArgs
@dataclass
class ModelArgs:
class ModelArgs(BaseModelArgs):
hidden_size: int = 2048
num_attention_heads: int = 16
num_hidden_layers: int = 24
@ -20,6 +18,11 @@ class ModelArgs:
intermediate_size: int = 11008
no_bias: bool = True
vocab_size: int = 151936
num_key_value_heads = None
def __post_init__(self):
if self.num_key_value_heads is None:
self.num_key_value_heads = self.num_attention_heads
class RMSNorm(nn.Module):
@ -95,7 +98,7 @@ class MLP(nn.Module):
args.hidden_size, args.intermediate_size // 2, bias=not args.no_bias
)
self.w2 = nn.Linear(
args.intermediate_size // 2, args.hidden_size, bias=not args.no_bias
args.hidden_size, args.intermediate_size // 2, bias=not args.no_bias
)
self.c_proj = nn.Linear(
args.intermediate_size // 2, args.hidden_size, bias=not args.no_bias
@ -128,17 +131,12 @@ class TransformerBlock(nn.Module):
return x, cache
class Qwen(nn.Module):
class QwenModel(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.embed_dim = args.hidden_size
self.wte = nn.Embedding(args.vocab_size, args.hidden_size)
self.h = [TransformerBlock(args) for _ in range(args.num_hidden_layers)]
self.ln_f = RMSNorm(self.embed_dim, eps=args.layer_norm_epsilon)
self.lm_head = nn.Linear(self.embed_dim, args.vocab_size, bias=False)
self.ln_f = RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
def __call__(self, inputs, mask=None, cache=None):
x = self.wte(inputs)
@ -156,123 +154,22 @@ class Qwen(nn.Module):
x, cache[e] = layer(x, mask, cache[e])
x = self.ln_f(x[:, T - 1 : T, :])
return self.lm_head(x), cache
return x, cache
def generate(prompt: mx.array, model: Qwen, temp: 0.0):
def sample(logits):
if temp == 0:
return mx.argmax(logits, axis=-1)
else:
return mx.random.categorical(logits * (1 / temp))
class Model(nn.Module):
def __init__(self, config: ModelArgs):
super().__init__()
self.transformer = QwenModel(config)
self.lm_head = nn.Linear(
config.hidden_size, config.vocab_size, bias=not config.no_bias
)
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_path: str, tokenizer_path: str = "Qwen/Qwen-1_8B"):
model_args = ModelArgs()
model_path = Path(model_path)
with open(model_path / "config.json", "r") as f:
config = json.load(f)
model_args.vocab_size = config["vocab_size"]
model_args.hidden_size = config["hidden_size"]
model_args.num_attention_heads = config["num_attention_heads"]
model_args.num_hidden_layers = config["num_hidden_layers"]
model_args.kv_channels = config["kv_channels"]
model_args.max_position_embeddings = config["max_position_embeddings"]
model_args.layer_norm_epsilon = config["layer_norm_epsilon"]
model_args.intermediate_size = config["intermediate_size"]
model_args.no_bias = config["no_bias"]
model = Qwen(model_args)
weights = mx.load(str(model_path / "weights.npz"))
if quantization := config.get("quantization", False):
nn.QuantizedLinear.quantize_module(model, **quantization)
model.update(tree_unflatten(list(weights.items())))
tokenizer = AutoTokenizer.from_pretrained(
tokenizer_path, trust_remote_code=True, eos_token="<|endoftext|>"
)
return model, tokenizer
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Qwen inference script")
parser.add_argument(
"--model-path",
type=str,
default="mlx_model",
help="The path to the model weights and config",
)
parser.add_argument(
"--tokenizer",
help="The tokenizer to be used, defaults to Qwen/Qwen-1_8B",
default="Qwen/Qwen-1_8B",
)
parser.add_argument(
"--prompt",
help="The message to be processed by the model",
# The example from the official huggingface repo of Qwen
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",
)
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(args.model_path, args.tokenizer)
prompt = tokenizer(
args.prompt,
return_tensors="np",
return_attention_mask=False,
)["input_ids"]
prompt = mx.array(prompt)
print(args.prompt, end="", flush=True)
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_token_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)
def __call__(
self,
x: mx.array,
mask: mx.array = None,
cache: mx.array = None,
) -> Tuple[mx.array, mx.array]:
y, cache = self.transformer(x, mask, cache)
return self.lm_head(y), cache

2
llms/mlx_lm/requirements.txt
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@ -1,4 +1,4 @@
mlx
numpy
transformers
transformers>=4.37.0
protobuf

25
llms/mlx_lm/utils.py
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@ -10,8 +10,7 @@ from huggingface_hub import snapshot_download
from transformers import AutoTokenizer, PreTrainedTokenizer
# Local imports
from .models import llama, mixtral, phi2
from .models.base import BaseModelArgs
from .models import llama, mixtral, phi2, qwen
# Constants
MODEL_MAPPING = {
@ -19,6 +18,7 @@ MODEL_MAPPING = {
"mistral": llama, # mistral is compatible with llama
"mixtral": mixtral,
"phi": phi2,
"qwen": qwen,
}
linear_class_predicate = (
@ -64,7 +64,13 @@ def get_model_path(path_or_hf_repo: str) -> Path:
model_path = Path(
snapshot_download(
repo_id=path_or_hf_repo,
allow_patterns=["*.json", "*.safetensors", "*.py", "tokenizer.model"],
allow_patterns=[
"*.json",
"*.safetensors",
"*.py",
"tokenizer.model",
"*.tiktoken",
],
)
)
return model_path
@ -196,15 +202,18 @@ def load_model(model_path: Path) -> nn.Module:
return model
def load(path_or_hf_repo: str) -> Tuple[nn.Module, PreTrainedTokenizer]:
def load(
path_or_hf_repo: str, tokenizer_config={}
) -> Tuple[nn.Module, PreTrainedTokenizer]:
"""
Load the model from a given path or a huggingface repository.
Args:
path_or_hf_repo (str): The path or the huggingface repository to load the model from.
model_path (Path): The path or the huggingface repository to load the model from.
tokenizer_config (dict, optional): Configuration parameters specifically for the tokenizer.
Defaults to an empty dictionary.
Returns:
Tuple[nn.Module, PreTrainedTokenizer]: The loaded model and tokenizer.
nn.Module: The loaded model.
Raises:
FileNotFoundError: If config file or safetensors are not found.
@ -213,5 +222,5 @@ def load(path_or_hf_repo: str) -> Tuple[nn.Module, PreTrainedTokenizer]:
model_path = get_model_path(path_or_hf_repo)
model = load_model(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path, **tokenizer_config)
return model, tokenizer

45
llms/qwen/README.md
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@ -1,45 +0,0 @@
# Qwen
Qwen (通义千问) are a family of language models developed by Alibaba Cloud.[^1]
The architecture of the Qwen models is similar to Llama except for the bias in
the attention layers.
## Setup
First download and convert the model with:
```sh
python convert.py
```
To generate a 4-bit quantized model, use ``-q``. For a full list of options:
The script downloads the model from Hugging Face. The default model is
`Qwen/Qwen-1_8B`. Check out the [Hugging Face
page](https://huggingface.co/Qwen) to see a list of available models.
By default, the conversion script will make the directory `mlx_model` and save
the converted `weights.npz` and `config.json` there.
## Generate
To generate text with the default prompt:
```sh
python qwen.py
```
If you change the model, make sure to pass the corresponding tokenizer. E.g.,
for Qwen 7B use:
```
python qwen.py --tokenizer Qwen/Qwen-7B
```
To see a list of options, run:
```sh
python qwen.py --help
```
[^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).

115
llms/qwen/convert.py
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@ -1,115 +0,0 @@
import argparse
import copy
import json
from pathlib import Path
import mlx.core as mx
import mlx.nn as nn
import numpy as np
import torch
from mlx.utils import tree_flatten, tree_map, tree_unflatten
from qwen import ModelArgs, Qwen
from transformers import AutoModelForCausalLM
def replace_key(key: str) -> str:
if key.startswith("transformer."):
# remove transformer prefix
key = key.replace("transformer.", "")
return key
def quantize(weights, config, args):
quantized_config = copy.deepcopy(config)
# Load the model:
model_args = ModelArgs()
model_args.vocab_size = config["vocab_size"]
model_args.hidden_size = config["hidden_size"]
model_args.num_attention_heads = config["num_attention_heads"]
model_args.num_hidden_layers = config["num_hidden_layers"]
model_args.kv_channels = config["kv_channels"]
model_args.max_position_embeddings = config["max_position_embeddings"]
model_args.layer_norm_epsilon = config["layer_norm_epsilon"]
model_args.intermediate_size = config["intermediate_size"]
model_args.no_bias = config["no_bias"]
model = Qwen(model_args)
weights = tree_map(mx.array, weights)
model.update(tree_unflatten(list(weights.items())))
# Quantize the model:
nn.QuantizedLinear.quantize_module(model, args.q_group_size, args.q_bits)
# Update the config:
quantized_config["quantization"] = {
"group_size": args.q_group_size,
"bits": args.q_bits,
}
quantized_weights = dict(tree_flatten(model.parameters()))
return quantized_weights, quantized_config
def convert(args):
mlx_path = Path(args.mlx_path)
mlx_path.mkdir(parents=True, exist_ok=True)
model = AutoModelForCausalLM.from_pretrained(
args.model, trust_remote_code=True, torch_dtype=torch.float16
)
state_dict = model.state_dict()
weights = {
replace_key(k): (
v.numpy() if v.dtype != torch.bfloat16 else v.to(torch.float32).numpy()
)
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)

7
llms/qwen/requirements.txt
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@ -1,7 +0,0 @@
einops
mlx
numpy
transformers>=4.35
transformers_stream_generator>=0.0.4
torch
tiktoken