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>
This commit is contained in:
Anchen
2024-01-22 15:00:07 -08:00
committed by GitHub
parent de15532da8
commit 30be4c4734
8 changed files with 80 additions and 309 deletions

View File

@@ -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)

175
llms/mlx_lm/models/qwen.py Normal file
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@@ -0,0 +1,175 @@
from dataclasses import dataclass
from typing import Tuple
import mlx.core as mx
import mlx.nn as nn
from .base import BaseModelArgs
@dataclass
class ModelArgs(BaseModelArgs):
hidden_size: int = 2048
num_attention_heads: int = 16
num_hidden_layers: int = 24
kv_channels: int = 128
max_position_embeddings: int = 8192
layer_norm_epsilon: float = 1e-6
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):
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__()
hidden_size = args.hidden_size
self.num_attention_heads = args.num_attention_heads
hidden_size_per_attention_head = hidden_size // self.num_attention_heads
self.rotary_emb = nn.RoPE(hidden_size_per_attention_head, traditional=False)
proj_size = args.kv_channels * self.num_attention_heads
self.c_attn = nn.Linear(hidden_size, proj_size * 3, bias=True)
self.c_proj = nn.Linear(hidden_size, proj_size, bias=not args.no_bias)
self.scale = hidden_size_per_attention_head**-0.5
def __call__(self, x, mask=None, cache=None):
qkv = self.c_attn(x)
q, k, v = mx.split(qkv, 3, axis=-1)
B, L, _ = q.shape
q = q.reshape(B, L, self.num_attention_heads, -1).transpose(0, 2, 1, 3)
k = k.reshape(B, L, self.num_attention_heads, -1).transpose(0, 2, 1, 3)
v = v.reshape(B, L, self.num_attention_heads, -1).transpose(0, 2, 1, 3)
if cache is not None:
k_cache, v_cache = cache
q = self.rotary_emb(q, offset=k_cache.shape[2])
k = self.rotary_emb(k, offset=k_cache.shape[2])
k = mx.concatenate([k_cache, k], axis=2)
v = mx.concatenate([v_cache, v], axis=2)
else:
q = self.rotary_emb(q)
k = self.rotary_emb(k)
scores = (q * self.scale) @ k.transpose(0, 1, 3, 2)
if mask is not None:
scores = scores + mask
scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
v_hat = (scores @ v).transpose(0, 2, 1, 3).reshape(B, L, -1)
return self.c_proj(v_hat), (k, v)
class MLP(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.w1 = nn.Linear(
args.hidden_size, args.intermediate_size // 2, bias=not args.no_bias
)
self.w2 = nn.Linear(
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
)
def __call__(self, x):
a1 = self.w1(x)
a2 = self.w2(x)
return self.c_proj(a1 * nn.silu(a2))
class TransformerBlock(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.ln_1 = RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
self.attn = Attention(args)
self.ln_2 = RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
self.mlp = MLP(args)
def __call__(self, x, mask=None, cache=None):
residual = x
x = self.ln_1(x)
x, cache = self.attn(x, mask=mask, cache=cache)
residual = x + residual
x = self.ln_2(residual)
x = self.mlp(x)
x = x + residual
return x, cache
class QwenModel(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
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(args.hidden_size, eps=args.layer_norm_epsilon)
def __call__(self, inputs, mask=None, cache=None):
x = self.wte(inputs)
mask = None
T = x.shape[1]
if T > 1:
mask = nn.MultiHeadAttention.create_additive_causal_mask(T)
mask = mask.astype(x.dtype)
if cache is None:
cache = [None] * len(self.h)
for e, layer in enumerate(self.h):
x, cache[e] = layer(x, mask, cache[e])
x = self.ln_f(x[:, T - 1 : T, :])
return x, cache
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
)
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

View File

@@ -1,4 +1,4 @@
mlx
numpy
transformers
transformers>=4.37.0
protobuf

View File

@@ -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