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Add PLaMo-13B model as an LLM example (#303)

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* Convert HF weights of PLaMo and load it to a plamo model in mlx

* Fix model inference part

* Add bos at the beginning of the prompt

* Fix convert.py to copy tokenizer.model into the converted dir

* Use the required insturction format in generate.py when "--instruct" option is specified

* Change filenames and update existing scripts

* Add README

* Add requirements.txt

* Fix plamo.py to stop generation when EOS appears

* Add quantization to convert.py

* Use mlx>=0.0.9 for mx.core.outer() in PLaMo model

* Update acknowledgements.md

* Fix card text in upload_to_hub()

* Not use prompt template when --instruct is not specified

* Ask if you trust_remote_code for loading tokenizer of PLaMo

* Check the user trusts the remote code when converting

* Remove plamo directory

* Update README

* Add PLaMo model file

* Fix the handling of cache in PLaMo and update README

* Ask if trust_remote_code only when the model is PLaMo

* Remove resolve_trust_remote_code from convert.py and use the latest transformers

* Remove code not to add EOS

* Update README to fix an example not to use noncommercial version of the model

* Remove unused imports

* Remove unnecessary description about the instruct model of PLaMo from README

* format, nits in README

* typo

---------

Co-authored-by: Shunta Saito <shunta@mitmul-mbp.local>
Co-authored-by: Awni Hannun <awni@apple.com>
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3
ACKNOWLEDGMENTS.md
View File

@ -8,4 +8,5 @@ with a short description of your contribution(s) below. For example:
MLX Examples was developed with contributions from the following individuals:
- Juarez Bochi: Added support for T5 models.
- Sarthak Yadav: Added the `cifar` and `speechcommands` examples.
- Sarthak Yadav: Added the `cifar` and `speechcommands` examples.
- Shunta Saito: Added support for PLaMo models.

29
llms/README.md
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@ -38,7 +38,7 @@ upload models to the Hugging Face Hub.
You can convert models in the Python API with:
```python
from mlx_lm import convert
from mlx_lm import convert
upload_repo = "mlx-community/My-Mistral-7B-v0.1-4bit"
@ -55,7 +55,7 @@ To see a description of all the arguments you can do:
>>> help(convert)
```
### Command Line
### Command Line
You can also use `mlx-lm` from the command line with:
@ -64,7 +64,7 @@ python -m mlx_lm.generate --model mistralai/Mistral-7B-v0.1 --prompt "hello"
```
This will download a Mistral 7B model from the Hugging Face Hub and generate
text using the given prompt.
text using the given prompt.
For a full list of options run:
@ -75,7 +75,7 @@ python -m mlx_lm.generate --help
To quantize a model from the command line run:
```
python -m mlx_lm.convert --hf-path mistralai/Mistral-7B-v0.1 -q
python -m mlx_lm.convert --hf-path mistralai/Mistral-7B-v0.1 -q
```
For more options run:
@ -85,7 +85,7 @@ python -m mlx_lm.convert --help
```
You can upload new models to Hugging Face by specifying `--upload-repo` to
`convert`. For example, to upload a quantized Mistral-7B model to the
`convert`. For example, to upload a quantized Mistral-7B model to the
[MLX Hugging Face community](https://huggingface.co/mlx-community) you can do:
```
@ -111,6 +111,8 @@ Here are a few examples of Hugging Face models that work with this example:
- [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)
- [pfnet/plamo-13b](https://huggingface.co/pfnet/plamo-13b)
- [pfnet/plamo-13b-instruct](https://huggingface.co/pfnet/plamo-13b-instruct)
Most
[Mistral](https://huggingface.co/models?library=transformers,safetensors&other=mistral&sort=trending),
@ -120,12 +122,17 @@ 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:
For some models (such as `Qwen` and `plamo`) the tokenizer requires you to
enable the `trust_remote_code` option. You can do this by passing
`--trust-remote-code` in the command line. If you don't specify the flag
explicitly, you will be prompted to trust remote code in the terminal when
running the model.
For `Qwen` models you must also specify the `eos_token`. You can do this by
passing `--eos-token "<|endoftext|>"` in the command
line.
These options can also be set in the Python API. For example:
```python
model, tokenizer = load(

365
llms/mlx_lm/models/plamo.py Normal file
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@ -0,0 +1,365 @@
from typing import Any, List, NamedTuple, Optional, Tuple, Union
import mlx.core as mx
import mlx.nn as nn
import numpy as np
from transformers import PretrainedConfig
class DecoderInput(NamedTuple):
hidden_states: mx.array
position_ids: mx.array
attention_mask: Optional[mx.array] = None
past_key_values: Optional[List[mx.array]] = None
output_hidden_states: Optional[bool] = False
output_attentions: Optional[bool] = False
use_cache: Optional[bool] = False
gradient_checkpointing: bool = False
class DecoderOutput(NamedTuple):
hidden_states: mx.array
all_hidden_states: Optional[Tuple[mx.array, ...]]
all_self_attns: Optional[Tuple[mx.array, ...]]
next_decoder_cache: Optional[Tuple[mx.array, ...]]
class ModelArgs(PretrainedConfig): # type: ignore
model_type: str = "plamo"
def __init__(
self,
vocab_size: int = 32000,
hidden_size: int = 4096,
intermediate_size: int = 13312,
num_hidden_layers: int = 32,
num_attention_heads: int = 32,
max_position_embeddings: int = 2048,
initializer_range: float = 0.02,
rms_norm_eps: float = 1e-6,
use_cache: bool = True,
tokenizer_class: str = "PlamoTokenizer",
pad_token_id: Optional[int] = None,
bos_token_id: int = 1,
eos_token_id: int = 2,
n_shared_head: int = 8,
tie_word_embeddings: bool = False,
**kwargs: Any,
) -> None:
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.n_shared_head = n_shared_head
super().__init__(
tokenizer_class=tokenizer_class,
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
class RotaryEmbedding:
def __init__(
self, dim: int, max_position_embeddings: int = 2048, base: int = 10000
) -> None:
super().__init__()
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
self.inv_freq = 1.0 / mx.power(
self.base, mx.arange(0, self.dim, 2, dtype=mx.float32) / self.dim
)
self.cos_cached = mx.zeros((1, 1, max_position_embeddings, dim))
self.sin_cached = mx.zeros((1, 1, max_position_embeddings, dim))
self._set_cos_sin_cache(max_position_embeddings)
def _set_cos_sin_cache(self, seq_len: int) -> None:
self.max_seq_len_cached = seq_len
t = mx.arange(self.max_seq_len_cached) # type: ignore
freqs = mx.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = mx.concatenate((freqs, freqs), axis=-1)
self.cos_cached = emb.cos()[None, None, :, :]
self.sin_cached = emb.sin()[None, None, :, :]
def __call__(self, x: mx.array, seq_len: int) -> Tuple[mx.array, mx.array]:
# x: [bs, num_attention_heads, seq_len, head_size]
if seq_len > self.max_seq_len_cached:
self._set_cos_sin_cache(seq_len)
return (
self.cos_cached[:, :, :seq_len, ...].astype(x.dtype), # type: ignore
self.sin_cached[:, :, :seq_len, ...].astype(x.dtype), # type: ignore
)
def _rotate_half(x: mx.array) -> mx.array:
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return mx.concatenate((-x2, x1), axis=-1)
def _rotary_pos_emb(
x: mx.array, cos: mx.array, sin: mx.array, position_ids: mx.array
) -> mx.array:
# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
cos = mx.squeeze(cos, (0, 1)) # [seq_len, dim]
sin = mx.squeeze(sin, (0, 1)) # [seq_len, dim]
cos = cos[position_ids][:, None] # [bs, 1, seq_len, dim]
sin = sin[position_ids][:, None] # [bs, 1, seq_len, dim]
x_embed = (x * cos) + (_rotate_half(x) * sin)
return x_embed
class RMSNorm(nn.Module):
def __init__(self, dims: int, eps: float = 1e-5):
super().__init__()
self.weight = mx.ones((dims,))
self.variance_epsilon = eps
def _norm(self, x):
return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.variance_epsilon)
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, config: ModelArgs) -> None:
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
head_dim = self.hidden_size // config.num_attention_heads
self.max_position_embeddings = config.max_position_embeddings
self.q_num_heads = config.num_attention_heads
self.qk_dim = self.v_dim = head_dim
self.k_num_heads = self.v_num_heads = int(
np.ceil(self.q_num_heads / config.n_shared_head)
)
self.scale = head_dim**-0.5
self.q_proj = nn.Linear(
self.hidden_size, self.q_num_heads * self.qk_dim, bias=False
)
self.k_proj = nn.Linear(
self.hidden_size, self.k_num_heads * self.qk_dim, bias=False
)
self.v_proj = nn.Linear(
self.hidden_size, self.v_num_heads * self.v_dim, bias=False
)
self.o_proj = nn.Linear(
self.q_num_heads * self.v_dim, self.hidden_size, bias=False
)
self.rotary_emb = RotaryEmbedding(
self.qk_dim, max_position_embeddings=self.max_position_embeddings
)
def __call__(
self,
hidden_states: mx.array,
attention_mask: Optional[mx.array] = None,
position_ids: Optional[mx.array] = None,
cache: Optional[Tuple[mx.array, mx.array]] = None,
) -> Tuple[mx.array, Tuple[mx.array, mx.array]]:
bsz, q_len, _ = hidden_states.shape
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
# Prepare the queries, keys and values for the attention computation
query_states = query_states.reshape(
bsz, q_len, self.q_num_heads, self.qk_dim
).transpose(0, 2, 1, 3)
key_states = key_states.reshape(
bsz, q_len, self.k_num_heads, self.qk_dim
).transpose(0, 2, 1, 3)
value_states = value_states.reshape(
bsz, q_len, self.v_num_heads, self.v_dim
).transpose(0, 2, 1, 3)
def _expand_kv(a: mx.array) -> mx.array:
a = mx.concatenate(
[mx.expand_dims(a, 1)] * self.config.n_shared_head, axis=1
)
return a.reshape([bsz, self.q_num_heads, q_len, -1])
# expand shared kv
assert self.k_num_heads == self.v_num_heads
key_states = _expand_kv(key_states)
value_states = _expand_kv(value_states)
kv_seq_len = key_states.shape[-2]
if cache is not None:
kv_seq_len += cache[0].shape[-2]
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
assert position_ids is not None
query_states = _rotary_pos_emb(query_states, cos, sin, position_ids)
key_states = _rotary_pos_emb(key_states, cos, sin, position_ids)
if cache is not None:
# reuse k, v, self_attention
key_states = mx.concatenate([cache[0], key_states], axis=2)
value_states = mx.concatenate([cache[1], value_states], axis=2)
scores = (query_states * self.scale) @ key_states.transpose(0, 1, 3, 2)
if attention_mask is not None:
scores += attention_mask
scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
output = (scores @ value_states).transpose(0, 2, 1, 3).reshape(bsz, q_len, -1)
return self.o_proj(output), (key_states, value_states)
class MLP(nn.Module):
def __init__(self, config: ModelArgs) -> None:
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = nn.silu
def __call__(self, x: mx.array) -> mx.array:
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) # type: ignore
class PlamoDecoderLayer(nn.Module):
def __init__(self, config: ModelArgs) -> None:
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.self_attn = Attention(config)
self.mlp = MLP(config)
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def __call__(
self,
hidden_states: mx.array,
attention_mask: Optional[mx.array] = None,
position_ids: Optional[mx.array] = None,
cache: Optional[Tuple[mx.array, mx.array]] = None,
) -> Tuple[Any, ...]:
# from LlamaDecoder
residual = hidden_states
hidden_states = self.norm(hidden_states)
# Self Attention
hidden_states_sa, cache = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
cache=cache,
)
# Fully Connected
hidden_states_mlp = self.mlp(hidden_states)
# Residual ("Parallel Layers" is used here, which is different from the normal residual connection)
# See "GPT-NeoX-20B: An Open-Source Autoregressive Language Model" for Parallel Layers
hidden_states = residual + hidden_states_sa + hidden_states_mlp
return hidden_states, cache # type: ignore
class PlamoDecoder(nn.Module):
def __init__(self, config: ModelArgs) -> None:
super().__init__()
self.layers = [
PlamoDecoderLayer(config) for _ in range(config.num_hidden_layers)
]
class PlamoModel(nn.Module):
config_class = ModelArgs
_no_split_modules: List[str]
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["PlamoDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
def __init__(self, config: ModelArgs):
super().__init__()
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = PlamoDecoder(config) # type: ignore
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
def __call__(
self,
inputs: mx.array,
cache: Optional[List[Union[Tuple[mx.array, mx.array], None]]] = None,
) -> Tuple[mx.array, Optional[List[Union[Tuple[mx.array, mx.array], None]]]]:
h = self.embed_tokens(inputs)
mask = None
if h.shape[1] > 1:
mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
mask = mask.astype(self.embed_tokens.weight.dtype)
if cache is None:
past_key_values_length = 0
cache = [None for _ in range(len(self.layers.layers))]
else:
if cache[0] is not None:
past_key_values_length = cache[0][0].shape[2]
position_ids = _create_position_ids(h.shape[1], past_key_values_length)
for e, layer in enumerate(self.layers.layers):
h, c = layer(h, mask, position_ids, cache[e])
if cache is not None:
cache[e] = c
else:
cache.append(c)
return self.norm(h), cache
def _create_position_ids(seq_length: int, past_key_values_length: int = 0) -> mx.array:
# create position_ids on the fly for batch generation
position_ids = mx.arange(
past_key_values_length, seq_length + past_key_values_length, dtype=mx.int64
)
position_ids = position_ids[None, ...].reshape(-1, seq_length)
return position_ids
class Model(nn.Module):
def __init__(self, config: PretrainedConfig) -> None:
super().__init__()
self.model = PlamoModel(config)
self.lm_head: nn.Module = nn.Linear(
config.hidden_size, config.vocab_size, bias=False
)
def __call__(
self,
inputs: mx.array,
cache: Optional[List[Tuple[mx.array, mx.array]]] = None,
) -> Tuple[mx.array, mx.array]:
out, cache = self.model(inputs, cache)
return self.lm_head(out), cache

3
llms/mlx_lm/utils.py
View File

@ -10,7 +10,7 @@ from huggingface_hub import snapshot_download
from transformers import AutoTokenizer, PreTrainedTokenizer
# Local imports
from .models import llama, mixtral, phi2, qwen
from .models import llama, mixtral, phi2, plamo, qwen
# Constants
MODEL_MAPPING = {
@ -19,6 +19,7 @@ MODEL_MAPPING = {
"mixtral": mixtral,
"phi": phi2,
"qwen": qwen,
"plamo": plamo,
}
linear_class_predicate = (