mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 09:21:18 +08:00
85c1ff8fd6
* 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>
366 lines
13 KiB
Python
366 lines
13 KiB
Python
from typing import Any, List, NamedTuple, Optional, Tuple, Union
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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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from transformers import PretrainedConfig
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class DecoderInput(NamedTuple):
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hidden_states: mx.array
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position_ids: mx.array
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attention_mask: Optional[mx.array] = None
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past_key_values: Optional[List[mx.array]] = None
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output_hidden_states: Optional[bool] = False
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output_attentions: Optional[bool] = False
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use_cache: Optional[bool] = False
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gradient_checkpointing: bool = False
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class DecoderOutput(NamedTuple):
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hidden_states: mx.array
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all_hidden_states: Optional[Tuple[mx.array, ...]]
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all_self_attns: Optional[Tuple[mx.array, ...]]
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next_decoder_cache: Optional[Tuple[mx.array, ...]]
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class ModelArgs(PretrainedConfig): # type: ignore
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model_type: str = "plamo"
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def __init__(
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self,
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vocab_size: int = 32000,
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hidden_size: int = 4096,
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intermediate_size: int = 13312,
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num_hidden_layers: int = 32,
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num_attention_heads: int = 32,
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max_position_embeddings: int = 2048,
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initializer_range: float = 0.02,
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rms_norm_eps: float = 1e-6,
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use_cache: bool = True,
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tokenizer_class: str = "PlamoTokenizer",
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pad_token_id: Optional[int] = None,
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bos_token_id: int = 1,
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eos_token_id: int = 2,
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n_shared_head: int = 8,
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tie_word_embeddings: bool = False,
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**kwargs: Any,
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) -> None:
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.n_shared_head = n_shared_head
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super().__init__(
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tokenizer_class=tokenizer_class,
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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class RotaryEmbedding:
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def __init__(
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self, dim: int, max_position_embeddings: int = 2048, base: int = 10000
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) -> None:
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super().__init__()
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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self.inv_freq = 1.0 / mx.power(
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self.base, mx.arange(0, self.dim, 2, dtype=mx.float32) / self.dim
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)
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self.cos_cached = mx.zeros((1, 1, max_position_embeddings, dim))
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self.sin_cached = mx.zeros((1, 1, max_position_embeddings, dim))
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self._set_cos_sin_cache(max_position_embeddings)
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def _set_cos_sin_cache(self, seq_len: int) -> None:
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self.max_seq_len_cached = seq_len
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t = mx.arange(self.max_seq_len_cached) # type: ignore
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freqs = mx.outer(t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = mx.concatenate((freqs, freqs), axis=-1)
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self.cos_cached = emb.cos()[None, None, :, :]
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self.sin_cached = emb.sin()[None, None, :, :]
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def __call__(self, x: mx.array, seq_len: int) -> Tuple[mx.array, mx.array]:
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# x: [bs, num_attention_heads, seq_len, head_size]
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if seq_len > self.max_seq_len_cached:
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self._set_cos_sin_cache(seq_len)
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return (
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self.cos_cached[:, :, :seq_len, ...].astype(x.dtype), # type: ignore
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self.sin_cached[:, :, :seq_len, ...].astype(x.dtype), # type: ignore
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)
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def _rotate_half(x: mx.array) -> mx.array:
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return mx.concatenate((-x2, x1), axis=-1)
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def _rotary_pos_emb(
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x: mx.array, cos: mx.array, sin: mx.array, position_ids: mx.array
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) -> mx.array:
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# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
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cos = mx.squeeze(cos, (0, 1)) # [seq_len, dim]
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sin = mx.squeeze(sin, (0, 1)) # [seq_len, dim]
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cos = cos[position_ids][:, None] # [bs, 1, seq_len, dim]
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sin = sin[position_ids][:, None] # [bs, 1, seq_len, dim]
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x_embed = (x * cos) + (_rotate_half(x) * sin)
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return x_embed
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class RMSNorm(nn.Module):
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def __init__(self, dims: int, eps: float = 1e-5):
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super().__init__()
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self.weight = mx.ones((dims,))
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self.variance_epsilon = eps
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def _norm(self, x):
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return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.variance_epsilon)
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def __call__(self, x):
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output = self._norm(x.astype(mx.float32)).astype(x.dtype)
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return self.weight * output
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class Attention(nn.Module):
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def __init__(self, config: ModelArgs) -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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head_dim = self.hidden_size // config.num_attention_heads
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self.max_position_embeddings = config.max_position_embeddings
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self.q_num_heads = config.num_attention_heads
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self.qk_dim = self.v_dim = head_dim
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self.k_num_heads = self.v_num_heads = int(
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np.ceil(self.q_num_heads / config.n_shared_head)
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)
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self.scale = head_dim**-0.5
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self.q_proj = nn.Linear(
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self.hidden_size, self.q_num_heads * self.qk_dim, bias=False
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)
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self.k_proj = nn.Linear(
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self.hidden_size, self.k_num_heads * self.qk_dim, bias=False
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)
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self.v_proj = nn.Linear(
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self.hidden_size, self.v_num_heads * self.v_dim, bias=False
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)
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self.o_proj = nn.Linear(
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self.q_num_heads * self.v_dim, self.hidden_size, bias=False
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)
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self.rotary_emb = RotaryEmbedding(
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self.qk_dim, max_position_embeddings=self.max_position_embeddings
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)
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def __call__(
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self,
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hidden_states: mx.array,
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attention_mask: Optional[mx.array] = None,
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position_ids: Optional[mx.array] = None,
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cache: Optional[Tuple[mx.array, mx.array]] = None,
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) -> Tuple[mx.array, Tuple[mx.array, mx.array]]:
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bsz, q_len, _ = hidden_states.shape
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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# Prepare the queries, keys and values for the attention computation
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query_states = query_states.reshape(
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bsz, q_len, self.q_num_heads, self.qk_dim
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).transpose(0, 2, 1, 3)
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key_states = key_states.reshape(
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bsz, q_len, self.k_num_heads, self.qk_dim
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).transpose(0, 2, 1, 3)
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value_states = value_states.reshape(
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bsz, q_len, self.v_num_heads, self.v_dim
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).transpose(0, 2, 1, 3)
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def _expand_kv(a: mx.array) -> mx.array:
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a = mx.concatenate(
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[mx.expand_dims(a, 1)] * self.config.n_shared_head, axis=1
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)
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return a.reshape([bsz, self.q_num_heads, q_len, -1])
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# expand shared kv
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assert self.k_num_heads == self.v_num_heads
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key_states = _expand_kv(key_states)
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value_states = _expand_kv(value_states)
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kv_seq_len = key_states.shape[-2]
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if cache is not None:
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kv_seq_len += cache[0].shape[-2]
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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assert position_ids is not None
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query_states = _rotary_pos_emb(query_states, cos, sin, position_ids)
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key_states = _rotary_pos_emb(key_states, cos, sin, position_ids)
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if cache is not None:
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# reuse k, v, self_attention
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key_states = mx.concatenate([cache[0], key_states], axis=2)
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value_states = mx.concatenate([cache[1], value_states], axis=2)
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scores = (query_states * self.scale) @ key_states.transpose(0, 1, 3, 2)
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if attention_mask is not None:
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scores += attention_mask
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scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
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output = (scores @ value_states).transpose(0, 2, 1, 3).reshape(bsz, q_len, -1)
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return self.o_proj(output), (key_states, value_states)
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class MLP(nn.Module):
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def __init__(self, config: ModelArgs) -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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self.act_fn = nn.silu
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def __call__(self, x: mx.array) -> mx.array:
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return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) # type: ignore
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class PlamoDecoderLayer(nn.Module):
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def __init__(self, config: ModelArgs) -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.self_attn = Attention(config)
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self.mlp = MLP(config)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def __call__(
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self,
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hidden_states: mx.array,
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attention_mask: Optional[mx.array] = None,
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position_ids: Optional[mx.array] = None,
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cache: Optional[Tuple[mx.array, mx.array]] = None,
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) -> Tuple[Any, ...]:
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# from LlamaDecoder
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residual = hidden_states
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hidden_states = self.norm(hidden_states)
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# Self Attention
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hidden_states_sa, cache = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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cache=cache,
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)
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# Fully Connected
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hidden_states_mlp = self.mlp(hidden_states)
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# Residual ("Parallel Layers" is used here, which is different from the normal residual connection)
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# See "GPT-NeoX-20B: An Open-Source Autoregressive Language Model" for Parallel Layers
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hidden_states = residual + hidden_states_sa + hidden_states_mlp
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return hidden_states, cache # type: ignore
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class PlamoDecoder(nn.Module):
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def __init__(self, config: ModelArgs) -> None:
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super().__init__()
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self.layers = [
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PlamoDecoderLayer(config) for _ in range(config.num_hidden_layers)
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]
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class PlamoModel(nn.Module):
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config_class = ModelArgs
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_no_split_modules: List[str]
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base_model_prefix = "model"
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supports_gradient_checkpointing = True
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_no_split_modules = ["PlamoDecoderLayer"]
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_skip_keys_device_placement = "past_key_values"
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_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.config = config
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self.padding_idx = config.pad_token_id
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self.vocab_size = config.vocab_size
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self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
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self.layers = PlamoDecoder(config) # type: ignore
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.gradient_checkpointing = False
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def __call__(
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self,
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inputs: mx.array,
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cache: Optional[List[Union[Tuple[mx.array, mx.array], None]]] = None,
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) -> Tuple[mx.array, Optional[List[Union[Tuple[mx.array, mx.array], None]]]]:
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h = self.embed_tokens(inputs)
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mask = None
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if h.shape[1] > 1:
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mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
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mask = mask.astype(self.embed_tokens.weight.dtype)
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if cache is None:
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past_key_values_length = 0
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cache = [None for _ in range(len(self.layers.layers))]
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else:
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if cache[0] is not None:
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past_key_values_length = cache[0][0].shape[2]
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position_ids = _create_position_ids(h.shape[1], past_key_values_length)
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for e, layer in enumerate(self.layers.layers):
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h, c = layer(h, mask, position_ids, cache[e])
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if cache is not None:
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cache[e] = c
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else:
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cache.append(c)
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return self.norm(h), cache
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def _create_position_ids(seq_length: int, past_key_values_length: int = 0) -> mx.array:
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# create position_ids on the fly for batch generation
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position_ids = mx.arange(
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past_key_values_length, seq_length + past_key_values_length, dtype=mx.int64
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)
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position_ids = position_ids[None, ...].reshape(-1, seq_length)
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return position_ids
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class Model(nn.Module):
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def __init__(self, config: PretrainedConfig) -> None:
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super().__init__()
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self.model = PlamoModel(config)
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self.lm_head: nn.Module = nn.Linear(
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config.hidden_size, config.vocab_size, bias=False
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)
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def __call__(
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self,
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inputs: mx.array,
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cache: Optional[List[Tuple[mx.array, mx.array]]] = None,
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) -> Tuple[mx.array, mx.array]:
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out, cache = self.model(inputs, cache)
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return self.lm_head(out), cache
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