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# From https://stackoverflow.com/a/23689767
# From https://github.com/pytorch/pytorch/issues/97899
# From https://github.com/facebookresearch/llama/blob/main/llama/model.py
import torch
from torch import nn
from torch.nn.functional import scaled_dot_product_attention
from xformers.ops import SwiGLU, memory_efficient_attention
from .rmsnorm import RMSNorm
from .rotary import precompute_freqs_cis, apply_rotary_emb
from transformers import PreTrainedModel, PretrainedConfig
from transformers.modeling_outputs import MaskedLMOutput
class DotDict(dict):
"""Dictionary that supports the dot notation to access attributes (similarly to HuggingFace)."""
__getattr__ = dict.get
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
class AMPLIFYConfig(PretrainedConfig):
model_type = "AMPLIFY"
# All config parameters must have a default value.
def __init__(
self,
hidden_size: int = 960,
num_hidden_layers: int = 32,
num_attention_heads: int = 15,
intermediate_size: int = 3840,
dropout_prob: float = 0,
embedding_init_range: float = 0.02,
decoder_init_range: float = 0.02,
rms_norm: bool = True,
norm_eps: float = 1e-05,
hidden_act: str = "SwiGLU",
layer_norm_after_embedding: bool = False,
layer_norm_before_last_layer: bool = True,
vocab_size: int = 27,
ffn_bias: bool = False,
att_bias: bool = False,
pad_token_id: int = 0,
max_length: int = 2048,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout_prob = dropout_prob
self.embedding_init_range = embedding_init_range
self.decoder_init_range = decoder_init_range
self.rms_norm = rms_norm
self.norm_eps = norm_eps
self.hidden_act = hidden_act
self.layer_norm_after_embedding = layer_norm_after_embedding
self.layer_norm_before_last_layer = layer_norm_before_last_layer
self.vocab_size = vocab_size
self.ffn_bias = ffn_bias
self.att_bias = att_bias
self.pad_token_id = pad_token_id
self.max_length = max_length
class EncoderBlock(nn.Module):
"""Transformer encoder block."""
def __init__(self, config: AMPLIFYConfig):
"""Initialize a EncoderBlock.
Args:
hidden_size (int): _description_
num_attention_heads (int): _description_
intermediate_size (int, optional): _description_. Defaults to 2048.
dropout_prob (float, optional): _description_. Defaults to 0.1.
activation (str, optional): _description_. Defaults to "relu".
rms_norm (bool, optional): _description_. Defaults to True.
norm_eps (float, optional): _description_. Defaults to 1e-5.
pad_token_id (int, optional): _description_. Defaults to 0.
max_length (int, optional): _description_. Defaults to 2048.
ffn_bias (bool, optional): _description_. Defaults to False.
att_bias (bool, optional): _description_. Defaults to False.
"""
super().__init__()
self.config = config
self.d_head = config.hidden_size // config.num_attention_heads
# Attention
self.q = nn.Linear(in_features=config.hidden_size, out_features=config.hidden_size, bias=config.att_bias)
self.k = nn.Linear(in_features=config.hidden_size, out_features=config.hidden_size, bias=config.att_bias)
self.v = nn.Linear(in_features=config.hidden_size, out_features=config.hidden_size, bias=config.att_bias)
self.wo = nn.Linear(in_features=config.hidden_size, out_features=config.hidden_size, bias=config.att_bias)
self.resid_dropout = nn.Dropout(config.dropout_prob)
# Feedforward network
match config.hidden_act.lower():
case "swiglu":
# To keep the number of parameters and the amount of computation constant, we reduce the number of
# hidden units by a factor of 2/3 (https://arxiv.org/pdf/2002.05202.pdf) and make it a multiple of 8 to
# avoid RuntimeError due to misaligned operand
multiple_of = 8
intermediate_size = int(2 * config.intermediate_size / 3)
intermediate_size = multiple_of * ((intermediate_size + multiple_of - 1) // multiple_of)
self.ffn = SwiGLU(config.hidden_size, intermediate_size, config.hidden_size, bias=config.ffn_bias)
case "relu":
self.ffn = nn.Sequential(
nn.Linear(config.hidden_size, config.intermediate_size, bias=config.ffn_bias),
nn.ReLU(),
nn.Linear(config.intermediate_size, config.hidden_size, bias=config.ffn_bias),
)
case "gelu":
self.ffn = nn.Sequential(
nn.Linear(config.hidden_size, config.intermediate_size, bias=config.ffn_bias),
nn.GELU(),
nn.Linear(config.intermediate_size, config.hidden_size, bias=config.ffn_bias),
)
self.attention_norm = (
RMSNorm(config.hidden_size, config.norm_eps)
if config.rms_norm
else nn.LayerNorm(config.hidden_size, config.norm_eps)
)
self.ffn_norm = (
RMSNorm(config.hidden_size, config.norm_eps)
if config.rms_norm
else nn.LayerNorm(config.hidden_size, config.norm_eps)
)
self.ffn_dropout = nn.Dropout(config.dropout_prob)
def forward(self, x: torch.Tensor, attention_mask: torch.Tensor, freqs_cis: torch.Tensor, output_attentions: bool):
attn, contact = self._att_block(self.attention_norm(x), attention_mask, freqs_cis, output_attentions)
x = x + attn
x = x + self._ff_block(self.ffn_norm(x))
return x, contact
def _att_block(
self, x: torch.Tensor, attention_mask: torch.Tensor, freqs_cis: torch.Tensor, output_attentions: bool
):
batch_size, seq_len, _ = x.shape
xq, xk, xv = self.q(x), self.k(x), self.v(x)
# Reshape for rotary embeddings
xq = xq.view(batch_size, seq_len, self.config.num_attention_heads, self.d_head)
xk = xk.view(batch_size, seq_len, self.config.num_attention_heads, self.d_head)
xv = xv.view(batch_size, seq_len, self.config.num_attention_heads, self.d_head)
xq, xk = apply_rotary_emb(xq, xk, freqs_cis)
# Compute the attention weight
attn_weights = None
if output_attentions:
attn_weights = xq.permute(0, 2, 1, 3) @ xk.permute(0, 2, 3, 1) / (xq.size(-1) ** 0.5)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
attn_weights = attn_weights.softmax(-1)
# Compute the attention using xformers if the tensors are on GPU
if x.is_cuda:
# Input and output are of dimension (B, M, H, K) where B is the batch size, M the sequence length,
# H the number of heads, and K the embeding size per head
attn = memory_efficient_attention(
query=xq,
key=xk,
value=xv,
attn_bias=attention_mask,
p=self.config.dropout_prob if self.training else 0,
)
else:
# Input and output are of dimension (B, H, M, K)
attn = scaled_dot_product_attention(
query=xq.transpose(1, 2),
key=xk.transpose(1, 2),
value=xv.transpose(1, 2),
attn_mask=attention_mask,
dropout_p=self.config.dropout_prob if self.training else 0,
).transpose(1, 2)
attn_scores = self.wo(attn.view(batch_size, seq_len, self.config.num_attention_heads * self.d_head))
return (self.resid_dropout(attn_scores), attn_weights)
def _ff_block(self, x: torch.Tensor):
return self.ffn_dropout(self.ffn(x))
class AMPLIFYPreTrainedModel(PreTrainedModel):
config_class = AMPLIFYConfig
def _init_weights(self, module):
if isinstance(module, nn.Linear):
module.weight.data.uniform_(-self.config.decoder_init_range, self.config.decoder_init_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.uniform_(-self.config.embedding_init_range, self.config.embedding_init_range)
class AMPLIFY(AMPLIFYPreTrainedModel):
"""The main model class.
Args:
config (amplify.model.amplify.AMPLIFYConfig): model configuration, usually defined from the Hydra configuration.
"""
def __init__(self, config: AMPLIFYConfig, **kwargs):
super().__init__(config)
self.config = config
self.encoder = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
if config.layer_norm_after_embedding:
self.layer_norm_1 = (
RMSNorm(config.hidden_size, config.norm_eps)
if config.rms_norm
else nn.LayerNorm(config.hidden_size, config.norm_eps)
)
self.transformer_encoder = nn.ModuleList()
for _ in range(config.num_hidden_layers):
self.transformer_encoder.append(EncoderBlock(config))
if config.layer_norm_before_last_layer:
self.layer_norm_2 = (
RMSNorm(config.hidden_size, config.norm_eps)
if config.rms_norm
else nn.LayerNorm(config.hidden_size, config.norm_eps)
)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
self.freqs_cis = precompute_freqs_cis(config.hidden_size // config.num_attention_heads, config.max_length)
# Initialize weights and apply final processing
self.post_init()
def forward(self, input_ids, attention_mask=None, output_hidden_states=False, output_attentions=False, **kwargs):
# Initialize
hidden_states, attentions = [], []
# Expand and repeat: (Batch, Length) -> (Batch, Heads, Length, Length)
if attention_mask is not None and not torch.all(attention_mask == 0):
attention_mask = (
attention_mask.unsqueeze(1)
.unsqueeze(1)
.repeat(1, self.config.num_attention_heads, attention_mask.size(-1), 1)
)
else:
attention_mask = None
# RoPE
self.freqs_cis = self.freqs_cis.to(input_ids.device, non_blocking=True)
freqs_cis = self.freqs_cis[: input_ids.shape[1]]
# Embedding
x = self.encoder(input_ids)
if self.config.layer_norm_after_embedding:
x = self.layer_norm_1(x)
# Transformer encoder
for layer in self.transformer_encoder:
x, attn = layer(x, attention_mask, freqs_cis, output_attentions)
if output_hidden_states:
hidden_states.append(x)
if output_attentions:
attentions.append(attn)
# Classification head with layer norm
logits = self.decoder(self.layer_norm_2(x) if self.config.layer_norm_before_last_layer else x)
# Return logits or the output of the last hidden layer
return MaskedLMOutput(logits=logits, hidden_states=hidden_states, attentions=attentions)
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