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ft_transformer.py
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ft_transformer.py
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# %%
import math
import typing as ty
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as nn_init
import zero
from torch import Tensor
import lib
# %%
class Tokenizer(nn.Module):
category_offsets: ty.Optional[Tensor]
def __init__(
self,
d_numerical: int,
categories: ty.Optional[ty.List[int]],
d_token: int,
bias: bool,
) -> None:
super().__init__()
if categories is None:
d_bias = d_numerical
self.category_offsets = None
self.category_embeddings = None
else:
d_bias = d_numerical + len(categories)
category_offsets = torch.tensor([0] + categories[:-1]).cumsum(0)
self.register_buffer('category_offsets', category_offsets)
self.category_embeddings = nn.Embedding(sum(categories), d_token)
nn_init.kaiming_uniform_(self.category_embeddings.weight, a=math.sqrt(5))
print(f'{self.category_embeddings.weight.shape=}')
# take [CLS] token into account
self.weight = nn.Parameter(Tensor(d_numerical + 1, d_token))
self.bias = nn.Parameter(Tensor(d_bias, d_token)) if bias else None
# The initialization is inspired by nn.Linear
nn_init.kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
nn_init.kaiming_uniform_(self.bias, a=math.sqrt(5))
@property
def n_tokens(self) -> int:
return len(self.weight) + (
0 if self.category_offsets is None else len(self.category_offsets)
)
def forward(self, x_num: Tensor, x_cat: ty.Optional[Tensor]) -> Tensor:
x_some = x_num if x_cat is None else x_cat
assert x_some is not None
x_num = torch.cat(
[torch.ones(len(x_some), 1, device=x_some.device)] # [CLS]
+ ([] if x_num is None else [x_num]),
dim=1,
)
x = self.weight[None] * x_num[:, :, None]
if x_cat is not None:
x = torch.cat(
[x, self.category_embeddings(x_cat + self.category_offsets[None])],
dim=1,
)
if self.bias is not None:
bias = torch.cat(
[
torch.zeros(1, self.bias.shape[1], device=x.device),
self.bias,
]
)
x = x + bias[None]
return x
class MultiheadAttention(nn.Module):
def __init__(
self, d: int, n_heads: int, dropout: float, initialization: str
) -> None:
if n_heads > 1:
assert d % n_heads == 0
assert initialization in ['xavier', 'kaiming']
super().__init__()
self.W_q = nn.Linear(d, d)
self.W_k = nn.Linear(d, d)
self.W_v = nn.Linear(d, d)
self.W_out = nn.Linear(d, d) if n_heads > 1 else None
self.n_heads = n_heads
self.dropout = nn.Dropout(dropout) if dropout else None
for m in [self.W_q, self.W_k, self.W_v]:
if initialization == 'xavier' and (n_heads > 1 or m is not self.W_v):
# gain is needed since W_qkv is represented with 3 separate layers
nn_init.xavier_uniform_(m.weight, gain=1 / math.sqrt(2))
nn_init.zeros_(m.bias)
if self.W_out is not None:
nn_init.zeros_(self.W_out.bias)
def _reshape(self, x: Tensor) -> Tensor:
batch_size, n_tokens, d = x.shape
d_head = d // self.n_heads
return (
x.reshape(batch_size, n_tokens, self.n_heads, d_head)
.transpose(1, 2)
.reshape(batch_size * self.n_heads, n_tokens, d_head)
)
def forward(
self,
x_q: Tensor,
x_kv: Tensor,
key_compression: ty.Optional[nn.Linear],
value_compression: ty.Optional[nn.Linear],
) -> Tensor:
q, k, v = self.W_q(x_q), self.W_k(x_kv), self.W_v(x_kv)
for tensor in [q, k, v]:
assert tensor.shape[-1] % self.n_heads == 0
if key_compression is not None:
assert value_compression is not None
k = key_compression(k.transpose(1, 2)).transpose(1, 2)
v = value_compression(v.transpose(1, 2)).transpose(1, 2)
else:
assert value_compression is None
batch_size = len(q)
d_head_key = k.shape[-1] // self.n_heads
d_head_value = v.shape[-1] // self.n_heads
n_q_tokens = q.shape[1]
q = self._reshape(q)
k = self._reshape(k)
attention = F.softmax(q @ k.transpose(1, 2) / math.sqrt(d_head_key), dim=-1)
if self.dropout is not None:
attention = self.dropout(attention)
x = attention @ self._reshape(v)
x = (
x.reshape(batch_size, self.n_heads, n_q_tokens, d_head_value)
.transpose(1, 2)
.reshape(batch_size, n_q_tokens, self.n_heads * d_head_value)
)
if self.W_out is not None:
x = self.W_out(x)
return x
class Transformer(nn.Module):
"""Transformer.
References:
- https://pytorch.org/docs/stable/generated/torch.nn.Transformer.html
- https://github.com/facebookresearch/pytext/tree/master/pytext/models/representations/transformer
- https://github.com/pytorch/fairseq/blob/1bba712622b8ae4efb3eb793a8a40da386fe11d0/examples/linformer/linformer_src/modules/multihead_linear_attention.py#L19
"""
def __init__(
self,
*,
# tokenizer
d_numerical: int,
categories: ty.Optional[ty.List[int]],
token_bias: bool,
# transformer
n_layers: int,
d_token: int,
n_heads: int,
d_ffn_factor: float,
attention_dropout: float,
ffn_dropout: float,
residual_dropout: float,
activation: str,
prenormalization: bool,
initialization: str,
# linformer
kv_compression: ty.Optional[float],
kv_compression_sharing: ty.Optional[str],
#
d_out: int,
) -> None:
assert (kv_compression is None) ^ (kv_compression_sharing is not None)
super().__init__()
self.tokenizer = Tokenizer(d_numerical, categories, d_token, token_bias)
n_tokens = self.tokenizer.n_tokens
def make_kv_compression():
assert kv_compression
compression = nn.Linear(
n_tokens, int(n_tokens * kv_compression), bias=False
)
if initialization == 'xavier':
nn_init.xavier_uniform_(compression.weight)
return compression
self.shared_kv_compression = (
make_kv_compression()
if kv_compression and kv_compression_sharing == 'layerwise'
else None
)
def make_normalization():
return nn.LayerNorm(d_token)
d_hidden = int(d_token * d_ffn_factor)
self.layers = nn.ModuleList([])
for layer_idx in range(n_layers):
layer = nn.ModuleDict(
{
'attention': MultiheadAttention(
d_token, n_heads, attention_dropout, initialization
),
'linear0': nn.Linear(
d_token, d_hidden * (2 if activation.endswith('glu') else 1)
),
'linear1': nn.Linear(d_hidden, d_token),
'norm1': make_normalization(),
}
)
if not prenormalization or layer_idx:
layer['norm0'] = make_normalization()
if kv_compression and self.shared_kv_compression is None:
layer['key_compression'] = make_kv_compression()
if kv_compression_sharing == 'headwise':
layer['value_compression'] = make_kv_compression()
else:
assert kv_compression_sharing == 'key-value'
self.layers.append(layer)
self.activation = lib.get_activation_fn(activation)
self.last_activation = lib.get_nonglu_activation_fn(activation)
self.prenormalization = prenormalization
self.last_normalization = make_normalization() if prenormalization else None
self.ffn_dropout = ffn_dropout
self.residual_dropout = residual_dropout
self.head = nn.Linear(d_token, d_out)
def _get_kv_compressions(self, layer):
return (
(self.shared_kv_compression, self.shared_kv_compression)
if self.shared_kv_compression is not None
else (layer['key_compression'], layer['value_compression'])
if 'key_compression' in layer and 'value_compression' in layer
else (layer['key_compression'], layer['key_compression'])
if 'key_compression' in layer
else (None, None)
)
def _start_residual(self, x, layer, norm_idx):
x_residual = x
if self.prenormalization:
norm_key = f'norm{norm_idx}'
if norm_key in layer:
x_residual = layer[norm_key](x_residual)
return x_residual
def _end_residual(self, x, x_residual, layer, norm_idx):
if self.residual_dropout:
x_residual = F.dropout(x_residual, self.residual_dropout, self.training)
x = x + x_residual
if not self.prenormalization:
x = layer[f'norm{norm_idx}'](x)
return x
def forward(self, x_num: Tensor, x_cat: ty.Optional[Tensor]) -> Tensor:
x = self.tokenizer(x_num, x_cat)
for layer_idx, layer in enumerate(self.layers):
is_last_layer = layer_idx + 1 == len(self.layers)
layer = ty.cast(ty.Dict[str, nn.Module], layer)
x_residual = self._start_residual(x, layer, 0)
x_residual = layer['attention'](
# for the last attention, it is enough to process only [CLS]
(x_residual[:, :1] if is_last_layer else x_residual),
x_residual,
*self._get_kv_compressions(layer),
)
if is_last_layer:
x = x[:, : x_residual.shape[1]]
x = self._end_residual(x, x_residual, layer, 0)
x_residual = self._start_residual(x, layer, 1)
x_residual = layer['linear0'](x_residual)
x_residual = self.activation(x_residual)
if self.ffn_dropout:
x_residual = F.dropout(x_residual, self.ffn_dropout, self.training)
x_residual = layer['linear1'](x_residual)
x = self._end_residual(x, x_residual, layer, 1)
assert x.shape[1] == 1
x = x[:, 0]
if self.last_normalization is not None:
x = self.last_normalization(x)
x = self.last_activation(x)
x = self.head(x)
x = x.squeeze(-1)
return x
# %%
if __name__ == "__main__":
args, output = lib.load_config()
args['model'].setdefault('token_bias', True)
args['model'].setdefault('kv_compression', None)
args['model'].setdefault('kv_compression_sharing', None)
# %%
zero.set_randomness(args['seed'])
dataset_dir = lib.get_path(args['data']['path'])
stats: ty.Dict[str, ty.Any] = {
'dataset': dataset_dir.name,
'algorithm': Path(__file__).stem,
**lib.load_json(output / 'stats.json'),
}
timer = zero.Timer()
timer.run()
D = lib.Dataset.from_dir(dataset_dir)
X = D.build_X(
normalization=args['data'].get('normalization'),
num_nan_policy='mean',
cat_nan_policy='new',
cat_policy=args['data'].get('cat_policy', 'indices'),
cat_min_frequency=args['data'].get('cat_min_frequency', 0.0),
seed=args['seed'],
)
if not isinstance(X, tuple):
X = (X, None)
zero.set_randomness(args['seed'])
Y, y_info = D.build_y(args['data'].get('y_policy'))
lib.dump_pickle(y_info, output / 'y_info.pickle')
X = tuple(None if x is None else lib.to_tensors(x) for x in X)
Y = lib.to_tensors(Y)
device = lib.get_device()
if device.type != 'cpu':
X = tuple(
None if x is None else {k: v.to(device) for k, v in x.items()} for x in X
)
Y_device = {k: v.to(device) for k, v in Y.items()}
else:
Y_device = Y
X_num, X_cat = X
del X
if not D.is_multiclass:
Y_device = {k: v.float() for k, v in Y_device.items()}
train_size = D.size(lib.TRAIN)
batch_size = args['training']['batch_size']
epoch_size = stats['epoch_size'] = math.ceil(train_size / batch_size)
eval_batch_size = args['training']['eval_batch_size']
chunk_size = None
loss_fn = (
F.binary_cross_entropy_with_logits
if D.is_binclass
else F.cross_entropy
if D.is_multiclass
else F.mse_loss
)
model = Transformer(
d_numerical=0 if X_num is None else X_num['train'].shape[1],
categories=lib.get_categories(X_cat),
d_out=D.info['n_classes'] if D.is_multiclass else 1,
**args['model'],
).to(device)
if torch.cuda.device_count() > 1: # type: ignore[code]
print('Using nn.DataParallel')
model = nn.DataParallel(model)
stats['n_parameters'] = lib.get_n_parameters(model)
def needs_wd(name):
return all(x not in name for x in ['tokenizer', '.norm', '.bias'])
for x in ['tokenizer', '.norm', '.bias']:
assert any(x in a for a in (b[0] for b in model.named_parameters()))
parameters_with_wd = [v for k, v in model.named_parameters() if needs_wd(k)]
parameters_without_wd = [v for k, v in model.named_parameters() if not needs_wd(k)]
optimizer = lib.make_optimizer(
args['training']['optimizer'],
(
[
{'params': parameters_with_wd},
{'params': parameters_without_wd, 'weight_decay': 0.0},
]
),
args['training']['lr'],
args['training']['weight_decay'],
)
stream = zero.Stream(lib.IndexLoader(train_size, batch_size, True, device))
progress = zero.ProgressTracker(args['training']['patience'])
training_log = {lib.TRAIN: [], lib.VAL: [], lib.TEST: []}
timer = zero.Timer()
checkpoint_path = output / 'checkpoint.pt'
def print_epoch_info():
print(f'\n>>> Epoch {stream.epoch} | {lib.format_seconds(timer())} | {output}')
print(
' | '.join(
f'{k} = {v}'
for k, v in {
'lr': lib.get_lr(optimizer),
'batch_size': batch_size,
'chunk_size': chunk_size,
'epoch_size': stats['epoch_size'],
'n_parameters': stats['n_parameters'],
}.items()
)
)
def apply_model(part, idx):
return model(
None if X_num is None else X_num[part][idx],
None if X_cat is None else X_cat[part][idx],
)
@torch.no_grad()
def evaluate(parts):
global eval_batch_size
model.eval()
metrics = {}
predictions = {}
for part in parts:
while eval_batch_size:
try:
predictions[part] = (
torch.cat(
[
apply_model(part, idx)
for idx in lib.IndexLoader(
D.size(part), eval_batch_size, False, device
)
]
)
.cpu()
.numpy()
)
except RuntimeError as err:
if not lib.is_oom_exception(err):
raise
eval_batch_size //= 2
print('New eval batch size:', eval_batch_size)
stats['eval_batch_size'] = eval_batch_size
else:
break
if not eval_batch_size:
RuntimeError('Not enough memory even for eval_batch_size=1')
metrics[part] = lib.calculate_metrics(
D.info['task_type'],
Y[part].numpy(), # type: ignore[code]
predictions[part], # type: ignore[code]
'logits',
y_info,
)
for part, part_metrics in metrics.items():
print(f'[{part:<5}]', lib.make_summary(part_metrics))
return metrics, predictions
def save_checkpoint(final):
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'stream': stream.state_dict(),
'random_state': zero.get_random_state(),
**{
x: globals()[x]
for x in [
'progress',
'stats',
'timer',
'training_log',
]
},
},
checkpoint_path,
)
lib.dump_stats(stats, output, final)
lib.backup_output(output)
# %%
timer.run()
for epoch in stream.epochs(args['training']['n_epochs']):
print_epoch_info()
model.train()
epoch_losses = []
for batch_idx in epoch:
loss, new_chunk_size = lib.train_with_auto_virtual_batch(
optimizer,
loss_fn,
lambda x: (apply_model(lib.TRAIN, x), Y_device[lib.TRAIN][x]),
batch_idx,
chunk_size or batch_size,
)
epoch_losses.append(loss.detach())
if new_chunk_size and new_chunk_size < (chunk_size or batch_size):
stats['chunk_size'] = chunk_size = new_chunk_size
print('New chunk size:', chunk_size)
epoch_losses = torch.stack(epoch_losses).tolist()
training_log[lib.TRAIN].extend(epoch_losses)
print(f'[{lib.TRAIN}] loss = {round(sum(epoch_losses) / len(epoch_losses), 3)}')
metrics, predictions = evaluate([lib.VAL, lib.TEST])
for k, v in metrics.items():
training_log[k].append(v)
progress.update(metrics[lib.VAL]['score'])
if progress.success:
print('New best epoch!')
stats['best_epoch'] = stream.epoch
stats['metrics'] = metrics
save_checkpoint(False)
for k, v in predictions.items():
np.save(output / f'p_{k}.npy', v)
elif progress.fail:
break
# %%
print('\nRunning the final evaluation...')
model.load_state_dict(torch.load(checkpoint_path)['model'])
stats['metrics'], predictions = evaluate(lib.PARTS)
for k, v in predictions.items():
np.save(output / f'p_{k}.npy', v)
stats['time'] = lib.format_seconds(timer())
save_checkpoint(True)
print('Done!')