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hparam_presets.py
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hparam_presets.py
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hparam_presets = {
'lstm-benchmark': {
'model': dict(
bidirectional_encoder=True,
d_model=1024,
d_embedding=512,
grad_clip_threshold=150.0,
init_scale=None,
kl_weight_start=0.2,
kl_annealing_steps=8000,
latent_depth=64,
lr=3e-4,
tie_embedding_weights=True,
tie_logit_weights=True,
transformer_encoder=False
),
'trainer': dict(
accumulate_grad_batches=2
)
},
'lstm-wikipedia': {
'data': dict(
dataset_name='wikipedia',
dataset_config='20200501.en',
tokens_per_batch=50_000,
min_tokens_per_sample=512,
max_tokens_per_sample=25_000
),
'model': dict(
bidirectional_encoder=True,
d_model=2048,
d_embedding=512,
grad_clip_threshold=150.0,
init_scale=None,
kl_weight_start=1.0,
kl_annealing_steps=0,
latent_depth=64,
lr=3e-4,
tie_embedding_weights=True,
tie_logit_weights=True,
transformer_encoder=False
),
'trainer': dict(
accumulate_grad_batches=2,
val_check_interval=0.25
)
},
'dense-benchmark': {
'data': dict(
dataset_name='wikipedia',
dataset_config='20200501.en',
tokens_per_batch=50_000,
min_tokens_per_sample=512,
max_tokens_per_sample=3_125
),
'model': dict(
d_model=512,
grad_checkpointing=True,
grad_clip_threshold=150.0,
init_scale=0.02,
kl_weight_start=0.3,
kl_weight_end=1.0,
kl_annealing_steps=8000,
latent_depth=64,
lr=3e-4,
num_layers=6,
sparse_self_attention=False,
tie_embedding_weights=True
),
'trainer': dict(
accumulate_grad_batches=2
)
},
'sparse-benchmark': {
'data': dict(
dataset_name='wikipedia',
dataset_config='20200501.en',
tokens_per_batch=50_000,
min_tokens_per_sample=512,
max_tokens_per_sample=3_125
),
'model': dict(
d_model=512,
grad_checkpointing=True,
grad_clip_threshold=150.0,
init_scale=0.02,
kl_weight_start=1.0,
kl_annealing_steps=0,
latent_depth=64,
lr=3e-4,
num_layers=6,
sparse_self_attention=True,
tie_embedding_weights=True
),
'trainer': dict(
accumulate_grad_batches=2
)
},
'nonvae-wikipedia': {
'data': dict(
dataset_name='wikipedia',
dataset_config='20200501.en',
tokens_per_batch=50_000,
min_tokens_per_sample=512,
max_tokens_per_sample=3_125
),
'model': dict(
d_model=512,
grad_checkpointing=True,
grad_clip_threshold=150.0,
init_scale=0.02,
lr=3e-4,
num_layers=6,
sparse_self_attention=False,
tie_embedding_weights=True
),
'trainer': dict(
accumulate_grad_batches=2,
val_check_interval=0.1
)
},
'wikipedia': {
'data': dict(
dataset_name='wikipedia',
dataset_config='20200501.en',
tokens_per_batch=100_000,
min_tokens_per_sample=512,
max_tokens_per_sample=50_000
),
'model': dict(
d_model=512,
grad_checkpointing=True,
grad_clip_threshold=150.0,
init_scale=0.02,
attn_window_size=8,
kl_weight_start=0.1,
kl_weight_end=1.0,
kl_annealing_steps=8000,
latent_depth=64,
lr=3e-4,
num_layers=6,
sparse_self_attention=True,
tie_embedding_weights=True
),
'trainer': dict(
accumulate_grad_batches=2,
val_check_interval=0.1
)
},
'pg19': {
'data': dict(
dataset_name='pg19',
dataset_config=None,
tokens_per_batch=102_912,
min_tokens_per_sample=512,
max_tokens_per_sample=102_400
),
'model': dict(
# adam_beta1=0.95,
d_model=512,
grad_checkpointing=True,
grad_clip_threshold=150.0,
init_scale=0.02,
attn_window_size=6,
kl_weight_start=0.1,
kl_weight_end=1.0,
kl_annealing_steps=8000,
latent_depth=64,
lr=3e-4,
num_layers=6,
sparse_self_attention=True,
tie_embedding_weights=True
),
'trainer': dict(
accumulate_grad_batches=4,
val_check_interval=0.5
)
},
'nonvae-pg19': {
'data': dict(
dataset_name='pg19',
dataset_config=None,
tokens_per_batch=92_672,
min_tokens_per_sample=512,
max_tokens_per_sample=92_160
),
'model': dict(
d_model=512,
grad_checkpointing=True,
grad_clip_threshold=150.0,
init_scale=0.02,
lr=3e-4,
num_layers=6,
sparse_self_attention=True,
tie_embedding_weights=True
),
'trainer': dict(
accumulate_grad_batches=4,
val_check_interval=0.5
)
}
}