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# Copyright (c) Meta Platforms, Inc. and affiliates.
import os
from dataclasses import replace
import numpy as np
import pytest
import torch
from bytelatent.constants import BLT_DATA
from bytelatent.data.data_types import Batch
from bytelatent.data.ngram_processor import NgramProcessor
from bytelatent.model.blt import (
ByteLatentTransformer,
ByteLatentTransformerArgs,
EmbeddingType,
compute_hash_embeddings,
create_global_transformer,
create_local_decoder,
create_local_encoder,
cross_attn_mask,
decoder_patch_ids_from_lengths,
get_blt_input,
init_embeddings,
patch_ids_from_lengths,
)
from bytelatent.model.latent_transformer import CrossAttention
from bytelatent.model.utils import create_causal_mask
from bytelatent.optim import OptimArgs, build_optimizer
from bytelatent.tokenizers.constants import EOS_ID
from bytelatent.train import compute_loss
def batch_to_tensors_and_gpu(batch):
x = torch.from_numpy(batch.x)
y = torch.from_numpy(batch.y)
mask = None if batch.mask is None else torch.from_numpy(batch.mask)
patch_lengths = (
None if batch.patch_lengths is None else torch.from_numpy(batch.patch_lengths)
)
ngram_ids = None if batch.ngram_ids is None else torch.from_numpy(batch.ngram_ids)
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
if mask is not None:
mask = mask.cuda()
if patch_lengths is not None:
patch_lengths = patch_lengths.cuda()
if ngram_ids is not None:
ngram_ids = ngram_ids.cuda()
return x, y, mask, patch_lengths, ngram_ids
def fake_batch():
batch_dict = torch.load(os.path.join(BLT_DATA, "test_batch.pt"), weights_only=False)
del batch_dict["x2"]
del batch_dict["y2"]
del batch_dict["src_names"]
return Batch(**batch_dict)
def create_args(cross_attention=False):
transformer_args = ByteLatentTransformerArgs(
# Base args provided
n_heads=8,
dim=512,
vocab_size=260,
# Additional args from command line
dim_token=256,
patch_size=6,
patching_mode="space",
tie_local_encoder_decoder_logits=False,
patch_in_forward=False,
max_encoder_seq_length=12288,
pad_to_max_length=True,
encoder_lm_loss=False,
patching_threshold=3.1439168453216553,
encoder_hash_byte_group_size=[4],
encoder_hash_byte_group_vocab=50002,
encoder_hash_byte_group_nb_functions=3,
cross_attn_encoder=cross_attention, # True,
cross_attn_decoder=cross_attention, # True,
cross_attn_window_encoder=512,
cross_attn_window_decoder=512,
dim_local_encoder=256,
dim_local_decoder=256,
cross_attn_k=8,
cross_attn_nheads=4,
cross_attn_all_layers_decoder=True,
cross_attn_all_layers_encoder=True,
cross_attn_use_flex_attention=True,
cross_attn_init_by_pooling=True,
log_patch_lengths=True,
non_linearity="swiglu",
use_rope=True,
recompute_fc1_out=False,
recompute_fc3_out=False,
recompute_attn=False,
custom_bwd=False,
layer_ckpt="none",
use_local_encoder_transformer=True,
init_use_gaussian=True,
init_use_depth="current",
attn_bias_type="block_causal",
attn_impl="xformers",
alpha_depth="disabled",
max_length=256,
local_attention_window_len=512,
max_seqlen=12288,
downsampling_by_pooling="max",
eos_id=EOS_ID,
)
return transformer_args
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is not available")
class TestByteLatentTransformer:
def test_local_encoder(self):
args = create_args()
device = torch.device("cuda")
local_encoder = create_local_encoder(args).to(device)
batch = fake_batch()
tokens, _, _, patch_lengths, _ = batch_to_tensors_and_gpu(batch)
local_encoder_tokens, _, _ = get_blt_input(
tokens=tokens,
enforce_patch_size_multiple=False,
nb_boe=0,
patch_size=local_encoder.patch_size,
boe_id=local_encoder.boe_id,
)
patch_ids = patch_ids_from_lengths(
patch_lengths, local_encoder_tokens.shape[-1]
)
encoder_hash_tok_embedding = init_embeddings(
args,
EmbeddingType.HASH_TOK,
local_encoder_dim=local_encoder.dim,
encoder_hash_byte_group_size=args.encoder_hash_byte_group_size,
).to(device)
local_encoder_embeds = compute_hash_embeddings(
local_encoder_tokens=local_encoder_tokens,
local_encoder=local_encoder,
encoder_hash_tok_embedding=encoder_hash_tok_embedding,
encoder_hash_byte_group_nb_functions=args.encoder_hash_byte_group_nb_functions,
encoder_hash_byte_group_size=args.encoder_hash_byte_group_size,
encoder_hash_byte_group_vocab=args.encoder_hash_byte_group_vocab,
)
reference_path = os.path.join(BLT_DATA, "local_encoder_tokens.pt")
reference_tokens = torch.load(reference_path).to(device)
torch.testing.assert_close(
local_encoder_tokens,
reference_tokens,
msg="Generated tokens don't match reference tokens",
)
(h_encoder, h_cross), cache_encoder = local_encoder(
tokens=local_encoder_tokens,
embeds=local_encoder_embeds,
patch_embeds=None,
cross_mask=None,
num_patches=patch_lengths.shape[1],
patch_ids=patch_ids,
)
assert h_encoder is not None
assert h_cross is None
assert cache_encoder is None
expected_shape = (
local_encoder_tokens.shape[0],
local_encoder_tokens.shape[1],
local_encoder.dim,
)
assert h_encoder.shape == expected_shape
def test_local_encoder_cross_attention(self):
args = create_args(cross_attention=True)
device = torch.device("cuda")
local_encoder = create_local_encoder(args).to(device)
batch = fake_batch()
tokens, _, _, patch_lengths, _ = batch_to_tensors_and_gpu(batch)
local_encoder_tokens, _, _ = get_blt_input(
tokens=tokens,
enforce_patch_size_multiple=False,
nb_boe=0,
patch_size=local_encoder.patch_size,
boe_id=local_encoder.boe_id,
)
patch_ids = patch_ids_from_lengths(
patch_lengths, local_encoder_tokens.shape[-1]
)
encoder_hash_tok_embedding = init_embeddings(
args,
EmbeddingType.HASH_TOK,
local_encoder_dim=local_encoder.dim,
encoder_hash_byte_group_size=args.encoder_hash_byte_group_size,
).to(device)
cross_attn_mask_enc = cross_attn_mask(
patch_ids,
patch_lengths,
local_encoder_tokens.shape[-1],
patches_as_queries=True,
cross_attn_k=args.cross_attn_k,
window=args.cross_attn_window_encoder,
block_mask=True,
)
local_encoder_embeds = compute_hash_embeddings(
local_encoder_tokens=local_encoder_tokens,
local_encoder=local_encoder,
encoder_hash_tok_embedding=encoder_hash_tok_embedding,
encoder_hash_byte_group_nb_functions=args.encoder_hash_byte_group_nb_functions,
encoder_hash_byte_group_size=args.encoder_hash_byte_group_size,
encoder_hash_byte_group_vocab=args.encoder_hash_byte_group_vocab,
)
(h_encoder, h_cross), cache_encoder = local_encoder(
tokens=local_encoder_tokens,
embeds=local_encoder_embeds,
patch_embeds=None,
cross_mask=cross_attn_mask_enc,
num_patches=patch_lengths.shape[1],
patch_ids=patch_ids,
)
assert h_encoder is not None
assert h_cross is not None
assert cache_encoder is None
expected_shape = (
local_encoder_tokens.shape[0],
local_encoder_tokens.shape[1],
local_encoder.dim,
)
assert h_encoder.shape == expected_shape
assert h_cross.shape == (2, 2048, local_encoder.dim)
def test_local_decoder_cross_attention(self):
args = create_args(cross_attention=True)
device = torch.device("cuda")
local_decoder = create_local_decoder(args).to(device)
test_files = {
"dec_embeds": "dec_embeds.pt",
"decoder_tokens": "local_decoder_tokens.pt",
"patch_embeds": "decoder_patch_cross_embeds.pt",
}
batch = fake_batch()
_, _, _, patch_lengths, _ = batch_to_tensors_and_gpu(batch)
tensors = {
name: torch.load(os.path.join(BLT_DATA, filename)).float().to(device)
for name, filename in test_files.items()
}
decoder_patch_ids = decoder_patch_ids_from_lengths(
patch_lengths, 0, tensors["decoder_tokens"].shape[-1]
)
cross_attn_mask_dec = cross_attn_mask(
decoder_patch_ids,
patch_lengths,
tensors["decoder_tokens"].shape[-1],
patches_as_queries=False,
cross_attn_k=args.cross_attn_k,
window=args.cross_attn_window_decoder,
block_mask=True,
)
output, _ = local_decoder(
embeds=tensors["dec_embeds"],
patch_embeds=tensors["patch_embeds"],
tokens=tensors["decoder_tokens"],
cross_mask=cross_attn_mask_dec,
cache=None,
)
assert output is not None
assert output.shape == (2, tensors["decoder_tokens"].shape[1], args.vocab_size)
def test_local_decoder(self):
args = create_args()
device = torch.device("cuda")
local_decoder = create_local_decoder(args).to(device)
test_files = {
"dec_embeds": "dec_embeds.pt",
"decoder_tokens": "local_decoder_tokens.pt",
"patch_embeds": "decoder_patch_embeds.pt",
}
tensors = {
name: torch.load(os.path.join(BLT_DATA, filename)).float().to(device)
for name, filename in test_files.items()
}
output, cache_decoder = local_decoder(
embeds=tensors["dec_embeds"],
patch_embeds=tensors["patch_embeds"],
tokens=tensors["decoder_tokens"],
cross_mask=None,
cache=None,
)
assert output is not None
expected_shape = (
tensors["decoder_tokens"].shape[0],
tensors["decoder_tokens"].shape[1],
args.vocab_size,
)
assert output.shape == expected_shape
assert cache_decoder is None
def test_global_transformer(self):
args = create_args()
device = torch.device("cuda")
global_transformer = create_global_transformer(args).to(device)
test_files = {
"global_embeds": "global_embeds.pt",
"global_tokens": "global_tokens.pt",
}
tensors = {
name: torch.load(os.path.join(BLT_DATA, filename)).float().to(device)
for name, filename in test_files.items()
}
h, cache = global_transformer(
embeds=tensors["global_embeds"], tokens=tensors["global_tokens"]
)
h is not None
assert h.shape == (2, 256, 512)
assert cache is None
def test_blt_transformer_init(self):
args = create_args()
model = ByteLatentTransformer(args)
assert model is not None
@pytest.mark.parametrize("attn_impl", ["sdpa", "xformers"])
def test_blt_transformer_forward(self, attn_impl):
args = create_args()
if attn_impl == "sdpa":
os.environ["BLT_SUPPRESS_ATTN_ERROR"] = "1"
else:
os.environ["BLT_SUPPRESS_ATTN_ERROR"] = "0"
args = args.model_copy(update=dict(attn_impl=attn_impl))
model = ByteLatentTransformer(args)
model = model.cuda()
batch = fake_batch()
x, _, _, patch_lengths, ngram_ids = batch_to_tensors_and_gpu(batch)
output = model(
tokens=x,
patch_lengths=patch_lengths,
ngram_ids=ngram_ids,
)
assert output is not None
expected_shape = (
x.shape[0],
x.shape[1],
args.vocab_size,
)
assert output.shape == expected_shape
def test_blt_transformer_cross_attn_forward(self):
args = create_args(cross_attention=True)
model = ByteLatentTransformer(args)
model = model.cuda()
batch = fake_batch()
x, y, mask, patch_lengths, ngram_ids = batch_to_tensors_and_gpu(batch)
output = model(
tokens=x,
patch_lengths=patch_lengths,
ngram_ids=ngram_ids,
)
assert output is not None
expected_shape = (
x.shape[0],
x.shape[1],
args.vocab_size,
)
assert output.shape == expected_shape
def test_cross_attention_rand(self):
x = torch.randn(2, 256, 512, device="cuda")
kv = torch.randn(2, 256, 512, device="cuda")
cross_attention = CrossAttention(
dim=512,
head_dim=64,
n_heads=8,
n_kv_heads=4,
norm_eps=1e-6,
).to("cuda")
mask = create_causal_mask(
x.shape[1], "flex_attention", None, sliding_window=None
)
output = cross_attention(x, kv, mask)
assert output is not None
assert output.shape == (2, 256, 512)
def test_ngram_embeddings(self):
ngram_to_size = {
2: 38396,
3: 50000,
4: 50000,
5: 50000,
6: 50000,
7: 50000,
8: 50000,
}
batch = fake_batch()
ngram_processor = NgramProcessor(BLT_DATA, ngram_to_size)
ngram_ids = ngram_processor.encode_token_ngrams(batch.x)
ngram_ids = np.stack(ngram_ids, axis=0)
batch = replace(batch, ngram_ids=ngram_ids)
args = create_args(cross_attention=True)
args = args.model_copy(
update=dict(
encoder_ngram_to_size_str="2:38396,3:50000,4:50000,5:50000,6:50000,7:50000,8:50000",
encoder_enable_byte_ngrams=True,
ngram_vocab_sizes=ngram_processor.ngram_vocab_sizes,
)
)
model = ByteLatentTransformer(args)
model = model.cuda()
x, _, _, patch_lengths, ngram_ids = batch_to_tensors_and_gpu(batch)
output = model(
tokens=x,
patch_lengths=patch_lengths,
ngram_ids=ngram_ids,
)
assert output is not None
expected_shape = (
x.shape[0],
x.shape[1],
args.vocab_size,
)
assert output.shape == expected_shape
def test_loss_backward(self):
args = create_args()
args = args.model_copy(update=dict(attn_impl="xformers"))
batch = fake_batch()
model = ByteLatentTransformer(args)
steps = 10
optimizer, scheduler = build_optimizer(model, OptimArgs(lr=4e-04), steps)
model = model.cuda()
x, y, mask, patch_lengths, ngram_ids = batch_to_tensors_and_gpu(batch)
initial_loss = None
final_loss = None
for step in range(steps):
output = model(
tokens=x,
patch_lengths=patch_lengths,
ngram_ids=ngram_ids,
)
loss, _ = compute_loss(output, y, mask, 1.0)
if step == 0:
initial_loss = loss.item()
if step == steps - 1:
final_loss = loss.item()
prev_loss = loss.item()
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
assert (
final_loss < initial_loss
), f"Training did not reduce loss: initial {initial_loss}, final {final_loss}"
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