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import os, sys; sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from bit_transformer import (
BitTransformerLM,
hil_safe_inference,
text_to_bits,
bits_to_text,
plot_telemetry,
infer_long_sequence,
diffusion_inference,
compress_bits,
)
from bit_transformer.safety import SafetyGate
import torch
import torch.nn.functional as F
import torch.nn as nn
import pytest
def test_forward_pass():
B, L = 2, 8
model = BitTransformerLM(d_model=32, nhead=4, num_layers=1, dim_feedforward=64, max_seq_len=L)
bits = torch.randint(0, 2, (B, L), dtype=torch.long)
logits, telemetry = model(bits)
assert logits.shape == (B, L, 2)
required_keys = {
"negentropy_input",
"lz_complexity_input",
"negentropy_logits",
"lz_complexity_logits",
"symbiosis_kl",
"symbiosis_score",
"attention_entropy",
"attention_entropy_mean",
}
assert required_keys.issubset(telemetry.keys())
pred = logits[:, :-1, :].reshape(-1, 2)
target = bits[:, 1:].reshape(-1)
loss = F.cross_entropy(pred, target)
assert torch.isfinite(loss)
def test_autocast_forward():
model = BitTransformerLM(
d_model=32,
nhead=4,
num_layers=1,
dim_feedforward=64,
max_seq_len=8,
use_autocast=True,
)
bits = torch.randint(0, 2, (1, 8), dtype=torch.long)
logits, _ = model(bits)
assert logits.shape == (1, 8, 2)
def test_act_forward():
model = BitTransformerLM(
d_model=32,
nhead=4,
num_layers=2,
dim_feedforward=64,
max_seq_len=8,
use_act=True,
)
bits = torch.randint(0, 2, (1, 8), dtype=torch.long)
logits, tele = model(bits)
assert logits.shape == (1, 8, 2)
assert "halt_probs" in tele
def test_act_skips_layers():
model = BitTransformerLM(
d_model=16,
nhead=4,
num_layers=3,
dim_feedforward=32,
max_seq_len=8,
use_act=True,
act_threshold=0.5,
)
for proj in model.halt_projs:
nn.init.constant_(proj.weight, 0.0)
nn.init.constant_(proj.bias, 10.0)
bits = torch.randint(0, 2, (1, 8), dtype=torch.long)
_, tele = model(bits)
assert len(tele["halt_probs"]) < model.num_layers
def test_hil_safety_gate():
model = BitTransformerLM(d_model=32, nhead=4, num_layers=1, dim_feedforward=64, max_seq_len=8)
bits = torch.randint(0, 2, (1, 8), dtype=torch.long)
# Expect gate triggered with high floors
raised = False
try:
hil_safe_inference(model, bits, c_floor=1.0, s_floor=1.0)
except RuntimeError:
raised = True
assert raised
def test_hil_safety_non_strict():
model = BitTransformerLM(d_model=32, nhead=4, num_layers=1, dim_feedforward=64, max_seq_len=8)
bits = torch.randint(0, 2, (1, 8), dtype=torch.long)
out, _ = hil_safe_inference(model, bits, c_floor=1.0, s_floor=1.0, strict=False)
assert out.shape == bits.shape
def test_safety_gate_burn_in():
model = BitTransformerLM(d_model=32, nhead=4, num_layers=1, dim_feedforward=64, max_seq_len=8)
bits = torch.randint(0, 2, (1, 8), dtype=torch.long)
gate = SafetyGate(c_floor=1.0, s_floor=1.0, burn_in=1)
hil_safe_inference(model, bits, gate=gate)
with pytest.raises(RuntimeError):
hil_safe_inference(model, bits, gate=gate)
def test_bit_io_roundtrip():
text = "hello"
bits = text_to_bits(text)
assert bits_to_text(bits) == text
def test_plot_telemetry():
log = {
"negentropy": [0.6, 0.7, 0.4],
"lz_complexity": [0.5, 0.45, 0.6],
"symbiosis_score": [0.55, 0.6, 0.3],
"clusters": [0, 0, 1],
}
fig, axes = plot_telemetry(log)
assert len(axes) == 3
fig.clf()
def test_metric_no_gradient_flow():
model = BitTransformerLM(d_model=32, nhead=4, num_layers=1, dim_feedforward=64, max_seq_len=8)
bits = torch.randint(0, 2, (2, 8), dtype=torch.long)
logits, _ = model(bits)
loss = model.negentropy_logits(logits).mean() + model.lz_complexity_logits(logits).mean()
assert not loss.requires_grad
with pytest.raises(RuntimeError):
loss.backward()
def test_negentropy_decompression_edge_case():
bits = torch.tensor([0, 1] * 8, dtype=torch.uint8)
comp = compress_bits(bits)
model = BitTransformerLM(d_model=16, nhead=2, num_layers=1, dim_feedforward=32, max_seq_len=bits.numel())
neg_comp = model.negentropy_kpi(comp.unsqueeze(0))
neg_raw = model.negentropy_kpi(bits.unsqueeze(0))
assert torch.allclose(neg_comp, neg_raw, atol=1e-6)
def test_dynamic_quantization():
model = BitTransformerLM(d_model=32, nhead=4, num_layers=1, dim_feedforward=64, max_seq_len=8)
from bit_transformer import quantize_dynamic
qmodel = quantize_dynamic(model)
bits = torch.randint(0, 2, (1, 8), dtype=torch.long)
logits, _ = qmodel(bits)
assert logits.shape == (1, 8, 2)
def test_qat_fx_roundtrip():
model = BitTransformerLM(d_model=32, nhead=4, num_layers=1, dim_feedforward=64, max_seq_len=8)
from bit_transformer import prepare_qat_fx, convert_qat_fx
example_bits = torch.randint(0, 2, (1, 8), dtype=torch.long)
qat_model = prepare_qat_fx(model)
qat_model.eval()
qmodel = convert_qat_fx(qat_model)
logits, _ = qmodel(example_bits)
assert logits.shape == (1, 8, 2)
def test_fsdp_wrap():
import os
import torch
import torch.distributed as dist
from bit_transformer import BitTransformerLM, wrap_fsdp
if not dist.is_initialized():
os.environ.setdefault("MASTER_ADDR", "localhost")
os.environ.setdefault("MASTER_PORT", "29500")
dist.init_process_group("gloo", rank=0, world_size=1)
if not torch.cuda.is_available():
pytest.skip("CUDA not available")
model = BitTransformerLM(d_model=32, nhead=4, num_layers=1, dim_feedforward=64, max_seq_len=8)
fsdp_model = wrap_fsdp(model)
bits = torch.randint(0, 2, (1, 8), dtype=torch.long)
logits, _ = fsdp_model(bits)
assert logits.shape == (1, 8, 2)
dist.destroy_process_group()
def test_make_pipeline():
import pytest
import torch.distributed.rpc as rpc
from bit_transformer import BitTransformerLM, make_pipeline
if not rpc._is_current_rpc_agent_set():
pytest.skip("RPC not initialized")
model = BitTransformerLM(d_model=32, nhead=4, num_layers=1, dim_feedforward=64, max_seq_len=8)
pipe_model = make_pipeline(model, chunks=1)
bits = torch.randint(0, 2, (1, 8), dtype=torch.long)
logits, _ = pipe_model(bits)
assert logits.shape == (1, 8, 2)
def test_causal_attention():
model = BitTransformerLM(d_model=32, nhead=4, num_layers=1, dim_feedforward=64, max_seq_len=8)
bits = torch.randint(0, 2, (1, 8), dtype=torch.long)
logits, tele = model(bits, causal=True)
assert logits.shape == (1, 8, 2)
attn = tele["attention_maps"][0]
upper = attn.triu(1)
assert torch.allclose(upper, torch.zeros_like(upper))
def test_scaling_helpers():
model = BitTransformerLM(d_model=32, nhead=4, num_layers=1, dim_feedforward=64, max_seq_len=8)
model = model.double_width()
assert model.d_model == 64
model = model.double_layers()
assert model.num_layers == 2
def test_expand_positional_encoding():
model = BitTransformerLM(d_model=16, nhead=4, num_layers=1, dim_feedforward=32, max_seq_len=8)
model.expand_positional_encoding(16)
assert model.pos_enc.pe.size(0) == 16
def test_infer_long_sequence():
model = BitTransformerLM(d_model=16, nhead=4, num_layers=1, dim_feedforward=32, max_seq_len=8)
bits = torch.randint(0, 2, (12,), dtype=torch.long)
preds, logs = infer_long_sequence(model, bits, ctx_bits=8, overlap=4)
assert len(preds) == 12
assert len(logs) >= 2
def test_chunking_disabled_when_non_causal():
model = BitTransformerLM(
d_model=32,
nhead=4,
num_layers=1,
dim_feedforward=64,
max_seq_len=8,
chunk_size=2,
full_attn_logging=True,
)
# Zero query/key/value projections so attention is uniformly distributed.
# This makes the test deterministic: any non-masked position receives equal
# weight, allowing us to rely solely on the chunking mask for the check.
nn.init.constant_(model.layers[0].self_attn.in_proj_weight, 0.0)
nn.init.constant_(model.layers[0].self_attn.in_proj_bias, 0.0)
# Disable dropout for deterministic attention weights.
model.eval()
for module in model.modules():
if isinstance(module, nn.Dropout):
module.p = 0.0
model.layers[0].self_attn.dropout = 0.0
bits = torch.randint(0, 2, (1, 8), dtype=torch.long)
_, tele_causal = model(bits, causal=True)
_, tele_noncausal = model(bits, causal=False)
attn_causal = tele_causal["attention_maps"][0]
attn_noncausal = tele_noncausal["attention_maps"][0]
# Causal mode keeps attention within chunk boundaries, while non-causal mode
# should permit cross-chunk attention.
assert attn_causal[0, 0, 0, 4] == 0
assert attn_noncausal[0, 0, 0, 4] > 0
def test_diffusion_inference_generates_bits():
model = BitTransformerLM(d_model=32, nhead=4, num_layers=1, dim_feedforward=64, max_seq_len=8)
out = diffusion_inference(model, length=8, steps=2, batch_size=2)
assert out.shape == (2, 8)
assert set(out.unique().tolist()).issubset({0, 1})
def test_diffusion_inference_cosine_schedule():
model = BitTransformerLM(d_model=32, nhead=4, num_layers=1, dim_feedforward=64, max_seq_len=8)
out = diffusion_inference(model, length=8, steps=2, schedule="cosine")
assert out.shape == (1, 8)
def test_chunking_restored_after_diffusion():
model = BitTransformerLM(
d_model=32,
nhead=4,
num_layers=1,
dim_feedforward=64,
max_seq_len=8,
chunk_size=2,
full_attn_logging=True,
)
bits = torch.randint(0, 2, (1, 8), dtype=torch.long)
_ = model(bits, causal=False)
assert model.layers[0].chunk_size == 2
_, tele = model(bits, causal=True)
attn = tele["attention_maps"][0]
assert attn[0, 0, 0, 4] == 0
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