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import json
import math
import os
import tempfile
import unittest
from pathlib import Path
from unittest import mock
import torch
from transformers import Qwen3Config, Qwen3ForCausalLM
import modeling_aha_qwen3 as aha_module
from modeling_aha_qwen3 import (
AHAQwen3Config,
AHAQwen3ForCausalLM,
aha_router_output_size,
)
from router_training_utils import RowWiseAdamW, configure_gate_only
def base_config() -> Qwen3Config:
config = Qwen3Config(
vocab_size=97,
hidden_size=32,
intermediate_size=64,
num_hidden_layers=2,
num_attention_heads=4,
num_key_value_heads=2,
head_dim=8,
max_position_embeddings=64,
attention_dropout=0.0,
attention_bias=True,
tie_word_embeddings=False,
)
config._attn_implementation = "eager"
return config
def aha_config(granularity: str, *, force_gate_value=None) -> AHAQwen3Config:
payload = base_config().to_dict()
payload.update(
aha_window_size=2,
aha_local_kind="sliding_window",
aha_mode="dynamic",
aha_router_granularity=granularity,
aha_force_gate_value=force_gate_value,
aha_reg_weight=1.0,
aha_ce_weight=0.0,
model_type="aha_qwen3",
)
config = AHAQwen3Config(**payload)
config._attn_implementation = "eager"
return config
def copy_base_weights(base: Qwen3ForCausalLM, target: AHAQwen3ForCausalLM) -> None:
source = base.state_dict()
destination = target.state_dict()
q_rows = target.config.num_attention_heads * target.config.head_dim
with torch.no_grad():
for name, value in source.items():
if name not in destination:
continue
if destination[name].shape == value.shape:
destination[name].copy_(value)
elif name.endswith("self_attn.q_proj.weight"):
destination[name][:q_rows].copy_(value)
destination[name][q_rows:].zero_()
elif name.endswith("self_attn.q_proj.bias"):
destination[name][:q_rows].copy_(value)
destination[name][q_rows:].zero_()
else:
raise AssertionError(f"unexpected shape mismatch for {name}")
target.load_state_dict(destination)
def aligned_models():
torch.manual_seed(7)
base = Qwen3ForCausalLM(base_config()).eval()
models = {}
for granularity in ("token", "token_kv_head"):
model = AHAQwen3ForCausalLM(
aha_config(granularity, force_gate_value=1.0)
).eval()
copy_base_weights(base, model)
models[granularity] = model
return base, models
class RouterGranularityTest(unittest.TestCase):
def test_auto_class_checkpoint_embeds_modeling_source(self):
AHAQwen3Config.register_for_auto_class()
AHAQwen3ForCausalLM.register_for_auto_class("AutoModelForCausalLM")
with tempfile.TemporaryDirectory() as tmp:
AHAQwen3ForCausalLM(aha_config("token")).save_pretrained(
tmp, safe_serialization=True
)
self.assertTrue((Path(tmp) / "modeling_aha_qwen3.py").exists())
def test_projection_and_effective_gate_shapes(self):
_, models = aligned_models()
input_ids = torch.tensor([[1, 2, 3, 4, 5]])
expected_q_rows = 4 * 8
for granularity, native_rows in (("token", 1), ("token_kv_head", 2)):
with self.subTest(granularity=granularity):
model = models[granularity]
attention = model.model.layers[0].self_attn
self.assertEqual(attention.q_proj.out_features, expected_q_rows + native_rows)
self.assertEqual(attention.aha_router_outputs, native_rows)
output = model.model(input_ids=input_ids, use_cache=False)
self.assertEqual(len(output.all_gate_soft), 2)
self.assertEqual(output.all_gate_soft[0].shape, (1, 5, 2))
self.assertEqual(output.all_gate_hard[0].shape, (1, 5, 2))
def test_force_open_matches_full_attention_logits(self):
base, models = aligned_models()
input_ids = torch.tensor([[1, 4, 2, 8, 3, 9]])
with torch.no_grad():
expected = base(input_ids=input_ids, use_cache=False).logits
for granularity, model in models.items():
with self.subTest(granularity=granularity):
actual = model(input_ids=input_ids, use_cache=False).logits
torch.testing.assert_close(actual, expected, atol=1e-6, rtol=1e-5)
def test_force_closed_uses_identical_local_branch(self):
_, models = aligned_models()
input_ids = torch.tensor([[1, 4, 2, 8, 3, 9]])
for model in models.values():
model.config.aha_force_gate_value = 0.0
with torch.no_grad():
token_logits = models["token"](input_ids=input_ids, use_cache=False).logits
head_logits = models["token_kv_head"](
input_ids=input_ids, use_cache=False
).logits
torch.testing.assert_close(token_logits, head_logits, atol=1e-6, rtol=1e-5)
def test_regularizer_uses_effective_kv_head_denominator(self):
_, models = aligned_models()
probability = 0.73
bias = math.log(probability / (1.0 - probability))
input_ids = torch.tensor([[1, 4, 2, 8, 3, 9]])
outputs = {}
for granularity, model in models.items():
model.train()
model.config.aha_force_gate_value = None
q_rows = model.config.num_attention_heads * model.config.head_dim
with torch.no_grad():
for layer in model.model.layers:
layer.self_attn.q_proj.weight[q_rows:].zero_()
layer.self_attn.q_proj.bias[q_rows:].fill_(bias)
outputs[granularity] = model(
input_ids=input_ids, labels=input_ids.clone(), use_cache=False
)
torch.testing.assert_close(
outputs["token"].gate_soft_mean,
outputs["token_kv_head"].gate_soft_mean,
)
torch.testing.assert_close(
outputs["token"].gate_aux_loss,
outputs["token_kv_head"].gate_aux_loss,
)
self.assertAlmostEqual(outputs["token"].gate_soft_mean.item(), probability, places=6)
def test_gate_only_updates_only_appended_rows(self):
for granularity, expected_rows in (("token", 1), ("token_kv_head", 2)):
with self.subTest(granularity=granularity):
model = AHAQwen3ForCausalLM(aha_config(granularity))
setup = configure_gate_only(model)
self.assertEqual(setup.gate_rows, expected_rows)
q_proj = model.model.layers[0].self_attn.q_proj
before = q_proj.weight.detach().clone()
q_proj.weight.sum().backward()
self.assertEqual(
torch.count_nonzero(q_proj.weight.grad[: setup.q_rows]).item(), 0
)
self.assertGreater(
torch.count_nonzero(q_proj.weight.grad[setup.q_rows :]).item(), 0
)
torch.optim.SGD(setup.parameters, lr=0.1).step()
torch.testing.assert_close(
q_proj.weight[: setup.q_rows], before[: setup.q_rows]
)
self.assertFalse(
torch.equal(q_proj.weight[setup.q_rows :], before[setup.q_rows :])
)
def test_rowwise_adamw_applies_real_ten_x_lr_ratio(self):
parameter = torch.nn.Parameter(torch.zeros(3, 1))
optimizer = RowWiseAdamW(
[{"params": [parameter], "lr": 0.1}],
row_scales=[(parameter, 2, 0.1)],
weight_decay=0.0,
betas=(0.9, 0.999),
)
parameter.grad = torch.ones_like(parameter)
optimizer.step()
backbone_update = parameter[:2].abs().mean().item()
gate_update = parameter[2:].abs().mean().item()
self.assertAlmostEqual(gate_update / backbone_update, 10.0, places=5)
def test_one_step_smoke_is_finite_and_reloadable(self):
input_ids = torch.tensor([[1, 4, 2, 8, 3, 9]])
for granularity in ("token", "token_kv_head"):
with self.subTest(granularity=granularity), tempfile.TemporaryDirectory() as tmp:
model = AHAQwen3ForCausalLM(aha_config(granularity)).train()
setup = configure_gate_only(model)
optimizer = torch.optim.AdamW(setup.parameters, lr=3e-5)
output = model(
input_ids=input_ids, labels=input_ids.clone(), use_cache=False
)
self.assertTrue(torch.isfinite(output.loss).item())
output.loss.backward()
self.assertTrue(
all(
parameter.grad is None
or torch.isfinite(parameter.grad).all().item()
for parameter in setup.parameters
)
)
optimizer.step()
model.save_pretrained(tmp, safe_serialization=True)
reloaded = AHAQwen3ForCausalLM.from_pretrained_aha(
tmp, torch_dtype=torch.float32, attn_implementation="eager"
)
self.assertEqual(
reloaded.config.aha_router_granularity, granularity
)
def test_legacy_default_and_save_load_roundtrip(self):
legacy = AHAQwen3Config(**base_config().to_dict())
self.assertEqual(legacy.aha_router_granularity, "token_kv_head")
self.assertEqual(aha_router_output_size(legacy), legacy.num_key_value_heads)
with tempfile.TemporaryDirectory() as tmp:
model = AHAQwen3ForCausalLM(aha_config("token"))
model.save_pretrained(tmp, safe_serialization=True)
loaded = AHAQwen3ForCausalLM.from_pretrained_aha(
tmp, torch_dtype=torch.float32, attn_implementation="eager"
)
self.assertEqual(loaded.config.aha_router_granularity, "token")
self.assertEqual(loaded.model.layers[0].self_attn.aha_router_outputs, 1)
def test_sparsity_tracker_records_native_and_effective_counts(self):
with tempfile.TemporaryDirectory() as tmp:
output = os.path.join(tmp, "sparsity.json")
with mock.patch.dict(os.environ, {"AHA_SPARSITY_STATS_PATH": output}):
tracker = aha_module._AHAInferenceSparsityTracker()
gate_hard = torch.tensor([[[1.0, 1.0], [0.0, 0.0]]])
gate_soft = torch.tensor([[[0.8, 0.8], [0.2, 0.2]]])
tracker.update(
gate_hard,
gate_soft,
layer_idx=0,
phase="prefill",
router_granularity="token",
native_router_width=1,
)
tracker.update(
gate_hard[:, :1],
gate_soft[:, :1],
layer_idx=0,
phase="decode",
router_granularity="token",
native_router_width=1,
)
tracker.write_stats()
payload = json.loads(Path(output).read_text(encoding="utf-8"))
self.assertEqual(payload["router_granularity"], "token")
self.assertEqual(payload["native_router_decisions"], 3)
self.assertEqual(payload["effective_router_decisions"], 6)
self.assertAlmostEqual(payload["sparsity"], 1 / 3)
self.assertEqual(payload["by_phase"]["decode"]["total_decisions"], 2)
if __name__ == "__main__":
unittest.main()
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