lars
68fb99bed8
Adds model.conditioning = "physical" | "embedding": physical mode routes particle mass/charge and material Z_eff/A_eff/density/X0/lambda_int through small MLPs to replace the learned PDG/material embedding tables, so the surrogate generalizes to PDG codes/materials outside the training vocab instead of memorizing it. "embedding" stays available as the comparison baseline (old checkpoints without the key default to it). Stage 2 now regresses a secondary's mass/charge directly against a fixed physics-derived target instead of a learned/snapped embedding, and uses no snapping at inference — the model's raw predicted (mass, charge) is the secondary's physical identity, including for its own further rollout steps. A separate reporting-only nearest-known-PDG lookup (never fed back into the model) populates output pdg columns / the embedding-mode rollout fallback. giant/materials.py's table is populated with Geant4's own built-in NIST constants (Z_eff, A_eff, density, X0, lambda_int), extracted directly from the Geant4 11.4.1 build vendored in minicalosim via G4NistManager rather than hand-typed literature values. G4_LYSO is left unfilled: confirmed (both by runtime lookup and by searching minicalosim's history) that it's never actually a constructed Geant4 material there, only documentation/UI color-map text. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
703 lines
23 KiB
Python
703 lines
23 KiB
Python
"""Tests for the mixture-of-experts routing prototype (giant/model/network.py)."""
|
|
|
|
import torch
|
|
|
|
from giant.constants import COND_DIM, K_MAX, SEC_DIM, X_DIM
|
|
from giant.model.network import (
|
|
ComposedRouter,
|
|
DenoisingMLP,
|
|
EnergyRouter,
|
|
PdgRouter,
|
|
ProcessRouter,
|
|
ROUTER_REGISTRY,
|
|
RoutedDenoisingMLP,
|
|
RoutedSecondaryDecoder,
|
|
SecondaryDecoder,
|
|
build_composed_router,
|
|
build_models,
|
|
build_router,
|
|
)
|
|
|
|
|
|
def _cond(B=8, pdg=3, mat=2):
|
|
cond_cont = torch.randn(B, COND_DIM)
|
|
cond_cat = torch.stack(
|
|
[torch.randint(0, pdg, (B,)), torch.randint(0, mat, (B,))], dim=1
|
|
)
|
|
return cond_cont, cond_cat
|
|
|
|
|
|
def _routed_stage1(n_experts=4, pdg=3, mat=2, **router_kwargs):
|
|
router = build_router("energy", n_experts, **router_kwargs)
|
|
return RoutedDenoisingMLP(
|
|
pdg_vocab=pdg,
|
|
mat_vocab=mat,
|
|
router=router,
|
|
expert_hidden_dim=16,
|
|
expert_n_blocks=2,
|
|
)
|
|
|
|
|
|
def _routed_sec_decoder(n_experts=4, pdg=3, mat=2, **router_kwargs):
|
|
router = build_router("energy", n_experts, **router_kwargs)
|
|
return RoutedSecondaryDecoder(
|
|
pdg_vocab=pdg,
|
|
mat_vocab=mat,
|
|
router=router,
|
|
expert_hidden_dim=16,
|
|
expert_n_blocks=2,
|
|
)
|
|
|
|
|
|
# ── Router / EnergyRouter contract ──────────────────────────────────────────
|
|
|
|
|
|
def test_energy_router_registered():
|
|
assert ROUTER_REGISTRY["energy"] is EnergyRouter
|
|
|
|
|
|
def test_energy_router_gate_partition_of_unity():
|
|
router = EnergyRouter(n_experts=4)
|
|
cond_cont, cond_cat = _cond(16)
|
|
g = router.gate(cond_cont, cond_cat)
|
|
assert g.shape == (16, 4)
|
|
torch.testing.assert_close(g.sum(dim=-1), torch.ones(16), atol=1e-5, rtol=0)
|
|
|
|
|
|
def test_energy_router_top1_matches_gate_argmax():
|
|
router = EnergyRouter(n_experts=4)
|
|
cond_cont, cond_cat = _cond(16)
|
|
assert torch.equal(
|
|
router.top1(cond_cont, cond_cat), router.gate(cond_cont, cond_cat).argmax(-1)
|
|
)
|
|
|
|
|
|
def test_energy_router_hardens_as_temperature_shrinks():
|
|
"""As tau -> 0 the soft gate should converge to a one-hot at the argmax."""
|
|
router = EnergyRouter(n_experts=4, temperature=1e-4)
|
|
cond_cont, cond_cat = _cond(16)
|
|
g = router.gate(cond_cont, cond_cat)
|
|
top1 = router.top1(cond_cont, cond_cat)
|
|
onehot = torch.nn.functional.one_hot(top1, num_classes=4).float()
|
|
torch.testing.assert_close(g, onehot, atol=1e-3, rtol=0)
|
|
|
|
|
|
def test_energy_router_balance_loss_is_nonnegative_scalar():
|
|
router = EnergyRouter(n_experts=4)
|
|
cond_cont, cond_cat = _cond(16)
|
|
loss = router.balance_loss(cond_cont, cond_cat)
|
|
assert loss.shape == ()
|
|
assert loss.item() >= 0.0
|
|
|
|
|
|
def test_build_router_ignores_unrecognized_kwargs():
|
|
# lambda_balance is a model_config.router key but not an EnergyRouter kwarg
|
|
router = build_router("energy", 4, temperature=0.3, lambda_balance=0.5)
|
|
assert isinstance(router, EnergyRouter)
|
|
assert router.temperature == 0.3
|
|
|
|
|
|
def test_build_router_unknown_type_raises():
|
|
try:
|
|
build_router("nonexistent", 4)
|
|
except ValueError:
|
|
return
|
|
raise AssertionError("expected ValueError for unknown router type")
|
|
|
|
|
|
# ── PdgRouter ────────────────────────────────────────────────────────────────
|
|
|
|
|
|
def test_pdg_router_registered():
|
|
assert ROUTER_REGISTRY["pdg"] is PdgRouter
|
|
|
|
|
|
def test_pdg_router_gate_partition_of_unity():
|
|
router = PdgRouter(n_experts=4, pdg_vocab=3)
|
|
cond_cont, cond_cat = _cond(16)
|
|
g = router.gate(cond_cont, cond_cat)
|
|
assert g.shape == (16, 4)
|
|
torch.testing.assert_close(g.sum(dim=-1), torch.ones(16), atol=1e-5, rtol=0)
|
|
|
|
|
|
def test_pdg_router_top1_matches_gate_argmax():
|
|
router = PdgRouter(n_experts=4, pdg_vocab=3)
|
|
cond_cont, cond_cat = _cond(16)
|
|
assert torch.equal(
|
|
router.top1(cond_cont, cond_cat), router.gate(cond_cont, cond_cat).argmax(-1)
|
|
)
|
|
|
|
|
|
def test_pdg_router_hardens_as_temperature_shrinks():
|
|
"""As tau -> 0 the soft gate should converge to a one-hot at the argmax."""
|
|
router = PdgRouter(n_experts=4, pdg_vocab=3, temperature=1e-4)
|
|
cond_cont, cond_cat = _cond(16)
|
|
g = router.gate(cond_cont, cond_cat)
|
|
top1 = router.top1(cond_cont, cond_cat)
|
|
onehot = torch.nn.functional.one_hot(top1, num_classes=4).float()
|
|
torch.testing.assert_close(g, onehot, atol=1e-3, rtol=0)
|
|
|
|
|
|
def test_pdg_router_balance_loss_is_nonnegative_scalar():
|
|
router = PdgRouter(n_experts=4, pdg_vocab=3)
|
|
cond_cont, cond_cat = _cond(16)
|
|
loss = router.balance_loss(cond_cont, cond_cat)
|
|
assert loss.shape == ()
|
|
assert loss.item() >= 0.0
|
|
|
|
|
|
def test_pdg_router_classify_loss_defaults_to_zero():
|
|
"""PDG is already known at gate time (unlike ProcessRouter's process
|
|
label), so no supervision is needed — falls back to Router's default."""
|
|
router = PdgRouter(n_experts=4, pdg_vocab=3)
|
|
cond_cont, cond_cat = _cond(16)
|
|
labels = torch.randint(0, 4, (16,))
|
|
loss = router.classify_loss(cond_cont, cond_cat, labels)
|
|
assert loss.shape == ()
|
|
assert loss.item() == 0.0
|
|
|
|
|
|
def test_pdg_router_only_reads_pdg_column():
|
|
"""Gate must depend on cond_cat[:, 0] (pdg) only, not cond_cont or material."""
|
|
router = PdgRouter(n_experts=4, pdg_vocab=3)
|
|
cond_cont, cond_cat = _cond(16)
|
|
g_before = router.gate(cond_cont, cond_cat)
|
|
|
|
cond_cont_perturbed = torch.randn_like(cond_cont)
|
|
cond_cat_diff_mat = cond_cat.clone()
|
|
cond_cat_diff_mat[:, 1] = (cond_cat_diff_mat[:, 1] + 1) % 2
|
|
g_after = router.gate(cond_cont_perturbed, cond_cat_diff_mat)
|
|
|
|
torch.testing.assert_close(g_before, g_after, atol=1e-6, rtol=0)
|
|
|
|
|
|
def test_build_router_pdg_type_uses_pdg_vocab():
|
|
router = build_router("pdg", 4, pdg_vocab=5, mat_vocab=3, emb_dim=8)
|
|
assert isinstance(router, PdgRouter)
|
|
assert router.pdg_emb.num_embeddings == 5
|
|
|
|
|
|
def test_build_models_routed_with_pdg_router():
|
|
model_config = dict(
|
|
pdg_vocab=4,
|
|
mat_vocab=2,
|
|
emb_dim=16,
|
|
dropout=0.1,
|
|
k_max=K_MAX,
|
|
expert_hidden_dim=16,
|
|
expert_n_blocks=2,
|
|
router={
|
|
"enabled": True,
|
|
"type": "pdg",
|
|
"n_experts": 3,
|
|
},
|
|
)
|
|
stage1, sec_decoder = build_models(model_config)
|
|
assert isinstance(stage1, RoutedDenoisingMLP)
|
|
assert isinstance(stage1.router, PdgRouter)
|
|
assert len(stage1.experts) == 3
|
|
assert stage1.router.pdg_emb.num_embeddings == 4
|
|
|
|
|
|
# ── ProcessRouter ────────────────────────────────────────────────────────────
|
|
|
|
|
|
def test_process_router_registered():
|
|
assert ROUTER_REGISTRY["process"] is ProcessRouter
|
|
|
|
|
|
def test_process_router_gate_partition_of_unity():
|
|
router = ProcessRouter(n_experts=4, pdg_vocab=3, mat_vocab=2)
|
|
cond_cont, cond_cat = _cond(16)
|
|
g = router.gate(cond_cont, cond_cat)
|
|
assert g.shape == (16, 4)
|
|
torch.testing.assert_close(g.sum(dim=-1), torch.ones(16), atol=1e-5, rtol=0)
|
|
|
|
|
|
def test_process_router_top1_matches_gate_argmax():
|
|
router = ProcessRouter(n_experts=4, pdg_vocab=3, mat_vocab=2)
|
|
cond_cont, cond_cat = _cond(16)
|
|
assert torch.equal(
|
|
router.top1(cond_cont, cond_cat), router.gate(cond_cont, cond_cat).argmax(-1)
|
|
)
|
|
|
|
|
|
def test_process_router_balance_loss_is_nonnegative_scalar():
|
|
router = ProcessRouter(n_experts=4, pdg_vocab=3, mat_vocab=2)
|
|
cond_cont, cond_cat = _cond(16)
|
|
loss = router.balance_loss(cond_cont, cond_cat)
|
|
assert loss.shape == ()
|
|
assert loss.item() >= 0.0
|
|
|
|
|
|
def test_process_router_classify_loss_decreases_with_training():
|
|
"""The classifier should be able to fit an arbitrary label assignment —
|
|
a sanity check that gradients actually flow to the process classifier."""
|
|
torch.manual_seed(0)
|
|
router = ProcessRouter(n_experts=4, pdg_vocab=3, mat_vocab=2)
|
|
cond_cont, cond_cat = _cond(32)
|
|
labels = torch.randint(0, 4, (32,))
|
|
|
|
opt = torch.optim.Adam(router.parameters(), lr=0.05)
|
|
first = router.classify_loss(cond_cont, cond_cat, labels).item()
|
|
for _ in range(50):
|
|
opt.zero_grad()
|
|
loss = router.classify_loss(cond_cont, cond_cat, labels)
|
|
loss.backward()
|
|
opt.step()
|
|
last = loss.item()
|
|
assert last < first
|
|
|
|
|
|
def test_energy_router_classify_loss_defaults_to_zero():
|
|
"""Routers with no supervised signal (EnergyRouter) fall back to the
|
|
Router base class's zero-loss default."""
|
|
router = EnergyRouter(n_experts=4)
|
|
cond_cont, cond_cat = _cond(16)
|
|
labels = torch.randint(0, 4, (16,))
|
|
loss = router.classify_loss(cond_cont, cond_cat, labels)
|
|
assert loss.shape == ()
|
|
assert loss.item() == 0.0
|
|
|
|
|
|
def test_build_router_process_type_uses_pdg_mat_vocab():
|
|
router = build_router("process", 4, pdg_vocab=5, mat_vocab=3, emb_dim=8)
|
|
assert isinstance(router, ProcessRouter)
|
|
assert router.pdg_emb.num_embeddings == 5
|
|
assert router.mat_emb.num_embeddings == 3
|
|
|
|
|
|
def test_build_models_routed_with_process_router():
|
|
model_config = dict(
|
|
pdg_vocab=4,
|
|
mat_vocab=2,
|
|
emb_dim=16,
|
|
dropout=0.1,
|
|
k_max=K_MAX,
|
|
expert_hidden_dim=16,
|
|
expert_n_blocks=2,
|
|
router={
|
|
"enabled": True,
|
|
"type": "process",
|
|
"n_experts": 3,
|
|
"lambda_proc": 1.0,
|
|
},
|
|
)
|
|
stage1, sec_decoder = build_models(model_config)
|
|
assert isinstance(stage1, RoutedDenoisingMLP)
|
|
assert isinstance(stage1.router, ProcessRouter)
|
|
assert len(stage1.experts) == 3
|
|
assert stage1.router.pdg_emb.num_embeddings == 4
|
|
assert stage1.router.mat_emb.num_embeddings == 2
|
|
|
|
|
|
# ── ComposedRouter ───────────────────────────────────────────────────────────
|
|
|
|
|
|
def test_composed_router_n_experts_is_product():
|
|
router = ComposedRouter(
|
|
[EnergyRouter(n_experts=4), PdgRouter(n_experts=3, pdg_vocab=5)]
|
|
)
|
|
assert router.n_experts == 12
|
|
|
|
|
|
def test_composed_router_gate_partition_of_unity():
|
|
router = ComposedRouter(
|
|
[EnergyRouter(n_experts=4), PdgRouter(n_experts=3, pdg_vocab=5)]
|
|
)
|
|
cond_cont, cond_cat = _cond(16, pdg=5)
|
|
g = router.gate(cond_cont, cond_cat)
|
|
assert g.shape == (16, 12)
|
|
torch.testing.assert_close(g.sum(dim=-1), torch.ones(16), atol=1e-5, rtol=0)
|
|
|
|
|
|
def test_composed_router_gate_is_outer_product_of_sub_gates():
|
|
energy_router = EnergyRouter(n_experts=4)
|
|
pdg_router = PdgRouter(n_experts=3, pdg_vocab=5)
|
|
router = ComposedRouter([energy_router, pdg_router])
|
|
cond_cont, cond_cat = _cond(16, pdg=5)
|
|
|
|
g_energy = energy_router.gate(cond_cont, cond_cat) # (16, 4)
|
|
g_pdg = pdg_router.gate(cond_cont, cond_cat) # (16, 3)
|
|
expected = (g_energy.unsqueeze(-1) * g_pdg.unsqueeze(1)).flatten(1) # (16, 12)
|
|
|
|
torch.testing.assert_close(router.gate(cond_cont, cond_cat), expected)
|
|
|
|
|
|
def test_composed_router_top1_factors_into_per_axis_argmax():
|
|
"""Joint argmax over the outer product must equal the pair of per-axis
|
|
argmaxes, flattened with the same row-major index convention as gate()."""
|
|
energy_router = EnergyRouter(n_experts=4)
|
|
pdg_router = PdgRouter(n_experts=3, pdg_vocab=5)
|
|
router = ComposedRouter([energy_router, pdg_router])
|
|
cond_cont, cond_cat = _cond(16, pdg=5)
|
|
|
|
joint_idx = router.top1(cond_cont, cond_cat)
|
|
energy_idx = energy_router.top1(cond_cont, cond_cat)
|
|
pdg_idx = pdg_router.top1(cond_cont, cond_cat)
|
|
expected = energy_idx * pdg_router.n_experts + pdg_idx
|
|
|
|
assert torch.equal(joint_idx, expected)
|
|
|
|
|
|
def test_composed_router_supports_different_expert_counts_per_axis():
|
|
router = ComposedRouter(
|
|
[EnergyRouter(n_experts=5), PdgRouter(n_experts=2, pdg_vocab=5)]
|
|
)
|
|
assert router.n_experts == 10
|
|
cond_cont, cond_cat = _cond(8, pdg=5)
|
|
assert router.gate(cond_cont, cond_cat).shape == (8, 10)
|
|
|
|
|
|
def test_composed_router_classify_loss_sums_sub_router_losses():
|
|
"""energy/pdg both default to zero, so the composed loss should too."""
|
|
router = ComposedRouter(
|
|
[EnergyRouter(n_experts=4), PdgRouter(n_experts=3, pdg_vocab=5)]
|
|
)
|
|
cond_cont, cond_cat = _cond(16, pdg=5)
|
|
labels = torch.randint(0, 4, (16,))
|
|
loss = router.classify_loss(cond_cont, cond_cat, labels)
|
|
assert loss.shape == ()
|
|
assert loss.item() == 0.0
|
|
|
|
|
|
def test_composed_router_rejects_empty_router_list():
|
|
try:
|
|
ComposedRouter([])
|
|
except ValueError:
|
|
return
|
|
raise AssertionError("expected ValueError for empty router list")
|
|
|
|
|
|
def test_composed_router_not_in_registry():
|
|
assert "composed" not in ROUTER_REGISTRY
|
|
|
|
|
|
# ── _parse_composed_axes (axis{i}_{field} flat-key config convention) ───────
|
|
|
|
|
|
def test_parse_composed_axes_groups_indexed_keys():
|
|
from giant.model.network import _parse_composed_axes
|
|
|
|
router_cfg = {
|
|
"enabled": True,
|
|
"type": "composed",
|
|
"axis0_type": "energy",
|
|
"axis0_n_experts": 4,
|
|
"axis0_temperature": 0.3,
|
|
"axis1_type": "pdg",
|
|
"axis1_n_experts": 3,
|
|
"axis1_emb_dim": 6,
|
|
}
|
|
axes = _parse_composed_axes(router_cfg)
|
|
assert axes == [
|
|
{"type": "energy", "n_experts": 4, "temperature": 0.3},
|
|
{"type": "pdg", "n_experts": 3, "emb_dim": 6},
|
|
]
|
|
|
|
|
|
def test_parse_composed_axes_ignores_unrelated_keys():
|
|
from giant.model.network import _parse_composed_axes
|
|
|
|
router_cfg = {
|
|
"enabled": True,
|
|
"type": "composed",
|
|
"lambda_balance": 0.0,
|
|
"axis0_type": "energy",
|
|
"axis0_n_experts": 4,
|
|
}
|
|
axes = _parse_composed_axes(router_cfg)
|
|
assert axes == [{"type": "energy", "n_experts": 4}]
|
|
|
|
|
|
def test_parse_composed_axes_raises_on_index_gap():
|
|
from giant.model.network import _parse_composed_axes
|
|
|
|
router_cfg = {
|
|
"type": "composed",
|
|
"axis0_type": "energy",
|
|
"axis0_n_experts": 4,
|
|
# axis1 missing entirely
|
|
"axis2_type": "pdg",
|
|
"axis2_n_experts": 3,
|
|
}
|
|
try:
|
|
_parse_composed_axes(router_cfg)
|
|
except ValueError:
|
|
return
|
|
raise AssertionError("expected ValueError for a gap in axis indices")
|
|
|
|
|
|
def test_build_composed_router_resolves_per_axis_specs():
|
|
router = build_composed_router(
|
|
[
|
|
{"type": "energy", "n_experts": 4, "temperature": 0.3},
|
|
{"type": "pdg", "n_experts": 3, "emb_dim": 6},
|
|
],
|
|
pdg_vocab=5,
|
|
mat_vocab=2,
|
|
)
|
|
assert isinstance(router, ComposedRouter)
|
|
assert router.n_experts == 12
|
|
energy_router, pdg_router = router.routers
|
|
assert isinstance(energy_router, EnergyRouter)
|
|
assert energy_router.temperature == 0.3
|
|
assert isinstance(pdg_router, PdgRouter)
|
|
assert pdg_router.pdg_emb.num_embeddings == 5
|
|
assert pdg_router.pdg_emb.embedding_dim == 6
|
|
|
|
|
|
def test_build_models_routed_with_composed_router():
|
|
model_config = dict(
|
|
pdg_vocab=5,
|
|
mat_vocab=2,
|
|
emb_dim=16,
|
|
dropout=0.1,
|
|
k_max=K_MAX,
|
|
expert_hidden_dim=16,
|
|
expert_n_blocks=2,
|
|
router={
|
|
"enabled": True,
|
|
"type": "composed",
|
|
"axis0_type": "energy",
|
|
"axis0_n_experts": 4,
|
|
"axis1_type": "pdg",
|
|
"axis1_n_experts": 3,
|
|
},
|
|
)
|
|
stage1, sec_decoder = build_models(model_config)
|
|
assert isinstance(stage1, RoutedDenoisingMLP)
|
|
assert isinstance(stage1.router, ComposedRouter)
|
|
assert len(stage1.experts) == 12
|
|
assert len(sec_decoder.experts) == 12
|
|
# stage1 and sec_decoder must not share router weights (same convention
|
|
# as the single-axis routers built by build_models).
|
|
assert stage1.router is not sec_decoder.router
|
|
|
|
|
|
def test_build_models_routed_pair_composed_router_is_drop_in_for_sample_flow():
|
|
from giant.sample import sample_flow, sample_secondaries
|
|
|
|
model_config = dict(
|
|
pdg_vocab=3,
|
|
mat_vocab=2,
|
|
emb_dim=16,
|
|
dropout=0.1,
|
|
k_max=K_MAX,
|
|
expert_hidden_dim=8,
|
|
expert_n_blocks=1,
|
|
router={
|
|
"enabled": True,
|
|
"type": "composed",
|
|
"axis0_type": "energy",
|
|
"axis0_n_experts": 2,
|
|
"axis1_type": "pdg",
|
|
"axis1_n_experts": 2,
|
|
},
|
|
)
|
|
stage1, sec_decoder = build_models(model_config)
|
|
B = 5
|
|
cond_cont, cond_cat = _cond(B, pdg=3, mat=2)
|
|
stage1_norm, n_sec_pred = sample_flow(stage1, cond_cont, cond_cat, steps=2)
|
|
assert stage1_norm.shape == (B, X_DIM)
|
|
assert n_sec_pred.shape == (B,)
|
|
|
|
sec_cont, sec_type_emb, sec_valid = sample_secondaries(
|
|
sec_decoder, cond_cont, cond_cat, stage1_norm, n_sec_pred, steps=2
|
|
)
|
|
assert sec_cont.shape == (B, K_MAX, 4)
|
|
assert sec_valid.shape == (B, K_MAX)
|
|
|
|
|
|
# ── RoutedDenoisingMLP ───────────────────────────────────────────────────────
|
|
|
|
|
|
def test_routed_denoising_mlp_output_shape_train_and_eval():
|
|
B = 8
|
|
model = _routed_stage1()
|
|
x_t = torch.randn(B, X_DIM)
|
|
t = torch.rand(B)
|
|
cond_cont, cond_cat = _cond(B)
|
|
|
|
model.train()
|
|
out_train = model(x_t, t, cond_cont, cond_cat)
|
|
assert out_train.shape == (B, X_DIM)
|
|
|
|
model.eval()
|
|
with torch.no_grad():
|
|
out_eval = model(x_t, t, cond_cont, cond_cat)
|
|
assert out_eval.shape == (B, X_DIM)
|
|
|
|
|
|
def test_routed_denoising_mlp_gradients_flow_in_train_mode():
|
|
"""Soft mixture in train mode should touch every expert's parameters."""
|
|
B = 8
|
|
model = _routed_stage1(n_experts=3)
|
|
x_t = torch.randn(B, X_DIM)
|
|
t = torch.rand(B)
|
|
cond_cont, cond_cat = _cond(B)
|
|
model.train()
|
|
flow_loss = model(x_t, t, cond_cont, cond_cat).sum()
|
|
nsec_loss = model.predict_n_sec(cond_cont, cond_cat).sum()
|
|
(flow_loss + nsec_loss).backward()
|
|
for name, p in model.named_parameters():
|
|
assert p.grad is not None, f"no grad for {name}"
|
|
|
|
|
|
def test_routed_denoising_mlp_eval_dispatch_matches_manual_grouping():
|
|
"""Eval-mode grouped top-1 dispatch must equal running each row through
|
|
its assigned expert individually (batch order shouldn't matter)."""
|
|
B = 12
|
|
model = _routed_stage1(n_experts=4)
|
|
model.eval()
|
|
x_t = torch.randn(B, X_DIM)
|
|
t = torch.rand(B)
|
|
cond_cont, cond_cat = _cond(B)
|
|
|
|
with torch.no_grad():
|
|
batched = model(x_t, t, cond_cont, cond_cat)
|
|
|
|
t_emb = model.time_emb(t)
|
|
c_emb = model.cond_enc(cond_cont, cond_cat)
|
|
cond = torch.cat([t_emb, c_emb], dim=-1)
|
|
idx = model.router.top1(cond_cont, cond_cat)
|
|
manual = torch.zeros_like(x_t)
|
|
for i in range(B):
|
|
manual[i] = model.experts[int(idx[i])](x_t[i : i + 1], cond[i : i + 1])[0]
|
|
|
|
torch.testing.assert_close(batched, manual, atol=1e-5, rtol=1e-4)
|
|
|
|
|
|
def test_routed_denoising_mlp_predict_n_sec_shape():
|
|
B = 6
|
|
model = _routed_stage1()
|
|
cond_cont, cond_cat = _cond(B)
|
|
logits = model.predict_n_sec(cond_cont, cond_cat)
|
|
assert logits.shape == (B, K_MAX + 1)
|
|
|
|
|
|
def test_routed_denoising_mlp_has_no_pdg_embedding_weight_method():
|
|
model = _routed_stage1(pdg=5, mat=2)
|
|
assert not hasattr(model, "pdg_embedding_weight")
|
|
|
|
|
|
# ── RoutedSecondaryDecoder ───────────────────────────────────────────────────
|
|
|
|
|
|
def test_routed_secondary_decoder_output_shape_train_and_eval():
|
|
B = 8
|
|
decoder = _routed_sec_decoder()
|
|
x_t = torch.randn(B, SEC_DIM)
|
|
t = torch.rand(B)
|
|
cond_cont, cond_cat = _cond(B)
|
|
stage1_out = torch.randn(B, X_DIM)
|
|
|
|
decoder.train()
|
|
out_train = decoder(x_t, t, cond_cont, cond_cat, stage1_out)
|
|
assert out_train.shape == (B, SEC_DIM)
|
|
|
|
decoder.eval()
|
|
with torch.no_grad():
|
|
out_eval = decoder(x_t, t, cond_cont, cond_cat, stage1_out)
|
|
assert out_eval.shape == (B, SEC_DIM)
|
|
|
|
|
|
def test_routed_secondary_decoder_gradients_flow():
|
|
B = 4
|
|
decoder = _routed_sec_decoder(n_experts=3)
|
|
x_t = torch.randn(B, SEC_DIM)
|
|
t = torch.rand(B)
|
|
cond_cont, cond_cat = _cond(B)
|
|
stage1_out = torch.randn(B, X_DIM)
|
|
decoder.train()
|
|
decoder(x_t, t, cond_cont, cond_cat, stage1_out).sum().backward()
|
|
for name, p in decoder.named_parameters():
|
|
assert p.grad is not None, f"no grad for {name}"
|
|
|
|
|
|
# ── build_models dispatch ────────────────────────────────────────────────────
|
|
|
|
|
|
def test_build_models_monolith_when_router_absent():
|
|
model_config = dict(
|
|
pdg_vocab=4,
|
|
mat_vocab=2,
|
|
hidden_dim=32,
|
|
n_blocks=2,
|
|
emb_dim=16,
|
|
dropout=0.1,
|
|
k_max=K_MAX,
|
|
)
|
|
stage1, sec_decoder = build_models(model_config)
|
|
assert isinstance(stage1, DenoisingMLP)
|
|
assert isinstance(sec_decoder, SecondaryDecoder)
|
|
|
|
|
|
def test_build_models_monolith_when_router_disabled():
|
|
model_config = dict(
|
|
pdg_vocab=4,
|
|
mat_vocab=2,
|
|
hidden_dim=32,
|
|
n_blocks=2,
|
|
emb_dim=16,
|
|
dropout=0.1,
|
|
k_max=K_MAX,
|
|
router={"enabled": False, "type": "energy", "n_experts": 4},
|
|
)
|
|
stage1, sec_decoder = build_models(model_config)
|
|
assert isinstance(stage1, DenoisingMLP)
|
|
assert isinstance(sec_decoder, SecondaryDecoder)
|
|
|
|
|
|
def test_build_models_routed_when_enabled():
|
|
model_config = dict(
|
|
pdg_vocab=4,
|
|
mat_vocab=2,
|
|
emb_dim=16,
|
|
dropout=0.1,
|
|
k_max=K_MAX,
|
|
expert_hidden_dim=16,
|
|
expert_n_blocks=2,
|
|
router={
|
|
"enabled": True,
|
|
"type": "energy",
|
|
"n_experts": 4,
|
|
"temperature": 0.5,
|
|
"learn_centers": True,
|
|
"lambda_balance": 0.0,
|
|
},
|
|
)
|
|
stage1, sec_decoder = build_models(model_config)
|
|
assert isinstance(stage1, RoutedDenoisingMLP)
|
|
assert isinstance(sec_decoder, RoutedSecondaryDecoder)
|
|
assert len(stage1.experts) == 4
|
|
assert len(sec_decoder.experts) == 4
|
|
|
|
|
|
def test_build_models_routed_pair_is_drop_in_for_sample_flow():
|
|
"""Exercise the exact calling convention giant/sample.py uses."""
|
|
from giant.sample import sample_flow, sample_secondaries
|
|
|
|
model_config = dict(
|
|
pdg_vocab=3,
|
|
mat_vocab=2,
|
|
emb_dim=16,
|
|
dropout=0.1,
|
|
k_max=K_MAX,
|
|
expert_hidden_dim=8,
|
|
expert_n_blocks=1,
|
|
router={"enabled": True, "type": "energy", "n_experts": 2},
|
|
)
|
|
stage1, sec_decoder = build_models(model_config)
|
|
B = 5
|
|
cond_cont, cond_cat = _cond(B, pdg=3, mat=2)
|
|
stage1_norm, n_sec_pred = sample_flow(stage1, cond_cont, cond_cat, steps=2)
|
|
assert stage1_norm.shape == (B, X_DIM)
|
|
assert n_sec_pred.shape == (B,)
|
|
|
|
sec_cont, sec_type_emb, sec_valid = sample_secondaries(
|
|
sec_decoder, cond_cont, cond_cat, stage1_norm, n_sec_pred, steps=2
|
|
)
|
|
assert sec_cont.shape == (B, K_MAX, 4)
|
|
assert sec_valid.shape == (B, K_MAX)
|