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The design doc and its followups doc are no longer needed as a live reference now that the v0.3.0 redesign is implemented — comments and docstrings across the codebase cited it extensively (file path, "design doc §X.Y", "decision N", or bare "§X.Y" section numbers) as design rationale. Removed docs/ and edited every citing comment/docstring to drop the now-dangling reference while keeping the substantive explanation next to it. CLAUDE.md's v0.3.0 roadmap bullet loses its trailing pointer to the deleted file. Verified: no remaining "docs/v0.3.0", "design doc", "decision N", or "§N.N" references (repo-wide grep); ruff and ty clean; full test suite on the heaviest-touched modules (network, sample, rollout, migration, config, train) passes. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
245 lines
8.4 KiB
Python
245 lines
8.4 KiB
Python
"""Tests for giant/sample.py's v0.3.0 stage-model sampling — the AR loop
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(`sample_secondaries_ar`) and non-"physical" `particle_type.target` coverage
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for the one-shot samplers."""
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import pytest
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import torch
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from giant.constants import COND_DIM, CONT_SLOT_DIM, K_MAX, PARTICLE_PHYS_DIM, X_DIM
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from giant.model.network import (
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Stage1Model,
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Stage2Autoregressive,
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Stage2OneShot,
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stage2_trunk_sec_dim,
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)
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from giant.sample import (
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sample_flow,
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sample_secondaries,
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sample_secondaries_ar,
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sample_secondaries_wgan,
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sample_wgan,
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)
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_PHYS_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1}
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def _particle_material_cfg(conditioning: str, emb_dim: int) -> tuple[dict, dict]:
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cfg = {"type": conditioning, "emb_dim": emb_dim, "n_layers": 1}
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return dict(cfg), dict(cfg)
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def _cond(B: int, pdg: int = 3, mat: int = 2) -> tuple[torch.Tensor, torch.Tensor]:
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cond_cont = torch.randn(B, COND_DIM)
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cond_cat = torch.stack(
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[torch.randint(0, pdg, (B,)), torch.randint(0, mat, (B,))], dim=1
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)
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return cond_cont, cond_cat
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def _conditioning_for(target: str) -> str:
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# target="embedding" regresses against the conditioning's own embedding
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# table — only meaningful when the
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# conditioning axis is itself "embedding".
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return "embedding" if target == "embedding" else "physical"
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def _stage2_oneshot(
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target: str, generator: str, emb_dim: int = 6, pdg: int = 3, mat: int = 2
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) -> Stage2OneShot:
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particle_cfg, material_cfg = _particle_material_cfg(
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_conditioning_for(target), emb_dim
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)
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particle_type_cfg = {"target": target}
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# build_models (giant/model/network.py) computes sec_dim this same way
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# before constructing Stage2OneShot — its own default (SEC_DIM, the
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# "physical" width) is only correct for target="physical".
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sec_dim = stage2_trunk_sec_dim(particle_type_cfg, generator, K_MAX, emb_dim)
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return Stage2OneShot(
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pdg_vocab=pdg,
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mat_vocab=mat,
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particle_cfg=particle_cfg,
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material_cfg=material_cfg,
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hidden_dim=32,
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n_res_blocks=2,
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generator=generator,
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time_dim=16,
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noise_dim=8,
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sec_dim=sec_dim,
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particle_type_cfg=particle_type_cfg,
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).eval()
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def _stage2_ar(
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target: str,
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generator: str,
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emb_dim: int = 6,
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pdg: int = 3,
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mat: int = 2,
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k_max: int = 5,
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history: str = "markov",
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) -> Stage2Autoregressive:
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particle_cfg, material_cfg = _particle_material_cfg(
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_conditioning_for(target), emb_dim
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)
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return Stage2Autoregressive(
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pdg_vocab=pdg,
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mat_vocab=mat,
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particle_cfg=particle_cfg,
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material_cfg=material_cfg,
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hidden_dim=32,
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n_res_blocks=2,
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generator=generator,
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time_dim=16,
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noise_dim=8,
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k_max=k_max,
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particle_type_cfg={"target": target},
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history=history,
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attn_n_heads=2,
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attn_n_layers=1,
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).eval()
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def _expected_type_dim(target: str, emb_dim: int) -> int:
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return PARTICLE_PHYS_DIM if target == "physical" else emb_dim
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# ── Stage-1 n_sec ownership ──────────────────────────────────────────────────
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def test_sample_flow_returns_none_n_sec_when_stage1_owns_no_head():
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model = Stage1Model(
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pdg_vocab=3,
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mat_vocab=2,
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particle_cfg=_PHYS_CFG,
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material_cfg=_PHYS_CFG,
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hidden_dim=16,
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n_res_blocks=1,
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)
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cond_cont, cond_cat = _cond(4)
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sample, n_sec = sample_flow(model, cond_cont, cond_cat, steps=2)
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assert sample.shape == (4, X_DIM)
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assert n_sec is None
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def test_sample_wgan_returns_none_n_sec_when_stage1_owns_no_head():
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model = Stage1Model(
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pdg_vocab=3,
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mat_vocab=2,
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particle_cfg=_PHYS_CFG,
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material_cfg=_PHYS_CFG,
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hidden_dim=16,
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n_res_blocks=1,
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generator="wgan",
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noise_dim=8,
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)
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cond_cont, cond_cat = _cond(4)
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sample, n_sec = sample_wgan(model, cond_cont, cond_cat)
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assert sample.shape == (4, X_DIM)
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assert n_sec is None
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def test_sample_flow_returns_n_sec_for_legacy_stage1():
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model = Stage1Model(
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pdg_vocab=3,
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mat_vocab=2,
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particle_cfg=_PHYS_CFG,
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material_cfg=_PHYS_CFG,
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hidden_dim=16,
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n_res_blocks=1,
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n_sec_head_k_max=K_MAX,
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)
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cond_cont, cond_cat = _cond(5)
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_, n_sec = sample_flow(model, cond_cont, cond_cat, steps=2)
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assert n_sec is not None and n_sec.shape == (5,)
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# ── Stage2OneShot: non-"physical" particle_type.target ──────────────────────
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@pytest.mark.parametrize("target", ["physical", "onehot", "embedding"])
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def test_sample_secondaries_flow_shapes_by_target(target):
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B, emb_dim = 5, 6
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decoder = _stage2_oneshot(target, "flow", emb_dim=emb_dim)
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cond_cont, cond_cat = _cond(B)
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stage1_out = torch.randn(B, X_DIM)
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n_sec_pred = torch.randint(0, K_MAX + 1, (B,))
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sec_cont, sec_type, sec_valid = sample_secondaries(
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decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2
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)
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assert sec_cont.shape == (B, K_MAX, CONT_SLOT_DIM)
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assert sec_type.shape == (B, K_MAX, _expected_type_dim(target, emb_dim))
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assert sec_valid.shape == (B, K_MAX)
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assert torch.isfinite(sec_cont).all()
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assert torch.isfinite(sec_type).all()
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@pytest.mark.parametrize("target", ["physical", "onehot", "embedding"])
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def test_sample_secondaries_wgan_shapes_by_target(target):
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B, emb_dim = 5, 6
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decoder = _stage2_oneshot(target, "wgan", emb_dim=emb_dim)
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cond_cont, cond_cat = _cond(B)
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stage1_out = torch.randn(B, X_DIM)
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n_sec_pred = torch.randint(0, K_MAX + 1, (B,))
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sec_cont, sec_type, sec_valid = sample_secondaries_wgan(
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decoder, cond_cont, cond_cat, stage1_out, n_sec_pred
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)
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assert sec_cont.shape == (B, K_MAX, CONT_SLOT_DIM)
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assert sec_type.shape == (B, K_MAX, _expected_type_dim(target, emb_dim))
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assert sec_valid.shape == (B, K_MAX)
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# ── Stage2Autoregressive ─────────────────────────────────────────────────────
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@pytest.mark.parametrize("history", ["markov", "attention"])
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@pytest.mark.parametrize("generator", ["flow", "wgan"])
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@pytest.mark.parametrize("target", ["physical", "onehot", "embedding"])
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def test_sample_secondaries_ar_shapes(target, generator, history):
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B, k_max, emb_dim = 4, 5, 6
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decoder = _stage2_ar(
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target, generator, emb_dim=emb_dim, k_max=k_max, history=history
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)
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cond_cont, cond_cat = _cond(B)
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stage1_out = torch.randn(B, X_DIM)
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n_sec_pred = torch.randint(0, k_max + 1, (B,))
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sec_cont, sec_type, sec_valid = sample_secondaries_ar(
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decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2
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)
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assert sec_cont.shape == (B, k_max, CONT_SLOT_DIM)
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assert sec_type.shape == (B, k_max, _expected_type_dim(target, emb_dim))
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assert sec_valid.shape == (B, k_max)
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assert torch.isfinite(sec_cont).all()
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assert torch.isfinite(sec_type).all()
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@pytest.mark.parametrize("generator", ["flow", "wgan"])
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@pytest.mark.parametrize("target", ["physical", "onehot", "embedding"])
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def test_sample_secondaries_ar_valid_mask_matches_n_sec(target, generator):
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B, k_max, emb_dim = 3, 5, 6
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decoder = _stage2_ar(target, generator, emb_dim=emb_dim, k_max=k_max)
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cond_cont, cond_cat = _cond(B)
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stage1_out = torch.randn(B, X_DIM)
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n_sec_pred = torch.tensor([0, 2, k_max])
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_, _, sec_valid = sample_secondaries_ar(
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decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2
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)
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for i, n in enumerate(n_sec_pred.tolist()):
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assert sec_valid[i, :n].all()
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assert not sec_valid[i, n:].any()
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def test_sample_secondaries_ar_first_slot_has_no_history():
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"""Slot 0 always has has_prev=False internally — nothing to assert on
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the public API directly, but a k_max=1 run should not crash on the
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"previous token" path at all (has_prev never true)."""
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B, emb_dim = 3, 6
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decoder = _stage2_ar("physical", "flow", emb_dim=emb_dim, k_max=1)
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cond_cont, cond_cat = _cond(B)
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stage1_out = torch.randn(B, X_DIM)
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n_sec_pred = torch.tensor([0, 1, 1])
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sec_cont, sec_type, sec_valid = sample_secondaries_ar(
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decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2
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)
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assert sec_cont.shape == (B, 1, CONT_SLOT_DIM)
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assert sec_valid.tolist() == [[False], [True], [True]]
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