Implement Phase 1: full data pipeline, model, training, and config support

- Data pipeline: loader (parquet→numpy), transforms (log, local-frame
  Rodrigues rotation, Normalizer), StepsDataset with event-ID-based split
- Model: SinusoidalEmbedding, ConditionEncoder, ResBlock, DenoisingMLP
- Schedule: cosine DDPM and conditional flow matching loss (Lipman 2022)
- Samplers: flow (Euler ODE), DDPM ancestral, DDIM deterministic
- Training loop: AdamW + cosine LR, grad clipping, best-val checkpoint
- Validation: per-dimension marginal summary (normalised space)
- CLI: TOML config support with CLI-overrides; hyperparam-encoded output
  directory; config.toml with git hash saved into each run's checkpoint dir
- 21 unit tests covering transforms, network, flow/DDPM losses, dataset splits

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

tests/test_network.py

@@ -0,0 +1,42 @@
+import torch
+import pytest
+from giant.model.network import DenoisingMLP, SinusoidalEmbedding
+
+
+def test_sinusoidal_embedding_shape():
+    emb = SinusoidalEmbedding(64)
+    t = torch.rand(16)
+    assert emb(t).shape == (16, 64)
+
+
+def test_sinusoidal_embedding_batch_1():
+    emb = SinusoidalEmbedding(32)
+    t = torch.tensor([0.5])
+    assert emb(t).shape == (1, 32)
+
+
+def test_denoising_mlp_output_shape():
+    B = 8
+    model = DenoisingMLP(pdg_vocab=5, mat_vocab=3)
+    x_t = torch.randn(B, 6)
+    t = torch.rand(B)
+    cond_cont = torch.randn(B, 9)
+    cond_cat = torch.stack([
+        torch.randint(0, 5, (B,)),
+        torch.randint(0, 3, (B,)),
+    ], dim=1)
+    out = model(x_t, t, cond_cont, cond_cat)
+    assert out.shape == (B, 6)
+
+
+def test_denoising_mlp_gradients_flow():
+    B = 4
+    model = DenoisingMLP(pdg_vocab=3, mat_vocab=2, hidden_dim=32, n_blocks=2)
+    x_t = torch.randn(B, 6)
+    t = torch.rand(B)
+    cond_cont = torch.randn(B, 9)
+    cond_cat = torch.zeros(B, 2, dtype=torch.long)
+    loss = model(x_t, t, cond_cont, cond_cat).sum()
+    loss.backward()
+    for name, p in model.named_parameters():
+        assert p.grad is not None, f"no grad for {name}"