Encode edep/secondary/post energy as a conservation-constrained simplex

Replaces the independent log_delta_e/log_edep targets with 2 additive-log-ratio
coordinates over the deposit/secondary/post-energy simplex (fractions of pre_E
summing to 1), so edep + e_sec + post_E == pre_E holds by construction after
decoding (softmax) rather than being learned approximately. Requires e_sec
(secondary energy) as a new conditioning input and a steps_to_parquet.py pass
to derive it from child track first-step energies.

tests/test_flow.py

@@ -1,4 +1,5 @@
 import torch
+from giant.constants import COND_DIM
 from giant.model.network import DenoisingMLP
 from giant.model.schedule import CosineSchedule, flow_matching_loss
 from giant.sample import sample_flow, sample_ddim
@@ -10,7 +11,7 @@ def _small_model():
 
 def _batch(B=8):
     x1 = torch.randn(B, 9)
-    cond_cont = torch.randn(B, 9)
+    cond_cont = torch.randn(B, COND_DIM)
     cond_cat = torch.zeros(B, 2, dtype=torch.long)
     return x1, cond_cont, cond_cat
 
@@ -36,7 +37,7 @@ def test_flow_matching_loss_has_grad():
 
 def test_sample_flow_shape():
     B = 6
-    cond_cont = torch.randn(B, 9)
+    cond_cont = torch.randn(B, COND_DIM)
     cond_cat = torch.zeros(B, 2, dtype=torch.long)
     out = sample_flow(_small_model(), cond_cont, cond_cat, steps=5)
     assert out.shape == (B, 9)
@@ -52,7 +53,7 @@ def test_ddpm_loss_nonneg():
 def test_sample_ddim_shape():
     B = 4
     schedule = CosineSchedule(T=50)
-    cond_cont = torch.randn(B, 9)
+    cond_cont = torch.randn(B, COND_DIM)
     cond_cat = torch.zeros(B, 2, dtype=torch.long)
     out = sample_ddim(_small_model(), cond_cont, cond_cat, schedule, steps=5)
     assert out.shape == (B, 9)