lars
8475199609
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.
46 lines
1.2 KiB
Python
46 lines
1.2 KiB
Python
import torch
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from giant.constants import COND_DIM
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from giant.model.network import DenoisingMLP, SinusoidalEmbedding
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def test_sinusoidal_embedding_shape():
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emb = SinusoidalEmbedding(64)
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t = torch.rand(16)
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assert emb(t).shape == (16, 64)
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def test_sinusoidal_embedding_batch_1():
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emb = SinusoidalEmbedding(32)
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t = torch.tensor([0.5])
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assert emb(t).shape == (1, 32)
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def test_denoising_mlp_output_shape():
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B = 8
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model = DenoisingMLP(pdg_vocab=5, mat_vocab=3)
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x_t = torch.randn(B, 9)
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t = torch.rand(B)
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cond_cont = torch.randn(B, COND_DIM)
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cond_cat = torch.stack(
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[
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torch.randint(0, 5, (B,)),
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torch.randint(0, 3, (B,)),
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],
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dim=1,
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)
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out = model(x_t, t, cond_cont, cond_cat)
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assert out.shape == (B, 9)
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def test_denoising_mlp_gradients_flow():
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B = 4
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model = DenoisingMLP(pdg_vocab=3, mat_vocab=2, hidden_dim=32, n_blocks=2)
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x_t = torch.randn(B, 9)
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t = torch.rand(B)
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cond_cont = torch.randn(B, COND_DIM)
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cond_cat = torch.zeros(B, 2, dtype=torch.long)
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loss = model(x_t, t, cond_cont, cond_cat).sum()
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loss.backward()
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for name, p in model.named_parameters():
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assert p.grad is not None, f"no grad for {name}"
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