lars
72bd65ff9f
step_length already encodes |post_pos - pre_pos| by definition, so a raw post_pos target would duplicate that magnitude and could drift inconsistent with step_length during sampling. Instead add travel_dir, a unit vector (local frame) giving only the direction of pre_pos->post_pos; post_pos is reconstructed at inference as pre_pos + step_length * travel_dir, keeping the two self-consistent. Target grows from 6D to 9D. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
import torch
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import pytest
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from giant.model.network import DenoisingMLP
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from giant.model.schedule import CosineSchedule, flow_matching_loss
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from giant.sample import sample_flow, sample_ddpm, sample_ddim
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def _small_model():
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return DenoisingMLP(pdg_vocab=3, mat_vocab=2, hidden_dim=32, n_blocks=2)
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def _batch(B=8):
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x1 = torch.randn(B, 9)
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cond_cont = torch.randn(B, 9)
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cond_cat = torch.zeros(B, 2, dtype=torch.long)
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return x1, cond_cont, cond_cat
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def test_flow_matching_loss_nonneg():
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x1, cond_cont, cond_cat = _batch()
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loss = flow_matching_loss(_small_model(), x1, cond_cont, cond_cat)
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assert loss.item() >= 0.0
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def test_flow_matching_loss_is_scalar():
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x1, cond_cont, cond_cat = _batch()
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loss = flow_matching_loss(_small_model(), x1, cond_cont, cond_cat)
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assert loss.shape == ()
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def test_flow_matching_loss_has_grad():
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model = _small_model()
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x1, cond_cont, cond_cat = _batch()
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flow_matching_loss(model, x1, cond_cont, cond_cat).backward()
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assert any(p.grad is not None for p in model.parameters())
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def test_sample_flow_shape():
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B = 6
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cond_cont = torch.randn(B, 9)
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cond_cat = torch.zeros(B, 2, dtype=torch.long)
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out = sample_flow(_small_model(), cond_cont, cond_cat, steps=5)
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assert out.shape == (B, 9)
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def test_ddpm_loss_nonneg():
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schedule = CosineSchedule(T=50)
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x1, cond_cont, cond_cat = _batch()
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loss = schedule.loss(_small_model(), x1, cond_cont, cond_cat)
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assert loss.item() >= 0.0
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def test_sample_ddim_shape():
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B = 4
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schedule = CosineSchedule(T=50)
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cond_cont = torch.randn(B, 9)
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cond_cat = torch.zeros(B, 2, dtype=torch.long)
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out = sample_ddim(_small_model(), cond_cont, cond_cat, schedule, steps=5)
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assert out.shape == (B, 9)
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