mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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241 lines
9.7 KiB
Python
241 lines
9.7 KiB
Python
# Copyright 2025 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Precision tests for v1 sigmoid-based DPO loss."""
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from types import SimpleNamespace
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import torch
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import torch.nn.functional as F
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from llamafactory.extras.constants import IGNORE_INDEX
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from llamafactory.train.dpo.trainer import CustomDPOTrainer
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from llamafactory.v1.trainers.dpo_trainer import DPOTrainer, compute_sigmoid_dpo_loss
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# ==============================================================================
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# Mock helpers
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# ==============================================================================
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def _make_mock_v1(
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pref_beta: float = 0.1,
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dpo_label_smoothing: float = 0.0,
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ld_alpha: float | None = None,
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) -> SimpleNamespace:
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return SimpleNamespace(
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pref_beta=pref_beta,
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dpo_label_smoothing=dpo_label_smoothing,
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ld_alpha=ld_alpha,
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device=torch.device("cpu"),
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)
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def _make_mock_v0_dpo(beta: float = 0.1, label_smoothing: float = 0.0) -> SimpleNamespace:
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mock = SimpleNamespace()
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mock.beta = beta
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mock.label_smoothing = label_smoothing
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mock.reference_free = False
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mock.f_divergence_type = "reverse_kl"
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mock.f_divergence_params = None
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mock.accelerator = SimpleNamespace()
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mock.accelerator.device = torch.device("cpu")
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return mock
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# ==============================================================================
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# Fixed test inputs
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# ==============================================================================
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P_CHOSEN = torch.tensor([-3.0, -2.5, -4.0, -1.5])
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P_REJECTED = torch.tensor([-5.0, -3.5, -6.0, -2.5])
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R_CHOSEN = torch.tensor([-2.8, -2.3, -3.8, -1.4])
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R_REJECTED = torch.tensor([-3.2, -2.7, -4.2, -1.8])
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# ==============================================================================
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# Test 1 — Core loss correctness (pure function ↔ v1 instance ↔ v0/TRL)
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# ==============================================================================
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def test_sigmoid_dpo_loss_correctness():
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"""Comprehensive correctness check for compute_sigmoid_dpo_loss and its wrapper."""
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# ---- 1a: pure function matches instance method ----
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v1 = _make_mock_v1(pref_beta=0.1)
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actual = DPOTrainer._sigmoid_dpo_loss(v1, P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED)
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expected = compute_sigmoid_dpo_loss(P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED, beta=0.1)
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torch.testing.assert_close(actual, expected, rtol=1e-6, atol=1e-6)
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# ---- 1b: v1 matches v0 (TRL) on fixed inputs ----
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v0 = _make_mock_v0_dpo(beta=0.1)
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v0_losses, _, _ = CustomDPOTrainer.dpo_loss(
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v0, P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED, loss_type="sigmoid",
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)
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torch.testing.assert_close(actual, v0_losses, rtol=1e-6, atol=1e-6)
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# ---- 1c: multiple beta values (v1 ↔ v0) ----
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for beta in [0.01, 0.1, 0.5, 1.0]:
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v0b = _make_mock_v0_dpo(beta=beta)
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v1b = _make_mock_v1(pref_beta=beta)
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vl, _, _ = CustomDPOTrainer.dpo_loss(
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v0b, P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED, loss_type="sigmoid",
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)
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v1l = DPOTrainer._sigmoid_dpo_loss(v1b, P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED)
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torch.testing.assert_close(v1l, vl, rtol=1e-6, atol=1e-6)
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# ---- 1d: label_smoothing sweep (v1 ↔ v0) ----
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for ls in [0.0, 0.1, 0.2, 0.3]:
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v0s = _make_mock_v0_dpo(beta=0.1, label_smoothing=ls)
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v1s = _make_mock_v1(pref_beta=0.1, dpo_label_smoothing=ls)
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vl, _, _ = CustomDPOTrainer.dpo_loss(
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v0s, P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED, loss_type="sigmoid",
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)
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v1l = DPOTrainer._sigmoid_dpo_loss(v1s, P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED)
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torch.testing.assert_close(v1l, vl, rtol=1e-6, atol=1e-6)
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# ---- 1e: label_smoothing=0.5 symmetry (swap chosen↔rejected same loss) ----
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v1s = _make_mock_v1(pref_beta=0.1, dpo_label_smoothing=0.5)
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fwd = DPOTrainer._sigmoid_dpo_loss(v1s, P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED)
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swp = DPOTrainer._sigmoid_dpo_loss(v1s, P_REJECTED, P_CHOSEN, R_REJECTED, R_CHOSEN)
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torch.testing.assert_close(fwd, swp, rtol=1e-6, atol=1e-6)
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# ---- 1f: chosen better → lower loss ----
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v1c = _make_mock_v1(pref_beta=0.1)
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loss_good = DPOTrainer._sigmoid_dpo_loss(
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v1c,
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torch.tensor([-1.0]), torch.tensor([-10.0]),
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torch.tensor([-3.0]), torch.tensor([-3.0]),
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)
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loss_bad = DPOTrainer._sigmoid_dpo_loss(
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v1c,
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torch.tensor([-10.0]), torch.tensor([-1.0]),
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torch.tensor([-3.0]), torch.tensor([-3.0]),
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)
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assert loss_good.item() < loss_bad.item()
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# ---- 1g: policy == ref → loss = log(2) ≈ 0.693 ----
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logps = torch.tensor([-3.0, -2.0, -4.0])
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losses = DPOTrainer._sigmoid_dpo_loss(v1c, logps, logps, logps, logps)
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expected_log2 = torch.full_like(logps, -F.logsigmoid(torch.tensor(0.0)).item())
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torch.testing.assert_close(losses, expected_log2, rtol=1e-5, atol=1e-5)
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# ---- 1h: non-negative ----
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assert (actual >= 0).all()
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# ---- 1i: extreme logps stay finite ----
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v1x = _make_mock_v1(pref_beta=0.1)
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x = DPOTrainer._sigmoid_dpo_loss(
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v1x,
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torch.tensor([-0.1, -50.0, -0.5, -100.0]),
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torch.tensor([-0.2, -5.0, -30.0, -1.0]),
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torch.tensor([-0.15, -3.0, -0.6, -2.0]),
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torch.tensor([-0.25, -4.0, -5.0, -1.5]),
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)
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assert torch.isfinite(x).all()
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# ==============================================================================
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# Test 2 — Random cross-validation & reward equivalence
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# ==============================================================================
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def test_cross_validate_and_rewards():
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"""Randomised v0↔v1 cross-validation (50 seeds) + reward-margin check."""
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torch.manual_seed(42)
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for _ in range(50):
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pc = -torch.rand(4) * 10 - 0.01
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pr = -torch.rand(4) * 15 - 0.01
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rc = -torch.rand(4) * 10 - 0.01
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rr = -torch.rand(4) * 12 - 0.01
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beta = 0.01 + torch.rand(1).item() * 0.5
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ls = torch.rand(1).item() * 0.3
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v0 = _make_mock_v0_dpo(beta=beta, label_smoothing=ls)
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v1 = _make_mock_v1(pref_beta=beta, dpo_label_smoothing=ls)
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v0_loss, _, _ = CustomDPOTrainer.dpo_loss(
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v0, pc, pr, rc, rr, loss_type="sigmoid",
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)
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v1_loss = DPOTrainer._sigmoid_dpo_loss(v1, pc, pr, rc, rr)
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torch.testing.assert_close(v1_loss, v0_loss, rtol=1e-5, atol=1e-5)
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# Reward margin = beta * (chosen_logratio - rejected_logratio)
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chosen_rewards = beta * (pc - rc)
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rejected_rewards = beta * (pr - rr)
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reward_margin = chosen_rewards - rejected_rewards
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logits = (pc - rc) - (pr - rr)
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torch.testing.assert_close(reward_margin, beta * logits, rtol=1e-6, atol=1e-6)
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# Fixed-input reward ordering
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cr = 0.1 * (P_CHOSEN - R_CHOSEN)
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rr = 0.1 * (P_REJECTED - R_REJECTED)
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assert (cr > rr).float().mean().item() == 1.0
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# ==============================================================================
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# Test 3 — End-to-end: log-prob extraction + synthetic batch + LD-DPO
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# ==============================================================================
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def _make_batch(num_pairs, seq_len, vocab_size, prompt_len=3, chosen_len=None, rejected_len=None):
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if chosen_len is None or rejected_len is None:
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rlen = (seq_len - prompt_len) // 2
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chosen_len = rlen
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rejected_len = rlen
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actual = prompt_len + chosen_len + rejected_len
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torch.manual_seed(42)
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input_ids = torch.randint(0, vocab_size, (num_pairs, actual))
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labels = input_ids.clone()
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labels[:, :prompt_len] = IGNORE_INDEX
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token_type_ids = torch.zeros(num_pairs, actual, dtype=torch.long)
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token_type_ids[:, prompt_len:prompt_len + chosen_len] = 1
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token_type_ids[:, prompt_len + chosen_len:] = 2
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torch.manual_seed(99)
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logits = torch.randn(num_pairs, actual, vocab_size)
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return input_ids, labels, token_type_ids, logits
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def test_logp_extraction_and_e2e_loss():
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"""Log-prob extraction shapes + e2e sigmoid loss (equal & unequal lengths)."""
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# --- equal-length batch ---
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ids, labels, tt_ids, logits = _make_batch(2, 12, 64, prompt_len=2)
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v1 = _make_mock_v1(pref_beta=0.1)
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c_lp, r_lp, c_avg, r_avg = DPOTrainer._extract_chosen_rejected_logps(v1, logits, labels, tt_ids)
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assert c_lp.shape == r_lp.shape == c_avg.shape == r_avg.shape == (2,)
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assert (c_lp <= 1e-6).all() and (r_lp <= 1e-6).all()
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# Create "ref" logits with small noise
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torch.manual_seed(123)
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ref_logits = logits + 0.1 * torch.randn_like(logits)
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rc_lp, rr_lp, _, _ = DPOTrainer._extract_chosen_rejected_logps(v1, ref_logits, labels, tt_ids)
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losses = DPOTrainer._sigmoid_dpo_loss(v1, c_lp, r_lp, rc_lp, rr_lp)
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assert torch.isfinite(losses).all() and (losses >= 0).all()
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# --- unequal-length (LD-DPO) batch ---
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ids2, labels2, tt_ids2, logits2 = _make_batch(
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1, 11, 64, prompt_len=2, chosen_len=6, rejected_len=3,
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)
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v1_ld = _make_mock_v1(pref_beta=0.1, ld_alpha=0.5)
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c_lp2, r_lp2, _, _ = DPOTrainer._extract_chosen_rejected_logps(v1_ld, logits2, labels2, tt_ids2)
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torch.manual_seed(123)
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ref2 = logits2 + 0.1 * torch.randn_like(logits2)
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rc2, rr2, _, _ = DPOTrainer._extract_chosen_rejected_logps(v1_ld, ref2, labels2, tt_ids2)
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losses2 = DPOTrainer._sigmoid_dpo_loss(v1_ld, c_lp2, r_lp2, rc2, rr2)
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assert torch.isfinite(losses2).all()
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