[v1][feature] add dpo trainer (#10544)

Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>

tests_v1/trainers/test_dpo_loss_precision.py

@@ -0,0 +1,240 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Precision tests for v1 sigmoid-based DPO loss."""
+
+from types import SimpleNamespace
+
+import torch
+import torch.nn.functional as F
+
+from llamafactory.extras.constants import IGNORE_INDEX
+from llamafactory.train.dpo.trainer import CustomDPOTrainer
+from llamafactory.v1.trainers.dpo_trainer import DPOTrainer, compute_sigmoid_dpo_loss
+
+
+# ==============================================================================
+# Mock helpers
+# ==============================================================================
+
+def _make_mock_v1(
+    pref_beta: float = 0.1,
+    dpo_label_smoothing: float = 0.0,
+    ld_alpha: float | None = None,
+) -> SimpleNamespace:
+    return SimpleNamespace(
+        pref_beta=pref_beta,
+        dpo_label_smoothing=dpo_label_smoothing,
+        ld_alpha=ld_alpha,
+        device=torch.device("cpu"),
+    )
+
+
+def _make_mock_v0_dpo(beta: float = 0.1, label_smoothing: float = 0.0) -> SimpleNamespace:
+    mock = SimpleNamespace()
+    mock.beta = beta
+    mock.label_smoothing = label_smoothing
+    mock.reference_free = False
+    mock.f_divergence_type = "reverse_kl"
+    mock.f_divergence_params = None
+    mock.accelerator = SimpleNamespace()
+    mock.accelerator.device = torch.device("cpu")
+    return mock
+
+
+# ==============================================================================
+# Fixed test inputs
+# ==============================================================================
+
+P_CHOSEN = torch.tensor([-3.0, -2.5, -4.0, -1.5])
+P_REJECTED = torch.tensor([-5.0, -3.5, -6.0, -2.5])
+R_CHOSEN = torch.tensor([-2.8, -2.3, -3.8, -1.4])
+R_REJECTED = torch.tensor([-3.2, -2.7, -4.2, -1.8])
+
+
+# ==============================================================================
+# Test 1 — Core loss correctness (pure function ↔ v1 instance ↔ v0/TRL)
+# ==============================================================================
+
+def test_sigmoid_dpo_loss_correctness():
+    """Comprehensive correctness check for compute_sigmoid_dpo_loss and its wrapper."""
+    # ---- 1a: pure function matches instance method ----
+    v1 = _make_mock_v1(pref_beta=0.1)
+    actual = DPOTrainer._sigmoid_dpo_loss(v1, P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED)
+    expected = compute_sigmoid_dpo_loss(P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED, beta=0.1)
+    torch.testing.assert_close(actual, expected, rtol=1e-6, atol=1e-6)
+
+    # ---- 1b: v1 matches v0 (TRL) on fixed inputs ----
+    v0 = _make_mock_v0_dpo(beta=0.1)
+    v0_losses, _, _ = CustomDPOTrainer.dpo_loss(
+        v0, P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED, loss_type="sigmoid",
+    )
+    torch.testing.assert_close(actual, v0_losses, rtol=1e-6, atol=1e-6)
+
+    # ---- 1c: multiple beta values (v1 ↔ v0) ----
+    for beta in [0.01, 0.1, 0.5, 1.0]:
+        v0b = _make_mock_v0_dpo(beta=beta)
+        v1b = _make_mock_v1(pref_beta=beta)
+        vl, _, _ = CustomDPOTrainer.dpo_loss(
+            v0b, P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED, loss_type="sigmoid",
+        )
+        v1l = DPOTrainer._sigmoid_dpo_loss(v1b, P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED)
+        torch.testing.assert_close(v1l, vl, rtol=1e-6, atol=1e-6)
+
+    # ---- 1d: label_smoothing sweep (v1 ↔ v0) ----
+    for ls in [0.0, 0.1, 0.2, 0.3]:
+        v0s = _make_mock_v0_dpo(beta=0.1, label_smoothing=ls)
+        v1s = _make_mock_v1(pref_beta=0.1, dpo_label_smoothing=ls)
+        vl, _, _ = CustomDPOTrainer.dpo_loss(
+            v0s, P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED, loss_type="sigmoid",
+        )
+        v1l = DPOTrainer._sigmoid_dpo_loss(v1s, P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED)
+        torch.testing.assert_close(v1l, vl, rtol=1e-6, atol=1e-6)
+
+    # ---- 1e: label_smoothing=0.5 symmetry (swap chosen↔rejected same loss) ----
+    v1s = _make_mock_v1(pref_beta=0.1, dpo_label_smoothing=0.5)
+    fwd = DPOTrainer._sigmoid_dpo_loss(v1s, P_CHOSEN, P_REJECTED, R_CHOSEN, R_REJECTED)
+    swp = DPOTrainer._sigmoid_dpo_loss(v1s, P_REJECTED, P_CHOSEN, R_REJECTED, R_CHOSEN)
+    torch.testing.assert_close(fwd, swp, rtol=1e-6, atol=1e-6)
+
+    # ---- 1f: chosen better → lower loss ----
+    v1c = _make_mock_v1(pref_beta=0.1)
+    loss_good = DPOTrainer._sigmoid_dpo_loss(
+        v1c,
+        torch.tensor([-1.0]), torch.tensor([-10.0]),
+        torch.tensor([-3.0]), torch.tensor([-3.0]),
+    )
+    loss_bad = DPOTrainer._sigmoid_dpo_loss(
+        v1c,
+        torch.tensor([-10.0]), torch.tensor([-1.0]),
+        torch.tensor([-3.0]), torch.tensor([-3.0]),
+    )
+    assert loss_good.item() < loss_bad.item()
+
+    # ---- 1g: policy == ref → loss = log(2) ≈ 0.693 ----
+    logps = torch.tensor([-3.0, -2.0, -4.0])
+    losses = DPOTrainer._sigmoid_dpo_loss(v1c, logps, logps, logps, logps)
+    expected_log2 = torch.full_like(logps, -F.logsigmoid(torch.tensor(0.0)).item())
+    torch.testing.assert_close(losses, expected_log2, rtol=1e-5, atol=1e-5)
+
+    # ---- 1h: non-negative ----
+    assert (actual >= 0).all()
+
+    # ---- 1i: extreme logps stay finite ----
+    v1x = _make_mock_v1(pref_beta=0.1)
+    x = DPOTrainer._sigmoid_dpo_loss(
+        v1x,
+        torch.tensor([-0.1, -50.0, -0.5, -100.0]),
+        torch.tensor([-0.2, -5.0, -30.0, -1.0]),
+        torch.tensor([-0.15, -3.0, -0.6, -2.0]),
+        torch.tensor([-0.25, -4.0, -5.0, -1.5]),
+    )
+    assert torch.isfinite(x).all()
+
+
+# ==============================================================================
+# Test 2 — Random cross-validation & reward equivalence
+# ==============================================================================
+
+def test_cross_validate_and_rewards():
+    """Randomised v0↔v1 cross-validation (50 seeds) + reward-margin check."""
+    torch.manual_seed(42)
+    for _ in range(50):
+        pc = -torch.rand(4) * 10 - 0.01
+        pr = -torch.rand(4) * 15 - 0.01
+        rc = -torch.rand(4) * 10 - 0.01
+        rr = -torch.rand(4) * 12 - 0.01
+        beta = 0.01 + torch.rand(1).item() * 0.5
+        ls = torch.rand(1).item() * 0.3
+
+        v0 = _make_mock_v0_dpo(beta=beta, label_smoothing=ls)
+        v1 = _make_mock_v1(pref_beta=beta, dpo_label_smoothing=ls)
+
+        v0_loss, _, _ = CustomDPOTrainer.dpo_loss(
+            v0, pc, pr, rc, rr, loss_type="sigmoid",
+        )
+        v1_loss = DPOTrainer._sigmoid_dpo_loss(v1, pc, pr, rc, rr)
+        torch.testing.assert_close(v1_loss, v0_loss, rtol=1e-5, atol=1e-5)
+
+        # Reward margin = beta * (chosen_logratio - rejected_logratio)
+        chosen_rewards = beta * (pc - rc)
+        rejected_rewards = beta * (pr - rr)
+        reward_margin = chosen_rewards - rejected_rewards
+        logits = (pc - rc) - (pr - rr)
+        torch.testing.assert_close(reward_margin, beta * logits, rtol=1e-6, atol=1e-6)
+
+    # Fixed-input reward ordering
+    cr = 0.1 * (P_CHOSEN - R_CHOSEN)
+    rr = 0.1 * (P_REJECTED - R_REJECTED)
+    assert (cr > rr).float().mean().item() == 1.0
+
+
+# ==============================================================================
+# Test 3 — End-to-end: log-prob extraction + synthetic batch + LD-DPO
+# ==============================================================================
+
+def _make_batch(num_pairs, seq_len, vocab_size, prompt_len=3, chosen_len=None, rejected_len=None):
+    if chosen_len is None or rejected_len is None:
+        rlen = (seq_len - prompt_len) // 2
+        chosen_len = rlen
+        rejected_len = rlen
+
+    actual = prompt_len + chosen_len + rejected_len
+
+    torch.manual_seed(42)
+    input_ids = torch.randint(0, vocab_size, (num_pairs, actual))
+    labels = input_ids.clone()
+    labels[:, :prompt_len] = IGNORE_INDEX
+
+    token_type_ids = torch.zeros(num_pairs, actual, dtype=torch.long)
+    token_type_ids[:, prompt_len:prompt_len + chosen_len] = 1
+    token_type_ids[:, prompt_len + chosen_len:] = 2
+
+    torch.manual_seed(99)
+    logits = torch.randn(num_pairs, actual, vocab_size)
+    return input_ids, labels, token_type_ids, logits
+
+
+def test_logp_extraction_and_e2e_loss():
+    """Log-prob extraction shapes + e2e sigmoid loss (equal & unequal lengths)."""
+    # --- equal-length batch ---
+    ids, labels, tt_ids, logits = _make_batch(2, 12, 64, prompt_len=2)
+    v1 = _make_mock_v1(pref_beta=0.1)
+
+    c_lp, r_lp, c_avg, r_avg = DPOTrainer._extract_chosen_rejected_logps(v1, logits, labels, tt_ids)
+    assert c_lp.shape == r_lp.shape == c_avg.shape == r_avg.shape == (2,)
+    assert (c_lp <= 1e-6).all() and (r_lp <= 1e-6).all()
+
+    # Create "ref" logits with small noise
+    torch.manual_seed(123)
+    ref_logits = logits + 0.1 * torch.randn_like(logits)
+    rc_lp, rr_lp, _, _ = DPOTrainer._extract_chosen_rejected_logps(v1, ref_logits, labels, tt_ids)
+
+    losses = DPOTrainer._sigmoid_dpo_loss(v1, c_lp, r_lp, rc_lp, rr_lp)
+    assert torch.isfinite(losses).all() and (losses >= 0).all()
+
+    # --- unequal-length (LD-DPO) batch ---
+    ids2, labels2, tt_ids2, logits2 = _make_batch(
+        1, 11, 64, prompt_len=2, chosen_len=6, rejected_len=3,
+    )
+    v1_ld = _make_mock_v1(pref_beta=0.1, ld_alpha=0.5)
+
+    c_lp2, r_lp2, _, _ = DPOTrainer._extract_chosen_rejected_logps(v1_ld, logits2, labels2, tt_ids2)
+
+    torch.manual_seed(123)
+    ref2 = logits2 + 0.1 * torch.randn_like(logits2)
+    rc2, rr2, _, _ = DPOTrainer._extract_chosen_rejected_logps(v1_ld, ref2, labels2, tt_ids2)
+
+    losses2 = DPOTrainer._sigmoid_dpo_loss(v1_ld, c_lp2, r_lp2, rc2, rr2)
+    assert torch.isfinite(losses2).all()