[v1] refactor registry plugin structure and params (#10641)

tests_v1/trainers/test_dpo_loss_precision.py

@@ -28,6 +28,7 @@ from llamafactory.v1.trainers.dpo_trainer import DPOTrainer, compute_sigmoid_dpo
 # Mock helpers
 # ==============================================================================
 
+
 def _make_mock_v1(
     pref_beta: float = 0.1,
     dpo_label_smoothing: float = 0.0,
@@ -67,6 +68,7 @@ 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 ----
@@ -78,7 +80,12 @@ def test_sigmoid_dpo_loss_correctness():
     # ---- 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",
+        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)
 
@@ -87,7 +94,12 @@ def test_sigmoid_dpo_loss_correctness():
         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",
+            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)
@@ -97,7 +109,12 @@ def test_sigmoid_dpo_loss_correctness():
         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",
+            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)
@@ -112,13 +129,17 @@ def test_sigmoid_dpo_loss_correctness():
     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]),
+        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]),
+        torch.tensor([-10.0]),
+        torch.tensor([-1.0]),
+        torch.tensor([-3.0]),
+        torch.tensor([-3.0]),
     )
     assert loss_good.item() < loss_bad.item()
 
@@ -147,6 +168,7 @@ def test_sigmoid_dpo_loss_correctness():
 # 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)
@@ -162,7 +184,12 @@ def test_cross_validate_and_rewards():
         v1 = _make_mock_v1(pref_beta=beta, dpo_label_smoothing=ls)
 
         v0_loss, _, _ = CustomDPOTrainer.dpo_loss(
-            v0, pc, pr, rc, rr, loss_type="sigmoid",
+            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)
@@ -184,6 +211,7 @@ def test_cross_validate_and_rewards():
 # 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
@@ -198,8 +226,8 @@ def _make_batch(num_pairs, seq_len, vocab_size, prompt_len=3, chosen_len=None, r
     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
+    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)
@@ -226,7 +254,12 @@ def test_logp_extraction_and_e2e_loss():
 
     # --- unequal-length (LD-DPO) batch ---
     ids2, labels2, tt_ids2, logits2 = _make_batch(
-        1, 11, 64, prompt_len=2, chosen_len=6, rejected_len=3,
+        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)
 

tests_v1/trainers/test_fsdp2_dpo_trainer.py

@@ -33,7 +33,6 @@ template: qwen3_nothink
 
 kernel_config:
     name: auto
-    include_kernels: auto
 
 quant_config: null
 

tests_v1/trainers/test_fsdp2_sft_trainer.py

@@ -30,7 +30,6 @@ model_class: llm
 
 kernel_config:
     name: auto
-    include_kernels: auto
 
 quant_config: null