claude_refactor_v3: Added .py-confs and all presets (nx5 .gin files). TODO: common gins-mapping and prepare to next step

src/conf/training_conf.py

@@ -1 +1,93 @@
+"""Training recipe: loss + optimizer + sampler.
+
+Three concerns kept together because they form one coherent recipe — they
+co-vary across experiments. Splitting Loss vs Optimizer vs Sampler can be
+done later if a need emerges.
+"""
+
+from __future__ import annotations
+
+import gin
+
+
+@gin.configurable
+class TrainingConfig:
+    """Loss + optimizer + sampler.
+
+    Selects between InfoNCELoss and WeightedInfoNCELoss via `loss_type`.
+    Selects between DSS / mutex / plain shuffle via `sampler_type`.
+    """
+
+    def __init__(
+        self,
+        # ---- Loss: shared between InfoNCELoss and WeightedInfoNCELoss ----
+        loss_type: str = "symmetric",            # 'symmetric' | 'weighted'
+        tau_init: float = 0.07,
+        tau_min: float = 0.01,
+        tau_max: float = 0.1,
+        learnable_temperature: bool = True,
+        label_smoothing: float = 0.1,
+        # ---- Loss: InfoNCELoss-only ----
+        tau_final: float = 0.01,                 # cosine-schedule final tau (when not learnable)
+        weight_q2g: float = 0.6,
+        weight_g2q: float = 0.4,
+        hard_mining_k: int = 0,
+        neg_bank_size: int = 0,
+        # ---- Loss: WeightedInfoNCELoss-only ----
+        weighted_loss_k: float = 5.0,            # sigmoid steepness for weight→eps mapping
+        # ---- Optimizer ----
+        learning_rate: float = 1e-4,
+        text_lr_factor: float = 0.1,             # lr * factor for DGTRS-CLIP/LoRA params
+        weight_decay: float = 1e-4,
+        grad_clip: float = 1.0,
+        # ---- Sampler ----
+        sampler_type: str = "mutex",             # 'mutex' | 'dss' | 'none'
+        dss_warmup_epochs: int = 1,
+        dss_reembed_every: int = 1,
+        dss_knn_device: str = "cuda",
+        dss_use_lsh: bool = False,
+        dss_lsh_num_tables: int = 8,
+        dss_lsh_num_bits: int = 14,
+        dss_cache_dir: str | None = None,
+        # Legacy alias (kept until train_gtauav.py is rewritten in step 4).
+        use_mutex_sampler: bool = True,
+    ) -> None:
+        # Loss (shared).
+        self.loss_type = loss_type
+        self.tau_init = tau_init
+        self.tau_min = tau_min
+        self.tau_max = tau_max
+        self.learnable_temperature = learnable_temperature
+        self.label_smoothing = label_smoothing
+        # Loss (InfoNCE-specific).
+        self.tau_final = tau_final
+        self.weight_q2g = weight_q2g
+        self.weight_g2q = weight_g2q
+        self.hard_mining_k = hard_mining_k
+        self.neg_bank_size = neg_bank_size
+        # Loss (WeightedInfoNCE-specific).
+        self.weighted_loss_k = weighted_loss_k
+        # Optimizer.
+        self.learning_rate = learning_rate
+        self.text_lr_factor = text_lr_factor
+        self.weight_decay = weight_decay
+        self.grad_clip = grad_clip
+        # Sampler.
+        self.sampler_type = sampler_type
+        self.dss_warmup_epochs = dss_warmup_epochs
+        self.dss_reembed_every = dss_reembed_every
+        self.dss_knn_device = dss_knn_device
+        self.dss_use_lsh = dss_use_lsh
+        self.dss_lsh_num_tables = dss_lsh_num_tables
+        self.dss_lsh_num_bits = dss_lsh_num_bits
+        self.dss_cache_dir = dss_cache_dir
+        self.use_mutex_sampler = use_mutex_sampler
+
+
+def get_training_cfg(path2cfg: str) -> TrainingConfig:
+    """Load ONLY training config (TESTING ONLY — production uses load_all_configs)."""
+    gin.clear_config()
+    gin.parse_config_file(f"{path2cfg}training.gin")
+    return TrainingConfig()
+