Update training/train_agent.py

training/train_agent.py

@@ -1,372 +1,301 @@
 """
 training/train_agent.py — SQL Database Engineer Agent
-FIXED: Uses local DatabaseSimulator for rewards (no HF Space calls)
-- No shared singleton state
-- Real delta rewards (0.0 for wrong actions, 40-75pts for correct)
-- Clear reward difference teaches model to prefer create_index over inspect_query
+Unsloth + GRPO training script.
+Run on venue GPU (April 25-26) with compute credits.
+ENV_URL points to live HF Space environment.
 """
 
-import os, json, sys, time
-from pathlib import Path
+import os
+import re
+import json
+import requests
+from datasets import Dataset
 
-# ── GPU check ─────────────────────────────────────────────────
-UNSLOTH_AVAILABLE = False
+# ── Try importing Unsloth (GPU only) ─────────────────────────
 try:
-    import torch
-    if not torch.cuda.is_available():
-        print("❌ No GPU. Unsloth requires CUDA GPU.")
-        sys.exit(1)
     from unsloth import FastLanguageModel
     from trl import GRPOTrainer, GRPOConfig
-    from datasets import Dataset
     UNSLOTH_AVAILABLE = True
-    print(f"✅ GPU: {torch.cuda.get_device_name(0)}")
-    print(f"✅ VRAM: {torch.cuda.get_device_properties(0).total_memory/1e9:.1f}GB")
-except ImportError as e:
-    print(f"❌ {e}\nRun: pip install unsloth trl transformers datasets accelerate")
-    sys.exit(1)
+except ImportError:
+    UNSLOTH_AVAILABLE = False
+    print("⚠️  Unsloth not available. Run: pip install unsloth trl")
 
-# Add project root so we can import DatabaseSimulator
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from env.db_simulator import DatabaseSimulator
+# ─────────────────────────────────────────────
+#  CONFIG
+# ─────────────────────────────────────────────
 
-# ── Config ────────────────────────────────────────────────────
 ENV_URL    = os.getenv("ENV_URL",    "https://junaid0600-sql-db-engineer-agent.hf.space")
 HF_TOKEN   = os.getenv("HF_TOKEN",  "")
-MODEL_NAME = os.getenv("MODEL_NAME", "unsloth/Qwen2.5-1.5B-Instruct")
+MODEL_NAME = os.getenv("MODEL_NAME", "unsloth/Qwen2.5-7B-Instruct")
 OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./sdea-trained")
-MAX_STEPS  = int(os.getenv("MAX_STEPS", "100"))
 
-print(f"\n[CONFIG] Model:     {MODEL_NAME}")
-print(f"[CONFIG] Max steps: {MAX_STEPS}")
-print(f"[CONFIG] Output:    {OUTPUT_DIR}\n")
+SYSTEM_PROMPT = """You are a senior database engineer. 
+Given the current database state with slow queries, choose the BEST action to improve performance.
+Think step by step:
+1. If you haven't inspected queries yet → use inspect_query
+2. If you haven't analyzed indexes → use analyze_indexes  
+3. If you know which index is missing → use create_index
+4. If query can be rewritten better → use rewrite_query
+5. If table is huge (1M+ rows) → use partition_table
+6. When performance target is reached → use submit_report
 
-# ── System prompt ─────────────────────────────────────────────
-SYSTEM_PROMPT = """You are a senior database engineer fixing slow database queries.
-You will see slow queries and table structures. Choose the BEST action.
+Respond with JSON only — no explanation, no markdown:
+{"action_type": "...", "payload": {...}}"""
 
-Key insight: create_index on the RIGHT columns fixes slow queries.
-Wrong columns = no improvement. Right columns = massive improvement.
 
-Respond with ONLY valid JSON:
-{"action_type": "create_index", "payload": {"table": "TABLE_NAME", "columns": ["COL1", "COL2"]}}
+# ─────────────────────────────────────────────
+#  REWARD FUNCTION (calls live HF Space)
+# ─────────────────────────────────────────────
 
-Available actions: inspect_query, analyze_indexes, create_index, rewrite_query, analyze_statistics, submit_report"""
+def parse_action(text: str) -> dict:
+    """Safely parse model output into an action dict.
 
-# ── Load all 15 scenarios ─────────────────────────────────────
-def load_all_scenarios() -> list:
-    scenarios = []
-    base = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-    for fname in ["easy_scenarios.json", "medium_scenarios.json", "hard_scenarios.json"]:
-        path = os.path.join(base, "dataset", fname)
-        try:
-            with open(path) as f:
-                data = json.load(f)
-                scenarios.extend(data)
-                print(f"  ✅ Loaded {len(data)} from {fname}")
-        except FileNotFoundError:
-            print(f"  ⚠️ {fname} not found")
-    print(f"  Total: {len(scenarios)} scenarios\n")
-    return scenarios
+    Supports markdown ```json blocks and returns a fallback action on failure.
+    """
+    if not isinstance(text, str):
+        text = str(text)
 
-ALL_SCENARIOS = load_all_scenarios()
+    candidate = text.strip()
+
+    fenced = re.search(r"```(?:json)?\s*(\{.*\})\s*```", candidate, re.S | re.I)
+    if fenced:
+        candidate = fenced.group(1).strip()
+    elif candidate.startswith("```") and candidate.endswith("```"):
+        candidate = candidate[3:-3].strip()
 
-# ── Parse LLM output ─────────────────────────────────────────
-def parse_action(text: str) -> dict:
-    """Parse LLM output into action dict."""
     try:
-        text = text.strip()
-        for marker in ["```json", "```"]:
-            if marker in text:
-                parts = text.split(marker)
-                text = parts[1] if len(parts) > 1 else parts[0]
-        text = text.strip()
-        data = json.loads(text)
-        if "action_type" in data and "payload" in data:
-            return data
-    except Exception:
+        action = json.loads(candidate)
+        if isinstance(action, dict):
+            action["_parse_fallback"] = False
+            return action
+    except json.JSONDecodeError:
         pass
-    return None  # None = invalid JSON = penalized
 
-
-# ── LOCAL reward function using DatabaseSimulator ─────────────
-def compute_local_reward(action: dict, scenario: dict) -> tuple:
-    """
-    Compute reward LOCALLY using DatabaseSimulator.
-    No HF Space calls. No shared state. Clean every time.
-    
-    Returns (reward_score, db_delta, milestone_bonus)
-    """
-    sim      = DatabaseSimulator(scenario)
-    baseline = sim.get_performance_score()
-    hints    = scenario.get("missing_index_hints", [])
-
-    action_type = action.get("action_type", "")
-    payload     = action.get("payload", {})
-
-    # Apply action to simulator
-    if action_type == "create_index":
-        result  = sim.apply_action("create_index", payload)
-        delta   = result.get("delta", 0.0)
-
-    elif action_type == "inspect_query":
-        # Investigation — small reward, no DB change
-        delta = 0.0
-
-    elif action_type == "analyze_indexes":
-        delta = 0.0
-
-    elif action_type == "rewrite_query":
-        result = sim.apply_action("rewrite_query", payload)
-        delta  = result.get("delta", 0.0)
-
-    elif action_type == "analyze_statistics":
-        result = sim.apply_action("analyze_statistics", payload)
-        delta  = result.get("delta", 0.0)
-
-    elif action_type == "partition_table":
-        result = sim.apply_action("partition_table", payload)
-        delta  = result.get("delta", 0.0)
-
-    elif action_type == "submit_report":
-        # Terminal: score based on how much DB improved so far
-        final = sim.get_performance_score()
-        improvement = max(0, final - baseline)
-        delta = improvement
-
-    else:
-        delta = -5.0  # Unknown action = penalty
-
-    final_score  = sim.get_performance_score()
-    improvement  = max(0.0, final_score - baseline)
-    max_possible = max(1.0, 100.0 - baseline)
-
-    # ── Reward components ─────────────────────────────────────
-    # 1. Step reward — different per action type
-    step_rewards = {
-        "inspect_query":     0.10,
-        "analyze_indexes":   0.10,
-        "create_index":      0.15,
-        "rewrite_query":     0.20,
-        "analyze_statistics":0.08,
-        "partition_table":   0.15,
-        "submit_report":     0.05,
+    start = candidate.find("{")
+    end = candidate.rfind("}")
+    if start != -1 and end != -1 and end > start:
+        try:
+            action = json.loads(candidate[start:end + 1])
+            if isinstance(action, dict):
+                action["_parse_fallback"] = False
+                return action
+        except json.JSONDecodeError:
+            pass
+
+    return {
+        "action_type": "inspect_query",
+        "payload": {"query_id": "q1"},
+        "_parse_fallback": True,
     }
-    step_r = step_rewards.get(action_type, 0.001)
 
-    # 2. Delta reward — proportional to actual improvement
-    delta_r = min(0.70, (improvement / max_possible) * 0.70)
 
-    # 3. Milestone bonus — one-time for big improvements
-    milestone_r = 0.0
-    if improvement / max_possible >= 0.75:
-        milestone_r = 0.40
-    elif improvement / max_possible >= 0.50:
-        milestone_r = 0.25
-    elif improvement / max_possible >= 0.25:
-        milestone_r = 0.15
+def compute_reward(action_dict: dict, obs: dict, env_response: dict) -> float:
+    """Apply shaping rules on top of environment reward.
 
-    # 4. Penalty for wrong index (delta=0 on create_index)
-    wrong_index_pen = 0.0
-    if action_type == "create_index" and delta <= 0.0:
-        wrong_index_pen = -0.15  # created useless index
+    This strengthens the signal, penalizes repeated inspection, empty payloads,
+    and bounds final reward between 0.0 and 1.0.
+    """
+    base_score = env_response.get("score", 0.0)
+    try:
+        base_score = float(base_score)
+    except (TypeError, ValueError):
+        base_score = 0.0
+
+    if action_dict.get("_parse_fallback", False):
+        return 0.0
+
+    action_type = action_dict.get("action_type", "")
+    payload = action_dict.get("payload") or {}
+
+    if action_type == "inspect_query":
+        query_id = payload.get("query_id")
+        inspected_queries = []
+        if isinstance(obs, dict):
+            inspected_queries = obs.get("inspected_queries") or obs.get("inspected_query_ids") or []
+
+        if isinstance(inspected_queries, list) and query_id in inspected_queries:
+            return max(0.0, base_score - 0.2)
+
+    if not payload or (isinstance(payload, dict) and len(payload) == 0):
+        return max(0.0, base_score - 0.3)
+
+    valid_actions = {
+        "inspect_query",
+        "analyze_indexes",
+        "create_index",
+        "fix_query",
+        "optimize_query",
+    }
+    if action_type not in valid_actions:
+        return 0.0
 
-    total = step_r + delta_r + milestone_r + wrong_index_pen
-    total = max(0.001, min(0.999, total))
+    if base_score > 0.9:
+        return min(1.0, base_score + 0.1)
 
-    return total, improvement, milestone_r
+    return base_score
 
 
-# ── GRPO reward function ──────────────────────────────────────
 def reward_fn(prompts, completions, **kwargs):
     """
-    LOCAL reward — no HTTP calls, no shared state.
-    Each completion gets its own fresh DatabaseSimulator.
-    
-    Reward differences:
-      inspect_query (always):     0.10 + 0.0  = 0.10
-      create_index (wrong col):   0.15 - 0.15 = 0.001
-      create_index (right col):   0.15 + 0.60 = 0.75+
-    
-    GRPO will learn: right create_index >> inspect_query >> wrong create_index
+    GRPO reward function — calls /step on live environment.
+    Returns list of float rewards, one per completion.
     """
     rewards = []
+    task_ids = kwargs.get("task_ids", ["easy_s001"] * len(prompts))
 
     for i, (prompt, completion) in enumerate(zip(prompts, completions)):
         try:
-            # Get text
-            if isinstance(completion, list):
-                text = completion[0].get("content", "") if completion else ""
-            else:
-                text = str(completion)
-
-            # Pick scenario (rotate through all)
-            scenario = ALL_SCENARIOS[i % len(ALL_SCENARIOS)]
-
-            # Parse action
+            text = completion[0]["content"] if isinstance(completion, list) else str(completion)
             action = parse_action(text)
 
-            if action is None:
-                # Invalid JSON output — penalize
-                rewards.append(0.001)
-                print(f"  [REWARD] scenario={scenario['id']} | "
-                      f"INVALID JSON | score=0.001")
-                continue
-
-            # Compute reward locally
-            score, improvement, milestone = compute_local_reward(action, scenario)
+            task_id = task_ids[i] if i < len(task_ids) else "easy_s001"
+            requests.post(
+                f"{ENV_URL}/reset",
+                json={"task_id": task_id},
+                timeout=15,
+            )
+
+            resp = requests.post(
+                f"{ENV_URL}/step",
+                json=action,
+                timeout=15,
+            )
+            env_response = resp.json()
+            score = compute_reward(
+                action_dict=action,
+                obs=env_response.get("observation", {}),
+                env_response=env_response,
+            )
+            score = max(0.0, min(1.0, float(score)))
             rewards.append(score)
 
-            print(f"  [REWARD] scenario={scenario['id']} | "
-                  f"action={action.get('action_type')} | "
-                  f"improvement=+{improvement:.1f}pts | "
-                  f"milestone=+{milestone:.2f} | "
-                  f"score={score:.3f}")
-
         except Exception as e:
-            print(f"  [REWARD] Error: {e}")
-            rewards.append(0.001)
+            print(f"Reward fn error: {e}")
+            rewards.append(0.0)
 
     return rewards
 
 
-# ── Build dataset ─────────────────────────────────────────────
-def build_dataset() -> Dataset:
-    examples = []
-    for i, s in enumerate(ALL_SCENARIOS):
-        tables_str  = json.dumps(s.get("tables", []))
-        queries_str = json.dumps(s.get("slow_queries", []))
-        hints_str   = json.dumps(s.get("missing_index_hints", []))
-
-        prompt = (
-            f"{SYSTEM_PROMPT}\n\n"
-            f"=== DATABASE STATE ===\n"
-            f"Scenario: {s['id']}\n"
-            f"Description: {s.get('description','')}\n"
-            f"Tables: {tables_str}\n"
-            f"Slow Queries: {queries_str}\n"
-            f"Missing Index Hints: {hints_str}\n"
-            f"Performance: {s.get('performance_score_baseline',0)}/100 "
-            f"→ Target: {s.get('target_score',85)}/100\n\n"
-            f"What action should you take? Output JSON only:"
-        )
-        examples.append({
-            "prompt":      prompt,
-            "scenario_id": s["id"],
-        })
+# ─────────────────────────────────────────────
+#  BUILD TRAINING DATASET
+# ─────────────────────────────────────────────
 
-    print(f"  ✅ Dataset: {len(examples)} examples")
-    return Dataset.from_list(examples)
+def build_dataset():
+    """Build training examples from all 15 Round 2 scenarios."""
+    scenarios = []
 
+    # Load all scenario files
+    for fname in ["dataset/easy_scenarios.json",
+                  "dataset/medium_scenarios.json",
+                  "dataset/hard_scenarios.json"]:
+        try:
+            with open(fname) as f:
+                scenarios.extend(json.load(f))
+        except FileNotFoundError:
+            print(f"{fname} not found, skipping")
 
-# ── Generate plots ────────────────────────────────────────────
-def generate_plots(trainer):
-    import matplotlib
-    matplotlib.use("Agg")
-    import matplotlib.pyplot as plt
+    if not scenarios:
+        # Fallback: fetch from live environment
+        resp = requests.get(f"{ENV_URL}/tasks", timeout=15)
+        tasks = resp.json().get("tasks", [])
+        scenarios = [{"id": t["id"], "description": t["description"]} for t in tasks]
 
-    logs = [l for l in trainer.state.log_history if "loss" in l]
-    if not logs:
-        print("⚠️ No logs for plotting")
-        return
+    examples = []
+    for s in scenarios:
+        prompt = f"""{SYSTEM_PROMPT}
 
-    steps  = [l.get("step", i) for i,l in enumerate(logs)]
-    losses = [l.get("loss", 0) for l in logs]
+Current Database State:
+- Scenario: {s.get('id', 'unknown')}
+- Description: {s.get('description', '')}
+- Tables: {json.dumps(s.get('tables', []))}
+- Slow Queries: {json.dumps(s.get('slow_queries', []))}
+- Performance Score: {s.get('performance_score_baseline', 0)} / 100
+- Target Score: {s.get('target_score', 85)}
 
-    fig, ax = plt.subplots(1, 1, figsize=(8, 4))
-    fig.suptitle("GRPO Training — SQL Database Engineer Agent",
-                 fontsize=13, fontweight="bold")
+What is your next action?"""
 
-    ax.plot(steps, losses, "b-o", lw=2, ms=4)
-    ax.set_xlabel("Training Step")
-    ax.set_ylabel("Loss")
-    ax.set_title("Training Loss (↓ = model learning DBA pattern)")
-    ax.grid(True, alpha=0.3)
+        examples.append({
+            "prompt":   prompt,
+            "task_id":  s.get("id", "easy_s001"),
+        })
 
-    if losses:
-        ax.annotate(f"Start: {losses[0]:.4f}",
-                   xy=(steps[0], losses[0]),
-                   xytext=(steps[0]+1, losses[0]*1.1),
-                   fontsize=9, color="red")
-        ax.annotate(f"End: {losses[-1]:.4f}",
-                   xy=(steps[-1], losses[-1]),
-                   xytext=(steps[-1]-5, losses[-1]*1.1),
-                   fontsize=9, color="green")
+    print(f"Built {len(examples)} training examples")
+    return Dataset.from_list(examples)
 
-    plt.tight_layout()
-    plt.savefig("loss_curve.png", dpi=150, bbox_inches="tight")
-    print("✅ loss_curve.png saved")
-    print(f"   Loss: {losses[0]:.4f} → {losses[-1]:.4f}")
 
+# ─────────────────────────────────────────────
+#  MAIN TRAINING
+# ─────────────────────────────────────────────
 
-# ── Main ──────────────────────────────────────────────────────
 def train():
-    if not ALL_SCENARIOS:
-        print("❌ No scenarios found. Check dataset/ folder.")
-        sys.exit(1)
+    if not UNSLOTH_AVAILABLE:
+        print("Cannot train — Unsloth not installed")
+        print("Run: pip install unsloth trl transformers datasets accelerate")
+        return
+
+    print(f"🚀 Loading model: {MODEL_NAME}")
+    print(f"🌐 Environment: {ENV_URL}")
 
-    print(f"⏳ Loading {MODEL_NAME}...")
+    # Load model with Unsloth 4-bit quantization
     model, tokenizer = FastLanguageModel.from_pretrained(
         model_name     = MODEL_NAME,
-        max_seq_length = 2048,
+        max_seq_length = 4096,
         load_in_4bit   = True,
         token          = HF_TOKEN or None,
     )
+
+    # Add LoRA adapters
     model = FastLanguageModel.get_peft_model(
         model,
-        r=16, lora_alpha=16,
-        target_modules=["q_proj","k_proj","v_proj","o_proj",
-                        "gate_proj","up_proj","down_proj"],
-        lora_dropout=0, bias="none",
-        use_gradient_checkpointing="unsloth",
-        random_state=42,
+        r              = 16,
+        lora_alpha     = 16,
+        target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                          "gate_proj", "up_proj", "down_proj"],
+        lora_dropout   = 0,
+        bias           = "none",
+        use_gradient_checkpointing = "unsloth",
     )
-    print("✅ Model ready\n")
 
+    # Build dataset
     dataset = build_dataset()
 
+    # GRPO config
     config = GRPOConfig(
         output_dir                  = OUTPUT_DIR,
-        max_steps                   = MAX_STEPS,
-        per_device_train_batch_size = 1,
-        gradient_accumulation_steps = 4,
-        learning_rate               = 5e-6,
-        max_completion_length       = 150,
-        num_generations             = 4,   # compare 4 actions per step
-        temperature                 = 1.0,
-        logging_steps               = 1,
-        save_steps                  = 25,
-        save_total_limit            = 3,
+        num_train_epochs            = 3,
+        per_device_train_batch_size = 2,
+        gradient_accumulation_steps = 8,
+        learning_rate               = 5e-5,
+        max_completion_length       = 256,
+        num_generations             = 4,
+        logging_steps               = 10,
+        save_steps                  = 50,
         warmup_ratio                = 0.1,
         report_to                   = "none",
-        remove_unused_columns       = False,
     )
 
+    # Reward function wrapper
+    def reward_wrapper(prompts, completions, **kwargs):
+        task_ids = [ex.get("task_id", "easy_s001") for ex in kwargs.get("batch", [])]
+        return reward_fn(prompts, completions, task_ids=task_ids)
+
+    # Train
     trainer = GRPOTrainer(
-        model         = model,
-        tokenizer     = tokenizer,
-        reward_funcs  = reward_fn,
-        args          = config,
+        model        = model,
+        tokenizer    = tokenizer,
+        reward_funcs = reward_wrapper,
+        args         = config,
         train_dataset = dataset,
     )
 
-    print(f"🏋️  GRPO training — {MAX_STEPS} steps")
-    print("Watch for: improvement > 0 and score > 0.5 on create_index\n")
+    print("🏋️  Starting GRPO training...")
     trainer.train()
-    print("\n✅ Training complete!")
 
-    Path(OUTPUT_DIR).mkdir(parents=True, exist_ok=True)
+    # Save
     model.save_pretrained(f"{OUTPUT_DIR}/final")
     tokenizer.save_pretrained(f"{OUTPUT_DIR}/final")
-    print(f"✅ Saved to {OUTPUT_DIR}/final")
-
-    generate_plots(trainer)
-
-    print("\n" + "="*50)
-    print("NEXT: python training/evaluate_agent.py")
-    print("THEN: git add loss_curve.png reward_curve.png")
-    print("="*50)
+    print(f"Training complete. Model saved to {OUTPUT_DIR}/final")
 
 
 if __name__ == "__main__":
-    train()
+    train()