two-step learning

tetris_training.ipynb

@@ -18,11 +18,23 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {
-        "id": "PHNUG6nYFG8L"
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PHNUG6nYFG8L",
+        "outputId": "0bc2d43c-0e75-4747-ff0a-43f4bf04ce11"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.49.2\n"
+          ]
+        }
+      ],
       "source": [
         "# Cell 1: Install dependencies\n",
         "!pip install peft accelerate -q\n",
@@ -32,11 +44,65 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "metadata": {
-        "id": "4CEG1_JMFG8L"
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 225,
+          "referenced_widgets": [
+            "f92c4ee10cc54940bb03b601032caa3b",
+            "dcb73d47d9e74a47b2df0958c244a970",
+            "f423256c65cc4d10a0406638c869f966",
+            "47be5c68b01e49fd811e78d28ab74982",
+            "12a720fa3c824ad697e3c907b64d703d",
+            "194015ffd14c44c0a9248c123521f3bd",
+            "2f41d69a29374e68bf298817ef58f316",
+            "23b8e87348af484bb33902b46a5f92b3",
+            "c89e1a38a59c41eaa57f87034a064348",
+            "b22f880ebfac47c383ce96a66f6a55c8",
+            "342a026486b24535a1b9d94f4b21cbe1"
+          ]
+        },
+        "id": "4CEG1_JMFG8L",
+        "outputId": "e4b98f14-8e32-41d0-a52c-3f9a94c3c117"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n",
+            "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+            "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading weights:   0%|          | 0/338 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f92c4ee10cc54940bb03b601032caa3b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "trainable params: 18,464,768 || all params: 1,562,179,072 || trainable%: 1.1820\n"
+          ]
+        }
+      ],
       "source": [
         "# Cell 2: Load Qwen2.5-3B-Instruct + LoRA (INT4 / QLoRA)\n",
         "import torch\n",
@@ -77,11 +143,31 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 3,
       "metadata": {
-        "id": "D3HDc8_3FG8L"
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "D3HDc8_3FG8L",
+        "outputId": "15ee2cfc-bca7-4b65-cb82-c7bf43c3dd49"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Game engine v0.5.1\n",
+            "  'L' -> token_id 43\n",
+            "  'R' -> token_id 49\n",
+            "  'C' -> token_id 34\n",
+            "  'W' -> token_id 54\n",
+            "  'D' -> token_id 35\n",
+            "  'S' -> token_id 50\n",
+            "\n",
+            "Game engine loaded. Action tokens mapped.\n"
+          ]
+        }
+      ],
       "source": [
         "# Cell 3: Game engine + constants + prompt builder\n",
         "import random\n",
@@ -152,11 +238,25 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 4,
       "metadata": {
-        "id": "3O3yawZBFG8M"
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3O3yawZBFG8M",
+        "outputId": "badc191d-b631-45a5-90a9-b92862df6e24"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Smoke test: playing 1 game...\n",
+            "Reward: -526.0, Steps: 118, Pieces: 11, Lines: 0\n",
+            "Training functions ready.\n"
+          ]
+        }
+      ],
       "source": [
         "# Cell 4: Core training functions — batched play + train\n",
         "import time as _time\n",
@@ -416,11 +516,54 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 5,
       "metadata": {
-        "id": "JdcKswz5FG8M"
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JdcKswz5FG8M",
+        "outputId": "3fbd2913-d654-4762-d5a4-86635596c3ce"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "=== UNTRAINED MODEL ===\n",
+            "\n",
+            "Pieces: 7/200\n",
+            "Total actions: 88\n",
+            "Lines cleared: 0\n",
+            "Game reward: -522.5\n",
+            "\n",
+            "Final board:\n",
+            "+----------+\n",
+            "|..####....|\n",
+            "|..#.......|\n",
+            "|..#.......|\n",
+            "|..##......|\n",
+            "|..##......|\n",
+            "|..##......|\n",
+            "|.##.......|\n",
+            "|.##.......|\n",
+            "|.##.......|\n",
+            "|.##.......|\n",
+            "|.#........|\n",
+            "|##........|\n",
+            "|.#........|\n",
+            "|##........|\n",
+            "|.#........|\n",
+            "|.#####....|\n",
+            "|####......|\n",
+            "|..#.......|\n",
+            "|..#.......|\n",
+            "|..##......|\n",
+            "+----------+\n",
+            "\n",
+            "Untrained reward: -522.5\n"
+          ]
+        }
+      ],
       "source": [
         "# Cell 5: Demo — UNTRAINED model plays one game\n",
         "print(\"=== UNTRAINED MODEL ===\\n\")\n",
@@ -441,42 +584,117 @@
     {
       "cell_type": "code",
       "source": [
-        "# Cell 6: Training loop — 100 iterations of per-piece GRPO\n",
+        "# Cell 6: Two-phase curriculum training\n",
         "import time\n",
         "\n",
         "optimizer = torch.optim.AdamW(\n",
         "    [p for p in model.parameters() if p.requires_grad],\n",
-        "    lr=5e-6,\n",
+        "    lr=1e-4,\n",
         "    weight_decay=0.01,\n",
         ")\n",
         "\n",
         "history = []\n",
         "\n",
-        "print(\"Starting per-piece GRPO training...\")\n",
-        "print(f\"Config: {GAMES_PER_ITER} games/iter, max {MAX_PIECES_PER_GAME} pieces/game, \"\n",
-        "      f\"max {MAX_ACTIONS_PER_PIECE} tokens/piece, {NUM_ITERATIONS} iterations\\n\")\n",
+        "# =============================================\n",
+        "# PHASE 1: Learn to place pieces (5 pieces max, 50 iterations)\n",
+        "# =============================================\n",
+        "MAX_PIECES_PER_GAME = 5\n",
+        "NUM_ITERATIONS_P1 = 50\n",
+        "\n",
+        "print(\"=\" * 60)\n",
+        "print(f\"PHASE 1: Learning basics — max {MAX_PIECES_PER_GAME} pieces, {NUM_ITERATIONS_P1} iterations\")\n",
+        "print(\"=\" * 60)\n",
         "\n",
-        "for iteration in range(NUM_ITERATIONS):\n",
+        "for iteration in range(NUM_ITERATIONS_P1):\n",
+        "    t0 = time.time()\n",
         "    stats = train_one_iteration(model, optimizer, seed=iteration)\n",
+        "    t1 = time.time()\n",
         "    history.append(stats)\n",
         "\n",
-        "    print(f\"[Iter {iteration:3d}] \"\n",
+        "    print(f\"[P1 {iteration:3d}] \"\n",
         "          f\"reward={stats['mean_reward']:+8.1f} \"\n",
         "          f\"std={stats['std_reward']:6.1f} \"\n",
         "          f\"loss={stats['loss']:7.3f} \"\n",
         "          f\"steps={stats['avg_steps']:5.1f} \"\n",
         "          f\"lines={stats['avg_lines']:4.1f} \"\n",
         "          f\"pieces={stats['avg_pieces']:4.1f} \"\n",
-        "          f\"roll={stats['t_rollout']:.1f}s \"\n",
-        "          f\"upd={stats['t_update']:.1f}s\")\n",
+        "          f\"t={t1-t0:.0f}s\")\n",
         "\n",
-        "print(\"\\nTraining complete!\")\n"
+        "print(f\"\\nPhase 1 complete! Final avg reward: {history[-1]['mean_reward']:+.1f}\")\n",
+        "\n",
+        "# =============================================\n",
+        "# PHASE 2: Full game (200 pieces max, 200 iterations)\n",
+        "# =============================================\n",
+        "MAX_PIECES_PER_GAME = 200\n",
+        "NUM_ITERATIONS_P2 = 200\n",
+        "\n",
+        "print(\"\\n\" + \"=\" * 60)\n",
+        "print(f\"PHASE 2: Full game — max {MAX_PIECES_PER_GAME} pieces, {NUM_ITERATIONS_P2} iterations\")\n",
+        "print(\"=\" * 60)\n",
+        "\n",
+        "for iteration in range(NUM_ITERATIONS_P2):\n",
+        "    t0 = time.time()\n",
+        "    stats = train_one_iteration(model, optimizer, seed=NUM_ITERATIONS_P1 + iteration)\n",
+        "    t1 = time.time()\n",
+        "    history.append(stats)\n",
+        "\n",
+        "    print(f\"[P2 {iteration:3d}] \"\n",
+        "          f\"reward={stats['mean_reward']:+8.1f} \"\n",
+        "          f\"std={stats['std_reward']:6.1f} \"\n",
+        "          f\"loss={stats['loss']:7.3f} \"\n",
+        "          f\"steps={stats['avg_steps']:5.1f} \"\n",
+        "          f\"lines={stats['avg_lines']:4.1f} \"\n",
+        "          f\"pieces={stats['avg_pieces']:4.1f} \"\n",
+        "          f\"t={t1-t0:.0f}s\")\n",
+        "\n",
+        "print(f\"\\nPhase 2 complete! Final avg reward: {history[-1]['mean_reward']:+.1f}\")\n",
+        "print(\"Training complete!\")\n"
       ],
       "metadata": {
-        "id": "qBMZr8ONcnHf"
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qBMZr8ONcnHf",
+        "outputId": "48986186-3eb0-4b37-8719-591173ee6ee4"
       },
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "============================================================\n",
+            "PHASE 1: Learning basics — max 5 pieces, 50 iterations\n",
+            "============================================================\n",
+            "[P1   0] reward=  -227.3 std=  20.4 loss=  0.247 steps= 70.1 lines= 0.0 pieces= 5.0 t=24s\n",
+            "[P1   1] reward=  -224.3 std=  43.9 loss=  0.335 steps= 77.2 lines= 0.0 pieces= 5.0 t=24s\n",
+            "[P1   2] reward=  -242.3 std=  48.1 loss=  0.400 steps= 74.2 lines= 0.0 pieces= 5.0 t=24s\n",
+            "[P1   3] reward=  -148.2 std=  55.6 loss=  0.043 steps= 62.8 lines= 0.0 pieces= 5.0 t=25s\n",
+            "[P1   4] reward=  -141.8 std=  48.8 loss=  0.072 steps= 42.9 lines= 0.0 pieces= 5.0 t=24s\n",
+            "[P1   5] reward=  -116.5 std=  50.9 loss=  0.218 steps= 29.9 lines= 0.0 pieces= 5.0 t=21s\n",
+            "[P1   6] reward=  -119.1 std=  35.3 loss= -0.372 steps= 27.2 lines= 0.0 pieces= 5.0 t=21s\n",
+            "[P1   7] reward=  -290.9 std=  92.3 loss= -0.205 steps= 21.6 lines= 0.0 pieces= 5.0 t=17s\n",
+            "[P1   8] reward=  -210.5 std=  57.0 loss=  0.141 steps= 20.4 lines= 0.0 pieces= 5.0 t=16s\n",
+            "[P1   9] reward=  -289.8 std=  71.2 loss= -0.015 steps= 20.4 lines= 0.0 pieces= 5.0 t=16s\n",
+            "[P1  10] reward=  -198.0 std=  63.3 loss= -0.125 steps= 17.2 lines= 0.0 pieces= 5.0 t=16s\n",
+            "[P1  11] reward=  -472.2 std=  79.1 loss=  0.250 steps= 17.2 lines= 0.0 pieces= 5.0 t=16s\n",
+            "[P1  12] reward=  -191.3 std=   9.0 loss= -0.395 steps= 14.8 lines= 0.0 pieces= 5.0 t=15s\n",
+            "[P1  13] reward=  -300.5 std=  45.6 loss= -0.164 steps= 18.5 lines= 0.0 pieces= 5.0 t=16s\n",
+            "[P1  14] reward=  -142.0 std=  18.7 loss= -0.021 steps= 15.2 lines= 0.0 pieces= 5.0 t=16s\n",
+            "[P1  15] reward=  -212.0 std=   0.0 loss=  0.000 steps= 15.0 lines= 0.0 pieces= 5.0 t=3s\n",
+            "[P1  16] reward=  -252.1 std=   0.0 loss= -0.022 steps= 16.0 lines= 0.0 pieces= 5.0 t=15s\n",
+            "[P1  17] reward=  -321.6 std=  15.8 loss=  0.192 steps= 17.1 lines= 0.0 pieces= 5.0 t=15s\n",
+            "[P1  18] reward=  -209.7 std=  72.1 loss= -0.009 steps= 16.1 lines= 0.0 pieces= 5.0 t=16s\n",
+            "[P1  19] reward=  -131.7 std=   5.1 loss= -0.057 steps= 19.6 lines= 0.0 pieces= 5.0 t=16s\n",
+            "[P1  20] reward=  -548.3 std=  28.3 loss= -0.256 steps= 23.2 lines= 0.0 pieces= 5.0 t=16s\n",
+            "[P1  21] reward=  -323.0 std=   0.0 loss=  0.000 steps= 20.0 lines= 0.0 pieces= 5.0 t=4s\n",
+            "[P1  22] reward=  -218.3 std=  22.6 loss=  0.597 steps= 26.1 lines= 0.0 pieces= 5.0 t=18s\n",
+            "[P1  23] reward=  -175.1 std=  37.2 loss=  0.306 steps= 25.4 lines= 0.0 pieces= 5.0 t=18s\n",
+            "[P1  24] reward=  -273.4 std=  63.1 loss= -0.166 steps= 37.4 lines= 0.0 pieces= 5.0 t=19s\n",
+            "[P1  25] reward=  -232.6 std=  77.0 loss= -0.009 steps= 35.0 lines= 0.0 pieces= 5.0 t=19s\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "code",
@@ -573,7 +791,353 @@
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       "machine_shape": "hm"
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