{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f754c96",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from xautodl import spaces\n",
    "from xautodl.xlayers import super_core\n",
    "\n",
    "def _create_stel(input_dim, output_dim, order):\n",
    "    return super_core.SuperSequential(\n",
    "        super_core.SuperLinear(input_dim, output_dim),\n",
    "        super_core.SuperTransformerEncoderLayer(\n",
    "            output_dim,\n",
    "            num_heads=spaces.Categorical(2, 4, 6),\n",
    "            mlp_hidden_multiplier=spaces.Categorical(1, 2, 4),\n",
    "            order=order,\n",
    "        ),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81d42f4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch, seq_dim, input_dim = 1, 4, 6\n",
    "order = super_core.LayerOrder.PreNorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8056b37c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SuperSequential(\n",
      "  (0): SuperSequential(\n",
      "    (0): SuperLinear(in_features=6, out_features=Categorical(candidates=[12, 24, 36], default_index=None), bias=True)\n",
      "    (1): SuperTransformerEncoderLayer(\n",
      "      (norm1): SuperLayerNorm1D(shape=Categorical(candidates=[12, 24, 36], default_index=None), eps=1e-06, elementwise_affine=True)\n",
      "      (mha): SuperSelfAttention(\n",
      "        input_dim=Categorical(candidates=[12, 24, 36], default_index=None), proj_dim=Categorical(candidates=[12, 24, 36], default_index=None), num_heads=Categorical(candidates=[2, 4, 6], default_index=None), mask=False, infinity=1000000000.0\n",
      "        (q_fc): SuperLinear(in_features=Categorical(candidates=[12, 24, 36], default_index=None), out_features=Categorical(candidates=[12, 24, 36], default_index=None), bias=False)\n",
      "        (k_fc): SuperLinear(in_features=Categorical(candidates=[12, 24, 36], default_index=None), out_features=Categorical(candidates=[12, 24, 36], default_index=None), bias=False)\n",
      "        (v_fc): SuperLinear(in_features=Categorical(candidates=[12, 24, 36], default_index=None), out_features=Categorical(candidates=[12, 24, 36], default_index=None), bias=False)\n",
      "        (attn_drop): SuperDrop(p=0.0, dims=[-1, -1, -1, -1], recover=True)\n",
      "      )\n",
      "      (drop): Dropout(p=0.0, inplace=False)\n",
      "      (norm2): SuperLayerNorm1D(shape=Categorical(candidates=[12, 24, 36], default_index=None), eps=1e-06, elementwise_affine=True)\n",
      "      (mlp): SuperMLPv2(\n",
      "        in_features=Categorical(candidates=[12, 24, 36], default_index=None), hidden_multiplier=Categorical(candidates=[1, 2, 4], default_index=None), out_features=Categorical(candidates=[12, 24, 36], default_index=None), drop=None, fc1 -> act -> drop -> fc2 -> drop,\n",
      "        (_params): ParameterDict(\n",
      "            (fc1_super_weight): Parameter containing: [torch.FloatTensor of size 144x36]\n",
      "            (fc1_super_bias): Parameter containing: [torch.FloatTensor of size 144]\n",
      "            (fc2_super_weight): Parameter containing: [torch.FloatTensor of size 36x144]\n",
      "            (fc2_super_bias): Parameter containing: [torch.FloatTensor of size 36]\n",
      "        )\n",
      "        (act): GELU()\n",
      "        (drop): Dropout(p=0.0, inplace=False)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (1): SuperSequential(\n",
      "    (0): SuperLinear(in_features=Categorical(candidates=[12, 24, 36], default_index=None), out_features=Categorical(candidates=[24, 36, 48], default_index=None), bias=True)\n",
      "    (1): SuperTransformerEncoderLayer(\n",
      "      (norm1): SuperLayerNorm1D(shape=Categorical(candidates=[24, 36, 48], default_index=None), eps=1e-06, elementwise_affine=True)\n",
      "      (mha): SuperSelfAttention(\n",
      "        input_dim=Categorical(candidates=[24, 36, 48], default_index=None), proj_dim=Categorical(candidates=[24, 36, 48], default_index=None), num_heads=Categorical(candidates=[2, 4, 6], default_index=None), mask=False, infinity=1000000000.0\n",
      "        (q_fc): SuperLinear(in_features=Categorical(candidates=[24, 36, 48], default_index=None), out_features=Categorical(candidates=[24, 36, 48], default_index=None), bias=False)\n",
      "        (k_fc): SuperLinear(in_features=Categorical(candidates=[24, 36, 48], default_index=None), out_features=Categorical(candidates=[24, 36, 48], default_index=None), bias=False)\n",
      "        (v_fc): SuperLinear(in_features=Categorical(candidates=[24, 36, 48], default_index=None), out_features=Categorical(candidates=[24, 36, 48], default_index=None), bias=False)\n",
      "        (attn_drop): SuperDrop(p=0.0, dims=[-1, -1, -1, -1], recover=True)\n",
      "      )\n",
      "      (drop): Dropout(p=0.0, inplace=False)\n",
      "      (norm2): SuperLayerNorm1D(shape=Categorical(candidates=[24, 36, 48], default_index=None), eps=1e-06, elementwise_affine=True)\n",
      "      (mlp): SuperMLPv2(\n",
      "        in_features=Categorical(candidates=[24, 36, 48], default_index=None), hidden_multiplier=Categorical(candidates=[1, 2, 4], default_index=None), out_features=Categorical(candidates=[24, 36, 48], default_index=None), drop=None, fc1 -> act -> drop -> fc2 -> drop,\n",
      "        (_params): ParameterDict(\n",
      "            (fc1_super_weight): Parameter containing: [torch.FloatTensor of size 192x48]\n",
      "            (fc1_super_bias): Parameter containing: [torch.FloatTensor of size 192]\n",
      "            (fc2_super_weight): Parameter containing: [torch.FloatTensor of size 48x192]\n",
      "            (fc2_super_bias): Parameter containing: [torch.FloatTensor of size 48]\n",
      "        )\n",
      "        (act): GELU()\n",
      "        (drop): Dropout(p=0.0, inplace=False)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (2): SuperSequential(\n",
      "    (0): SuperLinear(in_features=Categorical(candidates=[24, 36, 48], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=True)\n",
      "    (1): SuperTransformerEncoderLayer(\n",
      "      (norm1): SuperLayerNorm1D(shape=Categorical(candidates=[36, 72, 100], default_index=None), eps=1e-06, elementwise_affine=True)\n",
      "      (mha): SuperSelfAttention(\n",
      "        input_dim=Categorical(candidates=[36, 72, 100], default_index=None), proj_dim=Categorical(candidates=[36, 72, 100], default_index=None), num_heads=Categorical(candidates=[2, 4, 6], default_index=None), mask=False, infinity=1000000000.0\n",
      "        (q_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
      "        (k_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
      "        (v_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
      "        (attn_drop): SuperDrop(p=0.0, dims=[-1, -1, -1, -1], recover=True)\n",
      "      )\n",
      "      (drop): Dropout(p=0.0, inplace=False)\n",
      "      (norm2): SuperLayerNorm1D(shape=Categorical(candidates=[36, 72, 100], default_index=None), eps=1e-06, elementwise_affine=True)\n",
      "      (mlp): SuperMLPv2(\n",
      "        in_features=Categorical(candidates=[36, 72, 100], default_index=None), hidden_multiplier=Categorical(candidates=[1, 2, 4], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), drop=None, fc1 -> act -> drop -> fc2 -> drop,\n",
      "        (_params): ParameterDict(\n",
      "            (fc1_super_weight): Parameter containing: [torch.FloatTensor of size 400x100]\n",
      "            (fc1_super_bias): Parameter containing: [torch.FloatTensor of size 400]\n",
      "            (fc2_super_weight): Parameter containing: [torch.FloatTensor of size 100x400]\n",
      "            (fc2_super_bias): Parameter containing: [torch.FloatTensor of size 100]\n",
      "        )\n",
      "        (act): GELU()\n",
      "        (drop): Dropout(p=0.0, inplace=False)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "out1_dim = spaces.Categorical(12, 24, 36)\n",
    "out2_dim = spaces.Categorical(24, 36, 48)\n",
    "out3_dim = spaces.Categorical(36, 72, 100)\n",
    "layer1 = _create_stel(input_dim, out1_dim, order)\n",
    "layer2 = _create_stel(out1_dim, out2_dim, order)\n",
    "layer3 = _create_stel(out2_dim, out3_dim, order)\n",
    "model = super_core.SuperSequential(layer1, layer2, layer3)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fd53a7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/Users/xuanyidong/anaconda3/lib/python3.8/site-packages/xautodl-0.9.9-py3.8.egg/xautodl/xlayers/super_transformer.py\u001b[0m(116)\u001b[0;36mforward_raw\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m    114 \u001b[0;31m              \u001b[0;32mimport\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    115 \u001b[0;31m            \u001b[0;31m# feed-forward layer -- MLP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m--> 116 \u001b[0;31m            \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    117 \u001b[0;31m            \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmlp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    118 \u001b[0;31m        \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_order\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mLayerOrder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPostNorm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "ipdb> print(self)\n",
      "SuperTransformerEncoderLayer(\n",
      "  (norm1): SuperLayerNorm1D(shape=Categorical(candidates=[36, 72, 100], default_index=None), eps=1e-06, elementwise_affine=True)\n",
      "  (mha): SuperSelfAttention(\n",
      "    input_dim=Categorical(candidates=[36, 72, 100], default_index=None), proj_dim=Categorical(candidates=[36, 72, 100], default_index=None), num_heads=Categorical(candidates=[2, 4, 6], default_index=None), mask=False, infinity=1000000000.0\n",
      "    (q_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
      "    (k_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
      "    (v_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
      "    (attn_drop): SuperDrop(p=0.0, dims=[-1, -1, -1, -1], recover=True)\n",
      "  )\n",
      "  (drop): Dropout(p=0.0, inplace=False)\n",
      "  (norm2): SuperLayerNorm1D(shape=Categorical(candidates=[36, 72, 100], default_index=None), eps=1e-06, elementwise_affine=True)\n",
      "  (mlp): SuperMLPv2(\n",
      "    in_features=Categorical(candidates=[36, 72, 100], default_index=None), hidden_multiplier=Categorical(candidates=[1, 2, 4], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), drop=None, fc1 -> act -> drop -> fc2 -> drop,\n",
      "    (_params): ParameterDict(\n",
      "        (fc1_super_weight): Parameter containing: [torch.FloatTensor of size 400x100]\n",
      "        (fc1_super_bias): Parameter containing: [torch.FloatTensor of size 400]\n",
      "        (fc2_super_weight): Parameter containing: [torch.FloatTensor of size 100x400]\n",
      "        (fc2_super_bias): Parameter containing: [torch.FloatTensor of size 100]\n",
      "    )\n",
      "    (act): GELU()\n",
      "    (drop): Dropout(p=0.0, inplace=False)\n",
      "  )\n",
      ")\n",
      "ipdb> print(inputs.shape)\n",
      "torch.Size([1, 4, 100])\n",
      "ipdb> print(x.shape)\n",
      "torch.Size([1, 4, 96])\n",
      "ipdb> print(self.mha)\n",
      "SuperSelfAttention(\n",
      "  input_dim=Categorical(candidates=[36, 72, 100], default_index=None), proj_dim=Categorical(candidates=[36, 72, 100], default_index=None), num_heads=Categorical(candidates=[2, 4, 6], default_index=None), mask=False, infinity=1000000000.0\n",
      "  (q_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
      "  (k_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
      "  (v_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
      "  (attn_drop): SuperDrop(p=0.0, dims=[-1, -1, -1, -1], recover=True)\n",
      ")\n",
      "ipdb> print(self.mha.candidate)\n",
      "*** AttributeError: 'SuperSelfAttention' object has no attribute 'candidate'\n",
      "ipdb> print(self.mha.abstract_candidate)\n",
      "*** AttributeError: 'SuperSelfAttention' object has no attribute 'abstract_candidate'\n",
      "ipdb> print(self.mha._abstract_child)\n",
      "None\n",
      "ipdb> print(self.abstract_child)\n",
      "None\n",
      "ipdb> print(self.abstract_child.abstract_child)\n",
      "*** AttributeError: 'NoneType' object has no attribute 'abstract_child'\n",
      "ipdb> print(self.mha)\n",
      "SuperSelfAttention(\n",
      "  input_dim=Categorical(candidates=[36, 72, 100], default_index=None), proj_dim=Categorical(candidates=[36, 72, 100], default_index=None), num_heads=Categorical(candidates=[2, 4, 6], default_index=None), mask=False, infinity=1000000000.0\n",
      "  (q_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
      "  (k_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
      "  (v_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
      "  (attn_drop): SuperDrop(p=0.0, dims=[-1, -1, -1, -1], recover=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "inputs = torch.rand(batch, seq_dim, input_dim)\n",
    "outputs = model(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05332b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "abstract_space = model.abstract_search_space\n",
    "abstract_space.clean_last()\n",
    "abstract_child = abstract_space.random(reuse_last=True)\n",
    "# print(\"The abstract child program is:\\n{:}\".format(abstract_child))\n",
    "model.enable_candidate()\n",
    "model.set_super_run_type(super_core.SuperRunMode.Candidate)\n",
    "model.apply_candidate(abstract_child)\n",
    "outputs = model(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3289f938",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(outputs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36951cdf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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