{ "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": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }