tensor-sensor
1124 строки · 61.5 Кб
1{2"cells": [3{4"cell_type": "code",5"execution_count": 8,6"metadata": {},7"outputs": [8{9"ename": "RuntimeError",10"evalue": "mat1 and mat2 shapes cannot be multiplied (10x20 and 10x500)\nCause: @ on tensor operand W w/shape [10, 20] and operand X.T w/shape [10, 500]",11"output_type": "error",12"traceback": [13"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",14"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",15"\u001b[0;32m/var/folders/93/9kzk2ccm8xj8k70059b28jk80000gp/T/ipykernel_55352/3024069779.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtsensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexplain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'/tmp/mm.pdf'\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[0;32m----> 7\u001b[0;31m \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mW\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",16"\u001b[0;31mRuntimeError\u001b[0m: mat1 and mat2 shapes cannot be multiplied (10x20 and 10x500)\nCause: @ on tensor operand W w/shape [10, 20] and operand X.T w/shape [10, 500]"17]18}19],20"source": [21"import torch\n",22"import tsensor\n",23"W = torch.rand(10,20)\n",24"b = torch.rand(10,1)\n",25"X = torch.rand(500,10)\n",26"with tsensor.explain(savefig='/tmp/mm.svg'):\n",27" Y = W @ X.T + b"28]29},30{31"cell_type": 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46.390625 45.40625 43.1875 Q 47.5625 39.984375 48.625 35.46875 Q 49.703125 30.953125 49.703125 25.390625 z M 41.015625 25 Q 41.015625 28.859375 40.453125 32.078125 Q 39.890625 35.296875 38.671875 37.59375 Q 37.453125 39.890625 35.546875 41.1875 Q 33.640625 42.484375 31 42.484375 Q 29.390625 42.484375 27.734375 41.96875 Q 26.078125 41.453125 24.296875 40.25 Q 22.515625 39.0625 20.53125 37.078125 Q 18.5625 35.109375 16.3125 32.171875 L 16.3125 8.40625 Q 18.796875 7.421875 21.375 6.859375 Q 23.96875 6.296875 26.3125 6.296875 Q 29.25 6.296875 31.90625 7.21875 Q 34.578125 8.15625 36.59375 10.328125 Q 38.625 12.5 39.8125 16.078125 Q 41.015625 19.671875 41.015625 25 z \" id=\"Consolas-98\"/>\n",195" </defs>\n",196" <use xlink:href=\"#Consolas-98\"/>\n",197" </g>\n",198" </g>\n",199" <g id=\"text_16\">\n",200" <!-- , -->\n",201" <g style=\"fill:#444443;\" transform=\"translate(125.9964 9.451406)scale(0.13 -0.13)\">\n",202" <defs>\n",203" <path d=\"M 11.625 -10.015625 Q 14.109375 -10.109375 16.453125 -9.5625 Q 18.796875 -9.03125 20.578125 -7.984375 Q 22.359375 -6.9375 23.4375 -5.375 Q 24.515625 -3.8125 24.515625 -1.8125 Q 24.515625 0.25 23.796875 1.515625 Q 23.09375 2.78125 22.21875 3.859375 Q 21.34375 4.9375 20.625 6.15625 Q 19.921875 7.375 19.921875 9.421875 Q 19.921875 10.453125 20.3125 11.578125 Q 20.703125 12.703125 21.5 13.625 Q 22.3125 14.546875 23.5625 15.125 Q 24.8125 15.71875 26.515625 15.71875 Q 28.21875 15.71875 29.796875 15.015625 Q 31.390625 14.3125 32.59375 12.84375 Q 33.796875 11.375 34.5 9.171875 Q 35.203125 6.984375 35.203125 4 Q 35.203125 -0.046875 33.71875 -3.78125 Q 32.234375 -7.515625 29.265625 -10.390625 Q 26.3125 -13.28125 21.890625 -14.984375 Q 17.484375 -16.703125 11.625 -16.703125 z \" id=\"Consolas-44\"/>\n",204" </defs>\n",205" <use xlink:href=\"#Consolas-44\"/>\n",206" </g>\n",207" </g>\n",208" <g id=\"text_17\">\n",209" <!-- b -->\n",210" <g style=\"fill:#444443;\" transform=\"translate(143.0712 9.451406)scale(0.13 -0.13)\">\n",211" <use xlink:href=\"#Consolas-98\"/>\n",212" </g>\n",213" </g>\n",214" <g id=\"text_18\">\n",215" <!-- ) -->\n",216" <g style=\"fill:#a40227;\" transform=\"translate(160.146 9.451406)scale(0.13 -0.13)\">\n",217" <defs>\n",218" <path d=\"M 17.828125 72.703125 Q 40.328125 51.859375 40.328125 26.3125 Q 40.328125 21.046875 39.25 15.375 Q 38.1875 9.71875 35.59375 3.78125 Q 33.015625 -2.15625 28.640625 -8.25 Q 24.265625 -14.359375 17.625 -20.515625 L 12.703125 -15.484375 Q 22.21875 -6.0625 26.90625 4.1875 Q 31.59375 14.453125 31.59375 25.6875 Q 31.59375 48.921875 12.703125 67.484375 z \" id=\"Consolas-41\"/>\n",219" </defs>\n",220" <use xlink:href=\"#Consolas-41\"/>\n",221" </g>\n",222" </g>\n",223" <g id=\"text_19\">\n",224" <!-- + -->\n",225" <g style=\"fill:#b4b4b4;\" transform=\"translate(166.6746 9.451406)scale(0.13 -0.13)\">\n",226" <defs>\n",227" <path d=\"M 50.875 22.90625 L 31.6875 22.90625 L 31.6875 2.984375 L 23.296875 2.984375 L 23.296875 22.90625 L 4.109375 22.90625 L 4.109375 30.328125 L 23.296875 30.328125 L 23.296875 50.09375 L 31.6875 50.09375 L 31.6875 30.328125 L 50.875 30.328125 z \" id=\"Consolas-43\"/>\n",228" </defs>\n",229" <use xlink:href=\"#Consolas-43\"/>\n",230" </g>\n",231" </g>\n",232" <g id=\"text_20\">\n",233" <!-- -->\n",234" <g style=\"fill:#b4b4b4;\" transform=\"translate(173.2032 9.451406)scale(0.13 -0.13)\">\n",235" <use xlink:href=\"#Consolas-32\"/>\n",236" </g>\n",237" </g>\n",238" <g id=\"text_21\">\n",239" <!-- t -->\n",240" <g style=\"fill:#b4b4b4;\" transform=\"translate(179.7318 9.451406)scale(0.13 -0.13)\">\n",241" <use xlink:href=\"#Consolas-116\"/>\n",242" </g>\n",243" </g>\n",244" <g id=\"text_22\">\n",245" <!-- o -->\n",246" <g style=\"fill:#b4b4b4;\" transform=\"translate(186.2604 9.451406)scale(0.13 -0.13)\">\n",247" <use xlink:href=\"#Consolas-111\"/>\n",248" </g>\n",249" </g>\n",250" <g id=\"text_23\">\n",251" <!-- r -->\n",252" <g style=\"fill:#b4b4b4;\" transform=\"translate(192.789 9.451406)scale(0.13 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40.96875 18.546875 39.015625 Q 16.890625 37.0625 15.859375 34.328125 Q 14.84375 31.59375 14.59375 28.328125 z \" id=\"Consolas-101\"/>\n",279" </defs>\n",280" <use xlink:href=\"#Consolas-101\"/>\n",281" </g>\n",282" </g>\n",283" <g id=\"text_28\">\n",284" <!-- y -->\n",285" <g style=\"fill:#b4b4b4;\" transform=\"translate(225.432 9.451406)scale(0.13 -0.13)\">\n",286" <defs>\n",287" <path d=\"M 51.421875 49.03125 L 34.71875 5.171875 Q 32.125 -1.515625 29.359375 -6.34375 Q 26.609375 -11.1875 23.328125 -14.28125 Q 20.0625 -17.390625 16.15625 -18.84375 Q 12.25 -20.3125 7.421875 -20.3125 Q 6.15625 -20.3125 5.125 -20.265625 Q 4.109375 -20.21875 2.875 -20.125 L 2.875 -12.40625 Q 3.953125 -12.546875 5.21875 -12.671875 Q 6.5 -12.796875 7.90625 -12.796875 Q 10.25 -12.796875 12.28125 -12.109375 Q 14.3125 -11.421875 16.109375 -9.875 Q 17.921875 -8.34375 19.578125 -5.90625 Q 21.234375 -3.46875 22.796875 0 L 3.21875 49.03125 L 12.890625 49.03125 L 25.296875 16.609375 L 27.78125 8.984375 L 30.609375 16.796875 L 42.09375 49.03125 z \" id=\"Consolas-121\"/>\n",288" </defs>\n",289" <use xlink:href=\"#Consolas-121\"/>\n",290" </g>\n",291" </g>\n",292" <g id=\"text_29\">\n",293" <!-- e -->\n",294" <g style=\"fill:#b4b4b4;\" transform=\"translate(231.9606 9.451406)scale(0.13 -0.13)\">\n",295" <use xlink:href=\"#Consolas-101\"/>\n",296" </g>\n",297" </g>\n",298" <g id=\"text_30\">\n",299" <!-- ( -->\n",300" <g style=\"fill:#b4b4b4;\" transform=\"translate(238.4892 9.451406)scale(0.13 -0.13)\">\n",301" <use xlink:href=\"#Consolas-40\"/>\n",302" </g>\n",303" </g>\n",304" <g id=\"text_31\">\n",305" <!-- 2 -->\n",306" <g style=\"fill:#b4b4b4;\" transform=\"translate(245.0178 9.451406)scale(0.13 -0.13)\">\n",307" <defs>\n",308" <path d=\"M 49.21875 0 L 7.171875 0 L 7.171875 7.625 L 23.6875 24.03125 Q 27.734375 28.03125 30.265625 30.953125 Q 32.8125 33.890625 34.21875 36.296875 Q 35.640625 38.71875 36.125 40.859375 Q 36.625 43.015625 36.625 45.515625 Q 36.625 47.859375 35.984375 49.96875 Q 35.359375 52.09375 34.03125 53.703125 Q 32.71875 55.328125 30.609375 56.25 Q 28.515625 57.171875 25.59375 57.171875 Q 21.53125 57.171875 18.203125 55.359375 Q 14.890625 53.5625 12.109375 50.6875 L 7.421875 56.296875 Q 11.03125 60.109375 15.734375 62.40625 Q 20.453125 64.703125 26.703125 64.703125 Q 30.953125 64.703125 34.4375 63.421875 Q 37.9375 62.15625 40.46875 59.765625 Q 43.015625 57.375 44.40625 53.90625 Q 45.796875 50.4375 45.796875 46.09375 Q 45.796875 42.4375 44.8125 39.3125 Q 43.84375 36.1875 41.859375 33.078125 Q 39.890625 29.984375 36.890625 26.65625 Q 33.890625 23.34375 29.78125 19.390625 L 18.21875 8.109375 L 49.21875 8.109375 z \" id=\"Consolas-50\"/>\n",309" </defs>\n",310" <use xlink:href=\"#Consolas-50\"/>\n",311" </g>\n",312" </g>\n",313" <g id=\"text_32\">\n",314" <!-- , -->\n",315" <g style=\"fill:#b4b4b4;\" transform=\"translate(251.5464 9.451406)scale(0.13 -0.13)\">\n",316" <use xlink:href=\"#Consolas-44\"/>\n",317" </g>\n",318" </g>\n",319" <g id=\"text_33\">\n",320" <!-- 2 -->\n",321" <g style=\"fill:#b4b4b4;\" transform=\"translate(258.075 9.451406)scale(0.13 -0.13)\">\n",322" <use xlink:href=\"#Consolas-50\"/>\n",323" </g>\n",324" </g>\n",325" <g id=\"text_34\">\n",326" <!-- ) -->\n",327" <g style=\"fill:#b4b4b4;\" transform=\"translate(264.6036 9.451406)scale(0.13 -0.13)\">\n",328" <use xlink:href=\"#Consolas-41\"/>\n",329" </g>\n",330" </g>\n",331" <g id=\"text_35\">\n",332" <!-- @ -->\n",333" <g style=\"fill:#b4b4b4;\" transform=\"translate(271.1322 9.451406)scale(0.13 -0.13)\">\n",334" <use xlink:href=\"#Consolas-64\"/>\n",335" </g>\n",336" </g>\n",337" <g id=\"text_36\">\n",338" <!-- x -->\n",339" <g style=\"fill:#b4b4b4;\" transform=\"translate(277.6608 9.451406)scale(0.13 -0.13)\">\n",340" <defs>\n",341" <path d=\"M 52 0 L 40.71875 0 L 27.484375 18.703125 L 14.40625 0 L 3.421875 0 L 22.40625 24.609375 L 4.296875 49.03125 L 15.1875 49.03125 L 28.078125 30.171875 L 40.71875 49.03125 L 51.21875 49.03125 L 32.8125 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id=\"pf8405f6392\">\n",440" <rect height=\"41.491069\" width=\"310.3038\" x=\"0\" y=\"4.504646\"/>\n",441" </clipPath>\n",442" </defs>\n",443"</svg>"444],445"text/plain": [446"<IPython.core.display.SVG object>"447]448},449"metadata": {},450"output_type": "display_data"451},452{453"ename": "RuntimeError",454"evalue": "1D tensors expected, but got 2D and 2D tensors\nCause: torch.dot(b,b) tensor arg b w/shape [2, 1], arg b w/shape [2, 1]",455"output_type": "error",456"traceback": [457"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",458"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",459"\u001b[0;32m/var/folders/93/9kzk2ccm8xj8k70059b28jk80000gp/T/ipykernel_55352/2660314622.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtsensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclarify\u001b[0m\u001b[0;34m(\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[0;32m---> 14\u001b[0;31m \u001b[0mW\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meye\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;31m# W[33, 33] = 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mW\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mb\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",460"\u001b[0;31mRuntimeError\u001b[0m: 1D tensors expected, but got 2D and 2D tensors\nCause: torch.dot(b,b) tensor arg b w/shape [2, 1], arg b w/shape [2, 1]"461]462}463],464"source": [465"import torch\n",466"import tsensor\n",467"import sys\n",468"\n",469"W = torch.tensor([[1, 2], [3, 4]])\n",470"b = torch.tensor([9, 10]).reshape(2, 1)\n",471"x = torch.tensor([4, 5]).reshape(2, 1)\n",472"h = torch.tensor([1,2])\n",473"# z + z + W @ z\n",474"# W @ z\n",475"#torch.dot(b, 3)\n",476"\n",477"with tsensor.clarify():\n",478" W @ torch.dot(b,b)+ torch.eye(2,2)@x + z\n",479"# W[33, 33] = 3\n",480"b = torch.abs( W @ b + x )"481]482},483{484"cell_type": "code",485"execution_count": 4,486"metadata": {},487"outputs": [488{489"ename": "RuntimeError",490"evalue": "1D tensors expected, got 2D, 2D tensors at /tmp/pip-req-build-as628lz5/aten/src/TH/generic/THTensorEvenMoreMath.cpp:83\nCause: torch.dot(b,b) tensor arg b w/shape [2, 1], arg b w/shape [2, 1]",491"output_type": "error",492"traceback": [493"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",494"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",495"\u001b[0;32m<ipython-input-4-fd44e2c68a52>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtsensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclarify\u001b[0m\u001b[0;34m(\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[0;32m----> 2\u001b[0;31m \u001b[0mW\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meye\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",496"\u001b[0;31mRuntimeError\u001b[0m: 1D tensors expected, got 2D, 2D tensors at /tmp/pip-req-build-as628lz5/aten/src/TH/generic/THTensorEvenMoreMath.cpp:83\nCause: torch.dot(b,b) tensor arg b w/shape [2, 1], arg b w/shape [2, 1]"497]498}499],500"source": [501"with tsensor.clarify():\n",502" W @ torch.dot(b,b)+ torch.eye(2,2)@x + z"503]504},505{506"cell_type": "markdown",507"metadata": {},508"source": [509"## Graphviz"510]511},512{513"cell_type": "code",514"execution_count": 8,515"metadata": {},516"outputs": [517{518"name": "stdout",519"output_type": "stream",520"text": [521"[<NAME:b>, <EQUAL:=>, <NAME:W>, <AT:@>, <NAME:h>, <PLUS:+>, <NAME:torch>, <DOT:.>, <NAME:abs>, <LPAR:(>, <NAME:x>, <RPAR:)>, <STAR:*>, <NAME:h>, <DOT:.>, <NAME:dot>, <LPAR:(>, <NAME:h>, <RPAR:)>, <ENDMARKER:>]\n"522]523},524{525"data": {526"text/plain": [527"Assign(lhs=b,rhs=BinaryOp(op=<PLUS:+>,lhs=BinaryOp(op=<AT:@>,lhs=W,rhs=h),rhs=BinaryOp(op=<STAR:*>,lhs=Call(func=Member(obj=torch,member=abs),args=[x]),rhs=Call(func=Member(obj=h,member=dot),args=[h]))))"528]529},530"execution_count": 8,531"metadata": {},532"output_type": "execute_result"533}534],535"source": [536"p = tsensor.parse.PyExprParser(\"b = W @ h + torch.abs(x) *h.dot(h)\")\n",537"print(p.tokens)\n",538"root = p.parse()\n",539"root"540]541},542{543"cell_type": "code",544"execution_count": 9,545"metadata": {},546"outputs": [547{548"data": {549"text/plain": [550"tensor([[25, 31],\n",551" [30, 36]])"552]553},554"execution_count": 9,555"metadata": {},556"output_type": "execute_result"557}558],559"source": [560"frame = sys._getframe()\n",561"result = root.eval(frame)\n",562"result"563]564},565{566"cell_type": "code",567"execution_count": 7,568"metadata": {},569"outputs": [],570"source": [571"def postorder(t):\n",572" nodes = []\n",573" _postorder(t, nodes)\n",574" return nodes\n",575"def _postorder(t, nodes):\n",576" if t is None:\n",577" return\n",578" for sub in t.kids:\n",579" _postorder(sub, nodes)\n",580" nodes.append(t)"581]582},583{584"cell_type": "code",585"execution_count": null,586"metadata": {},587"outputs": [],588"source": [589"def leaves(t):\n",590" nodes = []\n",591" _leaves(t, nodes)\n",592" return nodes\n",593"def _leaves(t, nodes):\n",594" if t is None:\n",595" return\n",596" if len(t.kids)==0:\n",597" nodes.append(t)\n",598" return\n",599" for sub in t.kids:\n",600" _leaves(sub, nodes)"601]602},603{604"cell_type": "code",605"execution_count": null,606"metadata": {},607"outputs": [],608"source": [609"nodes = postorder(root)\n",610"atoms = leaves(root)\n",611"atoms"612]613},614{615"cell_type": "code",616"execution_count": null,617"metadata": {},618"outputs": [],619"source": [620"# map tokens to nodes\n",621"tok2node = {}\n",622"for nd in atoms:\n",623" tok2node[nd.token] = nd"624]625},626{627"cell_type": "code",628"execution_count": null,629"metadata": {},630"outputs": [],631"source": [632"def walk(t, pre=lambda x:None, post=lambda x:None):\n",633" if t is None:\n",634" return\n",635" pre(t)\n",636" for sub in t.kids:\n",637" walk(sub, pre, post)\n",638" post(t)"639]640},641{642"cell_type": "code",643"execution_count": null,644"metadata": {},645"outputs": [],646"source": [647"import graphviz\n",648"\n",649"s = \"\"\"\n",650"digraph G {\n",651" nodesep=.1;\n",652" ranksep=.3;\n",653" rankdir=BT;\n",654" ordering=out; # keep order of leaves\n",655" node [penwidth=\"0.5\", shape=plaintext, width=.1, height=.1];\n",656"\"\"\"\n",657"\n",658"nodesS = set(nodes)\n",659"atomsS = set(atoms)\n",660"ops = nodesS.difference(atomsS)\n",661"\n",662"# s += f'{{ rank=same;'\n",663"# for a in atoms:\n",664"# s += f' node{id(tok2node[a.token])};'\n",665"# s += '}\\n'\n",666"\n",667"s += f'{{ rank=same;'\n",668"for t in p.tokens:\n",669" if t.type!=tsensor.ENDMARKER:\n",670" x = tok2node[t] if t in tok2node else t\n",671" shape = \"\"\n",672" sh = tsensor._shape(x.value)\n",673" if x in atomsS and sh is not None:\n",674" shape = \"shape=box, fixedsize=shape \"\n",675" if len(sh)==1:\n",676" shape += f\"width={.15}, height={sh[0]/6.66}\"\n",677" elif len(sh)==2:\n",678" shape += f\"width={sh[1]/6.66}, height={sh[0]/6.66}\"\n",679"# print(sh, shape)\n",680" s += f'leaf{id(x)} [{shape} label=<<font face=\"Consolas\" color=\"#444443\" point-size=\"9\">{t.value}</font>>]\\n'\n",681"s += '}\\n'\n",682"\n",683"# for nd in ops:\n",684"# s += f'leaf{id(nd)} [label=<<font face=\"Consolas\" color=\"#444443\" point-size=\"9\">{str(nd)}</font>>]'\n",685"\n",686"for nd in nodes:\n",687" if nd in ops:\n",688" text = str(nd)\n",689"# if isinstance(nd, tsensor.Atom):\n",690"# text = str(nd)\n",691"# else:\n",692"# text = nd.__class__.__name__\n",693"# text = str(nd)#+\"\\n\"+str(nd.value)\n",694"# text = \" \"\n",695" shape = \"\"\n",696" sh = tsensor._shape(nd.value)\n",697" if sh is not None:\n",698" shape = \"shape=box, fixedsize=shape \"\n",699" if len(sh)==1:\n",700" shape += f\"width={.15}, height={sh[0]/6.66}\"\n",701" elif len(sh)==2:\n",702" shape += f\"width={sh[1]/6.66}, height={sh[0]/6.66}\"\n",703" if sh is not None:\n",704" text += \"<br/>\"+'x'.join(str(s) for s in sh)\n",705"# else:\n",706"# text += \"=\"+str(nd.value)\n",707" s += f'node{id(nd)} [{shape} label=<<font face=\"Consolas\" color=\"#444443\" point-size=\"9\">{text}</font>>]\\n'\n",708"\n",709"# link leaves left to right\n",710"for i in range(len(p.tokens)-2):\n",711" t = p.tokens[i]\n",712" t2 = p.tokens[i+1]\n",713" x = tok2node[t] if t in tok2node else t\n",714" x2 = tok2node[t2] if t2 in tok2node else t2\n",715" s += f'leaf{id(x)} -> leaf{id(x2)} [style=invis];\\n'\n",716" \n",717"for nd in nodes:\n",718" kids = nd.kids\n",719"# if isinstance(nd, tsensor.Call) and isinstance(nd.kids[0], tsensor.Member):\n",720"# print('ignore', nd)\n",721"# kids = kids[1:]\n",722" for sub in kids:\n",723" if sub in atomsS:\n",724" s += f'node{id(nd)} -> leaf{id(sub)} [dir=back, penwidth=\"0.5\", color=\"#444443\", arrowsize=.4];\\n'\n",725" else:\n",726" s += f'node{id(nd)} -> node{id(sub)} [dir=back, penwidth=\"0.5\", color=\"#444443\", arrowsize=.4];\\n'\n",727"s += \"}\\n\"\n",728"graphviz.Source(s)"729]730},731{732"cell_type": "code",733"execution_count": null,734"metadata": {},735"outputs": [],736"source": [737"import graphviz\n",738"\n",739"s = \"\"\"\n",740"digraph G {\n",741" nodesep=.1;\n",742" ranksep=.3;\n",743" rankdir=BT;\n",744" ordering=out; # keep order of leaves\n",745" node [penwidth=\"0.5\", shape=plaintext, width=.1, height=.1];\n",746"\"\"\"\n",747"\n",748"nodesS = set(nodes)\n",749"atomsS = set(atoms)\n",750"ops = nodesS.difference(atomsS)\n",751"\n",752"s += f'{{ rank=same;'\n",753"for a in atoms:\n",754" s += f'node{id(a)};'\n",755"s += '}\\n'\n",756"\n",757"for nd in nodes:\n",758" if isinstance(nd, tsensor.Atom):\n",759" text = str(nd)\n",760" else:\n",761" text = nd.__class__.__name__\n",762" shape = \"\"\n",763" sh = _shape(nd.value)\n",764" if sh is not None:\n",765" shape = \"shape=box, fixedsize=shape \"\n",766" if len(sh)==1:\n",767" shape += f\"width={.15}, height={sh[0]/6.66}\"\n",768" elif len(sh)==2:\n",769" shape += f\"width={sh[1]/6.66}, height={sh[0]/6.66}\"\n",770" s += f'node{id(nd)} [{shape} label=<<font face=\"Consolas\" color=\"#444443\" point-size=\"9\">{text}</font>>]\\n'\n",771" \n",772"for nd in nodes:\n",773" kids = nd.kids\n",774"# if isinstance(nd, tsensor.Call) and isinstance(nd.kids[0], tsensor.Member):\n",775"# print('ignore', nd)\n",776"# kids = kids[1:]\n",777" for sub in kids:\n",778" if sub in atomsS:\n",779" s += f'node{id(nd)} -> node{id(sub)} [dir=back, penwidth=\"0.5\", color=\"#444443\", arrowsize=.4];'\n",780" else:\n",781" s += f'node{id(nd)} -> node{id(sub)} [dir=back, penwidth=\"0.5\", color=\"#444443\", arrowsize=.4];'\n",782"s += \"}\\n\"\n",783"graphviz.Source(s)"784]785},786{787"cell_type": "code",788"execution_count": null,789"metadata": {},790"outputs": [],791"source": [792"W = torch.tensor([[1, 2], [3, 4], [5, 6]])\n",793"b = torch.tensor([9, 10]).reshape(2, 1)\n",794"x = torch.tensor([4, 5]).reshape(2, 1)\n",795"h = torch.tensor([1,2])\n",796"a = 3\n",797"\n",798"x = torch.tensor([4, 5]).reshape(2, 1)\n",799"p = tsensor.PyExprParser(\"a = a\")\n",800"p = tsensor.PyExprParser(\"b = W@b + torch.abs(x) + h.dot(h)\")\n",801"p = tsensor.PyExprParser(\"b = W@b + h.dot(h)\")\n",802"print(p.tokens)\n",803"root = p.parse()\n",804"nodes = postorder(root)\n",805"atoms = leaves(root)\n",806"# map tokens to nodes\n",807"tok2node = {}\n",808"for nd in atoms:\n",809" tok2node[nd.token] = nd\n",810"frame = sys._getframe()\n",811"result = root.eval(frame)\n",812"result"813]814},815{816"cell_type": "code",817"execution_count": null,818"metadata": {},819"outputs": [],820"source": [821"import graphviz\n",822"\n",823"def matrix_html(nrows, ncols, label, fontsize=12, fontname=\"Consolas\", dimfontsize=9, color=\"#cfe2d4\"):\n",824" isvec = ncols==None\n",825" if isvec:\n",826" sz = str(nrows)\n",827" ncols=nrows\n",828" nrows=1\n",829" else:\n",830" sz = f\"{nrows}x{ncols}\"\n",831" w = ncols*20\n",832" h = nrows*20\n",833" if ncols==1:\n",834" w = 15\n",835" if nrows==1:\n",836" h = 15\n",837" html = f\"\"\"\n",838" <table fixedsize=\"true\" width=\"{w}\" height=\"{h+2*fontsize*1.3}\" BORDER=\"0\" CELLPADDING=\"0\" CELLBORDER=\"1\" CELLSPACING=\"0\">\n",839" <tr>\n",840" <td fixedsize=\"true\" width=\"{w}\" height=\"{fontsize*1.3}\" cellspacing=\"0\" cellpadding=\"0\" border=\"0\" valign=\"top\" align=\"center\">\n",841" <font face=\"{fontname}\" color=\"#444443\" point-size=\"{dimfontsize}\">{sz}</font>\n",842" </td>\n",843" </tr>\n",844" <tr> \n",845" <td fixedsize=\"true\" width=\"{w}\" height=\"{h}\" cellspacing=\"0\" cellpadding=\"0\" bgcolor=\"{color}\" border=\"1\" sides=\"ltbr\" align=\"center\">\n",846" <font face=\"{fontname}\" color=\"#444443\" point-size=\"{fontsize}\">{label}</font>\n",847" </td>\n",848" </tr>\n",849" </table>\"\"\"\n",850" return html\n",851"\n",852"nodesS = set(nodes)\n",853"atomsS = set(atoms)\n",854"ops = nodesS.difference(atomsS)\n",855"\n",856"s = \"\"\"\n",857"digraph G {\n",858" nodesep=.0;\n",859" ranksep=.3;\n",860" rankdir=BT;\n",861" ordering=out; # keep order of leaves\n",862"\"\"\"\n",863"\n",864"fontname=\"Consolas\"\n",865"fontsize=12\n",866"spread = .2\n",867"\n",868"s += f'{{ rank=same; '\n",869"for t in p.tokens:\n",870" if t.type!=tsensor.ENDMARKER:\n",871" x = tok2node[t] if t in tok2node else t\n",872" shape = \"\"\n",873" sh = tsensor._shape(x.value)\n",874" label = f'<font face=\"{fontname}\" color=\"#444443\" point-size=\"{fontsize}\">{t.value}</font>'\n",875" matrixcolor=\"#cfe2d4\"\n",876" vectorcolor=\"#fefecd\"\n",877" if x in atomsS and sh is not None:\n",878" if len(sh)==1:\n",879" label = matrix_html(sh[0],None,t.value,fontname=fontname,fontsize=fontsize,color=vectorcolor)\n",880" elif len(sh)==2:\n",881" label = matrix_html(sh[0],sh[1],t.value,fontname=fontname,fontsize=fontsize,color=matrixcolor)\n",882" # margin/width don't seem to do anything for shape=plain\n",883" if t.type==tsensor.DOT:\n",884" spread=.1\n",885" if t.type==tsensor.EQUAL:\n",886" spread=.25\n",887" if t.type in tsensor.ADDOP:\n",888" spread=.5\n",889" if t.type in tsensor.MULOP:\n",890" spread=.2\n",891" s += f'leaf{id(x)} [shape=box penwidth=0 margin=.001 width={spread} label=<{label}>]\\n'\n",892"s += '}\\n'\n",893"\n",894"s += \"}\\n\"\n",895"# print(s)\n",896"graphviz.Source(s)"897]898},899{900"cell_type": "code",901"execution_count": null,902"metadata": {},903"outputs": [],904"source": [905"s = \"\"\"\n",906"digraph foo {\n",907" rankdir=TB\n",908" subgraph {\n",909" node1;\n",910" }\n",911" subgraph {\n",912" node2;\n",913" }\n",914" node1 -> node2\n",915"}\n",916"\"\"\"\n",917"display(graphviz.Source(s))\n",918"display(graphviz.Source(s))"919]920},921{922"cell_type": "markdown",923"metadata": {},924"source": [925"## Get string size in matplotlib"926]927},928{929"cell_type": "code",930"execution_count": null,931"metadata": {},932"outputs": [],933"source": [934"import matplotlib\n",935"import matplotlib.patches as patches\n",936"from matplotlib import pyplot as plt\n",937"def textdim(s, fontsize=11):\n",938" fig, ax = plt.subplots(1,1)\n",939" t = ax.text(0, 0, s, bbox={'lw':0}, fontsize=fontsize)\n",940" plt.savefig(\"/tmp/junk\")\n",941" plt.close()\n",942" bb = t.get_bbox_patch()\n",943" w, h = bb.get_width(), bb.get_height()\n",944" return w, h"945]946},947{948"cell_type": "code",949"execution_count": null,950"metadata": {},951"outputs": [],952"source": [953"textdim(\"test of foO\", fontsize=11)"954]955},956{957"cell_type": "code",958"execution_count": null,959"metadata": {},960"outputs": [],961"source": [962"import numpy as np\n",963"a = [[1,2,3],[3,4,5]]\n",964"objviz(a)"965]966},967{968"cell_type": "code",969"execution_count": null,970"metadata": {},971"outputs": [],972"source": [973"from lolviz import *\n",974"print(objviz(a).source)"975]976},977{978"cell_type": "code",979"execution_count": null,980"metadata": {},981"outputs": [],982"source": [983"s = \"\"\"\n",984"digraph G {\n",985" ranksep=0;\n",986" rankdir=BT;\n",987" ordering=out; # keep order of leaves\n",988" node1 [shape=plain, space=\"0.0\", margin=\"0.01\",label=<\n",989"<table BORDER=\"0\" CELLPADDING=\"0\" CELLBORDER=\"1\" CELLSPACING=\"0\">\n",990"<tr>\n",991"<td width=\"100\" height=\"12\" cellspacing=\"0\" cellpadding=\"0\" border=\"1\" sides=\"b\" valign=\"top\" align=\"center\">100x200</td>\n",992"</tr>\n",993"<tr>\n",994"<td width=\"100\" height=\"88\" cellspacing=\"0\" cellpadding=\"0\" border=\"1\" sides=\"lbr\" align=\"center\">self.W</td>\n",995"</tr>\n",996"</table>\n",997" >]\n",998"}\n",999"\"\"\"\n",1000"graphviz.Source(s)"1001]1002},1003{1004"cell_type": "markdown",1005"metadata": {},1006"source": [1007"## CSS (yuck)"1008]1009},1010{1011"cell_type": "code",1012"execution_count": null,1013"metadata": {},1014"outputs": [],1015"source": [1016"from IPython.core.display import display, HTML\n",1017"h = \"\"\"\n",1018"<h6 style=\"color: Green; position:absolute; top: 20vh; left: 25vw; height: auto; margin: auto;\">Actief</h6>\n",1019"<table>\n",1020"<tr>\n",1021" <td>b = torch.abs( W @ b +</td>\n",1022" <td><div style=\"width:100px;height:150px;border:1px solid #000;\">\n",1023" <table style=\"width:100px;height:150px\" border='0' cellborder='0' cellpadding=\"0\" cellspacing=\"0\">\n",1024" <tr>\n",1025" <td height=\"50%\" width=\"33%\"></td>\n",1026" <td width=\"33%\" cellpadding=\"0\" cellspacing=\"0\" align=center valign=top>40</td><td width=\"33%\"></td>\n",1027" </tr>\n",1028" <tr>\n",1029" <td width=\"33%\" cellpadding=\"0\" cellspacing=\"0\">100</td>\n",1030" <td width=\"33%\" cellpadding=\"0\" cellspacing=\"0\" align=\"center\">self.W</td>\n",1031" <td width=\"33%\"></td>\n",1032" </tr>\n",1033" </table>\n",1034" </div>\n",1035" </td>\n",1036" <td>)</td>\n",1037"</tr>\n",1038"\"\"\"\n",1039"t = HTML(h)\n",1040"t"1041]1042},1043{1044"cell_type": "code",1045"execution_count": null,1046"metadata": {},1047"outputs": [],1048"source": [1049"def matrix(nrows,ncols,text,dim_fontsize=9,fontsize=12):\n",1050" h = f\"\"\"\n",1051" <div style=\"position:relative; width:{ncols}px;height:{nrows*1.2}px; padding:0px\">\n",1052" <div style=\"position:relative; width:{ncols}px;height:{nrows}px;border:1px solid #000; padding:0px\">\n",1053" <div style=\"position:absolute; width:{ncols}px; top: -3px; text-align:center; font-size:{dim_fontsize}px\">\n",1054" {ncols}\n",1055" </div>\n",1056" <div style=\"position:absolute; top: 40%; left: -3px; transform: rotate(-90deg); font-size:{dim_fontsize}px\">\n",1057" {nrows}\n",1058" </div>\n",1059" </div>\n",1060" <div style=\"position:absolute; width:{ncols}px; bottom: 5px; text-align:center; font-size:{fontsize}px\">\n",1061" {text}\n",1062" </div>\n",1063" </div>\n",1064" \"\"\"\n",1065" sp = f\"\"\"\n",1066" <div style=\"position:relative; width:{ncols}px;height:{nrows*1.2}px; padding:0px\">\n",1067" <span style=\"position:relative; width:{ncols}px;height:{nrows}px;border:1px solid #000; padding:0px\">\n",1068" <span style=\"position:absolute; width:{ncols}px; top: -3px; text-align:center; font-size:{dim_fontsize}px\">\n",1069" {ncols}\n",1070" </span>\n",1071" <span style=\"position:absolute; top: 40%; left: -3px; transform: rotate(-90deg); font-size:{dim_fontsize}px\">\n",1072" {nrows}\n",1073" </span>\n",1074" </span>\n",1075" <span style=\"position:absolute; width:{ncols}px; bottom: 5px; text-align:center; font-size:{fontsize}px\">\n",1076" {text}\n",1077" </span>\n",1078" </div>\n",1079" \"\"\"\n",1080"\n",1081" return sp\n",1082"\n",1083"m = matrix(120,20,\"self.W\")\n",1084"m = f\"torch.relu({m})\"\n",1085"HTML(m)"1086]1087},1088{1089"cell_type": "code",1090"execution_count": null,1091"metadata": {},1092"outputs": [],1093"source": []1094},1095{1096"cell_type": "code",1097"execution_count": null,1098"metadata": {},1099"outputs": [],1100"source": []1101}1102],1103"metadata": {1104"kernelspec": {1105"display_name": "Python 3 (ipykernel)",1106"language": "python",1107"name": "python3"1108},1109"language_info": {1110"codemirror_mode": {1111"name": "ipython",1112"version": 31113},1114"file_extension": ".py",1115"mimetype": "text/x-python",1116"name": "python",1117"nbconvert_exporter": "python",1118"pygments_lexer": "ipython3",1119"version": "3.8.8"1120}1121},1122"nbformat": 4,1123"nbformat_minor": 41124}1125