├── .gitignore ├── LICENSE ├── README.md ├── Tensor Puzzlers.ipynb ├── Tensor Puzzlers_files ├── Tensor Puzzlers_10_0.svg ├── Tensor Puzzlers_11_0.svg ├── Tensor Puzzlers_12_0.png ├── Tensor Puzzlers_12_0.svg ├── Tensor Puzzlers_13_0.svg ├── Tensor Puzzlers_14_0.svg ├── Tensor Puzzlers_15_0.png ├── Tensor Puzzlers_15_0.svg ├── Tensor Puzzlers_16_0.svg ├── Tensor Puzzlers_17_0.svg ├── Tensor Puzzlers_18_0.png ├── Tensor Puzzlers_18_0.svg ├── Tensor Puzzlers_19_0.svg ├── Tensor Puzzlers_20_0.svg ├── Tensor Puzzlers_21_0.png ├── Tensor Puzzlers_21_0.svg ├── Tensor Puzzlers_22_0.svg ├── Tensor Puzzlers_23_0.svg ├── Tensor Puzzlers_24_0.png ├── Tensor Puzzlers_24_0.svg ├── Tensor Puzzlers_25_0.svg ├── Tensor Puzzlers_26_0.svg ├── Tensor Puzzlers_27_0.png ├── Tensor Puzzlers_27_0.svg ├── Tensor Puzzlers_27_1.png ├── Tensor Puzzlers_28_0.svg ├── Tensor Puzzlers_29_0.svg ├── Tensor Puzzlers_2_0.png ├── Tensor Puzzlers_30_0.png ├── Tensor Puzzlers_30_0.svg ├── Tensor Puzzlers_30_1.png ├── Tensor Puzzlers_31_0.svg ├── Tensor Puzzlers_32_0.svg ├── Tensor Puzzlers_33_0.png ├── Tensor Puzzlers_33_0.svg ├── Tensor Puzzlers_33_1.png ├── Tensor Puzzlers_34_0.svg ├── Tensor Puzzlers_35_0.svg ├── Tensor Puzzlers_36_0.png ├── Tensor Puzzlers_36_0.svg ├── Tensor Puzzlers_37_0.svg ├── Tensor Puzzlers_38_0.svg ├── Tensor Puzzlers_39_0.png ├── Tensor Puzzlers_39_0.svg ├── Tensor Puzzlers_3_0.png ├── Tensor Puzzlers_40_0.svg ├── Tensor Puzzlers_41_0.svg ├── Tensor Puzzlers_42_0.png ├── Tensor Puzzlers_42_0.svg ├── Tensor Puzzlers_43_0.svg ├── Tensor Puzzlers_44_0.svg ├── Tensor Puzzlers_45_0.png ├── Tensor Puzzlers_45_0.svg ├── Tensor Puzzlers_45_1.png ├── Tensor Puzzlers_46_0.svg ├── Tensor Puzzlers_47_0.svg ├── Tensor Puzzlers_48_0.png ├── Tensor Puzzlers_48_0.svg ├── Tensor Puzzlers_49_0.svg ├── Tensor Puzzlers_4_0.svg ├── Tensor Puzzlers_50_0.svg ├── Tensor Puzzlers_51_0.png ├── Tensor Puzzlers_51_0.svg ├── Tensor Puzzlers_52_0.svg ├── Tensor Puzzlers_53_0.svg ├── Tensor Puzzlers_54_0.png ├── Tensor Puzzlers_54_0.svg ├── Tensor Puzzlers_55_0.svg ├── Tensor Puzzlers_56_0.svg ├── Tensor Puzzlers_57_0.png ├── Tensor Puzzlers_57_0.svg ├── Tensor Puzzlers_57_1.png ├── Tensor Puzzlers_58_0.svg ├── Tensor Puzzlers_59_0.svg ├── Tensor Puzzlers_5_0.png ├── Tensor Puzzlers_5_0.svg ├── Tensor Puzzlers_5_1.png ├── Tensor Puzzlers_60_0.svg ├── Tensor Puzzlers_61_0.svg ├── Tensor Puzzlers_62_0.svg ├── Tensor Puzzlers_63_0.svg ├── Tensor Puzzlers_64_0.svg ├── Tensor Puzzlers_65_0.svg ├── Tensor Puzzlers_66_0.svg ├── Tensor Puzzlers_67_0.svg ├── Tensor Puzzlers_68_0.svg ├── Tensor Puzzlers_69_0.svg ├── Tensor Puzzlers_6_0.png ├── Tensor Puzzlers_6_1.png ├── Tensor Puzzlers_70_0.svg ├── Tensor Puzzlers_7_0.svg ├── Tensor Puzzlers_8_0.svg ├── Tensor Puzzlers_9_0.png ├── Tensor Puzzlers_9_0.svg └── Tensor Puzzlers_9_1.png ├── chess.jpeg ├── lib.py └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Sasha Rush 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /Tensor Puzzlers_files/Tensor Puzzlers_10_0.svg: -------------------------------------------------------------------------------- 1 | oneEx. 0Ex. 1Ex. 2target -------------------------------------------------------------------------------- /Tensor Puzzlers_files/Tensor Puzzlers_11_0.svg: -------------------------------------------------------------------------------- 1 | onetarget -------------------------------------------------------------------------------- /Tensor Puzzlers_files/Tensor Puzzlers_12_0.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | from hypothesis.extra.numpy import arrays 5 | from hypothesis.strategies import integers, lists, composite, floats 6 | from hypothesis import given 7 | import numpy as np 8 | import random 9 | import sys 10 | import typing 11 | import matplotlib.pyplot as plt 12 | 13 | import urllib 14 | import torch 15 | import time 16 | from chalk import * 17 | import chalk 18 | from colour import Color 19 | from IPython.display import display, SVG 20 | 21 | color = [Color("red")] * 50 22 | 23 | def color(v): 24 | d = rectangle(1, 1) 25 | if v == 0: 26 | return d 27 | elif v > 0: 28 | return d.fill_color(Color("orange")).fill_opacity(0.4 + 0.6 *( v / 10)) 29 | elif v < 0: 30 | return d.fill_color(Color("blue")).fill_opacity(0.4 + 0.6 * ( abs(v) / 10)) 31 | 32 | def draw_matrix(mat): 33 | return vcat((hcat((color(v) 34 | for j, v in enumerate(inner))) 35 | for i, inner in enumerate(mat))) 36 | 37 | def grid(diagrams): 38 | mhs = [0] * 100 39 | mws = [0] * 100 40 | for i, row in enumerate(diagrams): 41 | mh = 0 42 | for j, col in enumerate(row): 43 | env = col.get_envelope() 44 | mhs[i] = max(env.height, mhs[i]) 45 | mws[j] = max(mws[j], env.width) 46 | return vcat([hcat([col.center_xy().with_envelope(rectangle(mws[j], mhs[i])) 47 | for j, col in enumerate(row)], 1.0) for i, row in enumerate(diagrams)], 1.0) 48 | 49 | def draw_example(data): 50 | 51 | 52 | name = data["name"] 53 | keys = list(data["vals"][0].keys()) 54 | # cols = [[vstrut(0)] + [vstrut(0.5) / text(f"Ex. {i}", 0.5).fill_color(Color("black")).line_width(0.0) / vstrut(0.5) for i in range(len(data["vals"]))]] 55 | cols = [] 56 | for k in keys: 57 | mat = [(vstrut(0.5) / text(k, 0.5).fill_color(Color("black")).line_width(0.0) / vstrut(0.5))] 58 | for ex in data["vals"]: 59 | v2 = ex[k] 60 | mat.append(draw_matrix(v2)) 61 | cols.append(mat) 62 | 63 | full = grid(cols) 64 | 65 | full = ( 66 | vstrut(1) 67 | / text(name, 0.75).fill_color(Color("black")).line_width(0) 68 | / vstrut(1) 69 | / full.center_xy() 70 | ) 71 | full = full.pad(1.2).center_xy() 72 | env = full.get_envelope() 73 | set_svg_height(50 * env.height) 74 | height = 50 * env.height 75 | chalk.set_svg_height(300) 76 | return rectangle(env.width, env.height).fill_color(Color("white")) + full 77 | 78 | def draw_examples(name, examples): 79 | data = {"name":name, 80 | "vals" :[{k: [v.tolist()] if len(v.shape) == 1 else v.tolist() 81 | for k, v in example.items()} 82 | for example in examples ] } 83 | return draw_example(data) 84 | 85 | 86 | tensor = torch.tensor 87 | 88 | numpy_to_torch_dtype_dict = { 89 | bool: torch.bool, 90 | np.uint8: torch.uint8, 91 | np.int8: torch.int8, 92 | np.int16: torch.int16, 93 | np.int32: torch.int32, 94 | np.int64: torch.int64, 95 | np.float16: torch.float16, 96 | np.float32: torch.float32, 97 | np.float64: torch.float64, 98 | } 99 | torch_to_numpy_dtype_dict = {v: k for k, v in numpy_to_torch_dtype_dict.items()} 100 | 101 | 102 | @composite 103 | def spec(draw, x, min_size=1): 104 | # Get the type hints. 105 | if sys.version_info >= (3, 9): 106 | gth = typing.get_type_hints(x, include_extras=True) 107 | else: 108 | gth = typing.get_type_hints(x) 109 | 110 | # Collect all the dimension names. 111 | names = set() 112 | for k in gth: 113 | if not hasattr(gth[k], "__metadata__"): 114 | continue 115 | dims = gth[k].__metadata__[0]["details"][0].dims 116 | names.update([d.name for d in dims if isinstance(d.name, str)]) 117 | names = list(names) 118 | 119 | # draw sizes for each dim. 120 | size = integers(min_value=min_size, max_value=5) 121 | arr = draw(arrays(shape=(len(names),), unique=True, elements=size, dtype=np.int32)).tolist() 122 | sizes = dict(zip(names, arr)) 123 | for n in list(sizes.keys()): 124 | if '*' in n or '+' in n or '-' in n or '//' in n: 125 | i, op, j = n.split() 126 | i_val = i if i.isdigit() else sizes[i] 127 | j_val = j if j.isdigit() else sizes[j] 128 | sizes[n] = eval('{}{}{}'.format(i_val, op,j_val)) 129 | 130 | # Create tensors for each size. 131 | ret = {} 132 | for k in gth: 133 | if not hasattr(gth[k], "__metadata__"): 134 | continue 135 | shape = tuple( 136 | [ 137 | sizes[d.name] if isinstance(d.name, str) else d.size 138 | for d in gth[k].__metadata__[0]["details"][0].dims 139 | ] 140 | ) 141 | dtype = (torch_to_numpy_dtype_dict[ 142 | gth[k].__metadata__[0]["details"][1].dtype 143 | ] 144 | if len(gth[k].__metadata__[0]["details"]) >= 2 145 | else int) 146 | ret[k] = draw( 147 | arrays( 148 | shape=shape, 149 | dtype=dtype, 150 | elements=integers(min_value=-5, max_value=5) if 151 | dtype == int else None, 152 | unique=False 153 | ) 154 | ) 155 | ret[k] = np.nan_to_num(ret[k], nan=0, neginf=0, posinf=0) 156 | 157 | ret["return"][:] = 0 158 | return ret, sizes 159 | 160 | 161 | def make_test(name, problem, problem_spec, add_sizes=[], constraint=lambda d: d): 162 | examples = [] 163 | for i in range(3): 164 | example, sizes = spec(problem, 3).example() 165 | example = constraint(example) 166 | out = example["return"].tolist() 167 | del example["return"] 168 | problem_spec(*example.values(), out) 169 | 170 | for size in add_sizes: 171 | example[size] = sizes[size] 172 | 173 | yours = None 174 | try: 175 | yours = problem(*map(tensor, example.values())) 176 | 177 | except NotImplementedError: 178 | pass 179 | for size in add_sizes: 180 | del example[size] 181 | example["target"] = tensor(out) 182 | if yours is not None: 183 | example["yours"] = yours 184 | examples.append(example) 185 | 186 | diagram = draw_examples(name, examples) 187 | display(SVG(diagram._repr_svg_())) 188 | 189 | @given(spec(problem)) 190 | def test_problem(d): 191 | d, sizes = d 192 | d = constraint(d) 193 | out = d["return"].tolist() 194 | del d["return"] 195 | problem_spec(*d.values(), out) 196 | for size in add_sizes: 197 | d[size] = sizes[size] 198 | 199 | out2 = problem(*map(tensor, d.values())) 200 | out = tensor(out) 201 | out2 = torch.broadcast_to(out2, out.shape) 202 | assert torch.allclose( 203 | out, out2 204 | ), "Two tensors are not equal\n Spec: \n\t%s \n\t%s" % (out, out2) 205 | 206 | return test_problem 207 | 208 | 209 | def run_test(fn): 210 | fn() 211 | # Generate a random puppy video if you are correct. 212 | print("Correct!") 213 | from IPython.display import HTML 214 | pups = [ 215 | "2m78jPG", 216 | "pn1e9TO", 217 | "MQCIwzT", 218 | "udLK6FS", 219 | "ZNem5o3", 220 | "DS2IZ6K", 221 | "aydRUz8", 222 | "MVUdQYK", 223 | "kLvno0p", 224 | "wScLiVz", 225 | "Z0TII8i", 226 | "F1SChho", 227 | "9hRi2jN", 228 | "lvzRF3W", 229 | "fqHxOGI", 230 | "1xeUYme", 231 | "6tVqKyM", 232 | "CCxZ6Wr", 233 | "lMW0OPQ", 234 | "wHVpHVG", 235 | "Wj2PGRl", 236 | "HlaTE8H", 237 | "k5jALH0", 238 | "3V37Hqr", 239 | "Eq2uMTA", 240 | "Vy9JShx", 241 | "g9I2ZmK", 242 | "Nu4RH7f", 243 | "sWp0Dqd", 244 | "bRKfspn", 245 | "qawCMl5", 246 | "2F6j2B4", 247 | "fiJxCVA", 248 | "pCAIlxD", 249 | "zJx2skh", 250 | "2Gdl1u7", 251 | "aJJAY4c", 252 | "ros6RLC", 253 | "DKLBJh7", 254 | "eyxH0Wc", 255 | "rJEkEw4"] 256 | return HTML(""" 257 | 260 | """%(random.sample(pups, 1)[0])) 261 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | jupytext 2 | torchtyping 3 | hypothesis 4 | torch==1.10 5 | numpy 6 | pytest 7 | notebookjs 8 | jupyter 9 | --------------------------------------------------------------------------------