From 7aecea4f5694b09f783c2a96eb8558494edda724 Mon Sep 17 00:00:00 2001
From: chenyu <chenyu@fastmail.com>
Date: Thu, 13 Jun 2024 12:18:37 -0400
Subject: [PATCH] support creating Tensor from python tuple (#4944)

added a small fuzzer to test data with mixed tuple and list of numbers matched with numpy
---
 test/test_tensor.py | 14 +++++++++++++-
 tinygrad/tensor.py  |  2 +-
 2 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/test/test_tensor.py b/test/test_tensor.py
index 025731037d..453112d7bb 100644
--- a/test/test_tensor.py
+++ b/test/test_tensor.py
@@ -1,6 +1,6 @@
 import numpy as np
 import torch
-import unittest, copy, mmap
+import unittest, copy, mmap, random
 from tinygrad import Tensor, Device, dtypes
 from tinygrad.helpers import getenv, temp, CI
 from extra.gradcheck import numerical_jacobian, jacobian, gradcheck
@@ -288,6 +288,18 @@ class TestTinygrad(unittest.TestCase):
     with self.assertRaises(ValueError): Tensor([[[1,1,1],[1,1]]])
     with self.assertRaises(ValueError): Tensor([[1,1,1],[[1,1,1]]])
 
+  def test_tensor_mixed_list_tuple(self):
+    def _list_or_tuple(): return list if random.random() < 0.5 else tuple
+    def _generate_data(depth):
+      if depth == 0: return _list_or_tuple()()
+      if depth == 1: return _list_or_tuple()([random.random(), random.random()])
+      return _list_or_tuple()([_generate_data(depth-1), _generate_data(depth-1)])
+
+    for depth in range(7):
+      for _ in range(20):
+        data = _generate_data(depth)
+        np.testing.assert_allclose(Tensor(data).numpy(), np.array(data))
+
   def test_tensor_copy(self):
     x = copy.deepcopy(Tensor.ones((3,3,3)))
     np.testing.assert_allclose(x.numpy(), np.ones((3,3,3)))
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index d5ee96070a..596c9adb8b 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -108,7 +108,7 @@ class Tensor:
     elif isinstance(data, get_args(ConstType)): data = _loadop(LoadOps.CONST, tuple(), dtype or dtypes.from_py(data), device, data)
     elif isinstance(data, Variable): data = _loadop(LoadOps.CONST, tuple(), dtype or dtypes.from_py(data.unbind()[1]), device, data)
     elif isinstance(data, bytes): data = _fromcpu(np.frombuffer(data, np.uint8))
-    elif isinstance(data, list):
+    elif isinstance(data, (list, tuple)):
       if dtype is None:
         if (d := fully_flatten(data)) and all(isinstance(s, bool) for s in d): dtype = dtypes.bool
         else: dtype = dtypes.default_int if d and all_int(d) else dtypes.default_float