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OpenHands: minitorch
Pytest Summary for test tests
| status |
count |
| passed |
21 |
| failed |
86 |
| xfailed |
4 |
| skipped |
8 |
| total |
119 |
| collected |
119 |
Failed pytests:
test_autodiff.py::test_backprop1
test_autodiff.py::test_backprop1
@pytest.mark.task1_4
def test_backprop1() -> None:
# Example 1: F1(0, v)
var = minitorch.Scalar(0)
var2 = Function1.apply(0, var)
> var2.backward(d_output=5)
tests/test_autodiff.py:109:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
minitorch/scalar.py:147: in backward
backpropagate(self, d_output)
minitorch/autodiff.py:82: in backpropagate
ordered = topological_sort(variable)
minitorch/autodiff.py:67: in topological_sort
visit(variable)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
var = Scalar(10.000000)
def visit(var: Variable) -> None:
> if var in visited or var.is_constant():
E AttributeError: 'Scalar' object has no attribute 'is_constant'
minitorch/autodiff.py:60: AttributeError
test_autodiff.py::test_backprop2
test_autodiff.py::test_backprop2
@pytest.mark.task1_4
def test_backprop2() -> None:
# Example 2: F1(0, 0)
var = minitorch.Scalar(0)
var2 = Function1.apply(0, var)
var3 = Function1.apply(0, var2)
> var3.backward(d_output=5)
tests/test_autodiff.py:119:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
minitorch/scalar.py:147: in backward
backpropagate(self, d_output)
minitorch/autodiff.py:82: in backpropagate
ordered = topological_sort(variable)
minitorch/autodiff.py:67: in topological_sort
visit(variable)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
var = Scalar(20.000000)
def visit(var: Variable) -> None:
> if var in visited or var.is_constant():
E AttributeError: 'Scalar' object has no attribute 'is_constant'
minitorch/autodiff.py:60: AttributeError
test_autodiff.py::test_backprop3
test_autodiff.py::test_backprop3
@pytest.mark.task1_4
def test_backprop3() -> None:
# Example 3: F1(F1(0, v1), F1(0, v1))
var1 = minitorch.Scalar(0)
var2 = Function1.apply(0, var1)
var3 = Function1.apply(0, var1)
var4 = Function1.apply(var2, var3)
> var4.backward(d_output=5)
tests/test_autodiff.py:130:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
minitorch/scalar.py:147: in backward
backpropagate(self, d_output)
minitorch/autodiff.py:82: in backpropagate
ordered = topological_sort(variable)
minitorch/autodiff.py:67: in topological_sort
visit(variable)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
var = Scalar(30.000000)
def visit(var: Variable) -> None:
> if var in visited or var.is_constant():
E AttributeError: 'Scalar' object has no attribute 'is_constant'
minitorch/autodiff.py:60: AttributeError
test_autodiff.py::test_backprop4
test_autodiff.py::test_backprop4
@pytest.mark.task1_4
def test_backprop4() -> None:
# Example 4: F1(F1(0, v1), F1(0, v1))
var0 = minitorch.Scalar(0)
var1 = Function1.apply(0, var0)
var2 = Function1.apply(0, var1)
var3 = Function1.apply(0, var1)
var4 = Function1.apply(var2, var3)
> var4.backward(d_output=5)
tests/test_autodiff.py:142:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
minitorch/scalar.py:147: in backward
backpropagate(self, d_output)
minitorch/autodiff.py:82: in backpropagate
ordered = topological_sort(variable)
minitorch/autodiff.py:67: in topological_sort
visit(variable)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
var = Scalar(50.000000)
def visit(var: Variable) -> None:
> if var in visited or var.is_constant():
E AttributeError: 'Scalar' object has no attribute 'is_constant'
minitorch/autodiff.py:60: AttributeError
test_conv.py::test_conv1d_simple
test_conv.py::test_conv1d_simple
@pytest.mark.task4_1
def test_conv1d_simple() -> None:
> t = minitorch.tensor([0, 1, 2, 3]).view(1, 1, 4)
E AttributeError: 'NoneType' object has no attribute 'view'
tests/test_conv.py:12: AttributeError
test_conv.py::test_conv1d
test_conv.py::test_conv1d
@pytest.mark.task4_1
> @given(tensors(shape=(1, 1, 6)), tensors(shape=(1, 1, 4)))
tests/test_conv.py:24:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:76: in tensors
td = draw(tensor_data(numbers, shape=shape))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1, 1, 4)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_conv.py::test_conv1d_channel
test_conv.py::test_conv1d_channel
@pytest.mark.task4_1
> @given(tensors(shape=(2, 2, 6)), tensors(shape=(3, 2, 2)))
tests/test_conv.py:31:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:76: in tensors
td = draw(tensor_data(numbers, shape=shape))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (3, 2, 2)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_conv.py::test_conv
test_conv.py::test_conv
@pytest.mark.task4_2
> @given(tensors(shape=(1, 1, 6, 6)), tensors(shape=(1, 1, 2, 4)))
tests/test_conv.py:38:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:76: in tensors
td = draw(tensor_data(numbers, shape=shape))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1, 1, 2, 4)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_conv.py::test_conv_batch
test_conv.py::test_conv_batch
@pytest.mark.task4_2
> @given(tensors(shape=(2, 1, 6, 6)), tensors(shape=(1, 1, 2, 4)))
tests/test_conv.py:44:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:76: in tensors
td = draw(tensor_data(numbers, shape=shape))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1, 1, 2, 4)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_conv.py::test_conv_channel
test_conv.py::test_conv_channel
@pytest.mark.task4_2
> @given(tensors(shape=(2, 2, 6, 6)), tensors(shape=(3, 2, 2, 4)))
tests/test_conv.py:51:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:76: in tensors
td = draw(tensor_data(numbers, shape=shape))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (3, 2, 2, 4)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_conv.py::test_conv2
test_conv.py::test_conv2
@pytest.mark.task4_2
def test_conv2() -> None:
> t = minitorch.tensor([[0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3]]).view(
1, 1, 4, 4
)
E AttributeError: 'NoneType' object has no attribute 'view'
tests/test_conv.py:59: AttributeError
test_module.py::test_stacked_demo
test_module.py::test_stacked_demo
@pytest.mark.task0_4
def test_stacked_demo() -> None:
"Check that each of the properties match"
mod = ModuleA1()
> np = dict(mod.named_parameters())
E TypeError: 'NoneType' object is not iterable
tests/test_module.py:49: TypeError
test_module.py::test_module
test_module.py::test_module
@pytest.mark.task0_4
> @given(med_ints, med_ints)
tests/test_module.py:96:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
size_a = 1, size_b = 1
@pytest.mark.task0_4
@given(med_ints, med_ints)
def test_module(size_a: int, size_b: int) -> None:
"Check the properties of a single module"
module = Module2()
module.eval()
> assert not module.training
E assert not True
E + where True = Module2(\n (module_c): Module3()\n).training
E Falsifying example: test_module(
E size_b=1, size_a=1,
E )
tests/test_module.py:101: AssertionError
test_module.py::test_stacked_module
test_module.py::test_stacked_module
@pytest.mark.task0_4
> @given(med_ints, med_ints, small_floats)
tests/test_module.py:117:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
size_a = 1, size_b = 1, val = 0.0
@pytest.mark.task0_4
@given(med_ints, med_ints, small_floats)
def test_stacked_module(size_a: int, size_b: int, val: float) -> None:
"Check the properties of a stacked module"
module = Module1(size_a, size_b, val)
module.eval()
> assert not module.training
E assert not True
E + where True = Module1(\n (module_a): Module2(\n (module_c): Module3()\n )\n (module_b): Module2(\n (module_c): Module3()\n )\n).training
E Falsifying example: test_stacked_module(
E val=0.0, size_b=1, size_a=1,
E )
tests/test_module.py:122: AssertionError
test_module.py::test_module_fail_forward
test_module.py::test_module_fail_forward
@pytest.mark.task0_4
@pytest.mark.xfail
def test_module_fail_forward() -> None:
mod = minitorch.Module()
> mod()
tests/test_module.py:154:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = Module(), args = (), kwargs = {}
def __call__(self, *args: Any, **kwargs: Any) -> Any:
> return self.forward(*args, **kwargs)
E TypeError: 'NoneType' object is not callable
minitorch/module.py:79: TypeError
test_module.py::test_parameter
test_module.py::test_parameter
def test_parameter() -> None:
t = MockParam()
q = minitorch.Parameter(t)
print(q)
assert t.x
t2 = MockParam()
q.update(t2)
> assert t2.x
E assert False
E + where False = .x
tests/test_module.py:184: AssertionError
test_modules.py::test_linear
test_modules.py::test_linear
@given(lists(scalars(), max_size=10), integers(min_value=5, max_value=20))
> def test_linear(inputs: List[Scalar], out_size: int) -> None:
tests/test_modules.py:61:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_modules.py:63: in test_linear
mid = lin.forward(inputs)
tests/test_modules.py:53: in forward
y = [b.value for b in self.bias]
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
.0 =
> y = [b.value for b in self.bias]
E AttributeError: 'NoneType' object has no attribute 'value'
E Falsifying example: test_linear(
E out_size=5, inputs=[],
E )
tests/test_modules.py:53: AttributeError
test_modules.py::test_nn_size
test_modules.py::test_nn_size
def test_nn_size() -> None:
model = Network2()
> assert len(model.parameters()) == (
len(model.layer1.parameters()) + len(model.layer2.parameters())
)
E TypeError: object of type 'NoneType' has no len()
tests/test_modules.py:85: TypeError
test_nn.py::test_avg
test_nn.py::test_avg
@pytest.mark.task4_3
> @given(tensors(shape=(1, 1, 4, 4)))
tests/test_nn.py:12:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:76: in tensors
td = draw(tensor_data(numbers, shape=shape))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1, 1, 4, 4)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_nn.py::test_max
test_nn.py::test_max
@pytest.mark.task4_4
> @given(tensors(shape=(2, 3, 4)))
tests/test_nn.py:32:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:76: in tensors
td = draw(tensor_data(numbers, shape=shape))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (2, 3, 4)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_nn.py::test_max_pool
test_nn.py::test_max_pool
@pytest.mark.task4_4
> @given(tensors(shape=(1, 1, 4, 4)))
tests/test_nn.py:39:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:76: in tensors
td = draw(tensor_data(numbers, shape=shape))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1, 1, 4, 4)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_nn.py::test_drop
test_nn.py::test_drop
@pytest.mark.task4_4
> @given(tensors())
tests/test_nn.py:60:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:76: in tensors
td = draw(tensor_data(numbers, shape=shape))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_nn.py::test_softmax
test_nn.py::test_softmax
@pytest.mark.task4_4
> @given(tensors(shape=(1, 1, 4, 4)))
tests/test_nn.py:73:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:76: in tensors
td = draw(tensor_data(numbers, shape=shape))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1, 1, 4, 4)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_nn.py::test_log_softmax
test_nn.py::test_log_softmax
@pytest.mark.task4_4
> @given(tensors(shape=(1, 1, 4, 4)))
tests/test_nn.py:87:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:76: in tensors
td = draw(tensor_data(numbers, shape=shape))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1, 1, 4, 4)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_operators.py::test_same_as_python
test_operators.py::test_same_as_python
@pytest.mark.task0_1
> @given(small_floats, small_floats)
tests/test_operators.py:34:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_operators.py:37: in test_same_as_python
assert_close(mul(x, y), x * y)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
a = None, b = 0.0
def assert_close(a: float, b: float) -> None:
> assert minitorch.operators.is_close(a, b), "Failure x=%f y=%f" % (a, b)
E TypeError: must be real number, not NoneType
E Falsifying example: test_same_as_python(
E y=0.0, x=0.0,
E )
tests/strategies.py:16: TypeError
test_operators.py::test_relu
test_operators.py::test_relu
+ Exception Group Traceback (most recent call last):
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/runner.py", line 341, in from_call
| result: TResult | None = func()
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/runner.py", line 242, in
| lambda: runtest_hook(item=item, **kwds), when=when, reraise=reraise
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_hooks.py", line 513, in __call__
| return self._hookexec(self.name, self._hookimpls.copy(), kwargs, firstresult)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_manager.py", line 120, in _hookexec
| return self._inner_hookexec(hook_name, methods, kwargs, firstresult)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 182, in _multicall
| return outcome.get_result()
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_result.py", line 100, in get_result
| raise exc.with_traceback(exc.__traceback__)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 167, in _multicall
| teardown.throw(outcome._exception)
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/threadexception.py", line 92, in pytest_runtest_call
| yield from thread_exception_runtest_hook()
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/threadexception.py", line 68, in thread_exception_runtest_hook
| yield
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 167, in _multicall
| teardown.throw(outcome._exception)
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/unraisableexception.py", line 95, in pytest_runtest_call
| yield from unraisable_exception_runtest_hook()
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/unraisableexception.py", line 70, in unraisable_exception_runtest_hook
| yield
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 167, in _multicall
| teardown.throw(outcome._exception)
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/logging.py", line 846, in pytest_runtest_call
| yield from self._runtest_for(item, "call")
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/logging.py", line 829, in _runtest_for
| yield
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 167, in _multicall
| teardown.throw(outcome._exception)
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/capture.py", line 880, in pytest_runtest_call
| return (yield)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 167, in _multicall
| teardown.throw(outcome._exception)
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/skipping.py", line 257, in pytest_runtest_call
| return (yield)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 103, in _multicall
| res = hook_impl.function(*args)
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/runner.py", line 174, in pytest_runtest_call
| item.runtest()
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/python.py", line 1627, in runtest
| self.ihook.pytest_pyfunc_call(pyfuncitem=self)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_hooks.py", line 513, in __call__
| return self._hookexec(self.name, self._hookimpls.copy(), kwargs, firstresult)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_manager.py", line 120, in _hookexec
| return self._inner_hookexec(hook_name, methods, kwargs, firstresult)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 139, in _multicall
| raise exception.with_traceback(exception.__traceback__)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 103, in _multicall
| res = hook_impl.function(*args)
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/python.py", line 159, in pytest_pyfunc_call
| result = testfunction(**testargs)
| File "/testbed/tests/test_operators.py", line 46, in test_relu
| @given(small_floats)
| File "/testbed/.venv/lib/python3.10/site-packages/hypothesis/core.py", line 1257, in wrapped_test
| raise the_error_hypothesis_found
| exceptiongroup.ExceptionGroup: Hypothesis found 2 distinct failures. (2 sub-exceptions)
+-+---------------- 1 ----------------
| Traceback (most recent call last):
| File "/testbed/tests/test_operators.py", line 51, in test_relu
| assert relu(a) == 0.0
| AssertionError: assert None == 0.0
| + where None = relu(-1.0)
| Falsifying example: test_relu(
| a=-1.0,
| )
+---------------- 2 ----------------
| Traceback (most recent call last):
| File "/testbed/tests/test_operators.py", line 49, in test_relu
| assert relu(a) == a
| AssertionError: assert None == 1.0
| + where None = relu(1.0)
| Falsifying example: test_relu(
| a=1.0,
| )
+------------------------------------
test_operators.py::test_relu_back
test_operators.py::test_relu_back
+ Exception Group Traceback (most recent call last):
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/runner.py", line 341, in from_call
| result: TResult | None = func()
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/runner.py", line 242, in
| lambda: runtest_hook(item=item, **kwds), when=when, reraise=reraise
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_hooks.py", line 513, in __call__
| return self._hookexec(self.name, self._hookimpls.copy(), kwargs, firstresult)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_manager.py", line 120, in _hookexec
| return self._inner_hookexec(hook_name, methods, kwargs, firstresult)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 182, in _multicall
| return outcome.get_result()
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_result.py", line 100, in get_result
| raise exc.with_traceback(exc.__traceback__)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 167, in _multicall
| teardown.throw(outcome._exception)
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/threadexception.py", line 92, in pytest_runtest_call
| yield from thread_exception_runtest_hook()
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/threadexception.py", line 68, in thread_exception_runtest_hook
| yield
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 167, in _multicall
| teardown.throw(outcome._exception)
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/unraisableexception.py", line 95, in pytest_runtest_call
| yield from unraisable_exception_runtest_hook()
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/unraisableexception.py", line 70, in unraisable_exception_runtest_hook
| yield
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 167, in _multicall
| teardown.throw(outcome._exception)
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/logging.py", line 846, in pytest_runtest_call
| yield from self._runtest_for(item, "call")
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/logging.py", line 829, in _runtest_for
| yield
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 167, in _multicall
| teardown.throw(outcome._exception)
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/capture.py", line 880, in pytest_runtest_call
| return (yield)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 167, in _multicall
| teardown.throw(outcome._exception)
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/skipping.py", line 257, in pytest_runtest_call
| return (yield)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 103, in _multicall
| res = hook_impl.function(*args)
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/runner.py", line 174, in pytest_runtest_call
| item.runtest()
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/python.py", line 1627, in runtest
| self.ihook.pytest_pyfunc_call(pyfuncitem=self)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_hooks.py", line 513, in __call__
| return self._hookexec(self.name, self._hookimpls.copy(), kwargs, firstresult)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_manager.py", line 120, in _hookexec
| return self._inner_hookexec(hook_name, methods, kwargs, firstresult)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 139, in _multicall
| raise exception.with_traceback(exception.__traceback__)
| File "/testbed/.venv/lib/python3.10/site-packages/pluggy/_callers.py", line 103, in _multicall
| res = hook_impl.function(*args)
| File "/testbed/.venv/lib/python3.10/site-packages/_pytest/python.py", line 159, in pytest_pyfunc_call
| result = testfunction(**testargs)
| File "/testbed/tests/test_operators.py", line 55, in test_relu_back
| @given(small_floats, small_floats)
| File "/testbed/.venv/lib/python3.10/site-packages/hypothesis/core.py", line 1257, in wrapped_test
| raise the_error_hypothesis_found
| exceptiongroup.ExceptionGroup: Hypothesis found 2 distinct failures. (2 sub-exceptions)
+-+---------------- 1 ----------------
| Traceback (most recent call last):
| File "/testbed/tests/test_operators.py", line 60, in test_relu_back
| assert relu_back(a, b) == 0.0
| AssertionError: assert None == 0.0
| + where None = relu_back(-1.0, 0.0)
| Falsifying example: test_relu_back(
| b=0.0, a=-1.0,
| )
+---------------- 2 ----------------
| Traceback (most recent call last):
| File "/testbed/tests/test_operators.py", line 58, in test_relu_back
| assert relu_back(a, b) == b
| AssertionError: assert None == 0.0
| + where None = relu_back(1.0, 0.0)
| Falsifying example: test_relu_back(
| b=0.0, a=1.0,
| )
+------------------------------------
test_operators.py::test_id
test_operators.py::test_id
@pytest.mark.task0_1
> @given(small_floats)
tests/test_operators.py:64:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
a = 0.0
@pytest.mark.task0_1
@given(small_floats)
def test_id(a: float) -> None:
> assert id(a) == a
E assert None == 0.0
E + where None = id(0.0)
E Falsifying example: test_id(
E a=0.0,
E )
tests/test_operators.py:66: AssertionError
test_operators.py::test_lt
test_operators.py::test_lt
@pytest.mark.task0_1
> @given(small_floats)
tests/test_operators.py:70:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
a = 0.0
@pytest.mark.task0_1
@given(small_floats)
def test_lt(a: float) -> None:
"Check that a - 1.0 is always less than a"
> assert lt(a - 1.0, a) == 1.0
E assert None == 1.0
E + where None = lt((0.0 - 1.0), 0.0)
E Falsifying example: test_lt(
E a=0.0,
E )
tests/test_operators.py:73: AssertionError
test_operators.py::test_max
test_operators.py::test_max
@pytest.mark.task0_1
> @given(small_floats)
tests/test_operators.py:78:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
a = 0.0
@pytest.mark.task0_1
@given(small_floats)
def test_max(a: float) -> None:
> assert max(a - 1.0, a) == a
E assert None == 0.0
E + where None = max((0.0 - 1.0), 0.0)
E Falsifying example: test_max(
E a=0.0,
E )
tests/test_operators.py:80: AssertionError
test_operators.py::test_eq
test_operators.py::test_eq
@pytest.mark.task0_1
> @given(small_floats)
tests/test_operators.py:87:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
a = 0.0
@pytest.mark.task0_1
@given(small_floats)
def test_eq(a: float) -> None:
> assert eq(a, a) == 1.0
E assert None == 1.0
E + where None = eq(0.0, 0.0)
E Falsifying example: test_eq(
E a=0.0,
E )
tests/test_operators.py:89: AssertionError
test_operators.py::test_sigmoid
test_operators.py::test_sigmoid
@pytest.mark.task0_2
> @given(small_floats)
tests/test_operators.py:102:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
a = 0.0
@pytest.mark.task0_2
@given(small_floats)
def test_sigmoid(a: float) -> None:
"""Check properties of the sigmoid function, specifically
* It is always between 0.0 and 1.0.
* one minus sigmoid is the same as sigmoid of the negative
* It crosses 0 at 0.5
* It is strictly increasing.
"""
# TODO: Implement for Task 0.2.
> raise NotImplementedError('Need to implement for Task 0.2')
E NotImplementedError: Need to implement for Task 0.2
E Falsifying example: test_sigmoid(
E a=0.0,
E )
tests/test_operators.py:111: NotImplementedError
test_operators.py::test_transitive
test_operators.py::test_transitive
@pytest.mark.task0_2
> @given(small_floats, small_floats, small_floats)
tests/test_operators.py:115:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
a = 0.0, b = 0.0, c = 0.0
@pytest.mark.task0_2
@given(small_floats, small_floats, small_floats)
def test_transitive(a: float, b: float, c: float) -> None:
"Test the transitive property of less-than (a < b and b < c implies a < c)"
# TODO: Implement for Task 0.2.
> raise NotImplementedError('Need to implement for Task 0.2')
E NotImplementedError: Need to implement for Task 0.2
E Falsifying example: test_transitive(
E c=0.0, b=0.0, a=0.0,
E )
tests/test_operators.py:119: NotImplementedError
test_operators.py::test_symmetric
test_operators.py::test_symmetric
@pytest.mark.task0_2
def test_symmetric() -> None:
"""
Write a test that ensures that :func:`minitorch.operators.mul` is symmetric, i.e.
gives the same value regardless of the order of its input.
"""
# TODO: Implement for Task 0.2.
> raise NotImplementedError('Need to implement for Task 0.2')
E NotImplementedError: Need to implement for Task 0.2
tests/test_operators.py:129: NotImplementedError
test_operators.py::test_distribute
test_operators.py::test_distribute
@pytest.mark.task0_2
def test_distribute() -> None:
r"""
Write a test that ensures that your operators distribute, i.e.
:math:`z \times (x + y) = z \times x + z \times y`
"""
# TODO: Implement for Task 0.2.
> raise NotImplementedError('Need to implement for Task 0.2')
E NotImplementedError: Need to implement for Task 0.2
tests/test_operators.py:139: NotImplementedError
test_operators.py::test_other
test_operators.py::test_other
@pytest.mark.task0_2
def test_other() -> None:
"""
Write a test that ensures some other property holds for your functions.
"""
# TODO: Implement for Task 0.2.
> raise NotImplementedError('Need to implement for Task 0.2')
E NotImplementedError: Need to implement for Task 0.2
tests/test_operators.py:148: NotImplementedError
test_operators.py::test_zip_with
test_operators.py::test_zip_with
@pytest.mark.task0_3
> @given(small_floats, small_floats, small_floats, small_floats)
tests/test_operators.py:158:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
a = 0.0, b = 0.0, c = 0.0, d = 0.0
@pytest.mark.task0_3
@given(small_floats, small_floats, small_floats, small_floats)
def test_zip_with(a: float, b: float, c: float, d: float) -> None:
> x1, x2 = addLists([a, b], [c, d])
E TypeError: cannot unpack non-iterable NoneType object
E Falsifying example: test_zip_with(
E d=0.0, c=0.0, b=0.0, a=0.0,
E )
tests/test_operators.py:160: TypeError
test_operators.py::test_sum_distribute
test_operators.py::test_sum_distribute
@pytest.mark.task0_3
> @given(
lists(small_floats, min_size=5, max_size=5),
lists(small_floats, min_size=5, max_size=5),
)
tests/test_operators.py:167:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
ls1 = [0.0, 0.0, 0.0, 0.0, 0.0], ls2 = [0.0, 0.0, 0.0, 0.0, 0.0]
@pytest.mark.task0_3
@given(
lists(small_floats, min_size=5, max_size=5),
lists(small_floats, min_size=5, max_size=5),
)
def test_sum_distribute(ls1: List[float], ls2: List[float]) -> None:
"""
Write a test that ensures that the sum of `ls1` plus the sum of `ls2`
is the same as the sum of each element of `ls1` plus each element of `ls2`.
"""
# TODO: Implement for Task 0.3.
> raise NotImplementedError('Need to implement for Task 0.3')
E NotImplementedError: Need to implement for Task 0.3
E Falsifying example: test_sum_distribute(
E ls2=[0.0, 0.0, 0.0, 0.0, 0.0], ls1=[0.0, 0.0, 0.0, 0.0, 0.0],
E )
tests/test_operators.py:177: NotImplementedError
test_operators.py::test_sum
test_operators.py::test_sum
@pytest.mark.task0_3
> @given(lists(small_floats))
tests/test_operators.py:181:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_operators.py:183: in test_sum
assert_close(sum(ls), sum(ls))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
a = None, b = None
def assert_close(a: float, b: float) -> None:
> assert minitorch.operators.is_close(a, b), "Failure x=%f y=%f" % (a, b)
E TypeError: must be real number, not NoneType
E Falsifying example: test_sum(
E ls=[],
E )
tests/strategies.py:16: TypeError
test_operators.py::test_prod
test_operators.py::test_prod
@pytest.mark.task0_3
> @given(small_floats, small_floats, small_floats)
tests/test_operators.py:187:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_operators.py:189: in test_prod
assert_close(prod([x, y, z]), x * y * z)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
a = None, b = 0.0
def assert_close(a: float, b: float) -> None:
> assert minitorch.operators.is_close(a, b), "Failure x=%f y=%f" % (a, b)
E TypeError: must be real number, not NoneType
E Falsifying example: test_prod(
E z=0.0, y=0.0, x=0.0,
E )
tests/strategies.py:16: TypeError
test_operators.py::test_negList
test_operators.py::test_negList
@pytest.mark.task0_3
> @given(lists(small_floats))
tests/test_operators.py:193:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
ls = []
@pytest.mark.task0_3
@given(lists(small_floats))
def test_negList(ls: List[float]) -> None:
check = negList(ls)
> for i, j in zip(ls, check):
E TypeError: 'NoneType' object is not iterable
E Falsifying example: test_negList(
E ls=[],
E )
tests/test_operators.py:196: TypeError
test_scalar.py::test_central_diff
test_scalar.py::test_central_diff
@pytest.mark.task1_1
def test_central_diff() -> None:
> d = central_difference(operators.id, 5, arg=0)
tests/test_scalar.py:35:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
f = , arg = 0, epsilon = 1e-06, vals = (5,)
vals_plus = [5.000001]
def central_difference(f: Any, *vals: Any, arg: int=0, epsilon: float=1e-06) -> Any:
"""
Computes an approximation to the derivative of `f` with respect to one arg.
See :doc:`derivative` or https://en.wikipedia.org/wiki/Finite_difference for more details.
Args:
f : arbitrary function from n-scalar args to one value
*vals : n-float values $x_0 \\ldots x_{n-1}$
arg : the number $i$ of the arg to compute the derivative
epsilon : a small constant
Returns:
An approximation of $f'_i(x_0, \\ldots, x_{n-1})$
"""
vals_plus = list(vals)
vals_minus = list(vals)
vals_plus[arg] = vals[arg] + epsilon
vals_minus[arg] = vals[arg] - epsilon
> return (f(*vals_plus) - f(*vals_minus)) / (2.0 * epsilon)
E TypeError: unsupported operand type(s) for -: 'NoneType' and 'NoneType'
minitorch/autodiff.py:24: TypeError
test_scalar.py::test_simple
test_scalar.py::test_simple
@given(small_floats, small_floats)
> def test_simple(a: float, b: float) -> None:
tests/test_scalar.py:55:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_scalar.py:58: in test_simple
assert_close(c.data, a + b)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
a = 0.0, b = 0.0
def assert_close(a: float, b: float) -> None:
> assert minitorch.operators.is_close(a, b), "Failure x=%f y=%f" % (a, b)
E AssertionError: Failure x=0.000000 y=0.000000
E Falsifying example: test_simple(
E b=0.0, a=0.0,
E )
tests/strategies.py:16: AssertionError
test_scalar.py::test_two_args[fn0]
test_scalar.py::test_two_args[fn0]
fn = ('lt', )
@given(small_scalars, small_scalars)
> @pytest.mark.task1_2
tests/test_scalar.py:85:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
fn = ('lt', ), t1 = Scalar(0.000000)
t2 = Scalar(0.000000)
@given(small_scalars, small_scalars)
@pytest.mark.task1_2
@pytest.mark.parametrize("fn", two_arg)
def test_two_args(
fn: Tuple[str, Callable[[float, float], float], Callable[[Scalar, Scalar], Scalar]],
t1: Scalar,
t2: Scalar,
) -> None:
> name, base_fn, scalar_fn = fn
E ValueError: not enough values to unpack (expected 3, got 2)
E Falsifying example: test_two_args(
E t2=Scalar(0.000000), t1=Scalar(0.000000), fn=('lt', lt),
E )
tests/test_scalar.py:92: ValueError
test_scalar.py::test_two_args[fn1]
test_scalar.py::test_two_args[fn1]
fn = ('eq', )
@given(small_scalars, small_scalars)
> @pytest.mark.task1_2
tests/test_scalar.py:85:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
fn = ('eq', ), t1 = Scalar(0.000000)
t2 = Scalar(0.000000)
@given(small_scalars, small_scalars)
@pytest.mark.task1_2
@pytest.mark.parametrize("fn", two_arg)
def test_two_args(
fn: Tuple[str, Callable[[float, float], float], Callable[[Scalar, Scalar], Scalar]],
t1: Scalar,
t2: Scalar,
) -> None:
> name, base_fn, scalar_fn = fn
E ValueError: not enough values to unpack (expected 3, got 2)
E Falsifying example: test_two_args(
E t2=Scalar(0.000000), t1=Scalar(0.000000), fn=('eq', eq),
E )
tests/test_scalar.py:92: ValueError
test_scalar.py::test_two_args[fn2]
test_scalar.py::test_two_args[fn2]
fn = ('gt', . at 0x7fcab582eb00>)
@given(small_scalars, small_scalars)
> @pytest.mark.task1_2
tests/test_scalar.py:85:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
fn = ('gt', . at 0x7fcab582eb00>)
t1 = Scalar(0.000000), t2 = Scalar(0.000000)
@given(small_scalars, small_scalars)
@pytest.mark.task1_2
@pytest.mark.parametrize("fn", two_arg)
def test_two_args(
fn: Tuple[str, Callable[[float, float], float], Callable[[Scalar, Scalar], Scalar]],
t1: Scalar,
t2: Scalar,
) -> None:
> name, base_fn, scalar_fn = fn
E ValueError: not enough values to unpack (expected 3, got 2)
E Falsifying example: test_two_args(
E t2=Scalar(0.000000),
E t1=Scalar(0.000000),
E fn=('gt', lambda x, y: operators.lt(y, x)),
E )
tests/test_scalar.py:92: ValueError
test_scalar.py::test_two_derivative[fn0]
test_scalar.py::test_two_derivative[fn0]
fn = ('lt', )
@given(small_scalars, small_scalars)
> @pytest.mark.task1_4
tests/test_scalar.py:112:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
fn = ('lt', ), t1 = Scalar(0.000000)
t2 = Scalar(0.000000)
@given(small_scalars, small_scalars)
@pytest.mark.task1_4
@pytest.mark.parametrize("fn", two_arg)
def test_two_derivative(
fn: Tuple[str, Callable[[float, float], float], Callable[[Scalar, Scalar], Scalar]],
t1: Scalar,
t2: Scalar,
) -> None:
> name, _, scalar_fn = fn
E ValueError: not enough values to unpack (expected 3, got 2)
E Falsifying example: test_two_derivative(
E t2=Scalar(0.000000), t1=Scalar(0.000000), fn=('lt', lt),
E )
tests/test_scalar.py:119: ValueError
test_scalar.py::test_two_derivative[fn1]
test_scalar.py::test_two_derivative[fn1]
fn = ('eq', )
@given(small_scalars, small_scalars)
> @pytest.mark.task1_4
tests/test_scalar.py:112:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
fn = ('eq', ), t1 = Scalar(0.000000)
t2 = Scalar(0.000000)
@given(small_scalars, small_scalars)
@pytest.mark.task1_4
@pytest.mark.parametrize("fn", two_arg)
def test_two_derivative(
fn: Tuple[str, Callable[[float, float], float], Callable[[Scalar, Scalar], Scalar]],
t1: Scalar,
t2: Scalar,
) -> None:
> name, _, scalar_fn = fn
E ValueError: not enough values to unpack (expected 3, got 2)
E Falsifying example: test_two_derivative(
E t2=Scalar(0.000000), t1=Scalar(0.000000), fn=('eq', eq),
E )
tests/test_scalar.py:119: ValueError
test_scalar.py::test_two_derivative[fn2]
test_scalar.py::test_two_derivative[fn2]
fn = ('gt', . at 0x7fcab582eb00>)
@given(small_scalars, small_scalars)
> @pytest.mark.task1_4
tests/test_scalar.py:112:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
fn = ('gt', . at 0x7fcab582eb00>)
t1 = Scalar(0.000000), t2 = Scalar(0.000000)
@given(small_scalars, small_scalars)
@pytest.mark.task1_4
@pytest.mark.parametrize("fn", two_arg)
def test_two_derivative(
fn: Tuple[str, Callable[[float, float], float], Callable[[Scalar, Scalar], Scalar]],
t1: Scalar,
t2: Scalar,
) -> None:
> name, _, scalar_fn = fn
E ValueError: not enough values to unpack (expected 3, got 2)
E Falsifying example: test_two_derivative(
E t2=Scalar(0.000000),
E t1=Scalar(0.000000),
E fn=('gt', lambda x, y: operators.lt(y, x)),
E )
tests/test_scalar.py:119: ValueError
test_tensor.py::test_create
test_tensor.py::test_create
@given(lists(small_floats, min_size=1))
> def test_create(t1: List[float]) -> None:
tests/test_tensor.py:16:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
t1 = [0.0]
@given(lists(small_floats, min_size=1))
def test_create(t1: List[float]) -> None:
"Test the ability to create an index a 1D Tensor"
t2 = tensor(t1)
for i in range(len(t1)):
> assert t1[i] == t2[i]
E TypeError: 'NoneType' object is not subscriptable
E Falsifying example: test_create(
E t1=[0.0],
E )
tests/test_tensor.py:20: TypeError
test_tensor.py::test_two_args[fn0]
test_tensor.py::test_two_args[fn0]
fn = ('lt', )
@given(shaped_tensors(2))
> @pytest.mark.task2_3
tests/test_tensor.py:37:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_tensor.py::test_two_args[fn1]
test_tensor.py::test_two_args[fn1]
fn = ('eq', )
@given(shaped_tensors(2))
> @pytest.mark.task2_3
tests/test_tensor.py:37:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_tensor.py::test_two_args[fn2]
test_tensor.py::test_two_args[fn2]
fn = ('gt', . at 0x7fcab58e81f0>)
@given(shaped_tensors(2))
> @pytest.mark.task2_3
tests/test_tensor.py:37:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_tensor.py::test_permute
test_tensor.py::test_permute
@given(data(), tensors())
> @pytest.mark.task2_4
tests/test_tensor.py:62:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:76: in tensors
td = draw(tensor_data(numbers, shape=shape))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_tensor.py::test_grad_size
test_tensor.py::test_grad_size
def test_grad_size() -> None:
"Test the size of the gradient (from @WannaFy)"
a = tensor([1], requires_grad=True)
b = tensor([[1, 1]], requires_grad=True)
> c = (a * b).sum()
E TypeError: unsupported operand type(s) for *: 'NoneType' and 'NoneType'
tests/test_tensor.py:78: TypeError
test_tensor.py::test_two_grad[fn0]
test_tensor.py::test_two_grad[fn0]
fn = ('lt', )
@given(shaped_tensors(2))
> @pytest.mark.task2_4
tests/test_tensor.py:101:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_tensor.py::test_two_grad[fn1]
test_tensor.py::test_two_grad[fn1]
fn = ('eq', )
@given(shaped_tensors(2))
> @pytest.mark.task2_4
tests/test_tensor.py:101:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_tensor.py::test_two_grad[fn2]
test_tensor.py::test_two_grad[fn2]
fn = ('gt', . at 0x7fcab58e81f0>)
@given(shaped_tensors(2))
> @pytest.mark.task2_4
tests/test_tensor.py:101:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_tensor.py::test_two_grad_broadcast[fn0]
test_tensor.py::test_two_grad_broadcast[fn0]
fn = ('lt', )
@given(shaped_tensors(2))
> @pytest.mark.task2_4
tests/test_tensor.py:113:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_tensor.py::test_two_grad_broadcast[fn1]
test_tensor.py::test_two_grad_broadcast[fn1]
fn = ('eq', )
@given(shaped_tensors(2))
> @pytest.mark.task2_4
tests/test_tensor.py:113:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_tensor.py::test_two_grad_broadcast[fn2]
test_tensor.py::test_two_grad_broadcast[fn2]
fn = ('gt', . at 0x7fcab58e81f0>)
@given(shaped_tensors(2))
> @pytest.mark.task2_4
tests/test_tensor.py:113:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_tensor.py::test_fromlist
test_tensor.py::test_fromlist
def test_fromlist() -> None:
"Test longer from list conversion"
t = tensor([[2, 3, 4], [4, 5, 7]])
> assert t.shape == (2, 3)
E AttributeError: 'NoneType' object has no attribute 'shape'
tests/test_tensor.py:132: AttributeError
test_tensor.py::test_view
test_tensor.py::test_view
def test_view() -> None:
"Test view"
t = tensor([[2, 3, 4], [4, 5, 7]])
> assert t.shape == (2, 3)
E AttributeError: 'NoneType' object has no attribute 'shape'
tests/test_tensor.py:140: AttributeError
test_tensor.py::test_back_view
test_tensor.py::test_back_view
@given(tensors())
> def test_back_view(t1: Tensor) -> None:
tests/test_tensor.py:152:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/tensor_strategies.py:76: in tensors
td = draw(tensor_data(numbers, shape=shape))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_tensor.py::test_permute_view
test_tensor.py::test_permute_view
@pytest.mark.xfail
def test_permute_view() -> None:
t = tensor([[2, 3, 4], [4, 5, 7]])
> assert t.shape == (2, 3)
E AttributeError: 'NoneType' object has no attribute 'shape'
tests/test_tensor.py:165: AttributeError
test_tensor.py::test_index
test_tensor.py::test_index
@pytest.mark.xfail
def test_index() -> None:
t = tensor([[2, 3, 4], [4, 5, 7]])
> assert t.shape == (2, 3)
E AttributeError: 'NoneType' object has no attribute 'shape'
tests/test_tensor.py:173: AttributeError
test_tensor.py::test_fromnumpy
test_tensor.py::test_fromnumpy
def test_fromnumpy() -> None:
t = tensor([[2, 3, 4], [4, 5, 7]])
print(t)
> assert t.shape == (2, 3)
E AttributeError: 'NoneType' object has no attribute 'shape'
tests/test_tensor.py:180: AttributeError
test_tensor.py::test_reduce_forward_one_dim
test_tensor.py::test_reduce_forward_one_dim
@pytest.mark.task2_3
def test_reduce_forward_one_dim() -> None:
# shape (3, 2)
t = tensor([[2, 3], [4, 6], [5, 7]])
# here 0 means to reduce the 0th dim, 3 -> nothing
> t_summed = t.sum(0)
E AttributeError: 'NoneType' object has no attribute 'sum'
tests/test_tensor.py:196: AttributeError
test_tensor.py::test_reduce_forward_one_dim_2
test_tensor.py::test_reduce_forward_one_dim_2
@pytest.mark.task2_3
def test_reduce_forward_one_dim_2() -> None:
# shape (3, 2)
t = tensor([[2, 3], [4, 6], [5, 7]])
# here 1 means reduce the 1st dim, 2 -> nothing
> t_summed_2 = t.sum(1)
E AttributeError: 'NoneType' object has no attribute 'sum'
tests/test_tensor.py:209: AttributeError
test_tensor.py::test_reduce_forward_all_dims
test_tensor.py::test_reduce_forward_all_dims
@pytest.mark.task2_3
def test_reduce_forward_all_dims() -> None:
# shape (3, 2)
t = tensor([[2, 3], [4, 6], [5, 7]])
# reduce all dims, (3 -> 1, 2 -> 1)
> t_summed_all = t.sum()
E AttributeError: 'NoneType' object has no attribute 'sum'
tests/test_tensor.py:222: AttributeError
test_tensor_data.py::test_layout
test_tensor_data.py::test_layout
@pytest.mark.task2_1
def test_layout() -> None:
"Test basis properties of layout and strides"
data = [0] * 3 * 5
> tensor_data = minitorch.TensorData(data, (3, 5), (5, 1))
tests/test_tensor_data.py:19:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self =
storage = [0, 0, 0, 0, 0, 0, ...], shape = (3, 5), strides = (5, 1)
def __init__(self, storage: Union[Sequence[float], Storage], shape: UserShape, strides: Optional[UserStrides]=None):
if isinstance(storage, np.ndarray):
self._storage = storage
else:
self._storage = array(storage, dtype=float64)
if strides is None:
strides = strides_from_shape(shape)
assert isinstance(strides, tuple), 'Strides must be tuple'
assert isinstance(shape, tuple), 'Shape must be tuple'
if len(strides) != len(shape):
raise IndexingError(f'Len of strides {strides} must match {shape}.')
self._strides = array(strides)
self._shape = array(shape)
self.strides = strides
self.dims = len(strides)
> self.size = int(prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
minitorch/tensor_data.py:111: TypeError
test_tensor_data.py::test_layout_bad
test_tensor_data.py::test_layout_bad
@pytest.mark.xfail
def test_layout_bad() -> None:
"Test basis properties of layout and strides"
data = [0] * 3 * 5
> minitorch.TensorData(data, (3, 5), (6,))
tests/test_tensor_data.py:39:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self =
storage = [0, 0, 0, 0, 0, 0, ...], shape = (3, 5), strides = (6,)
def __init__(self, storage: Union[Sequence[float], Storage], shape: UserShape, strides: Optional[UserStrides]=None):
if isinstance(storage, np.ndarray):
self._storage = storage
else:
self._storage = array(storage, dtype=float64)
if strides is None:
strides = strides_from_shape(shape)
assert isinstance(strides, tuple), 'Strides must be tuple'
assert isinstance(shape, tuple), 'Shape must be tuple'
if len(strides) != len(shape):
> raise IndexingError(f'Len of strides {strides} must match {shape}.')
E minitorch.tensor_data.IndexingError: Len of strides (6,) must match (3, 5).
minitorch/tensor_data.py:106: IndexingError
test_tensor_data.py::test_enumeration
test_tensor_data.py::test_enumeration
@pytest.mark.task2_1
> @given(tensor_data())
tests/test_tensor_data.py:43:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-inf, max_value=inf, allow_nan=True, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_tensor_data.py::test_index
test_tensor_data.py::test_index
@pytest.mark.task2_1
> @given(tensor_data())
tests/test_tensor_data.py:61:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-inf, max_value=inf, allow_nan=True, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_tensor_data.py::test_permute
test_tensor_data.py::test_permute
@pytest.mark.task2_1
> @given(data())
tests/test_tensor_data.py:81:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_tensor_data.py:83: in test_permute
td = data.draw(tensor_data())
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1813: in draw
result = self.conjecture_data.draw(strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:946: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-inf, max_value=inf, allow_nan=True, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
E Falsifying example: test_permute(
E data=data(...),
E )
tests/tensor_strategies.py:49: TypeError
test_tensor_data.py::test_shape_broadcast
test_tensor_data.py::test_shape_broadcast
@pytest.mark.task2_2
def test_shape_broadcast() -> None:
c = minitorch.shape_broadcast((1,), (5, 5))
> assert c == (5, 5)
E assert None == (5, 5)
tests/test_tensor_data.py:100: AssertionError
test_tensor_data.py::test_string
test_tensor_data.py::test_string
@given(tensor_data())
> def test_string(tensor_data: TensorData) -> None:
tests/test_tensor_data.py:124:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-inf, max_value=inf, allow_nan=True, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
tests/tensor_strategies.py:49: TypeError
test_tensor_general.py::test_create[fast]
test_tensor_general.py::test_create[fast]
backend = 'fast'
@given(lists(small_floats, min_size=1))
> @pytest.mark.parametrize("backend", backend_tests)
tests/test_tensor_general.py:45:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
backend = 'fast', t1 = [0.0]
@given(lists(small_floats, min_size=1))
@pytest.mark.parametrize("backend", backend_tests)
def test_create(backend: str, t1: List[float]) -> None:
"Create different tensors."
t2 = minitorch.tensor(t1, backend=shared[backend])
for i in range(len(t1)):
> assert t1[i] == t2[i]
E TypeError: 'NoneType' object is not subscriptable
E Falsifying example: test_create(
E t1=[0.0], backend='fast',
E )
tests/test_tensor_general.py:50: TypeError
test_tensor_general.py::test_two_args[fast-fn0]
test_tensor_general.py::test_two_args[fast-fn0]
fn = ('lt', ), backend = 'fast'
@given(data())
> @settings(max_examples=100)
tests/test_tensor_general.py:71:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_tensor_general.py:80: in test_two_args
t1, t2 = data.draw(shaped_tensors(2, backend=shared[backend]))
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1813: in draw
result = self.conjecture_data.draw(strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:946: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
E Falsifying example: test_two_args(
E data=data(...), fn=('lt', lt), backend='fast',
E )
tests/tensor_strategies.py:49: TypeError
test_tensor_general.py::test_two_args[fast-fn1]
test_tensor_general.py::test_two_args[fast-fn1]
fn = ('eq', ), backend = 'fast'
@given(data())
> @settings(max_examples=100)
tests/test_tensor_general.py:71:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_tensor_general.py:80: in test_two_args
t1, t2 = data.draw(shaped_tensors(2, backend=shared[backend]))
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1813: in draw
result = self.conjecture_data.draw(strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:946: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
E Falsifying example: test_two_args(
E data=data(...), fn=('eq', eq), backend='fast',
E )
tests/tensor_strategies.py:49: TypeError
test_tensor_general.py::test_two_args[fast-fn2]
test_tensor_general.py::test_two_args[fast-fn2]
fn = ('gt', . at 0x7fcab58eab90>)
backend = 'fast'
@given(data())
> @settings(max_examples=100)
tests/test_tensor_general.py:71:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_tensor_general.py:80: in test_two_args
t1, t2 = data.draw(shaped_tensors(2, backend=shared[backend]))
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1813: in draw
result = self.conjecture_data.draw(strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:946: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
E Falsifying example: test_two_args(
E data=data(...), fn=('gt', lambda x, y: operators.lt(y, x)), backend='fast',
E )
tests/tensor_strategies.py:49: TypeError
test_tensor_general.py::test_two_grad[fast-fn0]
test_tensor_general.py::test_two_grad[fast-fn0]
fn = ('lt', ), backend = 'fast'
@given(data())
> @settings(max_examples=50)
tests/test_tensor_general.py:102:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_tensor_general.py:111: in test_two_grad
t1, t2 = data.draw(shaped_tensors(2, backend=shared[backend]))
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1813: in draw
result = self.conjecture_data.draw(strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:946: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
E Falsifying example: test_two_grad(
E data=data(...), fn=('lt', lt), backend='fast',
E )
tests/tensor_strategies.py:49: TypeError
test_tensor_general.py::test_two_grad[fast-fn1]
test_tensor_general.py::test_two_grad[fast-fn1]
fn = ('eq', ), backend = 'fast'
@given(data())
> @settings(max_examples=50)
tests/test_tensor_general.py:102:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_tensor_general.py:111: in test_two_grad
t1, t2 = data.draw(shaped_tensors(2, backend=shared[backend]))
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1813: in draw
result = self.conjecture_data.draw(strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:946: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
E Falsifying example: test_two_grad(
E data=data(...), fn=('eq', eq), backend='fast',
E )
tests/tensor_strategies.py:49: TypeError
test_tensor_general.py::test_two_grad[fast-fn2]
test_tensor_general.py::test_two_grad[fast-fn2]
fn = ('gt', . at 0x7fcab58eab90>)
backend = 'fast'
@given(data())
> @settings(max_examples=50)
tests/test_tensor_general.py:102:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_tensor_general.py:111: in test_two_grad
t1, t2 = data.draw(shaped_tensors(2, backend=shared[backend]))
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1813: in draw
result = self.conjecture_data.draw(strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:946: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
E Falsifying example: test_two_grad(
E data=data(...), fn=('gt', lambda x, y: operators.lt(y, x)), backend='fast',
E )
tests/tensor_strategies.py:49: TypeError
test_tensor_general.py::test_two_grad_broadcast[fast-fn0]
test_tensor_general.py::test_two_grad_broadcast[fast-fn0]
fn = ('lt', ), backend = 'fast'
@given(data())
> @settings(max_examples=25)
tests/test_tensor_general.py:308:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_tensor_general.py:317: in test_two_grad_broadcast
t1, t2 = data.draw(shaped_tensors(2, backend=shared[backend]))
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1813: in draw
result = self.conjecture_data.draw(strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:946: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
E Falsifying example: test_two_grad_broadcast(
E data=data(...), fn=('lt', lt), backend='fast',
E )
tests/tensor_strategies.py:49: TypeError
test_tensor_general.py::test_two_grad_broadcast[fast-fn1]
test_tensor_general.py::test_two_grad_broadcast[fast-fn1]
fn = ('eq', ), backend = 'fast'
@given(data())
> @settings(max_examples=25)
tests/test_tensor_general.py:308:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_tensor_general.py:317: in test_two_grad_broadcast
t1, t2 = data.draw(shaped_tensors(2, backend=shared[backend]))
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1813: in draw
result = self.conjecture_data.draw(strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:946: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
E Falsifying example: test_two_grad_broadcast(
E data=data(...), fn=('eq', eq), backend='fast',
E )
tests/tensor_strategies.py:49: TypeError
test_tensor_general.py::test_two_grad_broadcast[fast-fn2]
test_tensor_general.py::test_two_grad_broadcast[fast-fn2]
fn = ('gt', . at 0x7fcab58eab90>)
backend = 'fast'
@given(data())
> @settings(max_examples=25)
tests/test_tensor_general.py:308:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_tensor_general.py:317: in test_two_grad_broadcast
t1, t2 = data.draw(shaped_tensors(2, backend=shared[backend]))
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1813: in draw
result = self.conjecture_data.draw(strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:946: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
tests/tensor_strategies.py:90: in shaped_tensors
td = draw(tensor_data(numbers))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
E Falsifying example: test_two_grad_broadcast(
E data=data(...), fn=('gt', lambda x, y: operators.lt(y, x)), backend='fast',
E )
tests/tensor_strategies.py:49: TypeError
test_tensor_general.py::test_permute[fast]
test_tensor_general.py::test_permute[fast]
backend = 'fast'
@given(data())
> @settings(max_examples=100)
tests/test_tensor_general.py:328:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_tensor_general.py:332: in test_permute
t1 = data.draw(tensors(backend=shared[backend]))
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1813: in draw
result = self.conjecture_data.draw(strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:946: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
tests/tensor_strategies.py:76: in tensors
td = draw(tensor_data(numbers, shape=shape))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (1,)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
E Falsifying example: test_permute(
E data=data(...), backend='fast',
E )
tests/tensor_strategies.py:49: TypeError
test_tensor_general.py::test_mm2
test_tensor_general.py::test_mm2
@pytest.mark.task3_2
def test_mm2() -> None:
a = minitorch.rand((2, 3), backend=FastTensorBackend)
b = minitorch.rand((3, 4), backend=FastTensorBackend)
> c = a @ b
E TypeError: unsupported operand type(s) for @: 'NoneType' and 'NoneType'
tests/test_tensor_general.py:345: TypeError
test_tensor_general.py::test_bmm[fast]
test_tensor_general.py::test_bmm[fast]
backend = 'fast'
@given(data())
> @pytest.mark.parametrize("backend", matmul_tests)
tests/test_tensor_general.py:361:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_tensor_general.py:370: in test_bmm
a = data.draw(tensors(backend=shared[backend], shape=(D, A, B)))
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1813: in draw
result = self.conjecture_data.draw(strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:946: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
tests/tensor_strategies.py:76: in tensors
td = draw(tensor_data(numbers, shape=shape))
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/lazy.py:156: in do_draw
return data.draw(self.wrapped_strategy)
.venv/lib/python3.10/site-packages/hypothesis/internal/conjecture/data.py:941: in draw
return strategy.do_draw(self)
.venv/lib/python3.10/site-packages/hypothesis/strategies/_internal/core.py:1485: in do_draw
return self.definition(data.draw, *self.args, **self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
draw =
numbers = FloatStrategy(min_value=-100.0, max_value=100.0, allow_nan=False, smallest_nonzero_magnitude=5e-324)
shape = (2, 2, 2)
@composite
def tensor_data(
draw: DrawFn,
numbers: SearchStrategy[float] = floats(),
shape: Optional[UserShape] = None,
) -> TensorData:
if shape is None:
shape = draw(shapes())
> size = int(minitorch.prod(shape))
E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
E Falsifying example: test_bmm(
E data=data(...), backend='fast',
E )
E Draw 1: 2
E Draw 2: 2
E Draw 3: 2
E Draw 4: 2
tests/tensor_strategies.py:49: TypeError
Patch diff
diff --git a/minitorch/autodiff.py b/minitorch/autodiff.py
index cb9a430..84dbd21 100644
--- a/minitorch/autodiff.py
+++ b/minitorch/autodiff.py
@@ -17,11 +17,31 @@ def central_difference(f: Any, *vals: Any, arg: int=0, epsilon: float=1e-06) ->
Returns:
An approximation of $f'_i(x_0, \\ldots, x_{n-1})$
"""
- pass
+ vals_plus = list(vals)
+ vals_minus = list(vals)
+ vals_plus[arg] = vals[arg] + epsilon
+ vals_minus[arg] = vals[arg] - epsilon
+ return (f(*vals_plus) - f(*vals_minus)) / (2.0 * epsilon)
variable_count = 1
class Variable(Protocol):
- pass
+ """A variable in a computation graph."""
+
+ def accumulate_derivative(self, x: Any) -> None:
+ """Add `x` to the derivative accumulated on this variable."""
+ pass
+
+ def is_constant(self) -> bool:
+ """Is this a constant variable?"""
+ pass
+
+ def is_leaf(self) -> bool:
+ """Is this a leaf variable?"""
+ pass
+
+ def parents(self) -> Iterable["Variable"]:
+ """Get the parents of this variable."""
+ pass
def topological_sort(variable: Variable) -> Iterable[Variable]:
"""
@@ -33,7 +53,19 @@ def topological_sort(variable: Variable) -> Iterable[Variable]:
Returns:
Non-constant Variables in topological order starting from the right.
"""
- pass
+ visited = set()
+ order = []
+
+ def visit(var: Variable) -> None:
+ if var in visited or var.is_constant():
+ return
+ visited.add(var)
+ for parent in var.parents():
+ visit(parent)
+ order.insert(0, var)
+
+ visit(variable)
+ return order
def backpropagate(variable: Variable, deriv: Any) -> None:
"""
@@ -46,7 +78,26 @@ def backpropagate(variable: Variable, deriv: Any) -> None:
No return. Should write to its results to the derivative values of each leaf through `accumulate_derivative`.
"""
- pass
+ # Get variables in topological order
+ ordered = topological_sort(variable)
+
+ # Store derivatives for each variable
+ derivatives = {variable: deriv}
+
+ # Go through the variables in reverse order
+ for var in ordered:
+ if var.is_leaf():
+ var.accumulate_derivative(derivatives[var])
+ else:
+ # Get the derivative for this variable
+ d = derivatives[var]
+ # Get the parents and their derivatives through the chain rule
+ for parent, parent_deriv in var.chain_rule(d):
+ # Initialize derivative for parent if not seen before
+ if parent not in derivatives:
+ derivatives[parent] = 0.0
+ # Add to parent's derivative
+ derivatives[parent] += parent_deriv
@dataclass
class Context:
@@ -58,4 +109,25 @@ class Context:
def save_for_backward(self, *values: Any) -> None:
"""Store the given `values` if they need to be used during backpropagation."""
- pass
\ No newline at end of file
+ if not self.no_grad:
+ self.saved_values = values
+
+def grad_check(f: Any, *vals: Any, arg: int=0, epsilon: float=1e-06, rtol: float=0.001, atol: float=1e-08) -> bool:
+ """
+ Check that the computed gradient matches the numerical approximation.
+
+ Args:
+ f : arbitrary function from n-scalars to 1-scalar
+ *vals : n-float values $x_0 \ldots x_{n-1}$
+ arg : the argument to compute the gradient with respect to
+ epsilon : a small constant
+ rtol : relative tolerance
+ atol : absolute tolerance
+
+ Returns:
+ bool : whether the numerical and computed gradient are close
+ """
+ numerical = central_difference(f, *vals, arg=arg, epsilon=epsilon)
+ computed = f(*vals)
+ backpropagate(computed, 1.0)
+ return abs(numerical - vals[arg].derivative) <= (atol + rtol * abs(numerical))
\ No newline at end of file
diff --git a/minitorch/datasets.py b/minitorch/datasets.py
index 46322bd..1112bb6 100644
--- a/minitorch/datasets.py
+++ b/minitorch/datasets.py
@@ -8,4 +8,98 @@ class Graph:
N: int
X: List[Tuple[float, float]]
y: List[int]
+
+def simple(N: int = 100) -> Graph:
+ """Simple dataset with two linearly separable clouds of points"""
+ X = []
+ y = []
+ for i in range(N):
+ x = random.uniform(-1.0, 1.0)
+ y_val = random.uniform(-1.0, 1.0)
+ label = 1 if x + y_val > 0 else 0
+ X.append((x, y_val))
+ y.append(label)
+ return Graph(N, X, y)
+
+def diag(N: int = 100) -> Graph:
+ """Dataset with two diagonal lines of points"""
+ X = []
+ y = []
+ for i in range(N):
+ if random.random() > 0.5:
+ x = random.uniform(-1.0, 1.0)
+ y_val = x + 0.2 * random.uniform(-1.0, 1.0)
+ label = 1
+ else:
+ x = random.uniform(-1.0, 1.0)
+ y_val = -x + 0.2 * random.uniform(-1.0, 1.0)
+ label = 0
+ X.append((x, y_val))
+ y.append(label)
+ return Graph(N, X, y)
+
+def split(N: int = 100) -> Graph:
+ """Dataset with two distinct regions"""
+ X = []
+ y = []
+ for i in range(N):
+ x = random.uniform(-1.0, 1.0)
+ y_val = random.uniform(-1.0, 1.0)
+ if x < 0:
+ label = 1 if y_val > 0 else 0
+ else:
+ label = 1 if y_val < 0 else 0
+ X.append((x, y_val))
+ y.append(label)
+ return Graph(N, X, y)
+
+def xor(N: int = 100) -> Graph:
+ """Dataset with XOR pattern"""
+ X = []
+ y = []
+ for i in range(N):
+ x = random.uniform(-1.0, 1.0)
+ y_val = random.uniform(-1.0, 1.0)
+ if (x > 0 and y_val > 0) or (x < 0 and y_val < 0):
+ label = 1
+ else:
+ label = 0
+ X.append((x, y_val))
+ y.append(label)
+ return Graph(N, X, y)
+
+def circle(N: int = 100) -> Graph:
+ """Dataset with points in a circle"""
+ X = []
+ y = []
+ for i in range(N):
+ x = random.uniform(-1.0, 1.0)
+ y_val = random.uniform(-1.0, 1.0)
+ if x * x + y_val * y_val < 0.5:
+ label = 1
+ else:
+ label = 0
+ X.append((x, y_val))
+ y.append(label)
+ return Graph(N, X, y)
+
+def spiral(N: int = 100) -> Graph:
+ """Dataset with points in a spiral pattern"""
+ X = []
+ y = []
+ for i in range(N):
+ radius = random.uniform(0, 1)
+ angle = random.uniform(0, 4 * math.pi)
+ if random.random() > 0.5:
+ x = radius * math.cos(angle)
+ y_val = radius * math.sin(angle)
+ label = 1
+ else:
+ x = radius * math.cos(angle + math.pi)
+ y_val = radius * math.sin(angle + math.pi)
+ label = 0
+ X.append((x, y_val))
+ y.append(label)
+ return Graph(N, X, y)
+
datasets = {'Simple': simple, 'Diag': diag, 'Split': split, 'Xor': xor, 'Circle': circle, 'Spiral': spiral}
\ No newline at end of file
diff --git a/minitorch/fast_conv.py b/minitorch/fast_conv.py
index eddae84..137698a 100644
--- a/minitorch/fast_conv.py
+++ b/minitorch/fast_conv.py
@@ -41,7 +41,28 @@ def _tensor_conv1d(out: Tensor, out_shape: Shape, out_strides: Strides, out_size
weight_strides (Strides): strides for `input` tensor.
reverse (bool): anchor weight at left or right
"""
- pass
+ batch_, in_channels, width = input_shape
+ out_channels, _, k_width = weight_shape
+
+ # For each output position
+ for batch in prange(batch_):
+ for out_channel in prange(out_channels):
+ for w in prange(width):
+ # Sum up all the values of the input * weights
+ acc = 0.0
+ for in_channel in range(in_channels):
+ for k in range(k_width):
+ w_offset = k if not reverse else k_width - k - 1
+ if w + w_offset < width:
+ # Get input position
+ in_pos = index_to_position((batch, in_channel, w + w_offset), input_strides)
+ # Get weight position
+ w_pos = index_to_position((out_channel, in_channel, k), weight_strides)
+ # Add to accumulator
+ acc += input._tensor._storage[in_pos] * weight._tensor._storage[w_pos]
+ # Set output position
+ out_pos = index_to_position((batch, out_channel, w), out_strides)
+ out._tensor._storage[out_pos] = acc
tensor_conv1d = njit(parallel=True)(_tensor_conv1d)
class Conv1dFun(Function):
@@ -59,7 +80,28 @@ class Conv1dFun(Function):
Returns:
batch x out_channel x h x w
"""
- pass
+ ctx.save_for_backward(input, weight)
+ batch, in_channels, width = input.shape
+ out_channels, _, k_width = weight.shape
+
+ # Create output tensor
+ out = input.zeros((batch, out_channels, width))
+
+ # Call the conv1d implementation
+ tensor_conv1d(
+ out,
+ out.shape,
+ out.strides,
+ out.size,
+ input,
+ input.shape,
+ input.strides,
+ weight,
+ weight.shape,
+ weight.strides,
+ False,
+ )
+ return out
conv1d = Conv1dFun.apply
def _tensor_conv2d(out: Tensor, out_shape: Shape, out_strides: Strides, out_size: int, input: Tensor, input_shape: Shape, input_strides: Strides, weight: Tensor, weight_shape: Shape, weight_strides: Strides, reverse: bool) -> None:
@@ -95,7 +137,37 @@ def _tensor_conv2d(out: Tensor, out_shape: Shape, out_strides: Strides, out_size
weight_strides (Strides): strides for `input` tensor.
reverse (bool): anchor weight at top-left or bottom-right
"""
- pass
+ batch_, in_channels, height, width = input_shape
+ out_channels, _, k_height, k_width = weight_shape
+
+ # For each output position
+ for batch in prange(batch_):
+ for out_channel in prange(out_channels):
+ for h in prange(height):
+ for w in prange(width):
+ # Sum up all the values of the input * weights
+ acc = 0.0
+ for in_channel in range(in_channels):
+ for k_h in range(k_height):
+ for k_w in range(k_width):
+ h_offset = k_h if not reverse else k_height - k_h - 1
+ w_offset = k_w if not reverse else k_width - k_w - 1
+ if h + h_offset < height and w + w_offset < width:
+ # Get input position
+ in_pos = index_to_position(
+ (batch, in_channel, h + h_offset, w + w_offset),
+ input_strides,
+ )
+ # Get weight position
+ w_pos = index_to_position(
+ (out_channel, in_channel, k_h, k_w),
+ weight_strides,
+ )
+ # Add to accumulator
+ acc += input._tensor._storage[in_pos] * weight._tensor._storage[w_pos]
+ # Set output position
+ out_pos = index_to_position((batch, out_channel, h, w), out_strides)
+ out._tensor._storage[out_pos] = acc
tensor_conv2d = njit(parallel=True, fastmath=True)(_tensor_conv2d)
class Conv2dFun(Function):
@@ -113,5 +185,26 @@ class Conv2dFun(Function):
Returns:
(:class:`Tensor`) : batch x out_channel x h x w
"""
- pass
+ ctx.save_for_backward(input, weight)
+ batch, in_channels, height, width = input.shape
+ out_channels, _, k_height, k_width = weight.shape
+
+ # Create output tensor
+ out = input.zeros((batch, out_channels, height, width))
+
+ # Call the conv2d implementation
+ tensor_conv2d(
+ out,
+ out.shape,
+ out.strides,
+ out.size,
+ input,
+ input.shape,
+ input.strides,
+ weight,
+ weight.shape,
+ weight.strides,
+ False,
+ )
+ return out
conv2d = Conv2dFun.apply
\ No newline at end of file
diff --git a/minitorch/fast_ops.py b/minitorch/fast_ops.py
index ff6c24b..27cfb09 100644
--- a/minitorch/fast_ops.py
+++ b/minitorch/fast_ops.py
@@ -17,17 +17,75 @@ class FastOps(TensorOps):
@staticmethod
def map(fn: Callable[[float], float]) -> MapProto:
"""See `tensor_ops.py`"""
- pass
+ def _map(a: Tensor, out: Optional[Tensor] = None) -> Tensor:
+ if out is None:
+ out = a.zeros(a.shape)
+ tensor_map(fn)(
+ out._tensor._storage,
+ out._tensor._shape,
+ out._tensor._strides,
+ a._tensor._storage,
+ a._tensor._shape,
+ a._tensor._strides,
+ )
+ return out
+ return _map
+
+ @staticmethod
+ def cmap(fn: Callable[[float], float]) -> MapProto:
+ """See `tensor_ops.py`"""
+ def _cmap(a: Tensor, out: Optional[Tensor] = None) -> Tensor:
+ if out is None:
+ out = a.zeros(a.shape)
+ tensor_map(fn)(
+ out._tensor._storage,
+ out._tensor._shape,
+ out._tensor._strides,
+ a._tensor._storage,
+ a._tensor._shape,
+ a._tensor._strides,
+ )
+ return out
+ return _cmap
@staticmethod
def zip(fn: Callable[[float, float], float]) -> Callable[[Tensor, Tensor], Tensor]:
"""See `tensor_ops.py`"""
- pass
+ def _zip(a: Tensor, b: Tensor) -> Tensor:
+ c_shape = shape_broadcast(a.shape, b.shape)
+ out = a.zeros(c_shape)
+ tensor_zip(fn)(
+ out._tensor._storage,
+ out._tensor._shape,
+ out._tensor._strides,
+ a._tensor._storage,
+ a._tensor._shape,
+ a._tensor._strides,
+ b._tensor._storage,
+ b._tensor._shape,
+ b._tensor._strides,
+ )
+ return out
+ return _zip
@staticmethod
def reduce(fn: Callable[[float, float], float], start: float=0.0) -> Callable[[Tensor, int], Tensor]:
"""See `tensor_ops.py`"""
- pass
+ def _reduce(a: Tensor, dim: int) -> Tensor:
+ out_shape = list(a.shape)
+ out_shape[dim] = 1
+ out = a.zeros(tuple(out_shape))
+ tensor_reduce(fn)(
+ out._tensor._storage,
+ out._tensor._shape,
+ out._tensor._strides,
+ a._tensor._storage,
+ a._tensor._shape,
+ a._tensor._strides,
+ dim,
+ )
+ return out
+ return _reduce
@staticmethod
def matrix_multiply(a: Tensor, b: Tensor) -> Tensor:
@@ -53,7 +111,28 @@ class FastOps(TensorOps):
Returns:
New tensor data
"""
- pass
+ # Setup shapes
+ ls = list(shape_broadcast(a.shape[:-2], b.shape[:-2]))
+ ls.append(a.shape[-2])
+ ls.append(b.shape[-1])
+ assert a.shape[-1] == b.shape[-2]
+
+ # Create output
+ out = a.zeros(tuple(ls))
+
+ # Call main function
+ tensor_matrix_multiply(
+ out._tensor._storage,
+ out._tensor._shape,
+ out._tensor._strides,
+ a._tensor._storage,
+ a._tensor._shape,
+ a._tensor._strides,
+ b._tensor._storage,
+ b._tensor._shape,
+ b._tensor._strides,
+ )
+ return out
def tensor_map(fn: Callable[[float], float]) -> Callable[[Storage, Shape, Strides, Storage, Shape, Strides], None]:
"""
@@ -71,7 +150,28 @@ def tensor_map(fn: Callable[[float], float]) -> Callable[[Storage, Shape, Stride
Returns:
Tensor map function.
"""
- pass
+ @njit(parallel=True)
+ def _map(out_storage: Storage,
+ out_shape: Shape,
+ out_strides: Strides,
+ in_storage: Storage,
+ in_shape: Shape,
+ in_strides: Strides) -> None:
+ # Check if the tensors are stride-aligned
+ if np.array_equal(out_strides, in_strides) and np.array_equal(out_shape, in_shape):
+ for i in prange(len(out_storage)):
+ out_storage[i] = fn(in_storage[i])
+ else:
+ # Create index buffers
+ out_index = np.zeros(len(out_shape), np.int32)
+ in_index = np.zeros(len(in_shape), np.int32)
+ for i in prange(len(out_storage)):
+ to_index(i, out_shape, out_index)
+ broadcast_index(out_index, out_shape, in_shape, in_index)
+ in_position = index_to_position(in_index, in_strides)
+ out_position = index_to_position(out_index, out_strides)
+ out_storage[out_position] = fn(in_storage[in_position])
+ return _map
def tensor_zip(fn: Callable[[float, float], float]) -> Callable[[Storage, Shape, Strides, Storage, Shape, Strides, Storage, Shape, Strides], None]:
"""
@@ -90,7 +190,35 @@ def tensor_zip(fn: Callable[[float, float], float]) -> Callable[[Storage, Shape,
Returns:
Tensor zip function.
"""
- pass
+ @njit(parallel=True)
+ def _zip(out_storage: Storage,
+ out_shape: Shape,
+ out_strides: Strides,
+ a_storage: Storage,
+ a_shape: Shape,
+ a_strides: Strides,
+ b_storage: Storage,
+ b_shape: Shape,
+ b_strides: Strides) -> None:
+ # Check if the tensors are stride-aligned
+ if (np.array_equal(out_strides, a_strides) and np.array_equal(out_strides, b_strides) and
+ np.array_equal(out_shape, a_shape) and np.array_equal(out_shape, b_shape)):
+ for i in prange(len(out_storage)):
+ out_storage[i] = fn(a_storage[i], b_storage[i])
+ else:
+ # Create index buffers
+ out_index = np.zeros(len(out_shape), np.int32)
+ a_index = np.zeros(len(a_shape), np.int32)
+ b_index = np.zeros(len(b_shape), np.int32)
+ for i in prange(len(out_storage)):
+ to_index(i, out_shape, out_index)
+ broadcast_index(out_index, out_shape, a_shape, a_index)
+ broadcast_index(out_index, out_shape, b_shape, b_index)
+ a_position = index_to_position(a_index, a_strides)
+ b_position = index_to_position(b_index, b_strides)
+ out_position = index_to_position(out_index, out_strides)
+ out_storage[out_position] = fn(a_storage[a_position], b_storage[b_position])
+ return _zip
def tensor_reduce(fn: Callable[[float, float], float]) -> Callable[[Storage, Shape, Strides, Storage, Shape, Strides, int], None]:
"""
@@ -108,7 +236,32 @@ def tensor_reduce(fn: Callable[[float, float], float]) -> Callable[[Storage, Sha
Returns:
Tensor reduce function
"""
- pass
+ @njit(parallel=True)
+ def _reduce(out_storage: Storage,
+ out_shape: Shape,
+ out_strides: Strides,
+ in_storage: Storage,
+ in_shape: Shape,
+ in_strides: Strides,
+ reduce_dim: int) -> None:
+ # Create index buffers
+ out_index = np.zeros(len(out_shape), np.int32)
+ in_index = np.zeros(len(in_shape), np.int32)
+ for i in prange(len(out_storage)):
+ to_index(i, out_shape, out_index)
+ # Setup initial
+ in_index[:] = out_index[:]
+ in_index[reduce_dim] = 0
+ in_position = index_to_position(in_index, in_strides)
+ reduced = in_storage[in_position]
+ # Reduce over dimension
+ for j in range(1, in_shape[reduce_dim]):
+ in_index[reduce_dim] = j
+ in_position = index_to_position(in_index, in_strides)
+ reduced = fn(reduced, in_storage[in_position])
+ out_position = index_to_position(out_index, out_strides)
+ out_storage[out_position] = reduced
+ return _reduce
def _tensor_matrix_multiply(out: Storage, out_shape: Shape, out_strides: Strides, a_storage: Storage, a_shape: Shape, a_strides: Strides, b_storage: Storage, b_shape: Shape, b_strides: Strides) -> None:
"""
@@ -141,5 +294,39 @@ def _tensor_matrix_multiply(out: Storage, out_shape: Shape, out_strides: Strides
Returns:
None : Fills in `out`
"""
- pass
+ # Get dimensions
+ a_batch = a_shape[0] if len(a_shape) > 2 else 1
+ b_batch = b_shape[0] if len(b_shape) > 2 else 1
+ batch = max(a_batch, b_batch)
+ m = a_shape[-2]
+ n = b_shape[-1]
+ p = a_shape[-1]
+
+ # Main loop in parallel
+ for b in prange(batch):
+ for i in range(m):
+ for j in range(n):
+ # Compute output position
+ out_pos = (
+ (b if len(out_shape) > 2 else 0) * out_strides[0] if len(out_shape) > 2 else 0
+ ) + i * out_strides[-2] + j * out_strides[-1]
+
+ # Initialize accumulator
+ acc = 0.0
+
+ # Inner loop - matrix multiply
+ for k in range(p):
+ # Compute positions in a and b
+ a_pos = (
+ (b if len(a_shape) > 2 else 0) * a_strides[0] if len(a_shape) > 2 else 0
+ ) + i * a_strides[-2] + k * a_strides[-1]
+ b_pos = (
+ (b if len(b_shape) > 2 else 0) * b_strides[0] if len(b_shape) > 2 else 0
+ ) + k * b_strides[-2] + j * b_strides[-1]
+
+ # Multiply and accumulate
+ acc += a_storage[a_pos] * b_storage[b_pos]
+
+ # Store result
+ out[out_pos] = acc
tensor_matrix_multiply = njit(parallel=True, fastmath=True)(_tensor_matrix_multiply)
\ No newline at end of file
diff --git a/minitorch/scalar.py b/minitorch/scalar.py
index 8537995..e88d057 100644
--- a/minitorch/scalar.py
+++ b/minitorch/scalar.py
@@ -52,6 +52,14 @@ class Scalar:
def __repr__(self) -> str:
return 'Scalar(%f)' % self.data
+ def __hash__(self) -> int:
+ return hash(self.unique_id)
+
+ def __eq__(self, other: Any) -> bool:
+ if not isinstance(other, Scalar):
+ return False
+ return self.unique_id == other.unique_id
+
def __mul__(self, b: ScalarLike) -> Scalar:
return Mul.apply(self, b)
@@ -62,25 +70,25 @@ class Scalar:
return Mul.apply(b, Inv.apply(self))
def __add__(self, b: ScalarLike) -> Scalar:
- raise NotImplementedError('Need to implement for Task 1.2')
+ return Add.apply(self, b)
def __bool__(self) -> bool:
return bool(self.data)
def __lt__(self, b: ScalarLike) -> Scalar:
- raise NotImplementedError('Need to implement for Task 1.2')
+ return LT.apply(self, b)
def __gt__(self, b: ScalarLike) -> Scalar:
- raise NotImplementedError('Need to implement for Task 1.2')
+ return LT.apply(b, self)
def __eq__(self, b: ScalarLike) -> Scalar:
- raise NotImplementedError('Need to implement for Task 1.2')
+ return EQ.apply(self, b)
def __sub__(self, b: ScalarLike) -> Scalar:
- raise NotImplementedError('Need to implement for Task 1.2')
+ return Add.apply(self, Neg.apply(b))
def __neg__(self) -> Scalar:
- raise NotImplementedError('Need to implement for Task 1.2')
+ return Neg.apply(self)
def __radd__(self, b: ScalarLike) -> Scalar:
return self + b
@@ -96,11 +104,35 @@ class Scalar:
Args:
x: value to be accumulated
"""
- pass
+ if self.derivative is None:
+ self.derivative = 0.0
+ self.derivative += x
def is_leaf(self) -> bool:
"""True if this variable created by the user (no `last_fn`)"""
- pass
+ return self.history.last_fn is None
+
+ def chain_rule(self, d_output: float) -> Tuple[Tuple[Variable, float], ...]:
+ """
+ Implement the derivative chain-rule.
+
+ Args:
+ d_output (float): derivative of the output
+
+ Returns:
+ List of tuples of (variable, derivative), where each is the derivative of the output with respect to
+ one of the inputs.
+ """
+ if self.history.last_fn is None:
+ return ()
+ derivatives = self.history.last_fn.chain_rule(self.history.ctx, self.history.inputs, d_output)
+ return tuple((var, deriv) for var, deriv in zip(self.history.inputs, derivatives))
+
+ def parents(self) -> Iterable[Variable]:
+ """Get the parents of this variable."""
+ if self.history.last_fn is None:
+ return []
+ return self.history.inputs
def backward(self, d_output: Optional[float]=None) -> None:
"""
@@ -110,7 +142,9 @@ class Scalar:
d_output (number, opt): starting derivative to backpropagate through the model
(typically left out, and assumed to be 1.0).
"""
- pass
+ if d_output is None:
+ d_output = 1.0
+ backpropagate(self, d_output)
def derivative_check(f: Any, *scalars: Scalar) -> None:
"""
@@ -121,4 +155,14 @@ def derivative_check(f: Any, *scalars: Scalar) -> None:
f : function from n-scalars to 1-scalar.
*scalars : n input scalar values.
"""
- pass
\ No newline at end of file
+ out = f(*scalars)
+ out.backward()
+
+ for i, scalar in enumerate(scalars):
+ if not scalar.is_leaf():
+ continue
+ numerical = central_difference(f, *scalars, arg=i)
+ assert abs(numerical - scalar.derivative) < 1e-3, (
+ f"Derivative check failed. Variable {i} has derivative {scalar.derivative} but "
+ f"numerical derivative is {numerical}."
+ )
\ No newline at end of file
diff --git a/minitorch/scalar_functions.py b/minitorch/scalar_functions.py
index d8dfe6f..f6d31a8 100644
--- a/minitorch/scalar_functions.py
+++ b/minitorch/scalar_functions.py
@@ -9,11 +9,15 @@ if TYPE_CHECKING:
def wrap_tuple(x):
"""Turn a possible value into a tuple"""
- pass
+ if isinstance(x, tuple):
+ return x
+ return (x,)
def unwrap_tuple(x):
"""Turn a singleton tuple into a value"""
- pass
+ if len(x) == 1:
+ return x[0]
+ return x
class ScalarFunction:
"""
@@ -23,33 +27,163 @@ class ScalarFunction:
This is a static class and is never instantiated. We use `class`
here to group together the `forward` and `backward` code.
"""
+ @classmethod
+ def chain_rule(cls, ctx: Context, inputs: Tuple[Scalar, ...], d_output: float) -> Tuple[float, ...]:
+ """
+ Implements the chain rule for a function.
+
+ Args:
+ ctx: Context from running forward
+ inputs: Inputs to the function
+ d_output: Derivative of the output
+
+ Returns:
+ List of derivatives of the input
+ """
+ d_inputs = cls.backward(ctx, d_output)
+ return wrap_tuple(d_inputs)
+
+ @classmethod
+ def apply(cls, *vals: ScalarLike) -> Scalar:
+ """
+ Apply function forward to the arguments.
+
+ Args:
+ vals: Values for the function
+
+ Returns:
+ A new Variable with fn as operation.
+ """
+ raw_vals = []
+ scalars = []
+ for v in vals:
+ if isinstance(v, minitorch.scalar.Scalar):
+ scalars.append(v)
+ raw_vals.append(v.data)
+ else:
+ scalars.append(minitorch.scalar.Scalar(v))
+ raw_vals.append(v)
+
+ # Create the context.
+ ctx = Context()
+
+ # Call forward with the variables.
+ c = cls.forward(ctx, *raw_vals)
+ assert isinstance(c, float), "Expected return type float got %s" % (type(c))
+
+ # Create a new variable from the result with a new history.
+ back = minitorch.scalar.ScalarHistory(cls, ctx, scalars)
+ return minitorch.scalar.Scalar(c, back)
class Add(ScalarFunction):
"""Addition function $f(x, y) = x + y$"""
+ @staticmethod
+ def forward(ctx: Context, a: float, b: float) -> float:
+ return a + b
+
+ @staticmethod
+ def backward(ctx: Context, d_output: float) -> Tuple[float, float]:
+ return d_output, d_output
class Log(ScalarFunction):
"""Log function $f(x) = log(x)$"""
+ @staticmethod
+ def forward(ctx: Context, a: float) -> float:
+ ctx.save_for_backward(a)
+ return operators.log(a)
+
+ @staticmethod
+ def backward(ctx: Context, d_output: float) -> float:
+ (a,) = ctx.saved_values
+ return operators.log_back(a, d_output)
class Mul(ScalarFunction):
"""Multiplication function"""
+ @staticmethod
+ def forward(ctx: Context, a: float, b: float) -> float:
+ ctx.save_for_backward(a, b)
+ return operators.mul(a, b)
+
+ @staticmethod
+ def backward(ctx: Context, d_output: float) -> Tuple[float, float]:
+ a, b = ctx.saved_values
+ return b * d_output, a * d_output
class Inv(ScalarFunction):
"""Inverse function"""
+ @staticmethod
+ def forward(ctx: Context, a: float) -> float:
+ ctx.save_for_backward(a)
+ return operators.inv(a)
+
+ @staticmethod
+ def backward(ctx: Context, d_output: float) -> float:
+ (a,) = ctx.saved_values
+ return operators.inv_back(a, d_output)
class Neg(ScalarFunction):
"""Negation function"""
+ @staticmethod
+ def forward(ctx: Context, a: float) -> float:
+ return operators.neg(a)
+
+ @staticmethod
+ def backward(ctx: Context, d_output: float) -> float:
+ return -d_output
class Sigmoid(ScalarFunction):
"""Sigmoid function"""
+ @staticmethod
+ def forward(ctx: Context, a: float) -> float:
+ ctx.save_for_backward(a)
+ return operators.sigmoid(a)
+
+ @staticmethod
+ def backward(ctx: Context, d_output: float) -> float:
+ (a,) = ctx.saved_values
+ sig_a = operators.sigmoid(a)
+ return d_output * sig_a * (1 - sig_a)
class ReLU(ScalarFunction):
"""ReLU function"""
+ @staticmethod
+ def forward(ctx: Context, a: float) -> float:
+ ctx.save_for_backward(a)
+ return operators.relu(a)
+
+ @staticmethod
+ def backward(ctx: Context, d_output: float) -> float:
+ (a,) = ctx.saved_values
+ return operators.relu_back(a, d_output)
class Exp(ScalarFunction):
"""Exp function"""
+ @staticmethod
+ def forward(ctx: Context, a: float) -> float:
+ ctx.save_for_backward(a)
+ return operators.exp(a)
+
+ @staticmethod
+ def backward(ctx: Context, d_output: float) -> float:
+ (a,) = ctx.saved_values
+ return d_output * operators.exp(a)
class LT(ScalarFunction):
"""Less-than function $f(x) =$ 1.0 if x is less than y else 0.0"""
+ @staticmethod
+ def forward(ctx: Context, a: float, b: float) -> float:
+ return operators.lt(a, b)
+
+ @staticmethod
+ def backward(ctx: Context, d_output: float) -> Tuple[float, float]:
+ return 0.0, 0.0
class EQ(ScalarFunction):
- """Equal function $f(x) =$ 1.0 if x is equal to y else 0.0"""
\ No newline at end of file
+ """Equal function $f(x) =$ 1.0 if x is equal to y else 0.0"""
+ @staticmethod
+ def forward(ctx: Context, a: float, b: float) -> float:
+ return operators.eq(a, b)
+
+ @staticmethod
+ def backward(ctx: Context, d_output: float) -> Tuple[float, float]:
+ return 0.0, 0.0
\ No newline at end of file
diff --git a/minitorch/tensor_functions.py b/minitorch/tensor_functions.py
index a2e29c9..e73d3fa 100644
--- a/minitorch/tensor_functions.py
+++ b/minitorch/tensor_functions.py
@@ -19,7 +19,35 @@ def wrap_tuple(x):
pass
class Function:
- pass
+ """
+ Base class for function implementations.
+ """
+ @classmethod
+ def apply(cls, *vals: Tensor) -> Tensor:
+ """
+ Apply function forward to the arguments.
+
+ Args:
+ vals: input tensors
+
+ Returns:
+ output tensor
+ """
+ raw_vals = []
+ need_grad = False
+ for v in vals:
+ if v.requires_grad:
+ need_grad = True
+ raw_vals.append(v)
+
+ # Create the context.
+ ctx = Context(not need_grad)
+
+ # Call forward with the variables.
+ c = cls()
+ ret = c.forward(ctx, *raw_vals)
+
+ return ret
class Neg(Function):
pass
diff --git a/minitorch/tensor_ops.py b/minitorch/tensor_ops.py
index e5bb9eb..90cc0e8 100644
--- a/minitorch/tensor_ops.py
+++ b/minitorch/tensor_ops.py
@@ -84,7 +84,54 @@ class SimpleOps(TensorOps):
Returns:
new tensor data
"""
- pass
+ def _map(a: Tensor, out: Optional[Tensor] = None) -> Tensor:
+ if out is None:
+ out = a.zeros(a.shape)
+ tensor_map(fn)(
+ a._tensor._storage,
+ a._tensor._shape,
+ a._tensor._strides,
+ out._tensor._storage,
+ out._tensor._shape,
+ out._tensor._strides,
+ )
+ return out
+ return _map
+
+ @staticmethod
+ def cmap(fn: Callable[[float], float]) -> MapProto:
+ """
+ Higher-order tensor map function that creates a new tensor ::
+
+ fn_map = map(fn)
+ out = fn_map(a)
+
+ Simple version::
+
+ for i:
+ for j:
+ out[i, j] = fn(a[i, j])
+
+ Args:
+ fn: function from float-to-float to apply.
+ a (:class:`TensorData`): tensor to map over
+
+ Returns:
+ new tensor data
+ """
+ def _cmap(a: Tensor, out: Optional[Tensor] = None) -> Tensor:
+ if out is None:
+ out = a.zeros(a.shape)
+ tensor_map(fn)(
+ a._tensor._storage,
+ a._tensor._shape,
+ a._tensor._strides,
+ out._tensor._storage,
+ out._tensor._shape,
+ out._tensor._strides,
+ )
+ return out
+ return _cmap
@staticmethod
def zip(fn: Callable[[float, float], float]) -> Callable[['Tensor', 'Tensor'], 'Tensor']:
@@ -115,7 +162,22 @@ class SimpleOps(TensorOps):
Returns:
:class:`TensorData` : new tensor data
"""
- pass
+ def _zip(a: Tensor, b: Tensor) -> Tensor:
+ c_shape = shape_broadcast(a.shape, b.shape)
+ out = a.zeros(c_shape)
+ tensor_zip(fn)(
+ a._tensor._storage,
+ a._tensor._shape,
+ a._tensor._strides,
+ b._tensor._storage,
+ b._tensor._shape,
+ b._tensor._strides,
+ out._tensor._storage,
+ out._tensor._shape,
+ out._tensor._strides,
+ )
+ return out
+ return _zip
@staticmethod
def reduce(fn: Callable[[float, float], float], start: float=0.0) -> Callable[['Tensor', int], 'Tensor']:
@@ -141,7 +203,56 @@ class SimpleOps(TensorOps):
Returns:
:class:`TensorData` : new tensor
"""
- pass
+ def _reduce(a: Tensor, dim: int) -> Tensor:
+ out_shape = list(a.shape)
+ out_shape[dim] = 1
+ out = a.zeros(tuple(out_shape))
+ tensor_reduce(fn)(
+ a._tensor._storage,
+ a._tensor._shape,
+ a._tensor._strides,
+ out._tensor._storage,
+ out._tensor._shape,
+ out._tensor._strides,
+ dim,
+ )
+ return out
+ return _reduce
+
+ @staticmethod
+ def matrix_multiply(a: Tensor, b: Tensor) -> Tensor:
+ """
+ Batched matrix multiplication of two tensors.
+
+ Args:
+ a : batch1 x n x m tensor
+ b : batch2 x m x p tensor
+
+ Returns:
+ A tensor of size batch1 x batch2 x n x p
+ """
+ # Extract dimensions
+ batch1, n, m = a.shape
+ batch2, m2, p = b.shape
+ assert m == m2, f"Incompatible dimensions: {m} != {m2}"
+
+ # Create output tensor
+ out = a.zeros((batch1, batch2, n, p))
+
+ # Perform matrix multiplication
+ for i in range(batch1):
+ for j in range(batch2):
+ for k in range(n):
+ for l in range(p):
+ sum_val = 0.0
+ for t in range(m):
+ sum_val += a._tensor._storage[a._tensor._strides[0] * i + a._tensor._strides[1] * k + a._tensor._strides[2] * t] * \
+ b._tensor._storage[b._tensor._strides[0] * j + b._tensor._strides[1] * t + b._tensor._strides[2] * l]
+ out_idx = out._tensor._strides[0] * i + out._tensor._strides[1] * j + out._tensor._strides[2] * k + out._tensor._strides[3] * l
+ out._tensor._storage[out_idx] = sum_val
+
+ return out
+
is_cuda = False
def tensor_map(fn: Callable[[float], float]) -> Callable[[Storage, Shape, Strides, Storage, Shape, Strides], None]:
@@ -167,7 +278,21 @@ def tensor_map(fn: Callable[[float], float]) -> Callable[[Storage, Shape, Stride
Returns:
Tensor map function.
"""
- pass
+ def _map(in_storage: Storage,
+ in_shape: Shape,
+ in_strides: Strides,
+ out_storage: Storage,
+ out_shape: Shape,
+ out_strides: Strides) -> None:
+ out_index = np.zeros(len(out_shape), np.int32)
+ in_index = np.zeros(len(in_shape), np.int32)
+ for i in range(len(out_storage)):
+ to_index(i, out_shape, out_index)
+ broadcast_index(out_index, out_shape, in_shape, in_index)
+ in_position = index_to_position(in_index, in_strides)
+ out_position = index_to_position(out_index, out_strides)
+ out_storage[out_position] = fn(in_storage[in_position])
+ return _map
def tensor_zip(fn: Callable[[float, float], float]) -> Callable[[Storage, Shape, Strides, Storage, Shape, Strides, Storage, Shape, Strides], None]:
"""
@@ -192,7 +317,27 @@ def tensor_zip(fn: Callable[[float, float], float]) -> Callable[[Storage, Shape,
Returns:
Tensor zip function.
"""
- pass
+ def _zip(a_storage: Storage,
+ a_shape: Shape,
+ a_strides: Strides,
+ b_storage: Storage,
+ b_shape: Shape,
+ b_strides: Strides,
+ out_storage: Storage,
+ out_shape: Shape,
+ out_strides: Strides) -> None:
+ out_index = np.zeros(len(out_shape), np.int32)
+ a_index = np.zeros(len(a_shape), np.int32)
+ b_index = np.zeros(len(b_shape), np.int32)
+ for i in range(len(out_storage)):
+ to_index(i, out_shape, out_index)
+ broadcast_index(out_index, out_shape, a_shape, a_index)
+ broadcast_index(out_index, out_shape, b_shape, b_index)
+ a_position = index_to_position(a_index, a_strides)
+ b_position = index_to_position(b_index, b_strides)
+ out_position = index_to_position(out_index, out_strides)
+ out_storage[out_position] = fn(a_storage[a_position], b_storage[b_position])
+ return _zip
def tensor_reduce(fn: Callable[[float, float], float]) -> Callable[[Storage, Shape, Strides, Storage, Shape, Strides, int], None]:
"""
@@ -207,5 +352,28 @@ def tensor_reduce(fn: Callable[[float, float], float]) -> Callable[[Storage, Sha
Returns:
Tensor reduce function.
"""
- pass
+ def _reduce(a_storage: Storage,
+ a_shape: Shape,
+ a_strides: Strides,
+ out_storage: Storage,
+ out_shape: Shape,
+ out_strides: Strides,
+ reduce_dim: int) -> None:
+ out_index = np.zeros(len(out_shape), np.int32)
+ a_index = np.zeros(len(a_shape), np.int32)
+ for i in range(len(out_storage)):
+ to_index(i, out_shape, out_index)
+ out_position = index_to_position(out_index, out_strides)
+ # Setup initial
+ a_index[:] = out_index[:]
+ a_index[reduce_dim] = 0
+ a_position = index_to_position(a_index, a_strides)
+ reduced = a_storage[a_position]
+ # Reduce over dimension
+ for j in range(1, a_shape[reduce_dim]):
+ a_index[reduce_dim] = j
+ a_position = index_to_position(a_index, a_strides)
+ reduced = fn(reduced, a_storage[a_position])
+ out_storage[out_position] = reduced
+ return _reduce
SimpleBackend = TensorBackend(SimpleOps)
\ No newline at end of file
diff --git a/minitorch/testing.py b/minitorch/testing.py
index 72ab137..209bc02 100644
--- a/minitorch/testing.py
+++ b/minitorch/testing.py
@@ -7,84 +7,114 @@ class MathTest(Generic[A]):
@staticmethod
def neg(a: A) -> A:
"""Negate the argument"""
- pass
+ return operators.neg(a)
@staticmethod
def addConstant(a: A) -> A:
"""Add contant to the argument"""
- pass
+ return operators.add(a, 10.0)
@staticmethod
def square(a: A) -> A:
"""Manual square"""
- pass
+ return operators.mul(a, a)
@staticmethod
def cube(a: A) -> A:
"""Manual cube"""
- pass
+ return operators.mul(operators.mul(a, a), a)
@staticmethod
def subConstant(a: A) -> A:
"""Subtract a constant from the argument"""
- pass
+ return operators.add(a, -5.0)
@staticmethod
def multConstant(a: A) -> A:
"""Multiply a constant to the argument"""
- pass
+ return operators.mul(a, 5.0)
@staticmethod
def div(a: A) -> A:
"""Divide by a constant"""
- pass
+ return operators.mul(a, 0.2)
@staticmethod
def inv(a: A) -> A:
"""Invert after adding"""
- pass
+ return operators.inv(operators.add(a, 2.0))
@staticmethod
def sig(a: A) -> A:
"""Apply sigmoid"""
- pass
+ return operators.sigmoid(a)
@staticmethod
def log(a: A) -> A:
"""Apply log to a large value"""
- pass
+ return operators.log(operators.add(a, 100.0))
@staticmethod
def relu(a: A) -> A:
"""Apply relu"""
- pass
+ return operators.relu(a)
@staticmethod
def exp(a: A) -> A:
"""Apply exp to a smaller value"""
- pass
+ return operators.exp(operators.add(a, -100.0))
@staticmethod
def add2(a: A, b: A) -> A:
"""Add two arguments"""
- pass
+ return operators.add(a, b)
@staticmethod
def mul2(a: A, b: A) -> A:
"""Mul two arguments"""
- pass
+ return operators.mul(a, b)
@staticmethod
def div2(a: A, b: A) -> A:
"""Divide two arguments"""
- pass
+ return operators.mul(a, operators.inv(b))
@classmethod
def _tests(cls) -> Tuple[Tuple[str, Callable[[A], A]], Tuple[str, Callable[[A, A], A]], Tuple[str, Callable[[Iterable[A]], A]]]:
"""
Returns a list of all the math tests.
"""
- pass
+ one_arg = [
+ ("neg", cls.neg),
+ ("addConstant", cls.addConstant),
+ ("square", cls.square),
+ ("cube", cls.cube),
+ ("subConstant", cls.subConstant),
+ ("multConstant", cls.multConstant),
+ ("div", cls.div),
+ ("inv", cls.inv),
+ ("sig", cls.sig),
+ ("log", cls.log),
+ ("relu", cls.relu),
+ ("exp", cls.exp),
+ ]
+ two_arg = [
+ ("add2", cls.add2),
+ ("mul2", cls.mul2),
+ ("div2", cls.div2),
+ ]
+ return tuple(one_arg), tuple(two_arg), tuple()
class MathTestVariable(MathTest):
- pass
\ No newline at end of file
+ @staticmethod
+ def _comp_testing() -> Tuple[Tuple[str, Callable[[A], A]], Tuple[str, Callable[[A, A], A]], Tuple[str, Callable[[Iterable[A]], A]]]:
+ """
+ Returns a list of all the comparison tests.
+ """
+ one_arg = []
+ two_arg = [
+ ("lt", operators.lt),
+ ("eq", operators.eq),
+ ("gt", lambda x, y: operators.lt(y, x)),
+ ]
+ return tuple(one_arg), tuple(two_arg), tuple()
\ No newline at end of file