Format with black and ignore F841 for now

minitorch/autodiff.py

@@ -23,7 +23,7 @@ def central_difference(f: Any, *vals: Any, arg: int = 0, epsilon: float = 1e-6)
         An approximation of $f'_i(x_0, \ldots, x_{n-1})$
     """
     # TODO: Implement for Task 1.1.
-    raise NotImplementedError('Need to implement for Task 1.1')
+    raise NotImplementedError("Need to implement for Task 1.1")
 
 
 variable_count = 1
@@ -62,7 +62,7 @@ def topological_sort(variable: Variable) -> Iterable[Variable]:
         Non-constant Variables in topological order starting from the right.
     """
     # TODO: Implement for Task 1.4.
-    raise NotImplementedError('Need to implement for Task 1.4')
+    raise NotImplementedError("Need to implement for Task 1.4")
 
 
 def backpropagate(variable: Variable, deriv: Any) -> None:
@@ -77,7 +77,7 @@ def backpropagate(variable: Variable, deriv: Any) -> None:
     No return. Should write to its results to the derivative values of each leaf through `accumulate_derivative`.
     """
     # TODO: Implement for Task 1.4.
-    raise NotImplementedError('Need to implement for Task 1.4')
+    raise NotImplementedError("Need to implement for Task 1.4")
 
 
 @dataclass

minitorch/cuda_ops.py

@@ -154,7 +154,7 @@ def _map(
         in_index = cuda.local.array(MAX_DIMS, numba.int32)
         i = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x
         # TODO: Implement for Task 3.3.
-        raise NotImplementedError('Need to implement for Task 3.3')
+        raise NotImplementedError("Need to implement for Task 3.3")
 
     return cuda.jit()(_map)  # type: ignore
 
@@ -196,7 +196,7 @@ def _zip(
         i = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x
 
         # TODO: Implement for Task 3.3.
-        raise NotImplementedError('Need to implement for Task 3.3')
+        raise NotImplementedError("Need to implement for Task 3.3")
 
     return cuda.jit()(_zip)  # type: ignore
 
@@ -229,7 +229,7 @@ def _sum_practice(out: Storage, a: Storage, size: int) -> None:
     pos = cuda.threadIdx.x
 
     # TODO: Implement for Task 3.3.
-    raise NotImplementedError('Need to implement for Task 3.3')
+    raise NotImplementedError("Need to implement for Task 3.3")
 
 
 jit_sum_practice = cuda.jit()(_sum_practice)
@@ -279,7 +279,7 @@ def _reduce(
         pos = cuda.threadIdx.x
 
         # TODO: Implement for Task 3.3.
-        raise NotImplementedError('Need to implement for Task 3.3')
+        raise NotImplementedError("Need to implement for Task 3.3")
 
     return cuda.jit()(_reduce)  # type: ignore
 
@@ -316,7 +316,7 @@ def _mm_practice(out: Storage, a: Storage, b: Storage, size: int) -> None:
     """
     BLOCK_DIM = 32
     # TODO: Implement for Task 3.3.
-    raise NotImplementedError('Need to implement for Task 3.3')
+    raise NotImplementedError("Need to implement for Task 3.3")
 
 
 jit_mm_practice = cuda.jit()(_mm_practice)
@@ -386,7 +386,7 @@ def _tensor_matrix_multiply(
     #    b) Copy into shared memory for b matrix
     #    c) Compute the dot produce for position c[i, j]
     # TODO: Implement for Task 3.4.
-    raise NotImplementedError('Need to implement for Task 3.4')
+    raise NotImplementedError("Need to implement for Task 3.4")
 
 
 tensor_matrix_multiply = cuda.jit(_tensor_matrix_multiply)

minitorch/fast_conv.py

@@ -76,11 +76,11 @@ def _tensor_conv1d(
         and in_channels == in_channels_
         and out_channels == out_channels_
     )
-    s1 = input_strides
-    s2 = weight_strides
+    s1 = input_strides  # noqa: F841
+    s2 = weight_strides  # noqa: F841
 
     # TODO: Implement for Task 4.1.
-    raise NotImplementedError('Need to implement for Task 4.1')
+    raise NotImplementedError("Need to implement for Task 4.1")
 
 
 tensor_conv1d = njit(parallel=True)(_tensor_conv1d)
@@ -202,11 +202,11 @@ def _tensor_conv2d(
     s1 = input_strides
     s2 = weight_strides
     # inners
-    s10, s11, s12, s13 = s1[0], s1[1], s1[2], s1[3]
-    s20, s21, s22, s23 = s2[0], s2[1], s2[2], s2[3]
+    s10, s11, s12, s13 = s1[0], s1[1], s1[2], s1[3]  # noqa: F841
+    s20, s21, s22, s23 = s2[0], s2[1], s2[2], s2[3]  # noqa: F841
 
     # TODO: Implement for Task 4.2.
-    raise NotImplementedError('Need to implement for Task 4.2')
+    raise NotImplementedError("Need to implement for Task 4.2")
 
 
 tensor_conv2d = njit(parallel=True, fastmath=True)(_tensor_conv2d)

minitorch/fast_ops.py

@@ -162,7 +162,7 @@ def _map(
         in_strides: Strides,
     ) -> None:
         # TODO: Implement for Task 3.1.
-        raise NotImplementedError('Need to implement for Task 3.1')
+        raise NotImplementedError("Need to implement for Task 3.1")
 
     return njit(parallel=True)(_map)  # type: ignore
 
@@ -201,7 +201,7 @@ def _zip(
         b_strides: Strides,
     ) -> None:
         # TODO: Implement for Task 3.1.
-        raise NotImplementedError('Need to implement for Task 3.1')
+        raise NotImplementedError("Need to implement for Task 3.1")
 
     return njit(parallel=True)(_zip)  # type: ignore
 
@@ -235,7 +235,7 @@ def _reduce(
         reduce_dim: int,
     ) -> None:
         # TODO: Implement for Task 3.1.
-        raise NotImplementedError('Need to implement for Task 3.1')
+        raise NotImplementedError("Need to implement for Task 3.1")
 
     return njit(parallel=True)(_reduce)  # type: ignore
 
@@ -285,7 +285,7 @@ def _tensor_matrix_multiply(
     b_batch_stride = b_strides[0] if b_shape[0] > 1 else 0
 
     # TODO: Implement for Task 3.2.
-    raise NotImplementedError('Need to implement for Task 3.2')
+    raise NotImplementedError("Need to implement for Task 3.2")
 
 
 tensor_matrix_multiply = njit(parallel=True, fastmath=True)(_tensor_matrix_multiply)

minitorch/module.py

@@ -32,12 +32,12 @@ def modules(self) -> Sequence[Module]:
     def train(self) -> None:
         "Set the mode of this module and all descendent modules to `train`."
         # TODO: Implement for Task 0.4.
-        raise NotImplementedError('Need to implement for Task 0.4')
+        raise NotImplementedError("Need to implement for Task 0.4")
 
     def eval(self) -> None:
         "Set the mode of this module and all descendent modules to `eval`."
         # TODO: Implement for Task 0.4.
-        raise NotImplementedError('Need to implement for Task 0.4')
+        raise NotImplementedError("Need to implement for Task 0.4")
 
     def named_parameters(self) -> Sequence[Tuple[str, Parameter]]:
         """
@@ -48,12 +48,12 @@ def named_parameters(self) -> Sequence[Tuple[str, Parameter]]:
             The name and `Parameter` of each ancestor parameter.
         """
         # TODO: Implement for Task 0.4.
-        raise NotImplementedError('Need to implement for Task 0.4')
+        raise NotImplementedError("Need to implement for Task 0.4")
 
     def parameters(self) -> Sequence[Parameter]:
         "Enumerate over all the parameters of this module and its descendents."
         # TODO: Implement for Task 0.4.
-        raise NotImplementedError('Need to implement for Task 0.4')
+        raise NotImplementedError("Need to implement for Task 0.4")
 
     def add_parameter(self, k: str, v: Any) -> Parameter:
         """

minitorch/nn.py

@@ -24,7 +24,7 @@ def tile(input: Tensor, kernel: Tuple[int, int]) -> Tuple[Tensor, int, int]:
     assert height % kh == 0
     assert width % kw == 0
     # TODO: Implement for Task 4.3.
-    raise NotImplementedError('Need to implement for Task 4.3')
+    raise NotImplementedError("Need to implement for Task 4.3")
 
 
 def avgpool2d(input: Tensor, kernel: Tuple[int, int]) -> Tensor:
@@ -40,7 +40,7 @@ def avgpool2d(input: Tensor, kernel: Tuple[int, int]) -> Tensor:
     """
     batch, channel, height, width = input.shape
     # TODO: Implement for Task 4.3.
-    raise NotImplementedError('Need to implement for Task 4.3')
+    raise NotImplementedError("Need to implement for Task 4.3")
 
 
 max_reduce = FastOps.reduce(operators.max, -1e9)
@@ -68,13 +68,13 @@ class Max(Function):
     def forward(ctx: Context, input: Tensor, dim: Tensor) -> Tensor:
         "Forward of max should be max reduction"
         # TODO: Implement for Task 4.4.
-        raise NotImplementedError('Need to implement for Task 4.4')
+        raise NotImplementedError("Need to implement for Task 4.4")
 
     @staticmethod
     def backward(ctx: Context, grad_output: Tensor) -> Tuple[Tensor, float]:
         "Backward of max should be argmax (see above)"
         # TODO: Implement for Task 4.4.
-        raise NotImplementedError('Need to implement for Task 4.4')
+        raise NotImplementedError("Need to implement for Task 4.4")
 
 
 def max(input: Tensor, dim: int) -> Tensor:
@@ -97,7 +97,7 @@ def softmax(input: Tensor, dim: int) -> Tensor:
         softmax tensor
     """
     # TODO: Implement for Task 4.4.
-    raise NotImplementedError('Need to implement for Task 4.4')
+    raise NotImplementedError("Need to implement for Task 4.4")
 
 
 def logsoftmax(input: Tensor, dim: int) -> Tensor:
@@ -116,7 +116,7 @@ def logsoftmax(input: Tensor, dim: int) -> Tensor:
          log of softmax tensor
     """
     # TODO: Implement for Task 4.4.
-    raise NotImplementedError('Need to implement for Task 4.4')
+    raise NotImplementedError("Need to implement for Task 4.4")
 
 
 def maxpool2d(input: Tensor, kernel: Tuple[int, int]) -> Tensor:
@@ -132,7 +132,7 @@ def maxpool2d(input: Tensor, kernel: Tuple[int, int]) -> Tensor:
     """
     batch, channel, height, width = input.shape
     # TODO: Implement for Task 4.4.
-    raise NotImplementedError('Need to implement for Task 4.4')
+    raise NotImplementedError("Need to implement for Task 4.4")
 
 
 def dropout(input: Tensor, rate: float, ignore: bool = False) -> Tensor:
@@ -148,4 +148,4 @@ def dropout(input: Tensor, rate: float, ignore: bool = False) -> Tensor:
         tensor with random positions dropped out
     """
     # TODO: Implement for Task 4.4.
-    raise NotImplementedError('Need to implement for Task 4.4')
+    raise NotImplementedError("Need to implement for Task 4.4")

minitorch/scalar.py

@@ -93,30 +93,30 @@ def __rtruediv__(self, b: ScalarLike) -> Scalar:
 
     def __add__(self, b: ScalarLike) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def __bool__(self) -> bool:
         return bool(self.data)
 
     def __lt__(self, b: ScalarLike) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def __gt__(self, b: ScalarLike) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def __eq__(self, b: ScalarLike) -> Scalar:  # type: ignore[override]
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def __sub__(self, b: ScalarLike) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def __neg__(self) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def __radd__(self, b: ScalarLike) -> Scalar:
         return self + b
@@ -126,19 +126,19 @@ def __rmul__(self, b: ScalarLike) -> Scalar:
 
     def log(self) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def exp(self) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def sigmoid(self) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def relu(self) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     # Variable elements for backprop
 
@@ -174,7 +174,7 @@ def chain_rule(self, d_output: Any) -> Iterable[Tuple[Variable, Any]]:
         assert h.ctx is not None
 
         # TODO: Implement for Task 1.3.
-        raise NotImplementedError('Need to implement for Task 1.3')
+        raise NotImplementedError("Need to implement for Task 1.3")
 
     def backward(self, d_output: Optional[float] = None) -> None:
         """