made task4_4 simpler

minitorch/fast_conv.py

@@ -61,8 +61,14 @@ def _tensor_conv1d(
        `batch, out_channels, width`
 
     `reverse` decides if weight is anchored left (False) or right.
-    (See diagrams)
-
+    (See diagrams)    
+    Implementation hints:
+        - Use nested loops over batch, output channels, output width, input channels, and kernel width
+        - Use prange for appropriate outer loops to enable parallelization
+        - Calculate input position based on output position and kernel position
+        - Handle reverse flag to determine kernel indexing direction
+        - Apply bounds checking to avoid accessing invalid memory positions
+        - Use provided strides to compute correct memory offsets
     Args:
     ----
         out (Storage): storage for `out` tensor.
@@ -187,6 +193,13 @@ def _tensor_conv2d(
     `Reverse` decides if weight is anchored top-left (False) or bottom-right.
     (See diagrams)
 
+    Implementation hints:
+        - Use nested loops over batch, output channels, output height, output width, input channels, kernel height, and kernel width
+        - Use prange for appropriate outer loops to enable parallelization
+        - Calculate input positions based on output position and kernel positions
+        - Handle reverse flag to determine kernel indexing direction for both height and width
+        - Apply bounds checking to avoid accessing invalid memory positions for both dimensions
+        - Use provided strides to compute correct memory offsets for 4D tensors
 
     Args:
     ----

minitorch/nn.py

@@ -1,10 +1,11 @@
 from typing import Tuple
 
-from . import operators
-from .autodiff import Context
-from .fast_ops import FastOps
 from .tensor import Tensor
-from .tensor_functions import Function, rand, tensor
+from .tensor_functions import rand
+from typing import Optional
+from .tensor_functions import Function, tensor
+
+from .autodiff import Context
 
 
 # List of functions in this file:
@@ -39,4 +40,89 @@ def tile(input: Tensor, kernel: Tuple[int, int]) -> Tuple[Tensor, int, int]:
     raise NotImplementedError("Need to implement for Task 4.3")
 
 
-# TODO: Implement for Task 4.3.
+def avgpool2d(input: Tensor, kernel: Tuple[int, int]) -> Tensor:
+    """Tiled average pooling 2D
+
+    Args:
+    ----
+        input: batch x channel x height x width
+        kernel: height x width of pooling
+
+    Returns:
+    -------
+        Pooled tensor
+    """
+    # TODO: Implement for Task 4.3.
+    raise NotImplementedError("Need to implement for Task 4.3")
+
+
+class Max(Function):
+    @staticmethod
+    def forward(ctx: Context, a: 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")
+
+    @staticmethod
+    def backward(ctx: Context, grad_output: Tensor) -> Tuple[Tensor, Tensor]:
+        """Backward of max should be argmax (see argmax function)"""
+        # TODO: Implement for Task 4.4.
+        raise NotImplementedError("Need to implement for Task 4.4")
+
+
+def max(input: Tensor, dim: int) -> Tensor:
+    """Apply max reduction over a dimension"""
+    # TODO: Implement for Task 4.4.
+    raise NotImplementedError("Need to implement for Task 4.4")
+
+
+def argmax(input: Tensor, dim: int) -> Tensor:
+    """Compute the argmax as a 1-hot tensor"""
+    # TODO: Implement for Task 4.4.
+    raise NotImplementedError("Need to implement for Task 4.4")
+
+
+def softmax(input: Tensor, dim: int) -> Tensor:
+    """Compute the softmax as a tensor"""
+    # TODO: Implement for Task 4.4.
+    raise NotImplementedError("Need to implement for Task 4.4")
+
+
+def logsoftmax(input: Tensor, dim: int) -> Tensor:
+    """Compute the log of the softmax as a tensor
+    See https://en.wikipedia.org/wiki/LogSumExp#log-sum-exp_trick_for_log-domain_calculations """
+    # TODO: Implement for Task 4.4.
+    raise NotImplementedError("Need to implement for Task 4.4")
+
+
+def maxpool2d(input: Tensor, kernel: Tuple[int, int]) -> Tensor:
+    """Tiled max pooling 2D
+
+    Args:
+    ----
+        input: batch x channel x height x width
+        kernel: height x width of pooling
+
+    Returns:
+    -------
+        Pooled tensor
+    """
+    # TODO: Implement for Task 4.4.
+    raise NotImplementedError("Need to implement for Task 4.4")
+
+
+def dropout(input: Tensor, rate: float, ignore: bool = False) -> Tensor:
+    """Dropout positions based on random noise
+
+    Args:
+    ----
+        input: input tensor
+        rate: probability [0, 1) of dropping out each position
+        ignore: skip dropout, i.e. do nothing at all
+
+    Returns:
+    -------
+        tensor with random positions dropped out
+    """
+    # TODO: Implement for Task 4.4.
+    raise NotImplementedError("Need to implement for Task 4.4")