Added the support for max_unpool1d, max_unpool2d, and max_unpool3d #7524

experimental/torch_xla2/test/test_ops.py

@@ -113,9 +113,6 @@
     "nn.functional.max_pool1d",
     "nn.functional.max_pool2d",
     "nn.functional.max_pool3d",
-    "nn.functional.max_unpool1d",
-    "nn.functional.max_unpool2d",
-    "nn.functional.max_unpool3d",
     "nn.functional.multi_head_attention_forward",
     "nn.functional.multi_margin_loss",
     "nn.functional.multilabel_margin_loss",

experimental/torch_xla2/torch_xla2/ops/jaten.py

@@ -4026,3 +4026,60 @@ def _aten__fft_c2r(self, dim, normalization, last_dim_size):
   else:
     s = None
   return jnp.fft.irfftn(self, norm=norm, axes=dim, s=s)
+
+
+@op(torch.ops.aten.max_unpool3d)
+def _aten_max_unpool3d(input, indices, output_size, stride=None, padding=0):
+    if output_size is None:
+      raise ValueError("output_size value is not set correctly. It cannot be None or empty.")
+
+    output_size = [input.shape[0], input.shape[1]] + output_size
+
+    # Initialize an output array of zeros with the provided output_size
+    output = jnp.zeros(output_size, dtype=input.dtype)
+
+    # Use numpy.ndindex to iterate over all indices of the input tensor
+    for idx in np.ndindex(input.shape):
+        max_index = indices[idx]
+
+        # Get the spatial dimensions of the output
+        spatial_dims = output_size[2:]  # (D, H, W)
+
+        # Unravel the flat index to multi-dimensional index
+        unpooled_spatial_idx = np.unravel_index(max_index, spatial_dims)
+
+        # Combine batch, channel, and spatial indices
+        full_idx = idx[:2] + unpooled_spatial_idx
+
+        output = output.at[full_idx].set(input[idx])
+
+    return output
+
+@op(torch.ops.aten.max_unpool2d)
+def _aten_max_unpool2d(input, indices, output_size, stride=None, padding=0):
+    if output_size is None:
+        raise ValueError("output_size value is not set correctly. It cannot be None or empty.")
+
+    output_size = [input.shape[0], input.shape[1]] + output_size
+
+    # Initialize the output array with zeros
+    output = jnp.zeros(output_size, dtype=input.dtype)
+
+    # Use numpy.ndindex to iterate over all indices of the input tensor
+    for idx in np.ndindex(input.shape):
+        max_index = indices[idx]
+
+        # Get the spatial dimensions of the output (H, W)
+        spatial_dims = output_size[2:]
+
+        # Unravel the flat index to multi-dimensional index for 2D
+        unpooled_spatial_idx = np.unravel_index(max_index, spatial_dims)
+
+        # Combine batch, channel, and spatial indices
+        full_idx = idx[:2] + unpooled_spatial_idx
+
+        # Set the value in the output array at the corresponding location
+        output = output.at[full_idx].set(input[idx])
+
+    return output
+