tensordot: more doc

tltorch/functional/factorized_tensordot.py

@@ -7,42 +7,21 @@
 einsum_symbols = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
 
 
-# def tensor_dot_tucker(tensor, tucker, modes):
-#     modes_tensor, modes_tucker = _validate_contraction_modes(tl.shape(tensor), tucker.tensor_shape, modes)
-#     input_order = tensor.ndim
-#     weight_order = tucker.order
-
-#     sorted_modes_tucker = sorted(modes_tucker, reverse=True)
-#     sorted_modes_tensor = sorted(modes_tensor, reverse=True)
-
-#     # Symbol for dimensionality of the core
-#     rank_sym = [einsum_symbols[i] for i in range(weight_order)]
-
-#     # Symbols for tucker weight size
-#     tucker_sym = [einsum_symbols[i+weight_order] for i in range(weight_order)]
-
-#     # Symbolds for input tensor
-#     tensor_sym = [einsum_symbols[i+2*weight_order] for i in range(tensor.ndim)]
-
-#     # Output: input + weights symbols after removing contraction symbols
-#     output_sym = tensor_sym + tucker_sym
-#     for m in sorted_modes_tucker:
-#         output_sym.pop(m+input_order)
-#     for m in sorted_modes_tensor:
-#         output_sym.pop(m)
-#     for i, e in enumerate(modes_tensor):
-#         tensor_sym[e] = tucker_sym[modes_tucker[i]]
-
-#     # Form the actual equation: tensor, core, factors -> output
-#     eq = ''.join(tensor_sym)
-#     eq += ',' + ''.join(rank_sym)
-#     eq += ',' + ','.join(f'{s}{r}' for s,r in zip(tucker_sym,rank_sym))
-#     eq += '->' + ''.join(output_sym)
+def tensor_dot_tucker(tensor, tucker, modes, batched_modes=()):
+    """Batched tensor contraction between a dense tensor and a Tucker tensor on specified modes
     
-#     return tl.einsum(eq, tensor, tucker.core, *tucker.factors)
-
+    Parameters
+    ----------
+    tensor : DenseTensor
+    tucker : TuckerTensor
+    modes : int list or int
+        modes on which to contract tensor1 and tensor2
+    batched_modes : int or tuple[int]
 
-def tensor_dot_tucker(tensor, tucker, modes, batched_modes=()):
+    Returns
+    -------
+    contraction : tensor contracted with cp on the specified modes
+    """
     modes_tensor, modes_tucker = _validate_contraction_modes(
         tl.shape(tensor), tucker.tensor_shape, modes)
     input_order = tensor.ndim