Add nested tensor creation + svd and way to reverse SVD

more_math/Parser/MathExpr.g4

@@ -420,7 +420,14 @@ func1:
 		  flow_angle(x) - computes the angle of optical flow (basically angle() but real and imaginery is separate in 2 dimensions)
 		*/
 	| FLOW_ANG LPAREN expr RPAREN	# FlowAngFunc;
-
+	    /**
+		    as_nested(x) - converts a list to a nested tensor (special object containing tensors of different shapes behaving like a tensor)
+		  */
+	| AS_NESTED LPAREN expr RPAREN	# AsNestedFunc;
+	    /**
+		  svd(x) - computes the singular value decomposition of x. Returns a list of 3 tensors: U, S, V such that x = matmul(matmul(U, diagonal_matrix(S,shape(U))), V)
+		*/
+		| SVD LPAREN expr RPAREN	# SvdFunc;
 
 func2:
 		/**
@@ -718,6 +725,10 @@ func5:
 		  remap(v, i_min, i_max, o_min, o_max) - remaps values from input range to output range
 		*/
 	REMAP LPAREN expr COMMA expr COMMA expr COMMA expr COMMA expr RPAREN # RemapFunc;
+    	/**
+		  diagonal_matrix(x, shape, [offset], [dim1], [dim2]) - creates a diagonal matrix from x with specified shape. offset is the diagonal offset. dim1 and dim2 are the dimensions to place the diagonal on.
+		  */
+		  | DIAG LPAREN expr COMMA expr (COMMA expr (COMMA expr (COMMA expr)?)?)? RPAREN # DiagonalMatrixFunc;
 
 // N-argument functions
 funcN:
@@ -973,6 +984,9 @@ INTERPOLATE_LINEAR: 'interpolate_linear';
 INTERPOLATE_AREA: 'interpolate_area';
 INTERPOLATE_NEAREST: 'interpolate_nearest' | 'interpolate_nearest_exact';
 TEXT_IMAGE: 'text_image';
+AS_NESTED: 'as_nested_tensor';
+SVD: 'svd';
+DIAG: 'diagonal_matrix';
 
 IF: 'if';
 ELSE: 'else';

more_math/Parser/UnifiedMathVisitor.py

@@ -1,5 +1,6 @@
 import time
 import os
+from PIL.XVThumbImagePlugin import r
 import torch
 import math
 import inspect
@@ -4673,3 +4674,43 @@ class UnifiedMathVisitor(MathExprVisitor):
         )
         target_shape = tuple(int(x) for x in shape)
         return noise.view(target_shape)
+
+    def visitAsNestedFunc(self, ctx):
+        val = yield ctx.expr()
+        if self._is_list(val):
+            return NestedTensor(val)
+        if self._is_tensor(val):
+            return NestedTensor([val])
+        if isinstance(val, (int, float)):
+            return NestedTensor([torch.tensor(val, device=self.device)])
+
+    def visitSvdFunc(self, ctx):
+        """svd(x) - singular value decomposition"""
+        x = self._promote_to_tensor((yield ctx.expr()))
+        if x.ndim < 2:
+            raise ValueError(f"{ctx.start.line}:{ctx.start.column}: svd expects at least 2D input, got shape {tuple(x.shape)}")
+        u, s, vh = torch.linalg.svd(x, full_matrices=True)
+        v = vh.mvector_transpose(-2, -1).conj()
+        result = [u, s, v]
+        return result
+
+    def visitDiagonalMatrixFunc(self, ctx):
+        """diagonal_matrix(x) - create a diagonal matrix from a vector"""
+        x = self._promote_to_tensor((yield ctx.expr(0)))
+        shape = self._get_shape_from_ctx(ctx, 1)
+        offset = 0
+        dim1 = -2
+        dim2 = -1
+
+        if len(ctx.expr()) > 2:
+            offset = yield ctx.expr(2)
+        if len(ctx.expr()) > 3:
+            dim1 = yield ctx.expr(3)
+        if len(ctx.expr()) > 4:
+            dim2 = yield ctx.expr(4)
+
+        res = torch.zeros(shape, dtype=x.dtype, device=x.device)
+        diag_view = torch.diagonal(res, offset=offset, dim1=dim1, dim2=dim2)
+        diag_view.copy_(x)
+
+        return res