【Hackathon 6th No.4】Add Ormqr API to Paddle -part (#63227)

ADream-ki · sunzhongkai588 · web-flow · commit e5e4242cc463 · 2024-05-11T11:47:27.000+08:00
* ormqr API

* update

* test

* update code style

* update ormqr api

* code_style

* update check

* update ormqr

* finish

* finish

* finish

* update code_style

* update

* finish

* update name

* update

* update information

* update

* update

* Update python/paddle/tensor/linalg.py

---------

Co-authored-by: zachary sun &lt;70642955+sunzhongkai588@users.noreply.github.com&gt;
diff --git a/python/paddle/linalg.py b/python/paddle/linalg.py
@@ -34,6 +34,7 @@
     matrix_rank,
     multi_dot,
     norm,
+    ormqr,
     pca_lowrank,
     pinv,
     qr,
@@ -76,4 +77,5 @@
     'cholesky_solve',
     'triangular_solve',
     'lstsq',
+    'ormqr',
 ]
diff --git a/python/paddle/tensor/__init__.py b/python/paddle/tensor/__init__.py
@@ -88,6 +88,7 @@
     multi_dot,
     mv,
     norm,
+    ormqr,
     pca_lowrank,
     pinv,
     qr,
@@ -448,6 +449,7 @@
 tensor_method_func = [
     'create_parameter',
     'create_tensor',
+    'ormqr',
     'matmul',
     'dot',
     'cov',
diff --git a/python/paddle/tensor/linalg.py b/python/paddle/tensor/linalg.py
@@ -4351,6 +4351,7 @@ def householder_product(x, tau, name=None):
         x.dtype,
         'x',
         [
+            'float16',
             'float32',
             'float64',
             'complex64',
@@ -4362,6 +4363,7 @@ def householder_product(x, tau, name=None):
         tau.dtype,
         'tau',
         [
+            'float16',
             'float32',
             'float64',
             'complex64',
@@ -5041,3 +5043,94 @@ def __check_ranges(D, ranges):
         hist /= s
 
     return (hist, edges)
+
+
+def ormqr(x, tau, y, left=True, transpose=False, name=None):
+    r'''
+    Calculate the product of a normal matrix and a householder matrix.
+    Compute the product of the matrix C (given by y) with dimensions (m, n) and a matrix Q,
+    where Q is generated by the Householder reflection coefficient (x, tau). Returns a Tensor.
+
+    Args:
+        x (Tensor): Shape(\*,mn, k), when left is True, the value of mn is equal to m, otherwise the value of mn is equal to n. \* indicates that the length of the tensor on axis 0 is 0 or greater.
+        tau (Tensor): Shape (\*, min(mn, k)), where \* indicates that the length of the Tensor on axis 0 is 0 or greater, and its type is the same as input.
+        y (Tensor): Shape (\*m,n), where \* indicates that the length of the Tensor on axis 0 is 0 or greater, and its type is the same as input.
+        left (bool, optional): Determines the order in which the matrix product operations are operated. If left is true, the order of evaluation is op(Q) \* y, otherwise, the order of evaluation is y \* op(Q). Default value: True.
+        transpose (bool, optional): If true, the matrix Q is conjugated and transposed, otherwise, the conjugate transpose transformation is not performed. Default value: False.
+        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
+
+    Returns:
+        Tensor. Data type and dimension are equals with :attr:`y`.
+
+    Examples:
+        .. code-block:: python
+
+            >>> import paddle
+            >>> import numpy as np
+            >>> from paddle import  linalg
+
+            >>> input = paddle.to_tensor([[-114.6, 10.9, 1.1], [-0.304, 38.07, 69.38], [-0.45, -0.17, 62]])
+            >>> tau = paddle.to_tensor([1.55, 1.94, 3.0])
+            >>> y = paddle.to_tensor([[-114.6, 10.9, 1.1], [-0.304, 38.07, 69.38], [-0.45, -0.17, 62]])
+            >>> output = linalg.ormqr(input, tau, y)
+            >>> print(output)
+            Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
+                [[ 63.82712936 , -13.82312393 , -116.28614044],
+                [-53.65926361 , -28.15783691 , -70.42700958 ],
+                [-79.54292297 ,  24.00182915 , -41.34253311 ]])
+    '''
+
+    check_dtype(
+        y.dtype,
+        'y',
+        [
+            'float16',
+            'float32',
+            'float64',
+            'complex64',
+            'complex128',
+        ],
+        'ormqr',
+    )
+    check_type(left, 'left', bool, 'ormqr')
+    check_type(transpose, 'transpose', bool, 'ormqr')
+    assert (
+        x.dtype == tau.dtype and x.dtype == y.dtype
+    ), "The input tau and y must have the same dtype with the x.\n"
+    assert (
+        len(x.shape) >= 2 and len(y.shape) >= 2 and len(tau.shape) >= 1
+    ), "The input x and y must have more than 2 dimensions, and input tau must have more than 1 dimension"
+    assert len(x.shape) == len(tau.shape) + 1 and len(x.shape) == len(
+        y.shape
+    ), "the dimension of x is 1 larger than the dimension of tau\n and the dimension of x is equal to the dimension of input"
+    assert (
+        x.shape[-1] == tau.shape[-1]
+    ), "The innermost dimension of x and tau should be the same"
+    if transpose and left:
+        assert (
+            x.shape[-2] == y.shape[-2]
+        ), "The row dimensions of x and y should be the same"
+    elif not transpose and left:
+        assert (
+            x.shape[-1] == y.shape[-2]
+        ), "The column dimension of x and the row dimension of y should be the same"
+    elif transpose and not left:
+        assert (
+            x.shape[-2] == y.shape[-1]
+        ), "The row dimension of x and the column dimension of y should be the same"
+    else:
+        assert (
+            x.shape[-1] == y.shape[-1]
+        ), "The column dimensions of Impt and Osser's should be the same"
+    if len(x.shape) == 3:
+        assert (
+            x.shape[0] == y.shape[0] and x.shape[0] == tau.shape[0]
+        ), "The input and tau and y parameters should have the same batch"
+    Q = householder_product(x, tau)
+    if len(x.shape) == 2:
+        Q = Q.T if transpose else Q
+    else:
+        Q = paddle.transpose(Q, [0, 2, 1]) if transpose else Q
+    result = matmul(Q, y) if left else matmul(y, Q)
+
+    return result
diff --git a/test/legacy_test/test_ormqr.py b/test/legacy_test/test_ormqr.py
