Upadte cuda tests

examples/svd_tests.py

@@ -10,19 +10,33 @@ from timeit import default_timer as timer
 import numpy as np
 
 def test_svd():
-    d = 100
-    A = np.asarray(np.random.randint(1, 10,(d, d)), dtype=np.float64)
+    d = 1000
+    A = np.asarray(np.random.randint(1, 10,(d, d)), dtype=np.float32)
  
     # scipy
     print("\n****************** Scipy")
-    from scipy import linalg as sc_linalg
+    from scipy.linalg import svd as sc_svd
     start = timer()
-    u_sc, s_sc, v_sc = sc_linalg.svd(A)
+    u_sc, s_sc, v_sc = sc_svd(A)
     duration = timer() - start
     print("SVD scipy : " + str(duration))
     print(s_sc)
     print(type(s_sc))
     
+    # cupy
+    print("\n****************** Cupy")
+    import cupy as cp
+    from cupy.linalg import svd as cp_svd
+    start = timer()
+    A_cp = cp.asarray(A)
+    u_cp, s_cp, v_cp = cp_svd(A_cp)
+    u_cp, s_cp, v_cp = u_cp.get(), s_cp.get(), v_cp.get()
+    duration = timer() - start
+    print("SVD cupy : " + str(duration))
+    print(s_cp)
+    print(A_cp.device)
+    print(type(s_cp))
+
     # scikit-cuda
     print("\n****************** scikit-cuda")
     import pycuda.autoinit
@@ -40,30 +54,44 @@ def test_svd():
     print(s_sk)
     print(type(s_sk))
      
-    # tensorflow
-    print("\n****************** tensorflow")
-    import tensorflow as tf
-    from tensorflow import linalg as tf_linalg
-    start = timer()
-    with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
-        s_tf, u_tf, v_tf = tf_linalg.svd(A)
-        s_tf, u_tf, v_tf = s_tf.eval(), u_tf.eval(), v_tf.eval()
-    duration = timer() - start
-    print("SVD tensorflow : " + str(duration))
-    print(s_tf)
-    print(type(s_tf))
+#     # tensorflow
+#     print("\n****************** tensorflow")
+#     import tensorflow as tf
+#     start = timer()
+#     with tf.device("/cpu:0"):
+#         s_tf, u_tf, v_tf = tf.svd(A, compute_uv=True)
+#         s_tf, u_tf, v_tf = tf.Session().run(s_tf), tf.Session().run(u_tf), tf.Session().run(v_tf)
+#     duration = timer() - start
+#     print("SVD tensorflow CPU : " + str(duration))
+#     print(s_tf)
+#     print(type(s_tf))
+#     print()
+#     start = timer()
+#     with tf.device("/gpu:0"):
+#         s_tf, u_tf, v_tf = tf.svd(A, compute_uv=True)
+#         s_tf, u_tf, v_tf = tf.Session().run(s_tf), tf.Session().run(u_tf), tf.Session().run(v_tf)
+#     duration = timer() - start
+#     print("SVD tensorflow GPU : " + str(duration))
+#     print(s_tf)
+#     print(type(s_tf))
  
     # Theano
     print("\n****************** theano")
-    import theano.tensor as T
-    from theano import function
+    from theano import function, shared, tensor, config
     import theano.tensor.nlinalg as th_linalg
-    x = T.dmatrix('x')
+    x = tensor.dmatrix('x')
     u, s, v = th_linalg.svd(x)
     f = function([x], (u, s, v))
     start = timer()
     u_th, s_th, v_th = f(A)
     duration = timer() - start
+
+#     x = shared(np.asarray(A, config.floatX))
+#     f = function([], th_linalg.svd(x))
+#     start = timer()
+#     u_th, s_th, v_th = f()
+#     duration = timer() - start
+    
     print("SVD theano : " + str(duration))
     print(s_th)
     print(type(s_th))

splearn/hankel.py

@@ -363,8 +363,17 @@ class Hankel(object):
                               "the smaller dimension of the Hankel Matrix (" +
                              str(matrix_shape) + ")")
         if not self.sparse:
+            try:
+                import theano.tensor as T
+                from theano import function
+                import theano.tensor.nlinalg as th_linalg
+                x = T.dmatrix('x')
+                u, s, v = th_linalg.svd(x)
+                svd = function([x], (u, s, v))
+            except :
+                from scipy.linalg import svd
             hankel = self.lhankel[0]
-            [u, s, v] = np.linalg.svd(hankel)
+            [u, s, v] = svd(hankel)
             u = u[:, :rank]
             v = v[:rank, :]
             # ds = np.zeros((rank, rank), dtype=complex)