Refactor tests.tensor to use NumPy Generator

Author: kc611

tests/tensor/nnet/speed_test_conv.py

@@ -37,7 +37,7 @@ def flip(kern, kshp):
     return flip
 
 
-global_rng = np.random.RandomState(3423489)
+global_rng = np.random.default_rng(3423489)
 
 dmatrix4 = TensorType("float64", (False, False, False, False))
 
@@ -64,7 +64,7 @@ def exec_multilayer_conv_nnet_old(
         img = dmatrix()
 
     # build actual input images
-    imgval = global_rng.rand(bsize, imshp[0], imshp[1], imshp[2])
+    imgval = global_rng.random((bsize, imshp[0], imshp[1], imshp[2]))
 
     a = dmatrix()
     kerns = [a for i in nkerns]
@@ -82,7 +82,7 @@ def exec_multilayer_conv_nnet_old(
             print(conv_mode, ss, n_layer, kshp, nkern)
 
         # actual values
-        w = global_rng.random_sample(np.r_[nkern, imshp[0], kshp])
+        w = global_rng.random((np.r_[nkern, imshp[0], kshp]))
         w_flip = flip(w, kshp).reshape(w.shape)
 
         # manual implementation
@@ -193,7 +193,7 @@ def exec_multilayer_conv_nnet(
         img = dmatrix()
 
     # build actual input images
-    imgval = global_rng.rand(bsize, imshp[0], imshp[1], imshp[2])
+    imgval = global_rng.random((bsize, imshp[0], imshp[1], imshp[2]))
 
     a = dmatrix()
     kerns = [a for i in nkerns]
@@ -211,7 +211,7 @@ def exec_multilayer_conv_nnet(
             print(conv_mode, ss, n_layer, kshp, nkern)
 
         # actual values
-        w = global_rng.random_sample(np.r_[nkern, imshp[0], kshp])
+        w = global_rng.random((np.r_[nkern, imshp[0], kshp]))
         w_flip = flip(w, kshp).reshape(w.shape)
 
         outshp = np.hstack(

tests/tensor/nnet/test_abstract_conv.py

@@ -1903,7 +1903,7 @@ def test_interface(self):
 )
 class TestConv2dGrads:
     def setup_method(self):
-        self.random_stream = np.random.RandomState(utt.fetch_seed())
+        self.random_stream = np.random.default_rng(utt.fetch_seed())
 
         self.inputs_shapes = [(8, 1, 12, 12), (1, 1, 5, 5), (1, 1, 5, 6), (1, 1, 6, 6)]
         self.filters_shapes = [(5, 1, 2, 2), (1, 1, 3, 3)]
@@ -1928,12 +1928,12 @@ def test_conv2d_grad_wrt_inputs(self):
             for bm in self.border_modes:
                 for ss in self.subsamples:
                     for ff in self.filter_flip:
-                        input_val = self.random_stream.random_sample(in_shape).astype(
+                        input_val = self.random_stream.random(in_shape).astype(
+                            config.floatX
+                        )
+                        filter_val = self.random_stream.random(fltr_shape).astype(
                             config.floatX
                         )
-                        filter_val = self.random_stream.random_sample(
-                            fltr_shape
-                        ).astype(config.floatX)
                         out_grad_shape = (
                             aesara.tensor.nnet.abstract_conv.get_conv_output_shape(
                                 image_shape=in_shape,
@@ -1942,9 +1942,9 @@ def test_conv2d_grad_wrt_inputs(self):
                                 subsample=ss,
                             )
                         )
-                        out_grad_val = self.random_stream.random_sample(
-                            out_grad_shape
-                        ).astype(config.floatX)
+                        out_grad_val = self.random_stream.random(out_grad_shape).astype(
+                            config.floatX
+                        )
                         conv_out = aesara.tensor.nnet.conv2d(
                             self.x,
                             filters=self.w,
@@ -1994,12 +1994,12 @@ def test_conv2d_grad_wrt_weights(self):
             for bm in self.border_modes:
                 for ss in self.subsamples:
                     for ff in self.filter_flip:
-                        input_val = self.random_stream.random_sample(in_shape).astype(
+                        input_val = self.random_stream.random(in_shape).astype(
+                            config.floatX
+                        )
+                        filter_val = self.random_stream.random(fltr_shape).astype(
                             config.floatX
                         )
-                        filter_val = self.random_stream.random_sample(
-                            fltr_shape
-                        ).astype(config.floatX)
                         out_grad_shape = (
                             aesara.tensor.nnet.abstract_conv.get_conv_output_shape(
                                 image_shape=in_shape,
@@ -2008,9 +2008,9 @@ def test_conv2d_grad_wrt_weights(self):
                                 subsample=ss,
                             )
                         )
-                        out_grad_val = self.random_stream.random_sample(
-                            out_grad_shape
-                        ).astype(config.floatX)
+                        out_grad_val = self.random_stream.random(out_grad_shape).astype(
+                            config.floatX
+                        )
                         conv_out = aesara.tensor.nnet.conv2d(
                             self.x,
                             filters=self.w,