Use direct imports for theano.compile.io and theano.compile.mode objects

doc/cifarSC2011/advanced_theano.txt

@@ -29,9 +29,9 @@ Conditions
    z_lazy = ifelse(tt.lt(a,b), tt.mean(x), tt.mean(y))
 
    f_switch = theano.function([a,b,x,y], z_switch,
-                              mode=theano.Mode(linker='vm'))
+                              mode=theano.compile.mode.Mode(linker='vm'))
    f_lazyifelse = theano.function([a,b,x,y], z_lazy,
-                                  mode=theano.Mode(linker='vm'))
+                                  mode=theano.compile.mode.Mode(linker='vm'))
 
    val1 = 0.
    val2 = 1.

doc/tutorial/aliasing.txt

@@ -232,11 +232,13 @@ that control how ``theano.function`` handles its argument[s] and return value[s]
 
 .. testcode::
 
-    import theano, theano.tensor
+    import theano
+    import theano.tensor as tt
+    from theano.compile.io import In, Out
 
-    x = theano.tensor.matrix()
+    x = tt.matrix()
     y = 2 * x
-    f = theano.function([theano.In(x, borrow=True)], theano.Out(y, borrow=True))
+    f = theano.function([In(x, borrow=True)], Out(y, borrow=True))
 
 Borrowing an input means that Theano will treat the argument you provide as if
 it were part of Theano's pool of temporaries.  Consequently, your input

doc/tutorial/conditions.txt

@@ -31,9 +31,9 @@ IfElse vs Switch
    z_lazy = ifelse(tt.lt(a, b), tt.mean(x), tt.mean(y))
 
    f_switch = theano.function([a, b, x, y], z_switch,
-                              mode=theano.Mode(linker='vm'))
+                              mode=theano.compile.mode.Mode(linker='vm'))
    f_lazyifelse = theano.function([a, b, x, y], z_lazy,
-                                  mode=theano.Mode(linker='vm'))
+                                  mode=theano.compile.mode.Mode(linker='vm'))
 
    val1 = 0.
    val2 = 1.

doc/tutorial/modes.txt

@@ -210,7 +210,7 @@ The optimizers Theano provides are summarized below to indicate the trade-offs
 one might make between compilation time and execution time.
 
 These optimizers can be enabled globally with the Theano flag: ``optimizer=name``
-or per call to theano functions with ``theano.function(...mode=theano.Mode(optimizer="name"))``.
+or per call to theano functions with ``function(...mode=Mode(optimizer="name"))``.
 
 =================  ============  ==============  ==================================================
 optimizer          Compile time  Execution time  Description

tests/compile/function/test_types.py

@@ -11,6 +11,7 @@ import theano.tensor as tt
 from theano.compile.function import function
 from theano.compile.function.types import UnusedInputError
 from theano.compile.io import In, Out
+from theano.compile.mode import Mode
 from theano.configdefaults import config
 from theano.graph.basic import Constant
 from theano.graph.fg import MissingInputError
@@ -627,14 +628,12 @@ class TestFunction:
         f(o + 0.1)  # should not clobber the memory used to store four
         assert np.all(four == 4)
 
-        f = function(
-            [a], Out(a * 4, borrow=True), mode=theano.Mode("c|py_nogc", "fast_run")
-        )
+        f = function([a], Out(a * 4, borrow=True), mode=Mode("c|py_nogc", "fast_run"))
         o = np.ones((3, 3))
         four = f(o)
         assert np.all(four == 4)
         f(o + 0.1)  # should clobber the memory used to store four
-        if theano.config.cxx:
+        if config.cxx:
             assert not np.all(four == 4)
         else:
             # The Elemwise.perform method don't reuse memory
@@ -691,9 +690,7 @@ class TestFunction:
 
         a, b = dscalars("a", "b")
         c = a + b
-        func = theano.function(
-            [theano.In(a, name="first"), theano.In(b, value=1, name="second")], c
-        )
+        func = theano.function([In(a, name="first"), In(b, value=1, name="second")], c)
         x = func(first=1)
         try:
             func(second=2)
@@ -721,17 +718,17 @@ class TestFunction:
         # Assert cases we should check for aliased inputs
         for d in [
             dict(
-                inputs=[theano.In(x1, borrow=True)],
+                inputs=[In(x1, borrow=True)],
                 outputs=[x1 + 1],
                 updates=[(s2, s2 + 3)],
             ),
             dict(
-                inputs=[theano.In(x1, borrow=True, mutable=True)],
+                inputs=[In(x1, borrow=True, mutable=True)],
                 outputs=[x1 + 1],
                 updates=[(s2, s2 + 3)],
             ),
             dict(
-                inputs=[theano.In(x1, mutable=True)],
+                inputs=[In(x1, mutable=True)],
                 outputs=[x1 + 1],
                 updates=[(s2, s2 + 3)],
             ),
@@ -1146,12 +1143,12 @@ def test_empty_givens_updates():
     # triggering useless crashes at compile time.
     x = scalar()
     y = x * 2
-    function([theano.In(x)], y, givens={})
-    function([theano.In(x)], y, updates={})
+    function([In(x)], y, givens={})
+    function([In(x)], y, updates={})
 
 
 @pytest.mark.skipif(
-    not theano.gpuarray.pygpu_activated or theano.config.mode == "DEBUG_MODE",
+    not theano.gpuarray.pygpu_activated or config.mode == "DEBUG_MODE",
     reason="DEBUG_MODE forces synchronous behaviour which breaks this test",
 )
 def test_sync_update():

tests/compile/test_mode.py

@@ -33,5 +33,5 @@ def test_no_output_from_implace():
 
 
 def test_including():
-    mode = theano.Mode(optimizer="merge")
+    mode = Mode(optimizer="merge")
     mode.including("fast_compile")

tests/compile/test_modes.py

@@ -5,7 +5,7 @@ Test compilation modes
 import copy
 
 import theano
-from theano.compile import Mode
+from theano.compile.mode import Mode
 from theano.tensor.type import matrix, vector
 
 

tests/link/c/test_basic.py

@@ -2,6 +2,7 @@ import numpy as np
 import pytest
 
 import theano
+from theano.compile.mode import Mode
 from theano.graph import fg
 from theano.graph.basic import Apply, Constant, Variable
 from theano.graph.op import COp
@@ -220,7 +221,7 @@ def test_clinker_literal_inlining():
     not theano.config.cxx, reason="G++ not available, so we need to skip this test."
 )
 def test_clinker_literal_cache():
-    mode = theano.Mode(linker="c")
+    mode = Mode(linker="c")
 
     A = matrix()
     input1 = vector()
@@ -415,7 +416,7 @@ def test_shared_input_output():
     state = theano.shared(0)
     state.name = "state"
     linker = CLinker()
-    mode = theano.Mode(linker=linker)
+    mode = Mode(linker=linker)
     f = theano.function([inc], state, updates=[(state, state + inc)], mode=mode)
     g = theano.function([inc], state, updates=[(state, state + inc)])
 

tests/link/test_link.py

@@ -3,8 +3,9 @@ from copy import deepcopy
 import numpy as np
 
 import theano
-from theano.graph import basic, fg
-from theano.graph.basic import Apply, Constant, Variable
+from theano.compile.mode import Mode
+from theano.graph import fg
+from theano.graph.basic import Apply, Constant, Variable, clone
 from theano.graph.op import Op
 from theano.graph.type import Type
 from theano.link.basic import Container, PerformLinker, WrapLinker
@@ -121,7 +122,7 @@ class TestPerformLinker:
         x, y, z = inputs()
         a, d = add(x, y), div(x, y)
         e = mul(a, d)
-        fn = perform_linker(FunctionGraph(*basic.clone([x, y, a], [e]))).make_function()
+        fn = perform_linker(FunctionGraph(*clone([x, y, a], [e]))).make_function()
         assert fn(1.0, 2.0, 9.0) == 4.5
 
     def test_skiphole(self):
@@ -129,7 +130,7 @@ class TestPerformLinker:
         a = add(x, y)
         r = raise_err(a)
         e = add(r, a)
-        fn = perform_linker(FunctionGraph(*basic.clone([x, y, r], [e]))).make_function()
+        fn = perform_linker(FunctionGraph(*clone([x, y, r], [e]))).make_function()
         assert fn(1.0, 2.0, 4.5) == 7.5
 
 
@@ -186,7 +187,7 @@ def test_sort_schedule_fn():
         return cmp(str(a), str(b))  # lexicographical sort
 
     linker = OpWiseCLinker(schedule=sort_schedule_fn(str_cmp))
-    mode = theano.Mode(linker=linker)
+    mode = Mode(linker=linker)
     f = theano.function((x,), (y,), mode=mode)
 
     nodes = f.maker.linker.make_all()[-1]

tests/link/test_vm.py

@@ -8,7 +8,8 @@ import pytest
 import theano
 from theano import function
 from theano import tensor as tt
-from theano.compile import Mode
+from theano.compile.io import In
+from theano.compile.mode import Mode
 from theano.configdefaults import config
 from theano.graph.basic import Apply
 from theano.graph.op import Op
@@ -389,8 +390,8 @@ def test_vm_gc():
 
     x = vector()
     p = RunOnce()(x)
-    mode = theano.Mode(linker=VMLinker(lazy=True))
-    f = theano.function([theano.In(x, mutable=True)], [p + 1, p + 2], mode=mode)
+    mode = Mode(linker=VMLinker(lazy=True))
+    f = theano.function([In(x, mutable=True)], [p + 1, p + 2], mode=mode)
     f([1, 2, 3])
 
     p = RunOnce()(x)
@@ -408,7 +409,7 @@ def test_reallocation():
         VMLinker(allow_gc=False, lazy=False, use_cloop=False),
         VMLinker(allow_gc=True, lazy=False, use_cloop=False),
     ]:
-        m = theano.compile.get_mode(theano.Mode(linker=linker))
+        m = theano.compile.get_mode(Mode(linker=linker))
         m = m.excluding("fusion", "inplace")
 
         f = theano.function([x, y], z, name="test_reduce_memory", mode=m)
@@ -444,7 +445,7 @@ def test_no_recycling():
         VMLinker(use_cloop=False, lazy=False, allow_gc=False),
     ]:
 
-        mode = theano.Mode(optimizer="fast_compile", linker=lnk)
+        mode = Mode(optimizer="fast_compile", linker=lnk)
         f = theano.function([x], x + 1, mode=mode)
         f2 = theano.function([x], (x + 1) * 2, mode=mode)
         m1 = f.fn.thunks[0].thunk.module

tests/record.py

-from theano.compile import Mode
+from theano.compile.mode import Mode
 from theano.configdefaults import config
 from theano.link.basic import WrapLinkerMany
 from theano.link.vm import VMLinker

tests/scalar/test_basic.py

@@ -13,6 +13,7 @@ import pytest
 
 import tests.unittest_tools as utt
 import theano
+from theano.compile.mode import Mode
 from theano.graph.fg import FunctionGraph
 from theano.link.c.basic import DualLinker
 from theano.scalar.basic import (
@@ -453,7 +454,7 @@ def test_grad_abs():
     a = fscalar("a")
     b = theano.tensor.nnet.relu(a)
     c = theano.grad(b, a)
-    f = theano.function([a], c, mode=theano.Mode(optimizer=None))
+    f = theano.function([a], c, mode=Mode(optimizer=None))
     # Currently Theano return 0.5, but it isn't sure it won't change
     # in the futur.
     ret = f(0.0)

tests/scan/test_basic.py

@@ -26,6 +26,8 @@ import theano.scalar.sharedvar
 from tests import unittest_tools as utt
 from theano import tensor as tt
 from theano.compile.function.pfunc import rebuild_collect_shared
+from theano.compile.io import In
+from theano.compile.mode import FAST_RUN, Mode, get_default_mode, get_mode
 from theano.configdefaults import config
 from theano.misc.safe_asarray import _asarray
 from theano.scan.basic import scan
@@ -54,11 +56,11 @@ from theano.tensor.type import (
 
 
 if config.mode == "FAST_COMPILE":
-    mode_with_opt = theano.compile.mode.get_mode("FAST_RUN")
+    mode_with_opt = get_mode("FAST_RUN")
 else:
-    mode_with_opt = theano.compile.mode.get_default_mode()
+    mode_with_opt = get_default_mode()
 if config.mode in ("DEBUG_MODE", "DebugMode"):
-    mode_nodebug = theano.compile.mode.get_mode("FAST_RUN")
+    mode_nodebug = get_mode("FAST_RUN")
 else:
     mode_nodebug = mode_with_opt
 
@@ -234,9 +236,7 @@ class TestScan:
     # generator network, only one output , type scalar ; no sequence or
     # non sequence arguments
     @pytest.mark.skipif(
-        isinstance(
-            theano.compile.mode.get_default_mode(), theano.compile.debugmode.DebugMode
-        ),
+        isinstance(get_default_mode(), theano.compile.debugmode.DebugMode),
         reason="This test fails in DebugMode, because it is not yet picklable.",
     )
     def test_pickling(self):
@@ -920,14 +920,14 @@ class TestScan:
         u0 = vector("u0")
         u1 = vector("u1")
         u2 = vector("u2")
-        mu0 = theano.In(u0, mutable=False)
-        mu1 = theano.In(u1, mutable=True)
-        mu2 = theano.In(u2, mutable=True)
+        mu0 = In(u0, mutable=False)
+        mu1 = In(u1, mutable=True)
+        mu2 = In(u2, mutable=True)
         x0 = scalar("x0")
         x1 = scalar("y0")
         W_in = theano.shared(vW_in, "Win")
         W = theano.shared(vW, "W")
-        mode = theano.compile.mode.get_mode(None).including("inplace")
+        mode = get_mode(None).including("inplace")
 
         def f_rnn_shared(u0_t, u1_t, u2_t, x0_tm1, x1_tm1):
             return [
@@ -987,14 +987,14 @@ class TestScan:
         u0 = vector("u0")
         u1 = vector("u1")
         u2 = vector("u2")
-        mu0 = theano.In(u0, mutable=True)
-        mu1 = theano.In(u1, mutable=True)
-        mu2 = theano.In(u2, mutable=True)
+        mu0 = In(u0, mutable=True)
+        mu1 = In(u1, mutable=True)
+        mu2 = In(u2, mutable=True)
         x0 = scalar("x0")
         x1 = scalar("y0")
         W_in = theano.shared(vW_in, "Win")
         W = theano.shared(vW, "W")
-        mode = theano.compile.mode.get_mode(None).including("inplace")
+        mode = get_mode(None).including("inplace")
 
         def f_rnn_shared(u0_t, u1_t, u1_tp1, u2_tm1, u2_t, u2_tp1, x0_tm1, x1_tm1):
             return [
@@ -1057,7 +1057,7 @@ class TestScan:
         x0 = tt.constant(x0)
         to_replace = outputs[0].owner.inputs[0].owner.inputs[1]
         outputs = theano.clone(outputs, replace=[(to_replace, x0)])
-        mode = theano.compile.mode.get_mode(None).including("inplace")
+        mode = get_mode(None).including("inplace")
         f9 = theano.function([], outputs, updates=updates, mode=mode)
         scan_node = [x for x in f9.maker.fgraph.toposort() if isinstance(x.op, Scan)]
         assert 0 not in scan_node[0].op.destroy_map.keys()
@@ -2783,7 +2783,7 @@ class TestScan:
             x,
             tt.constant(np.asarray(0.0, dtype=config.floatX)),
         )
-        mode = theano.compile.mode.FAST_RUN
+        mode = FAST_RUN
         mode = mode.excluding("inplace")
         f1 = theano.function([], o, mode=mode)
         inputs, outputs = clone_optimized_graph(f1)
@@ -2817,7 +2817,7 @@ class TestScan:
             tt.constant(np.asarray(0.0, dtype=config.floatX)),
         )
 
-        mode = theano.compile.mode.FAST_RUN
+        mode = FAST_RUN
         mode = mode.excluding("inplace")
         f0 = theano.function([], o, mode=mode)
         inputs, outputs = clone_optimized_graph(f0)
@@ -2852,7 +2852,7 @@ class TestScan:
             tt.constant(np.asarray(0.0, dtype=config.floatX)),
         )
 
-        mode = theano.compile.mode.FAST_RUN
+        mode = FAST_RUN
         mode = mode.excluding("inplace")
         f1 = theano.function([], o, mode=mode)
         inputs, outputs = clone_optimized_graph(f1)
@@ -4186,7 +4186,7 @@ class TestScan:
             [U, x1, x2],
             [X1, X2, X3],
             updates=updates,
-            mode=theano.Mode(linker="py"),
+            mode=Mode(linker="py"),
             allow_input_downcast=True,
         )
         rng = np.random.RandomState(utt.fetch_seed())
@@ -4223,7 +4223,7 @@ class TestScan:
             [W, x1, x2],
             [X1, X2, X3],
             updates=updates,
-            mode=theano.Mode(linker="py"),
+            mode=Mode(linker="py"),
             allow_input_downcast=True,
         )
         rng = np.random.RandomState(utt.fetch_seed())
@@ -4594,7 +4594,7 @@ def test_speed():
     #     fn=lambda ri, rii: ri + rii,
     #     sequences=[s_r[1:]],
     #     outputs_info=tt.constant(r[0]),
-    #     mode=theano.Mode(linker="cvm"),
+    #     mode=Mode(linker="cvm"),
     # )
     # assert not updates
     #
@@ -4616,7 +4616,7 @@ def test_speed():
         [],
         [],
         updates=OrderedDict([(s_i, s_i + 1), (shared_r, s_rinc)]),
-        mode=theano.Mode(linker="cvm"),
+        mode=Mode(linker="cvm"),
     )
     f_cvm_shared._check_for_aliased_inputs = False
 
@@ -4652,11 +4652,11 @@ def test_speed_rnn():
     #     fn=lambda ri, rii: tt.tanh(tt.dot(rii, w)),
     #     sequences=[s_r[1:]],
     #     outputs_info=tt.constant(r[0]),
-    #     mode=theano.Mode(linker="cvm"),
+    #     mode=Mode(linker="cvm"),
     # )
     # assert not updates
     #
-    # f_cvm = theano.function([s_r], s_y, mode=theano.Mode(linker="cvm"))
+    # f_cvm = theano.function([s_r], s_y, mode=Mode(linker="cvm"))
     #
     # cvm_duration = timeit(lambda: f_cvm(r), number=n_timeit)
 
@@ -4675,7 +4675,7 @@ def test_speed_rnn():
         [],
         [],
         updates=OrderedDict([(s_i, s_i + 1), (shared_r, s_rinc)]),
-        mode=theano.Mode(linker="cvm"),
+        mode=Mode(linker="cvm"),
     )
 
     cvm_shared_duration = timeit(lambda: f_cvm_shared(), number=n_timeit)
@@ -4691,7 +4691,7 @@ def test_speed_batchrnn():
     This function prints out the speed of recurrent neural network
     calculations implemented in various ways.
 
-    We force the mode to theano.Mode(linker='cvm'). If you manually
+    We force the mode to Mode(linker='cvm'). If you manually
     change this code to use DebugMode this will test the correctness
     of the optimizations applied, but generally correctness-testing
     is not the goal of this test.
@@ -4725,7 +4725,7 @@ def test_speed_batchrnn():
         [],
         [],
         updates=[(s_i, s_i + 1), (shared_r, s_rinc)],
-        mode=theano.Mode(linker="cvm"),
+        mode=Mode(linker="cvm"),
     )
     f_fn = f.fn
     t2 = time.time()

tests/sparse/test_basic.py

@@ -11,6 +11,7 @@ from tests import unittest_tools as utt
 from tests.tensor.test_sharedvar import makeSharedTester
 from theano import sparse
 from theano.compile.function import function
+from theano.compile.io import In, Out
 from theano.configdefaults import config
 from theano.gradient import GradientError
 from theano.graph.basic import Apply, Constant
@@ -1357,7 +1358,7 @@ class TestStructuredDot:
                 a = SparseType(sparse_format_a, dtype=sparse_dtype)()
                 b = SparseType(sparse_format_b, dtype=sparse_dtype)()
                 d = tt.dot(a, b)
-                f = theano.function([a, b], theano.Out(d, borrow=True))
+                f = theano.function([a, b], Out(d, borrow=True))
                 for M, N, K, nnz in [
                     (4, 3, 2, 3),
                     (40, 30, 20, 3),
@@ -1379,7 +1380,7 @@ class TestStructuredDot:
         a = SparseType("csc", dtype=sparse_dtype)()
         b = matrix(dtype=dense_dtype)
         d = tt.dot(a, b)
-        f = theano.function([a, b], theano.Out(d, borrow=True))
+        f = theano.function([a, b], Out(d, borrow=True))
 
         for M, N, K, nnz in [
             (4, 3, 2, 3),
@@ -1928,9 +1929,7 @@ def test_sparse_shared_memory():
     sdot = sparse.structured_dot
     z = sdot(x * 3, m1) + sdot(y * 2, m2)
 
-    f = theano.function(
-        [theano.In(x, mutable=True), theano.In(y, mutable=True)], z, mode="FAST_RUN"
-    )
+    f = theano.function([In(x, mutable=True), In(y, mutable=True)], z, mode="FAST_RUN")
 
     def f_(x, y, m1=m1, m2=m2):
         return ((x * 3) * m1) + ((y * 2) * m2)
@@ -2243,9 +2242,7 @@ class TestRemove0(utt.InferShapeTester):
 
                 # the In thingy has to be there because theano has as rule not
                 # to optimize inputs
-                f = theano.function(
-                    [theano.In(x, borrow=True, mutable=True)], Remove0()(x)
-                )
+                f = theano.function([In(x, borrow=True, mutable=True)], Remove0()(x))
 
                 # assert optimization local_inplace_remove0 is applied in
                 # modes with optimization

tests/sparse/test_opt.py

@@ -10,6 +10,7 @@ import theano.tensor as tt
 from tests import unittest_tools as utt
 from tests.sparse.test_basic import random_lil
 from theano import sparse
+from theano.compile.mode import Mode, get_default_mode
 from theano.configdefaults import config
 from theano.tensor.type import ivector, matrix, vector
 
@@ -17,7 +18,7 @@ from theano.tensor.type import ivector, matrix, vector
 def test_local_csm_properties_csm():
     data = vector()
     indices, indptr, shape = (ivector(), ivector(), ivector())
-    mode = theano.compile.mode.get_default_mode()
+    mode = get_default_mode()
     mode = mode.including("specialize", "local_csm_properties_csm")
     for CS, cast in [
         (sparse.CSC, sp.sparse.csc_matrix),
@@ -43,10 +44,10 @@ def test_local_csm_properties_csm():
 def test_local_csm_grad_c():
     data = vector()
     indices, indptr, shape = (ivector(), ivector(), ivector())
-    mode = theano.compile.mode.get_default_mode()
+    mode = get_default_mode()
 
     if theano.config.mode == "FAST_COMPILE":
-        mode = theano.compile.Mode(linker="c|py", optimizer="fast_compile")
+        mode = Mode(linker="c|py", optimizer="fast_compile")
 
     mode = mode.including("specialize", "local_csm_grad_c")
     for CS, cast in [
@@ -68,7 +69,7 @@ def test_local_csm_grad_c():
     not theano.config.cxx, reason="G++ not available, so we need to skip this test."
 )
 def test_local_mul_s_d():
-    mode = theano.compile.mode.get_default_mode()
+    mode = get_default_mode()
     mode = mode.including("specialize", "local_mul_s_d")
 
     for sp_format in sparse.sparse_formats:
@@ -85,7 +86,7 @@ def test_local_mul_s_d():
     not theano.config.cxx, reason="G++ not available, so we need to skip this test."
 )
 def test_local_mul_s_v():
-    mode = theano.compile.mode.get_default_mode()
+    mode = get_default_mode()
     mode = mode.including("specialize", "local_mul_s_v")
 
     for sp_format in ["csr"]:  # Not implemented for other format
@@ -102,7 +103,7 @@ def test_local_mul_s_v():
     not theano.config.cxx, reason="G++ not available, so we need to skip this test."
 )
 def test_local_structured_add_s_v():
-    mode = theano.compile.mode.get_default_mode()
+    mode = get_default_mode()
     mode = mode.including("specialize", "local_structured_add_s_v")
 
     for sp_format in ["csr"]:  # Not implemented for other format
@@ -120,7 +121,7 @@ def test_local_structured_add_s_v():
     not theano.config.cxx, reason="G++ not available, so we need to skip this test."
 )
 def test_local_sampling_dot_csr():
-    mode = theano.compile.mode.get_default_mode()
+    mode = get_default_mode()
     mode = mode.including("specialize", "local_sampling_dot_csr")
 
     for sp_format in ["csr"]:  # Not implemented for other format
@@ -147,7 +148,7 @@ def test_local_sampling_dot_csr():
 
 
 def test_local_dense_from_sparse_sparse_from_dense():
-    mode = theano.compile.mode.get_default_mode()
+    mode = get_default_mode()
     mode = mode.including("local_dense_from_sparse_sparse_from_dense")
 
     m = matrix()

tests/tensor/nnet/test_abstract_conv.py

@@ -4,6 +4,7 @@ import pytest
 import theano
 import theano.tensor as tt
 from tests import unittest_tools as utt
+from theano.compile.mode import Mode
 from theano.configdefaults import config
 from theano.graph.opt import check_stack_trace
 from theano.tensor.nnet import abstract_conv as conv
@@ -1047,7 +1048,7 @@ class TestAbstractConvNoOptim(BaseTestConv2d):
 
     def run_test_case(self, i, f, s, b, flip, provide_shape, fd=(1, 1)):
         o = self.get_output_shape(i, f, s, b, fd)
-        mode = theano.Mode(optimizer=None)
+        mode = Mode(optimizer=None)
         self.run_fwd(
             inputs_shape=i,
             filters_shape=f,
@@ -1093,7 +1094,7 @@ class TestAbstractConvNoOptim(BaseTestConv2d):
     def run_test_case_gi(
         self, i, f, o, s, b, flip, provide_shape, fd=(1, 1), expect_error=False
     ):
-        mode = theano.Mode(optimizer=None)
+        mode = Mode(optimizer=None)
         if not expect_error:
             self.run_gradinput(
                 inputs_shape=i,
@@ -2059,7 +2060,7 @@ class TestGroupedConvNoOptim:
     conv_op = theano.tensor.nnet.abstract_conv.AbstractConv2d
     conv_gradw_op = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights
     conv_gradi_op = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs
-    mode = theano.Mode(optimizer=None)
+    mode = Mode(optimizer=None)
     is_dnn = False
 
     def setup_method(self):
@@ -2269,7 +2270,7 @@ class TestGroupedConv3dNoOptim(TestGroupedConvNoOptim):
     conv_op = theano.tensor.nnet.abstract_conv.AbstractConv3d
     conv_gradw_op = theano.tensor.nnet.abstract_conv.AbstractConv3d_gradWeights
     conv_gradi_op = theano.tensor.nnet.abstract_conv.AbstractConv3d_gradInputs
-    mode = theano.Mode(optimizer=None)
+    mode = Mode(optimizer=None)
 
     def setup_method(self):
         self.num_groups = [3, 2, 4, 4]
@@ -2509,7 +2510,7 @@ class TestUnsharedConv:
     conv2d_gradw_op = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights
     conv2d_gradi_op = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs
 
-    mode = theano.compile.mode.Mode(optimizer="None")
+    mode = Mode(optimizer="None")
 
     def setup_method(self):
         self.img_shape = [(2, 2, 4, 4), (3, 2, 4, 2), (3, 3, 5, 3), (3, 4, 4, 4)]
@@ -2741,7 +2742,7 @@ class TestAsymmetricPadding:
     conv2d_gradw_op = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights
     conv2d_gradi_op = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs
 
-    mode = theano.compile.mode.Mode(optimizer="None")
+    mode = Mode(optimizer="None")
 
     img_shape = [(2, 2, 4, 4), (3, 2, 4, 2), (3, 3, 5, 3)]
     kern_shape = [(4, 2, 2, 2), (2, 2, 4, 2), (2, 3, 3, 3)]
@@ -2911,7 +2912,7 @@ class TestAsymmetricPadding:
 
 
 class TestCausalConv:
-    mode = theano.compile.mode.Mode(optimizer="None")
+    mode = Mode(optimizer="None")
 
     img = np.array(
         [

tests/tensor/nnet/test_conv.py

@@ -6,6 +6,7 @@ import pytest
 import theano
 import theano.tensor as tt
 from tests import unittest_tools as utt
+from theano.compile.mode import Mode
 from theano.tensor.basic import NotScalarConstantError, _allclose
 from theano.tensor.nnet import conv, conv2d
 from theano.tensor.type import dmatrix, dtensor3, dtensor4, dvector, scalar, tensor4
@@ -607,7 +608,7 @@ class TestConv2D(utt.InferShapeTester):
                             unroll_patch=True,
                             openmp=openmp,
                         )
-                        mode = theano.Mode(
+                        mode = Mode(
                             linker=theano.link.vm.VMLinker(
                                 allow_gc=False, use_cloop=True
                             )

tests/tensor/test_basic.py

@@ -13,7 +13,6 @@ from numpy.testing import assert_allclose, assert_almost_equal, assert_array_equ
 import theano
 import theano.scalar as ts
 import theano.tensor as tt
-import theano.tensor.var as var
 from tests import unittest_tools as utt
 from tests.tensor.utils import (
     ALL_DTYPES,
@@ -73,6 +72,7 @@ from tests.tensor.utils import (
 from theano import compile, config, function, shared
 from theano.assert_op import Assert
 from theano.compile import DeepCopyOp
+from theano.compile.io import In, Out
 from theano.compile.mode import get_default_mode
 from theano.gradient import grad, hessian, numeric_grad
 from theano.graph.basic import Apply, Variable
@@ -170,6 +170,7 @@ from theano.tensor import (
     triu,
     true_div,
     unbroadcast,
+    var,
     vertical_stack,
 )
 from theano.tensor.elemwise import DimShuffle, Elemwise
@@ -3224,7 +3225,7 @@ def test_join_inplace():
     join = Join(view=0)
     c = join(0, x, z, z)
 
-    f = theano.function([theano.In(x, borrow=True), s], theano.Out(c, borrow=True))
+    f = theano.function([In(x, borrow=True), s], Out(c, borrow=True))
 
     data = np.array([3, 4, 5], dtype=config.floatX)
     print(f(data, 0))

tests/tensor/test_blas.py

@@ -25,6 +25,7 @@ from tests import unittest_tools
 from tests.tensor.utils import inplace_func
 from theano import shared
 from theano.compile.io import In
+from theano.compile.mode import Mode
 from theano.configdefaults import config
 from theano.graph.fg import FunctionGraph
 from theano.misc.safe_asarray import _asarray
@@ -139,7 +140,7 @@ class TestGemm:
                 f = inplace_func(
                     [tz, ta, tx, ty, tb],
                     gemm_inplace(tz, ta, tx, ty, tb),
-                    mode=theano.compile.Mode(optimizer=None, linker=l),
+                    mode=Mode(optimizer=None, linker=l),
                 )
                 f(z, a, x, y, b)
                 z_after = self._gemm(z_orig, a, x, y, b)
@@ -304,12 +305,12 @@ class TestGemm:
             tz, ta, tx, ty, tb = [shared(p) for p in (z, a, x, y, b)]
 
             # f = inplace_func([tz,ta,tx,ty,tb], gemm_inplace(tz,ta,tx,ty,tb),
-            #                 mode = theano.compile.Mode(optimizer = None, linker=l))
+            #                 mode = Mode(optimizer = None, linker=l))
             # f(z, a, x, y, b)
             f = inplace_func(
                 [],
                 gemm_inplace(tz, ta, tx, ty, tb),
-                mode=theano.compile.Mode(optimizer=None, linker=l),
+                mode=Mode(optimizer=None, linker=l),
             )
             f()
             unittest_tools.assert_allclose(z_after, tz.get_value(borrow=True))
@@ -366,7 +367,7 @@ class TestGemm:
                 f_i = inplace_func(
                     [],
                     gemm_inplace(tz[:, :, i], ta, tx[:, :, i], ty[:, :, i], tb),
-                    mode=theano.compile.Mode(optimizer=None, linker=l),
+                    mode=Mode(optimizer=None, linker=l),
                 )
                 for j in range(3):
                     # tz will not _always_ be overwritten,
@@ -385,7 +386,7 @@ class TestGemm:
                     [],
                     tz_i,
                     updates=[(tz, tt.set_subtensor(tz[:, :, i], tz_i))],
-                    mode=theano.compile.Mode(optimizer=None, linker=l),
+                    mode=Mode(optimizer=None, linker=l),
                 )
                 for j in range(3):
                     g_i()
@@ -645,7 +646,7 @@ def just_gemm(i, o, ishapes=None, max_graphlen=0, expected_nb_gemm=1):
     g = inplace_func(
         i,
         o,
-        mode=theano.compile.Mode(linker="py", optimizer=None),
+        mode=Mode(linker="py", optimizer=None),
         allow_input_downcast=True,
         on_unused_input="ignore",
     )
@@ -734,7 +735,7 @@ def test_gemm_opt_double_gemm():
     g = inplace_func(
         i,
         o,
-        mode=theano.compile.Mode(linker="py", optimizer=None),
+        mode=Mode(linker="py", optimizer=None),
         on_unused_input="ignore",
     )
 

tests/tensor/test_casting.py

@@ -3,7 +3,7 @@ import numpy as np
 import theano
 import theano.tensor.basic as basic
 from theano import function
-from theano.compile import In
+from theano.compile.io import In
 from theano.misc.safe_asarray import _asarray
 from theano.tensor.basic import (
     _convert_to_float32,

tests/tensor/test_elemwise.py

@@ -1284,9 +1284,7 @@ class TestElemwise(unittest_tools.InferShapeTester):
         # it overflowed in this case.
         a, b, c, d, e, f = vectors("abcdef")
         s = a + b + c + d + e + f
-        g = theano.function(
-            [a, b, c, d, e, f], s, mode=theano.compile.Mode(linker="py")
-        )
+        g = theano.function([a, b, c, d, e, f], s, mode=Mode(linker="py"))
         g(*[np.zeros(2 ** 11, config.floatX) for i in range(6)])
 
 

tests/tensor/test_gc.py

@@ -4,6 +4,7 @@ import time
 import numpy as np
 
 import theano
+from theano.compile.mode import Mode
 from theano.link.basic import PerformLinker
 from theano.link.c.basic import OpWiseCLinker
 from theano.tensor.type import dvector, lvector
@@ -42,12 +43,8 @@ def test_gc_never_pickles_temporaries():
 
         # g_linker has no garbage collection
 
-        f = theano.function(
-            [x], r, mode=theano.Mode(optimizer=optimizer, linker=f_linker)
-        )
-        g = theano.function(
-            [x], r, mode=theano.Mode(optimizer=optimizer, linker=g_linker)
-        )
+        f = theano.function([x], r, mode=Mode(optimizer=optimizer, linker=f_linker))
+        g = theano.function([x], r, mode=Mode(optimizer=optimizer, linker=g_linker))
 
         pre_f = pickle.dumps(f)
         # pre_g = pickle.dumps(g)

tests/tensor/test_keepdims.py

@@ -4,6 +4,7 @@ import pytest
 import theano
 import theano.tensor as tt
 from theano import function
+from theano.compile.mode import Mode
 from theano.tensor.elemwise import DimShuffle
 from theano.tensor.type import dtensor3
 
@@ -43,7 +44,7 @@ class TestKeepDims:
         a = np.random.rand(3, 2, 4)
         # We don't need to test all opt and C code, as this is tested
         # by the ops tests.
-        mode = theano.compile.Mode(optimizer="fast_compile", linker="py")
+        mode = Mode(optimizer="fast_compile", linker="py")
 
         # 'max_and_argmax' has two outputs and can be specified with either
         # a single or every axis:
@@ -177,7 +178,7 @@ class TestKeepDims:
 
         x = dtensor3()
         a = np.random.rand(3, 2, 4).astype(theano.config.floatX)
-        mode = theano.compile.Mode(optimizer="fast_compile", linker="py")
+        mode = Mode(optimizer="fast_compile", linker="py")
 
         for axis in [
             0,

tests/tensor/test_mpi.py

@@ -4,6 +4,7 @@ import subprocess
 import pytest
 
 import theano
+from theano.compile.mode import Mode
 from theano.configdefaults import config
 from theano.graph.sched import sort_schedule_fn
 from theano.link.c.basic import OpWiseCLinker
@@ -21,7 +22,7 @@ from theano.tensor.type import matrix
 
 mpi_scheduler = sort_schedule_fn(*mpi_cmps)
 mpi_linker = OpWiseCLinker(schedule=mpi_scheduler)
-mpi_mode = theano.Mode(linker=mpi_linker)
+mpi_mode = Mode(linker=mpi_linker)
 
 
 @config.change_flags(compute_test_value="off")

tests/tensor/test_sort.py

@@ -6,6 +6,7 @@ import pytest
 
 import theano
 from tests import unittest_tools as utt
+from theano.compile.mode import Mode
 from theano.tensor.sort import (
     ArgSortOp,
     SortOp,
@@ -428,7 +429,7 @@ class TestTopK:
         # So don't use DebugMode here.
         mode = self.mode
         if isinstance(self.mode, theano.compile.debugmode.DebugMode):
-            mode = theano.Mode(optimizer=mode.optimizer)
+            mode = Mode(optimizer=mode.optimizer)
         fn = theano.function([x], y, mode=mode)
         assert any(
             [isinstance(n.op, self.op_class) for n in fn.maker.fgraph.apply_nodes]

tests/tensor/test_subtensor.py

@@ -12,6 +12,7 @@ import theano.tensor as tt
 from tests import unittest_tools as utt
 from tests.tensor.utils import inplace_func, rand, randint_ranged
 from theano.compile import DeepCopyOp, shared
+from theano.compile.io import In
 from theano.configdefaults import config
 from theano.graph.op import get_test_value
 from theano.graph.toolbox import is_same_graph
@@ -1265,7 +1266,7 @@ class TestSubtensor(utt.OptimizationTestMixin):
                                     data_copy[idx] = inc_num
                                 else:
                                     data_copy[idx] += inc_num
-                        data_var = theano.In(data_var, mutable=True)
+                        data_var = In(data_var, mutable=True)
 
                         # Remember data for the Theano function (see below).
                         all_inputs_var += [data_var, idx_var, inc_var]

tests/test_config.py

@@ -249,7 +249,7 @@ def test_mode_apply():
     with pytest.raises(ValueError, match="Expected one of"):
         configdefaults._filter_mode("not_a_mode")
 
-    # test with theano.Mode instance
+    # test with Mode instance
     import theano.compile.mode
 
     assert (

theano/compile/function/pfunc.py

@@ -282,8 +282,7 @@ class Param(In):
         borrow=None,
     ):
         warnings.warn(
-            "The Param class is deprecated. Replace Param(default=N)"
-            " by theano.In(value=N)",
+            "The Param class is deprecated. Replace Param(default=N)" " by In(value=N)",
             stacklevel=2,
         )
         super().__init__(
@@ -322,7 +321,7 @@ def pfunc(
         Function parameters, these are not allowed to be shared variables.
     outputs : list of Variables or Out instances
         Expressions to compute.
-    mode : string or `theano.compile.Mode` instance
+    mode : string or `theano.compile.mode.Mode` instance
         Compilation mode.
     updates : iterable over pairs (shared_variable, new_expression). List, tuple or dict.
         Update the values for SharedVariable inputs according to these

theano/configdefaults.py

@@ -111,14 +111,14 @@ def _filter_mode(val):
     if val in str_options:
         return val
     # This can be executed before Theano is completly imported, so
-    # theano.Mode is not always available.
-    # Instead of isinstance(val, theano.Mode),
+    # theano.compile.mode.Mode is not always available.
+    # Instead of isinstance(val, theano.compile.mode.Mode),
     # we can inspect the __mro__ of the object!
     for type_ in type(val).__mro__:
         if "theano.compile.mode.Mode" in str(type_):
             return val
     raise ValueError(
-        f"Expected one of {str_options}, or an instance of theano.Mode. "
+        f"Expected one of {str_options}, or an instance of theano.compile.mode.Mode. "
         f"Instead got: {val}."
     )
 

theano/gpuarray/dnn.py

@@ -10,6 +10,8 @@ import theano
 import theano.pathparse
 import theano.tensor as tt
 from theano.assert_op import Assert
+from theano.compile.io import Out
+from theano.compile.mode import Mode
 from theano.compile.ops import shape_i, shape_i_op
 from theano.configdefaults import SUPPORTED_DNN_CONV_ALGO_RUNTIME, config
 from theano.gpuarray import cudnn_defs, pygpu
@@ -285,7 +287,7 @@ class MakerCDataType(CDataType):
                 self._fn = theano.function(
                     [v],
                     CDataMaker(self)(v),
-                    mode=theano.Mode(optimizer=None),
+                    mode=Mode(optimizer=None),
                     profile=False,
                 )
         return self._fn
@@ -421,9 +423,7 @@ def version(raises=True):
             return -1
 
     if version.v is None:
-        f = theano.function(
-            [], DnnVersion()(), theano.Mode(optimizer=None), profile=False
-        )
+        f = theano.function([], DnnVersion()(), Mode(optimizer=None), profile=False)
         v = f()
         if v[0] != v[1]:
             raise RuntimeError(
@@ -2632,7 +2632,7 @@ def _make_dropout_desc(dropout, seed, context_name):
     desc, states = theano.function(
         [],
         _DropoutDescriptor(context_name)(dropout, seed, context_name),
-        theano.Mode(optimizer=None),
+        Mode(optimizer=None),
         profile=False,
     )()
     return desc, states
@@ -2740,7 +2740,7 @@ def _make_rnn_desc(
         _RNNDescriptor(context_name)(
             hidden_size, num_layers, ddesc, input_mode, direction_mode, rnn_mode, dtype
         ),
-        theano.Mode(optimizer=None),
+        Mode(optimizer=None),
         profile=False,
     )()
     return desc
@@ -2774,7 +2774,7 @@ def _get_param_size(desc, input_size, dtype, context_name):
     return theano.function(
         [],
         _RNNParamSize(context_name)(desc, input_size, typecode),
-        theano.Mode(optimizer=None),
+        Mode(optimizer=None),
         profile=False,
     )()
 
@@ -3016,8 +3016,8 @@ class _RNNSplitParams(DnnBase):
 def _split_rnn_params(w, desc, layer, input_size, dtype, rnn_mode):
     typecode = gpuarray.dtype_to_typecode(dtype)
     outs = _RNNSplitParams(rnn_mode)(w, desc, layer, input_size, typecode)
-    outs = [theano.Out(o, borrow=True) for o in outs]
-    return theano.function([], outs, theano.Mode(optimizer=None), profile=False)()
+    outs = [Out(o, borrow=True) for o in outs]
+    return theano.function([], outs, Mode(optimizer=None), profile=False)()
 
 
 class GpuDnnRNNOp(DnnBase):

theano/printing.py

@@ -14,8 +14,8 @@ from io import StringIO
 
 import numpy as np
 
-import theano
 from theano.compile import Function, SharedVariable, debugmode
+from theano.compile.io import In, Out
 from theano.configdefaults import config
 from theano.graph.basic import (
     Apply,
@@ -176,7 +176,7 @@ def debugprint(
             order.extend([topo for item in obj.outputs])
         elif isinstance(obj, (int, float, np.ndarray)):
             print(obj, file=_file)
-        elif isinstance(obj, (theano.In, theano.Out)):
+        elif isinstance(obj, (In, Out)):
             results_to_print.append(obj.variable)
             profile_list.append(None)
             smap.append(None)