[REFACTOR][PY][API-CHANGE] Remove legacy python files.

Remove legacy python files.
Use the te namespace for most of the tensor expression primitives.

- tvm.create_schedule -> tvm.te.create_schedule
- tvm.placeholder -> tvm.te.placeholder
- tvm.compute -> tvm.te.compute

apps/android_rpc/tests/android_rpc_test.py

@@ -22,6 +22,7 @@
 """
 
 import tvm
+from tvm import te
 import os
 from tvm import rpc
 from tvm.contrib import util, ndk
@@ -44,9 +45,9 @@
 
 def test_rpc_module():
     # graph
-    n = tvm.convert(1024)
-    A = tvm.placeholder((n,), name='A')
-    B = tvm.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
+    n = tvm.runtime.convert(1024)
+    A = te.placeholder((n,), name='A')
+    B = te.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
     a_np = np.random.uniform(size=1024).astype(A.dtype)
     temp = util.tempdir()
 
@@ -56,7 +57,7 @@ def test_rpc_module():
                              session_timeout=60)
 
     # Compile the Graph for CPU target
-    s = tvm.create_schedule(B.op)
+    s = te.create_schedule(B.op)
     xo, xi = s[B].split(B.op.axis[0], factor=64)
     s[B].parallel(xi)
     s[B].pragma(xo, "parallel_launch_point")
@@ -79,10 +80,10 @@ def test_rpc_module():
 
     # Compile the Graph for OpenCL target
     if test_opencl:
-        s = tvm.create_schedule(B.op)
+        s = te.create_schedule(B.op)
         xo, xi = s[B].split(B.op.axis[0], factor=64)
-        s[B].bind(xi, tvm.thread_axis("threadIdx.x"))
-        s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
+        s[B].bind(xi, te.thread_axis("threadIdx.x"))
+        s[B].bind(xo, te.thread_axis("blockIdx.x"))
         # Build the dynamic lib.
         # If we don't want to do metal and only use cpu, just set target to be target
         f = tvm.build(s, [A, B], "opencl", target_host=target, name="myadd")
@@ -102,10 +103,10 @@ def test_rpc_module():
 
     # Compile the Graph for Vulkan target
     if test_vulkan:
-        s = tvm.create_schedule(B.op)
+        s = te.create_schedule(B.op)
         xo, xi = s[B].split(B.op.axis[0], factor=64)
-        s[B].bind(xi, tvm.thread_axis("threadIdx.x"))
-        s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
+        s[B].bind(xi, te.thread_axis("threadIdx.x"))
+        s[B].bind(xo, te.thread_axis("blockIdx.x"))
         # Build the dynamic lib.
         # If we don't want to do metal and only use cpu, just set target to be target
         f = tvm.build(s, [A, B], "vulkan", target_host=target, name="myadd")

apps/benchmark/arm_cpu_imagenet_bench.py

@@ -22,6 +22,7 @@
 import numpy as np
 
 import tvm
+from tvm import te
 from tvm.contrib.util import tempdir
 import tvm.contrib.graph_runtime as runtime
 from tvm import relay

apps/benchmark/gpu_imagenet_bench.py

@@ -23,6 +23,7 @@
 import numpy as np
 
 import tvm
+from tvm import te
 import tvm.contrib.graph_runtime as runtime
 from tvm import relay
 

apps/benchmark/mobile_gpu_imagenet_bench.py

@@ -22,6 +22,7 @@
 import numpy as np
 
 import tvm
+from tvm import te
 from tvm.contrib.util import tempdir
 import tvm.contrib.graph_runtime as runtime
 from tvm import relay

apps/bundle_deploy/build_model.py

@@ -20,6 +20,7 @@
 import os
 from tvm import relay
 import tvm
+from tvm import te
 import logging
 
 

apps/dso_plugin_module/test_plugin_module.py

@@ -15,6 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 import tvm
+from tvm import te
 import os
 
 def test_plugin_module():

apps/extension/python/tvm_ext/__init__.py

@@ -21,6 +21,7 @@
 import ctypes
 # Import TVM first to get library symbols
 import tvm
+from tvm import te
 
 def load_lib():
     """Load library, the functions will be registered into TVM"""

apps/extension/tests/test_ext.py

@@ -16,6 +16,8 @@
 # under the License.
 import tvm_ext
 import tvm
+import tvm._ffi.registry
+from tvm import te
 import numpy as np
 
 def test_bind_add():
@@ -26,9 +28,9 @@ def add(a, b):
 
 def test_ext_dev():
     n = 10
-    A = tvm.placeholder((n,), name='A')
-    B = tvm.compute((n,), lambda *i: A(*i) + 1.0, name='B')
-    s = tvm.create_schedule(B.op)
+    A = te.placeholder((n,), name='A')
+    B = te.compute((n,), lambda *i: A(*i) + 1.0, name='B')
+    s = te.create_schedule(B.op)
     def check_llvm():
         if not tvm.runtime.enabled("llvm"):
             return
@@ -43,8 +45,8 @@ def check_llvm():
 
 
 def test_sym_add():
-    a = tvm.var('a')
-    b = tvm.var('b')
+    a = te.var('a')
+    b = te.var('b')
     c = tvm_ext.sym_add(a, b)
     assert c.a == a and c.b == b
 
@@ -59,19 +61,20 @@ def ivec_cb(v2):
         assert(isinstance(v2, tvm_ext.IntVec))
         assert v2[2] == 3
 
-    tvm.convert(ivec_cb)(ivec)
+    tvm.runtime.convert(ivec_cb)(ivec)
 
 
 def test_extract_ext():
-    fdict = tvm.extract_ext_funcs(tvm_ext._LIB.TVMExtDeclare)
+    fdict = tvm._ffi.registry.extract_ext_funcs(
+        tvm_ext._LIB.TVMExtDeclare)
     assert fdict["mul"](3, 4) == 12
 
 
 def test_extern_call():
     n = 10
-    A = tvm.placeholder((n,), name='A')
-    B = tvm.compute((n,), lambda *i: tvm.call_extern("float32", "TVMTestAddOne", A(*i)), name='B')
-    s = tvm.create_schedule(B.op)
+    A = te.placeholder((n,), name='A')
+    B = te.compute((n,), lambda *i: tvm.tir.call_extern("float32", "TVMTestAddOne", A(*i)), name='B')
+    s = te.create_schedule(B.op)
 
     def check_llvm():
         if not tvm.runtime.enabled("llvm"):

apps/howto_deploy/prepare_test_libs.py

@@ -16,13 +16,14 @@
 # under the License.
 """Script to prepare test_addone.so"""
 import tvm
+from tvm import te
 import os
 
 def prepare_test_libs(base_path):
-    n = tvm.var("n")
-    A = tvm.placeholder((n,), name='A')
-    B = tvm.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
-    s = tvm.create_schedule(B.op)
+    n = te.var("n")
+    A = te.placeholder((n,), name='A')
+    B = te.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
+    s = te.create_schedule(B.op)
     # Compile library as dynamic library
     fadd_dylib = tvm.build(s, [A, B], "llvm", name="addone")
     dylib_path = os.path.join(base_path, "test_addone_dll.so")

apps/howto_deploy/python_deploy.py

@@ -19,6 +19,7 @@
 # file python_deploy.py
 
 import tvm
+from tvm import te
 import numpy as np
 
 def verify(mod, fname):

apps/ios_rpc/tests/ios_rpc_test.py

@@ -21,6 +21,7 @@
 """
 
 import tvm
+from tvm import te
 import os
 import re
 import sys
@@ -54,14 +55,14 @@ def compile_metal(src):
 
 def test_rpc_module():
     # graph
-    n = tvm.convert(1024)
-    A = tvm.placeholder((n,), name='A')
-    B = tvm.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
+    n = tvm.runtime.convert(1024)
+    A = te.placeholder((n,), name='A')
+    B = te.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
     temp = util.tempdir()
-    s = tvm.create_schedule(B.op)
+    s = te.create_schedule(B.op)
     xo, xi = s[B].split(B.op.axis[0], factor=64)
-    s[B].bind(xi, tvm.thread_axis("threadIdx.x"))
-    s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
+    s[B].bind(xi, te.thread_axis("threadIdx.x"))
+    s[B].bind(xo, te.thread_axis("blockIdx.x"))
     # Build the dynamic lib.
     # If we don't want to do metal and only use cpu, just set target to be target
     f = tvm.build(s, [A, B], "metal", target_host=target, name="myadd")
@@ -70,7 +71,7 @@ def test_rpc_module():
                      arch=arch, sdk=sdk)
     xcode.codesign(path_dso1)
 
-    s = tvm.create_schedule(B.op)
+    s = te.create_schedule(B.op)
     xo, xi = s[B].split(B.op.axis[0], factor=64)
     s[B].parallel(xi)
     s[B].pragma(xo, "parallel_launch_point")

apps/sgx/enclave/src/build_model.py

@@ -23,6 +23,7 @@
 from tvm import relay
 from tvm.relay import testing
 import tvm
+from tvm import te
 
 
 def main():

apps/sgx/run_model.py

@@ -17,6 +17,7 @@
 import os.path as osp
 import numpy as np
 import tvm
+from tvm import te
 
 CWD = osp.abspath(osp.dirname(__file__))
 

docs/api/python/te.rst

@@ -23,6 +23,7 @@ tvm.te
    :members:
    :imported-members:
    :exclude-members:
+      any, all, min_value, max_value, trace,
       exp, erf, tanh, sigmoid, log, cos, sin, atan, sqrt, rsqrt, floor, ceil,
       trunc, abs, round, nearbyint, isnan, power, popcount, fmod, if_then_else,
       div, indexdiv, indexmod, truncdiv, truncmod, floordiv, floormod,

docs/api/python/tir.rst

@@ -20,5 +20,5 @@ tvm.tir
 .. automodule:: tvm.tir
    :members:
    :imported-members:
-   :exclude-members: PrimExpr
+   :exclude-members: PrimExpr, const
    :autosummary:

docs/conf.py

@@ -61,6 +61,7 @@
 os.environ['TVM_BUILD_DOC'] = '1'
 # Version information.
 import tvm
+from tvm import te
 version = tvm.__version__
 release = tvm.__version__
 

golang/sample/deploy.py

@@ -21,6 +21,7 @@
 from __future__ import absolute_import, print_function
 
 import tvm
+from tvm import te
 import numpy as np
 
 # Global declarations of environment.
@@ -31,15 +32,15 @@
 ######################################################################
 # Describe the Computation
 # ------------------------
-n = tvm.var("n")
-A = tvm.placeholder((n,), name='A')
-B = tvm.placeholder((n,), name='B')
-C = tvm.compute(A.shape, lambda i: A[i] + B[i], name="C")
+n = te.var("n")
+A = te.placeholder((n,), name='A')
+B = te.placeholder((n,), name='B')
+C = te.compute(A.shape, lambda i: A[i] + B[i], name="C")
 
 ######################################################################
 # Schedule the Computation
 # ------------------------
-s = tvm.create_schedule(C.op)
+s = te.create_schedule(C.op)
 
 ######################################################################
 # Compilation

jvm/core/src/test/scripts/test_add_cpu.py

@@ -17,14 +17,15 @@
 import os
 
 import tvm
+from tvm import te
 from tvm.contrib import cc, util
 
 def test_add(target_dir):
-    n = tvm.var("n")
-    A = tvm.placeholder((n,), name='A')
-    B = tvm.placeholder((n,), name='B')
-    C = tvm.compute(A.shape, lambda i: A[i] + B[i], name="C")
-    s = tvm.create_schedule(C.op)
+    n = te.var("n")
+    A = te.placeholder((n,), name='A')
+    B = te.placeholder((n,), name='B')
+    C = te.compute(A.shape, lambda i: A[i] + B[i], name="C")
+    s = te.create_schedule(C.op)
     fadd = tvm.build(s, [A, B, C], "llvm", target_host="llvm", name="myadd")
 
     fadd.save(os.path.join(target_dir, "add_cpu.o"))

jvm/core/src/test/scripts/test_add_gpu.py

@@ -17,22 +17,23 @@
 import os
 
 import tvm
+from tvm import te
 from tvm.contrib import cc, util
 
 def test_add(target_dir):
     if not tvm.runtime.enabled("cuda"):
         print("skip %s because cuda is not enabled..." % __file__)
         return
-    n = tvm.var("n")
-    A = tvm.placeholder((n,), name='A')
-    B = tvm.placeholder((n,), name='B')
-    C = tvm.compute(A.shape, lambda i: A[i] + B[i], name="C")
+    n = te.var("n")
+    A = te.placeholder((n,), name='A')
+    B = te.placeholder((n,), name='B')
+    C = te.compute(A.shape, lambda i: A[i] + B[i], name="C")
 
-    s = tvm.create_schedule(C.op)
+    s = te.create_schedule(C.op)
 
     bx, tx = s[C].split(C.op.axis[0], factor=64)
-    s[C].bind(bx, tvm.thread_axis("blockIdx.x"))
-    s[C].bind(tx, tvm.thread_axis("threadIdx.x"))
+    s[C].bind(bx, te.thread_axis("blockIdx.x"))
+    s[C].bind(tx, te.thread_axis("threadIdx.x"))
     fadd_cuda = tvm.build(s, [A, B, C], "cuda", target_host="llvm", name="myadd")
 
     fadd_cuda.save(os.path.join(target_dir, "add_gpu.o"))

jvm/core/src/test/scripts/test_graph_runtime.py

@@ -17,14 +17,15 @@
 import os
 
 import tvm
+from tvm import te
 import json
 from tvm.contrib import graph_runtime
 
 def dump_graph_lib(target_dir):
     dim = 4
-    A = tvm.placeholder((dim,), name='A')
-    B = tvm.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
-    sched = tvm.create_schedule(B.op)
+    A = te.placeholder((dim,), name='A')
+    B = te.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
+    sched = te.create_schedule(B.op)
 
     node0 = {"op": "null", "name": "x", "inputs": []}
     node1 = {"op": "tvm_op", "name": "add",

python/tvm/__init__.py

@@ -24,7 +24,7 @@
 # tvm._ffi
 from ._ffi.base import TVMError, __version__
 from ._ffi.runtime_ctypes import TypeCode, DataType
-from ._ffi.registry import register_object, register_func, register_extension
+from ._ffi import register_object, register_func, register_extension, get_global_func
 
 # top-level alias
 # tvm.runtime
@@ -65,12 +65,8 @@
 from . import arith
 
 # backward compact for topi, to be removed later
-from .api import *
 from .tir import expr, stmt, ir_builder, ir_pass, generic
 from .te import tensor, schedule
-from .tir.op import *
-from . import intrin
-from . import make
 
 # Contrib initializers
 from .contrib import rocm as _rocm, nvcc as _nvcc, sdaccel as _sdaccel