Docs and Readme updated as per new namespace change (#4989)

docs/deploy/aocl_fpga.md

@@ -27,16 +27,16 @@ We use two python scripts for this tutorial.
 - build.py - a script to synthesize FPGA bitstream.
 ```
 import tvm
-
+from tvm import te
 tgt_host="llvm"
 tgt="aocl_sw_emu"
 
-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)
 px, x = s[C].split(C.op.axis[0], nparts=1)
 
 s[C].bind(px, tvm.thread_axis("pipeline"))

docs/deploy/aws_fpga.md

@@ -27,16 +27,17 @@ We use two python scripts for this tutorial.
 - build.py - a script to synthesize FPGA bitstream.
 ```python
 import tvm
+from tvm import te
 
 tgt_host="llvm"
 tgt="sdaccel"
 
-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)
 px, x = s[C].split(C.op.axis[0], nparts=1)
 
 s[C].bind(px, tvm.thread_axis("pipeline"))

docs/dev/codebase_walkthrough.rst

@@ -51,9 +51,9 @@ We use a simple example that uses the low level TVM API directly. The example is
 ::
 
    n = 1024
-   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")
+   A = tvm.te.placeholder((n,), name='A')
+   B = tvm.te.placeholder((n,), name='B')
+   C = tvm.te.compute(A.shape, lambda i: A[i] + B[i], name="C")
 
 Here, types of ``A``, ``B``, ``C`` are ``tvm.tensor.Tensor``, defined in ``python/tvm/te/tensor.py``. The Python ``Tensor`` is backed by C++ ``Tensor``, implemented in ``include/tvm/te/tensor.h`` and ``src/te/tensor.cc``. All Python types in TVM can be thought of as a handle to the underlying C++ type with the same name. If you look at the definition of Python ``Tensor`` type below, you can see it is a subclass of ``Object``.
 

docs/dev/inferbound.rst

@@ -623,15 +623,16 @@ Above, we discussed the behavior of PassUpDomain on Split relations only. In the
 ::
 
    import tvm
+   from tvm import te
 
    n = 4
    m = 4
 
-   A = tvm.placeholder((n, m), name='A')
-   B = tvm.compute((n, m), lambda bi, bj: A[bi, bj]+2, name='B')
-   C = tvm.compute((n, m), lambda ci, cj: B[ci, cj]*3, name='C')
+   A = te.placeholder((n, m), name='A')
+   B = te.compute((n, m), lambda bi, bj: A[bi, bj]+2, name='B')
+   C = te.compute((n, m), lambda ci, cj: B[ci, cj]*3, name='C')
 
-   s = tvm.create_schedule(C.op)
+   s = te.create_schedule(C.op)
 
    fused_axes = s[C].fuse(C.op.axis[0], C.op.axis[1])
    xo, xi = s[C].split(fused_axes, 4)

docs/dev/runtime.rst

@@ -260,8 +260,9 @@ For example, in the following code, we accessed the op field of the TensorNode.
 .. code:: python
 
     import tvm
+    from tvm import te
 
-    x = tvm.placeholder((3,4), name="x")
+    x = te.placeholder((3,4), name="x")
     # access the op field of TensorNode
     print(x.op.name)
 

docs/langref/hybrid_script.rst

@@ -64,8 +64,8 @@ The current parse interface looks like:
 
 .. code-block:: python
 
-   a = tvm.placeholder((100, ), name='a')
-   b = tvm.placeholder((99, ), name='b')
+   a = tvm.te.placeholder((100, ), name='a')
+   b = tvm.te.placeholder((99, ), name='b')
    parser = tvm.hybrid.parse(outer_product, [a, b]) # return the parser of this function
 
 
@@ -74,8 +74,8 @@ or ``tvm.container.Array``, to this function, it returns a op node:
 
 .. code-block:: python
 
-   a = tvm.placeholder((100, ), name='a')
-   b = tvm.placeholder((99, ), name='b')
+   a = tvm.te.placeholder((100, ), name='a')
+   b = tvm.te.placeholder((99, ), name='b')
    c = outer_product(a, b, c) # return the output tensor(s) of the operator
 
 You can use any methods that can be applied on a TVM ``OpNode``, like create_schedule, although
@@ -90,7 +90,7 @@ Follow up the example above, you can use some tvm like interfaces to tune the co
 .. code-block:: python
 
    i, j = c.op.axis
-   sch = tvm.create_schedule(op)
+   sch = te.create_schedule(op)
    jo, ji = sch.split(j, 4)
    sch.vectorize(ji)
 

jvm/README.md

@@ -96,14 +96,15 @@ There's nothing special for this part. The following Python snippet generate add
 ```python
 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"))
@@ -175,4 +176,4 @@ Server server = new Server(proxyHost, proxyPort, "key");
 server.start();
 ```
 
-You can also use `StandaloneServerProcessor` and `ConnectProxyServerProcessor` to build your own RPC server. Refer to [Android RPC Server](https://github.com/apache/incubator-tvm/blob/master/apps/android_rpc/app/src/main/java/org/apache/tvm/tvmrpc/RPCProcessor.java) for more details.
+You can also use `StandaloneServerProcessor` and `ConnectProxyServerProcessor` to build your own RPC server. Refer to [Android RPC Server](https://github.com/apache/incubator-tvm/blob/master/apps/android_rpc/app/src/main/java/org/apache/tvm/tvmrpc/RPCProcessor.java) for more details.
\ No newline at end of file

rust/frontend/README.md

@@ -122,17 +122,18 @@ One can use the following Python snippet to generate `add_gpu.so` which add two
 ```python
 import os
 import tvm
+from tvm import te
 from tvm.contrib import cc
 
 def test_add(target_dir):
     if not tvm.runtime.enabled("cuda"):
         print("skip {__file__} because cuda is not enabled...".format(__file__=__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")
-    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)
     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"))

web/README.md

@@ -87,16 +87,17 @@ the compilation process.
 
 ```python
 import tvm
+from tvm import te
 from tvm.contrib import emscripten
 import os
 def prepare_test_libs(base_path):
     target = "llvm -target=asmjs-unknown-emscripten -system-lib"
     if not tvm.runtime.enabled(target):
         raise RuntimeError("Target %s is not enbaled" % target)
-    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)
     fadd1 = tvm.build(s, [A, B], target, name="add_one")
     obj_path = os.path.join(base_path, "test_add_one.bc")
     fadd1.save(obj_path)