[Refactor] Migrate build API to tvm.compile (#17718)

* tvm.build -> tvm.compile

* relax.build -> tvm.compile

* update

Author: Siyuan Feng

apps/android_rpc/tests/android_rpc_test.py

@@ -64,7 +64,7 @@ def test_rpc_module():
     sch.bind(xi, "threadIdx.x")
 
     if test_opencl:
-        f = tvm.build(sch.mod, target=tvm.target.Target("opencl", host=target))
+        f = tvm.compile(sch.mod, target=tvm.target.Target("opencl", host=target))
         path_dso_cl = temp.relpath("dev_lib_cl.so")
         f.export_library(path_dso_cl, fcompile=ndk.create_shared)
 

apps/ios_rpc/tests/ios_rpc_test.py

@@ -59,7 +59,7 @@ def test_rpc_module(host, port, key, mode):
 
     # 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(sch.mod, target=tvm.target.Target("metal", host=target))
+    f = tvm.compile(sch.mod, target=tvm.target.Target("metal", host=target))
     path_dso1 = temp.relpath("dev_lib.dylib")
     f.export_library(path_dso1, fcompile=xcode.create_dylib, arch=arch, sdk=sdk)
 

docs/deep_dive/tensor_ir/tutorials/tir_creation.py

@@ -212,7 +212,7 @@ def evaluate_dynamic_shape(lib: tvm.runtime.Module, m: int, n: int, k: int):
 
 
 # Compile lib only once
-dyn_shape_lib = tvm.build(DynamicShapeModule, target="llvm")
+dyn_shape_lib = tvm.compile(DynamicShapeModule, target="llvm")
 # Able to handle different shapes
 print(evaluate_dynamic_shape(dyn_shape_lib, m=4, n=4, k=4))
 print(evaluate_dynamic_shape(dyn_shape_lib, m=64, n=64, k=128))

docs/deep_dive/tensor_ir/tutorials/tir_transformation.py

@@ -78,7 +78,7 @@ def main(
 
 
 def evaluate(mod: tvm.IRModule):
-    lib = tvm.build(mod, target="llvm")
+    lib = tvm.tir.build(mod, target="llvm")
     # check correctness
     lib(a_nd, b_nd, c_nd)
     np.testing.assert_allclose(c_nd.numpy(), c_np, rtol=1e-5)

docs/get_started/tutorials/ir_module.py

@@ -232,7 +232,7 @@ def main(
 # ~~~~~~~~~~~~~
 # We can deploy the IRModule on CPU by specifying the target as ``llvm``.
 
-exec = relax.build(mod, target="llvm")
+exec = tvm.compile(mod, target="llvm")
 dev = tvm.cpu()
 vm = relax.VirtualMachine(exec, dev)
 
@@ -263,7 +263,7 @@ def main(
 ######################################################################
 # Now we can compile the IRModule on GPU, the similar way as we did on CPU.
 
-exec = relax.build(gpu_mod, target="cuda")
+exec = tvm.compile(gpu_mod, target="cuda")
 dev = tvm.device("cuda", 0)
 vm = relax.VirtualMachine(exec, dev)
 # Need to allocate data and params on GPU device

docs/get_started/tutorials/quick_start.py

@@ -137,7 +137,7 @@ def forward(self, x):
 import numpy as np
 
 target = tvm.target.Target("llvm")
-ex = relax.build(mod, target)
+ex = tvm.compile(mod, target)
 device = tvm.cpu()
 vm = relax.VirtualMachine(ex, device)
 data = np.random.rand(1, 784).astype("float32")

docs/how_to/tutorials/cross_compilation_and_rpc.py

@@ -119,7 +119,7 @@
 else:
     target = "llvm -mtriple=armv7l-linux-gnueabihf"
 
-func = tvm.build(mod, target=target)
+func = tvm.compile(mod, target=target)
 # save the lib at a local temp folder
 temp = utils.tempdir()
 path = temp.relpath("lib.tar")
@@ -237,7 +237,7 @@ def run_opencl():
     xo, xi = sch.split(i, [None, 32])
     sch.bind(x, "blockIdx.x")
     sch.bind(x, "threadIdx.x")
-    func = tvm.build(sch.mod, target=target)
+    func = tvm.compile(sch.mod, target=target)
 
     remote = rpc.connect(opencl_device_host, opencl_device_port)
 

docs/how_to/tutorials/customize_opt.py

@@ -205,7 +205,7 @@ def transform_module(self, mod: IRModule, _ctx: tvm.transform.PassContext) -> IR
 # --------------------------
 # We can build and deploy the optimized model to the TVM runtime.
 
-ex = relax.build(mod, target="cuda")
+ex = tvm.compile(mod, target="cuda")
 dev = tvm.device("cuda", 0)
 vm = relax.VirtualMachine(ex, dev)
 # Need to allocate data and params on GPU device

docs/how_to/tutorials/e2e_opt_model.py

@@ -113,7 +113,7 @@
 # We skip this step in the CI environment.
 
 if not IS_IN_CI:
-    ex = relax.build(mod, target="cuda")
+    ex = tvm.compile(mod, target="cuda")
     dev = tvm.device("cuda", 0)
     vm = relax.VirtualMachine(ex, dev)
     # Need to allocate data and params on GPU device

docs/reference/api/python/driver.rst

@@ -19,4 +19,4 @@ tvm.driver
 ----------
 .. automodule:: tvm.driver
 
-.. autofunction:: tvm.build
+.. autofunction:: tvm.compile