[TARGET] Move target_host usage to new target style. (#9497)

- Add deprecation warnings to functions with target_host parameters.
- Update the build usage to new target style.

apps/android_camera/models/prepare_model.py

@@ -98,7 +98,7 @@ def main(model_str, output_path):
         pass
     print("building...")
     with tvm.transform.PassContext(opt_level=3):
-        graph, lib, params = relay.build(net, target, target_host=target_host, params=params)
+        graph, lib, params = relay.build(net, tvm.target.Target(target, target_host), params=params)
     print("dumping lib...")
     lib.export_library(output_path_str + "/" + "deploy_lib_cpu.so", ndk.create_shared)
     print("dumping graph...")

apps/android_rpc/tests/android_rpc_test.py

@@ -86,7 +86,7 @@ def test_rpc_module():
         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")
+        f = tvm.build(s, [A, B], tvm.target.Target("opencl", host=target), name="myadd")
         path_dso_cl = temp.relpath("dev_lib_cl.so")
         f.export_library(path_dso_cl, ndk.create_shared)
 
@@ -109,7 +109,7 @@ def test_rpc_module():
         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")
+        f = tvm.build(s, [A, B], tvm.target.Target("vulkan", host=target), name="myadd")
         path_dso_vulkan = temp.relpath("dev_lib_vulkan.so")
         f.export_library(path_dso_vulkan, ndk.create_shared)
 

apps/benchmark/arm_cpu_imagenet_bench.py

@@ -40,7 +40,7 @@ def evaluate_network(network, target, target_host, repeat):
 
     print_progress("%-20s building..." % network)
     with tvm.transform.PassContext(opt_level=3):
-        lib = relay.build(net, target=target, target_host=target_host, params=params)
+        lib = relay.build(net, target=tvm.target.Target(target, host=target_host), params=params)
 
     tmp = tempdir()
     if "android" in str(target):

apps/benchmark/mobile_gpu_imagenet_bench.py

@@ -40,7 +40,7 @@ def evaluate_network(network, target, target_host, dtype, repeat):
 
     print_progress("%-20s building..." % network)
     with tvm.transform.PassContext(opt_level=3):
-        lib = relay.build(net, target=target, target_host=target_host, params=params)
+        lib = relay.build(net, target=tvm.target.Target(target, host=target_host), params=params)
 
     tmp = tempdir()
     if "android" in str(target) or "android" in str(target_host):

apps/hexagon_launcher/README.md

@@ -19,7 +19,7 @@
 ## Compilation
 
 The launcher consists of two parts: part running on Hexagon, and part running
-on Android. Each component must be compiled separately. 
+on Android. Each component must be compiled separately.
 
 The supported Snapdragon architectures are 855, 865, and 888.
 
@@ -37,9 +37,9 @@ Building the Hexagon launcher application as a component of the main TVM build
 used for Hexagon codegen can be achieved by setting `USE_HEXAGON_LAUNCHER=ON`.
 This option will compile core tvm, the android launcher binary and its corresponding
 tvm_runtime, as well as the Hexagon launcher shared library and its corresponding
-tvm_runtime. As described in the [Manual compilation](#Manual compilation) section 
-each component requires Hexagon and android dependencies. When building the launcher 
-along with TVM these configurations must be providing when invoking cmake. A minimal 
+tvm_runtime. As described in the [Manual compilation](#Manual compilation) section
+each component requires Hexagon and android dependencies. When building the launcher
+along with TVM these configurations must be providing when invoking cmake. A minimal
 example invocation for compiling TVM along with the Hexagon launcher is included below:
 
 ```
@@ -59,9 +59,9 @@ cmake -DCMAKE_C_COMPILER=/path/to/clang \
 ```
 
 where `v65|v66|v68` means "one of" these architecture versions.
-The Hexagon launcher application is an android binary and thus requires the use 
-of an android toolchain for compilation. Similarly, the Hexagon tvm runtime 
-requires the use of the Hexagon toolchain and depends on the Hexagon SDK. The 
+The Hexagon launcher application is an android binary and thus requires the use
+of an android toolchain for compilation. Similarly, the Hexagon tvm runtime
+requires the use of the Hexagon toolchain and depends on the Hexagon SDK. The
 resulting hexagon launcher binaries can be found in the `apps_hexagon_launcher`
 subdirectory of the cmake build directory. Please note that the above command
 will not build support for Hexagon codegen in the TVM library, for that please
@@ -70,7 +70,7 @@ additionally define the `USE_HEXAGON_DEVICE` variable. Also, the LLVM used in
 
 ### Manual compilation
 
-Since some source files are shared between the Hexagon and android builds, 
+Since some source files are shared between the Hexagon and android builds,
 make sure to delete all object files between compilations. Compile the Hexagon
 code first.
 
@@ -157,7 +157,7 @@ mod, params = relay.frontend.from_tflite(
 
 target = tvm.target.hexagon('v68', link_params=True)
 with tvm.transform.PassContext(opt_level=3):
-    lib = relay.build(mod, target, target_host=target, params=params, mod_name="default")
+    lib = relay.build(mod, tvm.target.Target(target, host=target), params=params, mod_name="default")
 
 # Save model.so and model.json:
 with open('model.json', 'w') as f:

apps/ios_rpc/tests/ios_rpc_mobilenet.py

@@ -93,7 +93,9 @@ def test_mobilenet(host, port, key, mode):
 
     def run(mod, target):
         with relay.build_config(opt_level=3):
-            lib = relay.build(mod, target=target, target_host=target_host, params=params)
+            lib = relay.build(
+                mod, target=tvm.target.Target(target, host=target_host), params=params
+            )
         path_dso = temp.relpath("deploy.dylib")
         lib.export_library(path_dso, xcode.create_dylib, arch=arch, sdk=sdk)
 

apps/ios_rpc/tests/ios_rpc_test.py

@@ -55,7 +55,7 @@ def test_rpc_module(host, port, key, mode):
     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")
+    f = tvm.build(s, [A, B], tvm.target.Target("metal", host=target), name="myadd")
     path_dso1 = temp.relpath("dev_lib.dylib")
     f.export_library(path_dso1, xcode.create_dylib, arch=arch, sdk=sdk)
 

apps/topi_recipe/gemm/android_gemm_square.py

@@ -120,7 +120,7 @@ def test_gemm_gpu(N, times, bn, num_block, num_thread):
 
     print(tvm.lower(s, [A, B, C], simple_mode=True))
 
-    f = tvm.build(s, [A, B, C], "opencl", target_host=target, name="gemm_gpu")
+    f = tvm.build(s, [A, B, C], tvm.target.Target("opencl", host=target), name="gemm_gpu")
     temp = utils.tempdir()
     path_dso = temp.relpath("gemm_gpu.so")
     f.export_library(path_dso, ndk.create_shared)

docs/how_to/deploy/bnns.rst

@@ -130,7 +130,7 @@ After that you need to compile new module with target corresponding to required
 
     model = partition_for_bnns(model, params=params)  # to markup operations to be offloaded to BNNS
     with tvm.transform.PassContext(opt_level=3):
-        lib = relay.build(model, target=target, target_host=target, params=params)
+        lib = relay.build(model, target=target, params=params)
 
 Export the module.
 

docs/how_to/deploy/hls.rst

@@ -34,8 +34,7 @@ We use two python scripts for this tutorial.
       import tvm
       from tvm import te
 
-      tgt_host="llvm"
-      tgt="sdaccel"
+      tgt= tvm.target.Target("sdaccel", host="llvm")
 
       n = te.var("n")
       A = te.placeholder((n,), name='A')
@@ -47,7 +46,7 @@ We use two python scripts for this tutorial.
 
       s[C].bind(px, tvm.te.thread_axis("pipeline"))
 
-      fadd = tvm.build(s, [A, B, C], tgt, target_host=tgt_host, name="myadd")
+      fadd = tvm.build(s, [A, B, C], tgt, name="myadd")
 
       fadd.save("myadd.o")
       fadd.imported_modules[0].save("myadd.xclbin")

golang/sample/deploy.py

@@ -26,7 +26,6 @@
 
 # Global declarations of environment.
 
-tgt_host = "llvm"
 tgt = "llvm"
 
 ######################################################################
@@ -45,7 +44,7 @@
 ######################################################################
 # Compilation
 # -----------
-fadd = tvm.build(s, [A, B, C], tgt, target_host=tgt_host, name="myadd")
+fadd = tvm.build(s, [A, B, C], tgt, name="myadd")
 
 ######################################################################
 # Save Compiled Module

jvm/README.md

@@ -105,7 +105,7 @@ def test_add(target_dir):
     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 = tvm.build(s, [A, B, C], "llvm", name="myadd")
 
     fadd.save(os.path.join(target_dir, "add_cpu.o"))
     cc.create_shared(os.path.join(target_dir, "add_cpu.so"),

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

@@ -27,7 +27,7 @@ def test_add(target_dir):
     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 = tvm.build(s, [A, B, C], "llvm", name="myadd")
 
     fadd.save(os.path.join(target_dir, "add_cpu.o"))
     cc.create_shared(

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

@@ -35,7 +35,7 @@ def test_add(target_dir):
     bx, tx = s[C].split(C.op.axis[0], factor=64)
     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 = tvm.build(s, [A, B, C], tvm.target.Target("cuda", host="llvm"), name="myadd")
 
     fadd_cuda.save(os.path.join(target_dir, "add_cuda.o"))
     fadd_cuda.imported_modules[0].save(os.path.join(target_dir, "add_cuda.ptx"))

python/tvm/auto_scheduler/relay_integration.py

@@ -25,6 +25,7 @@
 import json
 import logging
 import threading
+import warnings
 
 import tvm
 from tvm import autotvm, transform
@@ -109,6 +110,11 @@ def extract_tasks(
         The weight (i.e. the number of appearance) of extracted tasks
     """
     # pylint: disable=import-outside-toplevel
+    if target_host is not None:
+        warnings.warn(
+            "target_host parameter is going to be deprecated. "
+            "Please pass in tvm.target.Target(target, host=target_host) instead."
+        )
 
     target, target_host = Target.check_and_update_host_consist(target, target_host)
 

python/tvm/autotvm/task/relay_integration.py

@@ -22,6 +22,7 @@
 """
 import threading
 import logging
+import warnings
 
 import tvm
 from tvm.autotvm.task.dispatcher import DispatchContext, FallbackContext
@@ -77,6 +78,11 @@ def extract_from_program(mod, params, target, target_host=None, ops=None):
     task: Array of autotvm.task.Task
         collected tasks
     """
+    if target_host is not None:
+        warnings.warn(
+            "target_host parameter is going to be deprecated. "
+            "Please pass in tvm.target.Target(target, host=target_host) instead."
+        )
     target, target_host = Target.check_and_update_host_consist(target, target_host)
     return extract_from_multiple_program([mod], [params], target, ops=ops)
 

python/tvm/driver/build_module.py

@@ -17,6 +17,7 @@
 
 # pylint: disable=invalid-name
 """The build utils in python."""
+import warnings
 
 from typing import Union, Optional, List, Mapping
 
@@ -206,7 +207,7 @@ def build(
           s2 = topi.cuda.schedule_injective(cuda_tgt, [C])
           m1 = tvm.lower(s1, [A, B, C], name="test_add1")
           m2 = tvm.lower(s2, [A, B, C], name="test_add2")
-          rt_mod = tvm.build({"llvm": m1, "cuda": m2}, target_host="llvm")
+          rt_mod = tvm.build({"llvm": m1, "cuda": m2})
 
     Note
     ----
@@ -229,6 +230,12 @@ def build(
             f"but got {type(inputs)}."
         )
 
+    if target_host is not None:
+        warnings.warn(
+            "target_host parameter is going to be deprecated. "
+            "Please pass in tvm.target.Target(target, host=target_host) instead."
+        )
+
     if not isinstance(inputs, (dict, container.Map)):
         target = Target.current() if target is None else target
         target = target if target else "llvm"

python/tvm/relay/backend/vm.py

@@ -20,6 +20,8 @@
 
 Implements a Python interface to compiling and executing on the Relay VM.
 """
+import warnings
+
 import numpy as np
 
 import tvm
@@ -63,6 +65,11 @@ def compile(mod, target=None, target_host=None, params=None):
     exec : tvm.runtime.vm.Executable
         The VM executable that contains both library code and bytecode.
     """
+    if target_host is not None:
+        warnings.warn(
+            "target_host parameter is going to be deprecated. "
+            "Please pass in tvm.target.Target(target, host=target_host) instead."
+        )
     target, target_host = Target.check_and_update_host_consist(
         target, target_host, target_is_dict_key=False
     )
@@ -132,6 +139,11 @@ def lower(self, mod, target=None, target_host=None):
             By default, llvm is used if it is enabled,
             otherwise a stackvm intepreter is used.
         """
+        if target_host is not None:
+            warnings.warn(
+                "target_host parameter is going to be deprecated. "
+                "Please pass in tvm.target.Target(target, host=target_host) instead."
+            )
         target = self._update_target(target)
         target_host = self._update_target_host(target, target_host)
         target, target_host = Target.check_and_update_host_consist(
@@ -173,6 +185,11 @@ def optimize(self, mod, target=None, target_host=None, params=None):
         params : dict
             The parameters of the final module.
         """
+        if target_host is not None:
+            warnings.warn(
+                "target_host parameter is going to be deprecated. "
+                "Please pass in tvm.target.Target(target, host=target_host) instead."
+            )
         target = self._update_target(target)
         target_host = self._update_target_host(target, target_host)
         target, target_host = Target.check_and_update_host_consist(

python/tvm/relay/build_module.py

@@ -148,6 +148,11 @@ def build(
         params : dict
             The parameters of the final graph.
         """
+        if target_host is not None:
+            warnings.warn(
+                "target_host parameter is going to be deprecated. "
+                "Please pass in tvm.target.Target(target, host=target_host) instead."
+            )
         target = build_target_by_device_type_map(target)
         target, target_host = Target.check_and_update_host_consist(
             target, target_host, target_is_dict_key=False
@@ -332,16 +337,23 @@ def build(ir_mod, target=None, target_host=None, params=None, mod_name="default"
             "instead of deprecated parameter mod (tvm.relay.function.Function)",
             DeprecationWarning,
         )
+
+    if target_host is not None:
+        warnings.warn(
+            "target_host parameter is going to be deprecated. "
+            "Please pass in tvm.target.Target(target, host=target_host) instead."
+        )
+
+    target, target_host = Target.check_and_update_host_consist(
+        target, target_host, target_is_dict_key=False
+    )
+
     target = build_target_by_device_type_map(target)
     if isinstance(target_host, (str, Target)):
         target_host = Target(target_host)
     elif target_host:
         raise ValueError("target host must be the type of str, " + "tvm.target.Target, or None")
 
-    target, target_host = Target.check_and_update_host_consist(
-        target, target_host, target_is_dict_key=False
-    )
-
     # Retrieve the executor from the target
     executor = get_executor_from_target(target, target_host)
 

rust/tvm/tests/basics/src/tvm_add.py

@@ -36,7 +36,7 @@ def main(target, out_dir):
         s[C].bind(bx, te.thread_axis("blockIdx.x"))
         s[C].bind(tx, te.thread_axis("threadIdx.x"))
 
-    fadd = tvm.build(s, [A, B, C], target, target_host="llvm", name="myadd")
+    fadd = tvm.build(s, [A, B, C], tvm.target.Target(target, host="llvm"), name="myadd")
     fadd.save(osp.join(out_dir, "test_add.o"))
     if target == "cuda":
         fadd.imported_modules[0].save(osp.join(out_dir, "test_add.ptx"))

tests/python/contrib/test_bnns/infrastructure.py

@@ -143,7 +143,7 @@ def build_module(mod, target, params=None, enable_bnns=True, tvm_ops=0):
         if enable_bnns:
             mod = partition_for_bnns(mod)
         relay.backend.te_compiler.get().clear()
-        return relay.build(mod, target=target, target_host=target, params=params)
+        return relay.build(mod, target=target, params=params)
 
 
 def build_and_run(

tests/python/contrib/test_bnns/test_onnx_topologies.py

@@ -109,7 +109,7 @@ def run(mod, target, simplify=True, with_bnns=False):
                 mod = simplify_model(mod)
             if with_bnns:
                 mod = partition_for_bnns(mod)
-            graph_module = relay.build(mod, target=target, target_host=target, params=params)
+            graph_module = relay.build(mod, target=target, params=params)
 
         lib_name = "deploy.tar"
         path_dso = temp.relpath(lib_name)

tests/python/contrib/test_hexagon/infrastructure.py

@@ -64,7 +64,9 @@ def get_packed_filter_layout(out_channel, in_channel, kernel_h, kernel_w):
 def build_and_run(inputs, func, target, target_host, *args, **kwargs):
     schedule, placeholders, binds = func(*args, **kwargs)
 
-    func = tvm.build(schedule, placeholders, target=target, target_host=target_host, binds=binds)
+    func = tvm.build(
+        schedule, placeholders, target=tvm.target.Target(target, host=target_host), binds=binds
+    )
     dev = tvm.device(target)
     tensors = []
     for tensor in inputs: