[Target] Add support for target object with host field compatible with previous api

include/tvm/target/target.h

@@ -24,6 +24,8 @@
 #ifndef TVM_TARGET_TARGET_H_
 #define TVM_TARGET_TARGET_H_
 
+#include <tvm/ir/expr.h>
+#include <tvm/ir/module.h>
 #include <tvm/node/node.h>
 #include <tvm/support/with.h>
 #include <tvm/target/target_kind.h>
@@ -35,6 +37,7 @@
 namespace tvm {
 
 class TargetInternal;
+class Target;
 
 /*!
  * \brief Compilation target.
@@ -60,6 +63,8 @@ class TargetNode : public Object {
   TVM_DLL const std::string& str() const;
   /*! \return Export target to JSON-like configuration */
   TVM_DLL Map<String, ObjectRef> Export() const;
+  /*! \return The Optional<Target> typed target host of the TargetNode */
+  TVM_DLL Optional<Target> GetHost() const;
 
   void VisitAttrs(AttrVisitor* v) {
     v->Visit("kind", &kind);
@@ -150,6 +155,13 @@ class Target : public ObjectRef {
    */
   TVM_DLL explicit Target(Target target, Target host);
   TVM_DEFINE_OBJECT_REF_METHODS(Target, ObjectRef, TargetNode);
+  /*!
+   * \brief Create a new Target object with given target (w.o host) and target host.
+   * \param target The current Target typed object target, with or without host field.
+   * \param host The given Target typed object target host
+   * \return The new Target object with the given target and host field of given host.
+   */
+  static Target WithHost(const Target& target, const Target& host);
 
  private:
   // enable with syntax.
@@ -167,6 +179,29 @@ class Target : public ObjectRef {
    */
   TVM_DLL void ExitWithScope();
 };
-
+/*!
+ * \brief Check and update host field of the given legacy target and target host pair.
+ *  Note that this function is for legacy target api compatibility issue only, not
+ *  recommended for other use.
+ * \param target The pointer to a Target typed object with host field to be updated
+ * \param host The pointer to a Target typed object for target host to be updated
+ */
+void CheckAndUpdateHostConsistency(Target* target, Target* host);
+/*!
+ * \brief Check and update host field of the given legacy heterogeneous targets and
+ *  target host.Note that this function is for legacy target api compatibility issue only,
+ *  not recommended for other use.
+ * \param target The pointer to a Map objects with values being Target objects
+ * \param host The Target typed object for target host to be updated
+ */
+void CheckAndUpdateHostConsistency(Map<Integer, Target>* target, Target* host);
+/*!
+ * \brief Check and update host field of the given legacy heterogeneous targets and
+ *  target host.Note that this function is for legacy target api compatibility issue only,
+ *  not recommended for other use.
+ * \param target The pointer to a Map objects with keys being Target objects
+ * \param host The Target typed object for target host to be updated
+ */
+void CheckAndUpdateHostConsistency(Map<Target, IRModule>* target, Target* host);
 }  // namespace tvm
 #endif  // TVM_TARGET_TARGET_H_

python/tvm/auto_scheduler/measure.py

@@ -44,6 +44,8 @@
 from tvm.ir import transform
 from tvm.autotvm.measure.measure_methods import set_cuda_target_arch
 from tvm.contrib import tar, ndk
+from tvm.target import Target
+
 
 from . import _ffi_api
 from .loop_state import StateObject
@@ -221,10 +223,12 @@ def recover_measure_input(inp, rebuild_state=False):
     from .search_task import SearchTask  # lazily import to avoid recursive dependency
 
     task = inp.task
+    task.target, task.target_host = Target.check_and_update_host_consist(
+        task.target, task.target_host
+    )
     new_task = SearchTask(
         workload_key=task.workload_key,
         target=task.target,
-        target_host=task.target_host,
         hardware_params=task.hardware_params,
         layout_rewrite_option=task.layout_rewrite_option,
         task_inputs=list(task.task_input_names),
@@ -602,6 +606,9 @@ def _timed_func(inp_serialized, build_func, verbose):
     tic = time.time()
     inp = MeasureInput.deserialize(inp_serialized)
     task = inp.task
+    task.target, task.target_host = Target.check_and_update_host_consist(
+        task.target, task.target_host
+    )
 
     error_no = MeasureErrorNo.NO_ERROR
     error_msg = None
@@ -622,9 +629,7 @@ def _timed_func(inp_serialized, build_func, verbose):
 
         try:
             with transform.PassContext():
-                func = build_module.build(
-                    sch, args, target=task.target, target_host=task.target_host
-                )
+                func = build_module.build(sch, args, target=task.target)
             func.export_library(filename, build_func)
         # pylint: disable=broad-except
         except Exception:

python/tvm/auto_scheduler/relay_integration.py

@@ -29,9 +29,11 @@
 from tvm import autotvm, transform
 from tvm.ir.transform import PassContext
 from tvm.runtime import convert_to_object
+
 from tvm.te.tensor import ComputeOp, PlaceholderOp, Tensor
 from tvm.tir import Reduce
 from tvm.tir import expr as _expr
+from tvm.target import Target
 
 from . import _ffi_api
 from .compute_dag import ComputeDAG, LayoutRewriteOption
@@ -108,10 +110,7 @@ def extract_tasks(
     """
     # pylint: disable=import-outside-toplevel
 
-    if isinstance(target, str):
-        target = tvm.target.Target(target)
-    if isinstance(target_host, str):
-        target_host = tvm.target.Target(target_host)
+    target, target_host = Target.check_and_update_host_consist(target, target_host)
 
     # Run the compiler to collect all TOPI calls during compilation.
     env = TracingEnvironment(
@@ -137,7 +136,6 @@ def extract_tasks(
             SearchTask(
                 workload_key=wkl_key,
                 target=target,
-                target_host=target_host,
                 hardware_params=hardware_params,
                 # When auto scheduler is used in end to end network, try to apply layout rewrite
                 # to improve the overall performance

python/tvm/auto_scheduler/search_task.py

@@ -398,10 +398,8 @@ def __init__(
             compute_dag = ComputeDAG(workload_key)
 
         assert target is not None, "Must specify a target."
-        if isinstance(target, str):
-            target = Target(target)
-        if isinstance(target_host, str):
-            target_host = Target(target_host)
+
+        target, target_host = Target.check_and_update_host_consist(target, target_host)
 
         if layout_rewrite_option is None:
             layout_rewrite_option = LayoutRewriteOption.get_target_default(target)
@@ -511,6 +509,9 @@ def print_best(self, log_file, print_mode="schedule"):
         raise ValueError("Invalid print_mode: %s" % print_mode)
 
     def __getstate__(self):
+        self.target, self.target_host = Target.check_and_update_host_consist(
+            self.target, self.target_host
+        )
         return {
             "compute_dag": self.compute_dag,
             "workload_key": self.workload_key,
@@ -535,12 +536,15 @@ def __setstate__(self, state):
         if workload[0] not in WORKLOAD_FUNC_REGISTRY:
             register_workload_tensors(state["workload_key"], state["compute_dag"].tensors)
 
+        state["target"], state["target_host"] = Target.check_and_update_host_consist(
+            state["target"], state["target_host"]
+        )
         self.__init_handle_by_constructor__(
             _ffi_api.SearchTask,
             state["compute_dag"],
             state["workload_key"],
             state["target"],
-            state["target_host"],
+            state["target"].host,
             state["hardware_params"],
             state["layout_rewrite_option"],
             state["task_input_names"],

python/tvm/autotvm/graph_tuner/base_graph_tuner.py

@@ -28,6 +28,7 @@
 from tvm.autotvm.task import get_config
 from tvm.autotvm.record import encode, load_from_file
 from tvm.autotvm.measure import MeasureResult, MeasureInput
+from tvm.target import Target
 
 from ...target import Target
 from .utils import (
@@ -439,6 +440,8 @@ def benchmark_layout_transform(
             This might bring performance loss comparing to benchmarking layout transformation.
         """
         self._logger.info("Start to benchmark layout transformation...")
+        self._target, target_host = Target.check_and_update_host_consist(self._target, target_host)
+
         if layout_records is None and infer_layout:
             raise RuntimeError("Requires some records to infer layout transformation time.")
 
@@ -525,9 +528,7 @@ def _callback(_, inputs, results):
                 continue
 
             records = []
-            task = autotvm.task.create(
-                "layout_transform", args=args, target=self._target, target_host=target_host
-            )
+            task = autotvm.task.create("layout_transform", args=args, target=self._target)
             tuner = autotvm.tuner.GridSearchTuner(task)
             tuner.tune(n_trial=1, measure_option=measure_option, callbacks=[_log_to_list(records)])
             if not isinstance(records[0][1].costs[0], float):

python/tvm/autotvm/graph_tuner/utils/traverse_graph.py

@@ -63,7 +63,7 @@ def expr2graph(expr, target_ops, node_dict, node_list):
         for node_entry in node_list:
             if node_entry["op"] in target_ops:
                 task_name, args = env.task_collection[task_pos]
-                task = autotvm.task.create(task_name, args, target="llvm", target_host=None)
+                task = autotvm.task.create(task_name, args, target="llvm")
                 node_entry["workloads"] = [task.workload]
                 node_entry["topi_op"] = [task_name]
                 task_pos += 1

python/tvm/autotvm/measure/measure_methods.py

@@ -40,6 +40,7 @@
 from tvm.error import TVMError
 from tvm.driver import build
 from tvm.contrib import nvcc, ndk, tar
+from tvm.target import Target
 
 from ..utils import get_const_tuple
 from ..env import AutotvmGlobalScope
@@ -418,6 +419,8 @@ def set_task(self, task):
 def _build_func_common(measure_input, check_gpu=None, cuda_arch=None, build_option=None):
     """Common part for building a configuration"""
     target, task, config = measure_input
+    target, task.target_host = Target.check_and_update_host_consist(target, task.target_host)
+
     with target:
         s, args = task.instantiate(config)
 

python/tvm/autotvm/task/relay_integration.py

@@ -25,6 +25,7 @@
 
 import tvm
 from tvm.autotvm.task.dispatcher import DispatchContext, FallbackContext
+from tvm.target import Target
 from .task import create
 from .topi_integration import TaskExtractEnv
 
@@ -89,7 +90,8 @@ def extract_from_program(mod, params, target, target_host=None, ops=None):
     task: Array of autotvm.task.Task
         collected tasks
     """
-    return extract_from_multiple_program([mod], [params], target, target_host, ops)
+    target, target_host = Target.check_and_update_host_consist(target, target_host)
+    return extract_from_multiple_program([mod], [params], target, ops=ops)
 
 
 def extract_from_multiple_program(mods, params, target, target_host=None, ops=None):
@@ -122,6 +124,9 @@ def extract_from_multiple_program(mods, params, target, target_host=None, ops=No
 
     env = TaskExtractEnv.get()
 
+    # merge target and target host
+    target, target_host = Target.check_and_update_host_consist(target, target_host)
+
     # run compiler to collect all TOPI calls during compilation
     env.reset(ops)
     with env:
@@ -152,7 +157,7 @@ def extract_from_multiple_program(mods, params, target, target_host=None, ops=No
     tasks = []
     for task_name, args in env.get_tasks():
         try:
-            tsk = create(task_name, args, target=target, target_host=target_host)
+            tsk = create(task_name, args, target=target)
             tasks.append(tsk)
         except topi.InvalidShapeError:
             logger.warning("Invalid shape during AutoTVM task creation")

python/tvm/autotvm/task/task.py

@@ -175,14 +175,17 @@ def __getstate__(self):
         # and restore the function by name when unpickling it.
         import cloudpickle  # pylint: disable=import-outside-toplevel
 
+        self.target, self.target_host = Target.check_and_update_host_consist(
+            self.target, self.target_host
+        )
         return {
             "name": self.name,
             "args": self.args,
             "kwargs": self.kwargs,
             "config_space": self.config_space,
             "flop": self.flop,
             "target": self.target,
-            "target_host": self.target_host,
+            "target_host": self.target.host,
             "func": cloudpickle.dumps(self.func),
         }
 
@@ -195,8 +198,9 @@ def __setstate__(self, state):
         self.config_space = state["config_space"]
         self.func = cloudpickle.loads(state["func"])
         self.flop = state["flop"]
-        self.target = state["target"]
-        self.target_host = state["target_host"]
+        self.target, self.target_host = Target.check_and_update_host_consist(
+            state["target"], state["target_host"]
+        )
 
     def __repr__(self):
         return "Task(func_name=%s, args=%s, kwargs=%s, workload=%s)" % (
@@ -448,6 +452,8 @@ def create(task_name, args, target, target_host=None):
     if isinstance(target, str):
         target = Target(target)
 
+    target, target_host = Target.check_and_update_host_consist(target, target_host)
+
     # init config space
     ret.config_space = ConfigSpace()
 
@@ -459,7 +465,7 @@ def create(task_name, args, target, target_host=None):
 
     ret.flop = ret.config_space.flop or compute_flop(sch)
     ret.target = target
-    ret.target_host = target_host
+    ret.target_host = target.host
 
     return ret
 

python/tvm/contrib/peak.py

@@ -20,6 +20,7 @@
 import logging
 import tvm
 from tvm import te
+from tvm.target import Target
 from . import utils
 from .. import rpc
 
@@ -86,6 +87,8 @@ def measure_bandwidth_sum(
     GBPS: float
          gigabyte per second
     """
+    target, target_host = Target.check_and_update_host_consist(target, target_host)
+
     n, m = total_item, item_per_thread
     n //= lanes
 
@@ -107,7 +110,7 @@ def measure_bandwidth_sum(
     s[y].unroll(k)
 
     try:
-        func = tvm.build(s, [x, y], target, target_host=target_host)
+        func = tvm.build(s, [x, y], target)
 
         x = tvm.nd.empty((n,), dtype=dtype, device=dev)
         y = tvm.nd.empty((n // m,), dtype=dtype, device=dev)
@@ -151,6 +154,7 @@ def measure_bandwidth_all_types(
     result: list
         a list of (type_name, GBPS) pairs
     """
+    target, target_host = Target.check_and_update_host_consist(target, target_host)
     max_threads = target.max_num_threads
 
     result = []
@@ -221,6 +225,7 @@ def measure_compute_mad(
     GOPS: float
          giga operation per second
     """
+    target, target_host = Target.check_and_update_host_consist(target, target_host)
 
     n = total_item
 
@@ -272,7 +277,7 @@ def mad_func(x, y):
     s = te.create_schedule(y.op)
 
     try:
-        func = tvm.build(s, [y], target, target_host=target_host)
+        func = tvm.build(s, [y], target)
         func = _convert_to_remote(func, remote)
         time_f = func.time_evaluator(func.entry_name, dev, number=n_times)
         y = tvm.nd.empty((n,), dtype=dtype, device=dev)
@@ -313,6 +318,8 @@ def measure_compute_all_types(
     result: list
         a list of (type_name, GFLOPS/GIOPS) pairs
     """
+    target, target_host = Target.check_and_update_host_consist(target, target_host)
+
     result = []
     for base_type in ["float", "int"]:
         for bits in [16, 32, 64]:
@@ -357,7 +364,7 @@ def measure_peak_all(target, target_host, host, port):
     port: int
     """
 
-    target = tvm.target.Target(target)
+    target, target_host = Target.check_and_update_host_consist(target, target_host)
     remote = rpc.connect(host, port)
     n_times = 20