[CUDA][THRUST] Enforce -libs=thrust to allow thrust offload

apps/topi_recipe/gemm/cuda_gemm_square.py

@@ -21,6 +21,7 @@
 from tvm.contrib import nvcc
 from tvm.contrib import spirv
 import numpy as np
+import tvm.testing
 
 TASK = "gemm"
 USE_MANUAL_CODE = False

python/tvm/contrib/thrust.py

@@ -0,0 +1,45 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Utilities for thrust"""
+import logging
+
+from tvm._ffi import get_global_func
+
+
+def maybe_warn(target, func_name):
+    if get_global_func(func_name, allow_missing=True) and not "thrust" in target.libs:
+        logging.warning("TVM is built with thrust but thrust is not used.")
+    if "thrust" in target.libs and get_global_func(func_name, allow_missing=True) is None:
+        logging.warning("thrust is requested but TVM is not built with thrust.")
+
+
+def can_use_thrust(target, func_name):
+    maybe_warn(target, func_name)
+    return (
+        target.kind.name in ["cuda", "nvptx"]
+        and "thrust" in target.libs
+        and get_global_func(func_name, allow_missing=True)
+    )
+
+
+def can_use_rocthrust(target, func_name):
+    maybe_warn(target, func_name)
+    return (
+        target.kind.name == "rocm"
+        and "thrust" in target.libs
+        and get_global_func(func_name, allow_missing=True)
+    )

python/tvm/relay/op/strategy/cuda.py

@@ -20,7 +20,7 @@
 from tvm.auto_scheduler import is_auto_scheduler_enabled
 from tvm.te import SpecializedCondition
 from tvm.contrib import nvcc
-from tvm._ffi import get_global_func
+from tvm.contrib.thrust import can_use_thrust
 from .generic import *
 from .. import op as _op
 
@@ -791,9 +791,7 @@ def scatter_cuda(attrs, inputs, out_type, target):
     rank = len(inputs[0].shape)
 
     with SpecializedCondition(rank == 1):
-        if target.kind.name == "cuda" and get_global_func(
-            "tvm.contrib.thrust.stable_sort_by_key", allow_missing=True
-        ):
+        if can_use_thrust(target, "tvm.contrib.thrust.stable_sort_by_key"):
             strategy.add_implementation(
                 wrap_compute_scatter(topi.cuda.scatter_via_sort),
                 wrap_topi_schedule(topi.cuda.schedule_scatter_via_sort),
@@ -838,9 +836,7 @@ def sort_strategy_cuda(attrs, inputs, out_type, target):
         wrap_topi_schedule(topi.cuda.schedule_sort),
         name="sort.cuda",
     )
-    if target.kind.name == "cuda" and get_global_func(
-        "tvm.contrib.thrust.sort", allow_missing=True
-    ):
+    if can_use_thrust(target, "tvm.contrib.thrust.sort"):
         strategy.add_implementation(
             wrap_compute_sort(topi.cuda.sort_thrust),
             wrap_topi_schedule(topi.cuda.schedule_sort),
@@ -859,9 +855,7 @@ def argsort_strategy_cuda(attrs, inputs, out_type, target):
         wrap_topi_schedule(topi.cuda.schedule_argsort),
         name="argsort.cuda",
     )
-    if target.kind.name == "cuda" and get_global_func(
-        "tvm.contrib.thrust.sort", allow_missing=True
-    ):
+    if can_use_thrust(target, "tvm.contrib.thrust.sort"):
         strategy.add_implementation(
             wrap_compute_argsort(topi.cuda.argsort_thrust),
             wrap_topi_schedule(topi.cuda.schedule_argsort),
@@ -880,9 +874,7 @@ def topk_strategy_cuda(attrs, inputs, out_type, target):
         wrap_topi_schedule(topi.cuda.schedule_topk),
         name="topk.cuda",
     )
-    if target.kind.name == "cuda" and get_global_func(
-        "tvm.contrib.thrust.sort", allow_missing=True
-    ):
+    if can_use_thrust(target, "tvm.contrib.thrust.sort"):
         strategy.add_implementation(
             wrap_compute_topk(topi.cuda.topk_thrust),
             wrap_topi_schedule(topi.cuda.schedule_topk),

python/tvm/relay/op/strategy/rocm.py

@@ -19,7 +19,8 @@
 from tvm import topi
 from tvm.auto_scheduler import is_auto_scheduler_enabled
 from tvm.te import SpecializedCondition
-from tvm._ffi import get_global_func
+from tvm.contrib.thrust import can_use_rocthrust
+
 from .generic import *
 from .. import op as _op
 from .cuda import judge_winograd, naive_schedule
@@ -223,14 +224,6 @@ def batch_matmul_strategy_rocm(attrs, inputs, out_type, target):
     return strategy
 
 
-def can_use_thrust(target, func_name):
-    return (
-        target.kind.name == "rocm"
-        and "thrust" in target.libs
-        and get_global_func(func_name, allow_missing=True)
-    )
-
-
 @argsort_strategy.register(["rocm"])
 def argsort_strategy_cuda(attrs, inputs, out_type, target):
     """argsort rocm strategy"""
@@ -240,7 +233,7 @@ def argsort_strategy_cuda(attrs, inputs, out_type, target):
         wrap_topi_schedule(topi.cuda.schedule_argsort),
         name="argsort.rocm",
     )
-    if can_use_thrust(target, "tvm.contrib.thrust.sort"):
+    if can_use_rocthrust(target, "tvm.contrib.thrust.sort"):
         strategy.add_implementation(
             wrap_compute_argsort(topi.cuda.argsort_thrust),
             wrap_topi_schedule(topi.cuda.schedule_argsort),
@@ -264,7 +257,7 @@ def scatter_cuda(attrs, inputs, out_type, target):
     rank = len(inputs[0].shape)
 
     with SpecializedCondition(rank == 1):
-        if can_use_thrust(target, "tvm.contrib.thrust.stable_sort_by_key"):
+        if can_use_rocthrust(target, "tvm.contrib.thrust.stable_sort_by_key"):
             strategy.add_implementation(
                 wrap_compute_scatter(topi.cuda.scatter_via_sort),
                 wrap_topi_schedule(topi.cuda.schedule_scatter_via_sort),
@@ -283,7 +276,7 @@ def sort_strategy_cuda(attrs, inputs, out_type, target):
         wrap_topi_schedule(topi.cuda.schedule_sort),
         name="sort.rocm",
     )
-    if can_use_thrust(target, "tvm.contrib.thrust.sort"):
+    if can_use_rocthrust(target, "tvm.contrib.thrust.sort"):
         strategy.add_implementation(
             wrap_compute_sort(topi.cuda.sort_thrust),
             wrap_topi_schedule(topi.cuda.schedule_sort),
@@ -303,7 +296,7 @@ def topk_strategy_cuda(attrs, inputs, out_type, target):
         name="topk.rocm",
     )
 
-    if can_use_thrust(target, "tvm.contrib.thrust.sort"):
+    if can_use_rocthrust(target, "tvm.contrib.thrust.sort"):
         strategy.add_implementation(
             wrap_compute_topk(topi.cuda.topk_thrust),
             wrap_topi_schedule(topi.cuda.schedule_topk),

python/tvm/topi/cuda/nms.py

@@ -19,9 +19,9 @@
 """Non-maximum suppression operator"""
 import tvm
 from tvm import te
-
+from tvm.contrib.thrust import can_use_thrust, can_use_rocthrust
 from tvm.tir import if_then_else
-from .sort import argsort, argsort_thrust, is_thrust_available
+from .sort import argsort, argsort_thrust
 from .scan import exclusive_scan
 from ..utils import ceil_div
 
@@ -610,8 +610,10 @@ def _get_sorted_indices(data, data_buf, score_index, score_shape):
     )
 
     target = tvm.target.Target.current()
-    # TODO(masahi): Check -libs=thrust option
-    if target and target.kind.name in ["cuda", "rocm"] and is_thrust_available():
+    if target and (
+        can_use_thrust(target, "tvm.contrib.thrust.sort")
+        or can_use_rocthrust(target, "tvm.contrib.thrust.sort")
+    ):
         sort_tensor = argsort_thrust(score_tensor, axis=1, is_ascend=False, dtype="int32")
     else:
         sort_tensor = argsort(score_tensor, axis=1, is_ascend=False, dtype="int32")

python/tvm/topi/cuda/scan.py

@@ -18,7 +18,7 @@
 "Scan related operators"
 import tvm
 from tvm import te
-from tvm._ffi import get_global_func
+from tvm.contrib.thrust import can_use_thrust, can_use_rocthrust
 from ..transform import expand_dims, squeeze, transpose, reshape
 from ..utils import ceil_div, swap, prod, get_const_int
 from ..math import cast
@@ -249,11 +249,6 @@ def ir(data, data_ex_scan, reduction):
     return reduction
 
 
-def is_thrust_available():
-    """Test if thrust based scan ops are available."""
-    return get_global_func("tvm.contrib.thrust.sum_scan", allow_missing=True) is not None
-
-
 def scan_thrust(
     data, output_dtype, exclusive=True, return_reduction=False, binop=tvm.tir.generic.add
 ):
@@ -352,8 +347,10 @@ def exclusive_scan(
 
     def do_scan(data, output_dtype):
         target = tvm.target.Target.current()
-        # TODO(masahi): Check -libs=thrust option
-        if target and target.kind.name in ["cuda", "rocm"] and is_thrust_available():
+        if target and (
+            can_use_thrust(target, "tvm.contrib.thrust.sum_scan")
+            or can_use_rocthrust(target, "tvm.contrib.thrust.sum_scan")
+        ):
             return scan_thrust(
                 data, output_dtype, exclusive=True, return_reduction=return_reduction, binop=binop
             )

python/tvm/topi/cuda/scatter.py

@@ -21,7 +21,7 @@
 from ..scatter import _verify_scatter_nd_inputs
 from ..generic import schedule_extern
 from .nms import atomic_add
-from .sort import stable_sort_by_key_thrust, is_thrust_available
+from .sort import stable_sort_by_key_thrust
 from ..utils import prod, ceil_div
 
 
@@ -565,7 +565,6 @@ def scatter_via_sort(cfg, data, indices, updates, axis=0):
     if axis < 0:
         axis += len(data.shape)
     assert axis == 0 and len(data.shape) == 1, "sorting based scatter only supported for 1d input"
-    assert is_thrust_available(), "Thrust is required for this op"
 
     cfg.add_flop(1)  # A dummy value to satisfy AutoTVM
 

python/tvm/topi/cuda/sort.py

@@ -18,7 +18,6 @@
 """Sort related operators """
 import tvm
 from tvm import te
-from tvm._ffi import get_global_func
 
 from .injective import schedule_injective_from_existing
 from ..transform import strided_slice, transpose
@@ -879,10 +878,3 @@ def stable_sort_by_key_thrust(keys, values, for_scatter=False):
         tag="stable_sort_by_key",
     )
     return out[0], out[1]
-
-
-def is_thrust_available():
-    """
-    Test if thrust based sorting ops are available.
-    """
-    return get_global_func("tvm.contrib.thrust.sort", allow_missing=True) is not None