[Arith] ConstIntBound was incorrectly assuming bounds were over int64… (

#13918)

[Arith] ConstIntBound was incorrectly assuming bounds were over int64_t range

This commit improved the floormod and floordiv conversion check to be
simpler for the negative range and adds a  test to cover all integer data types.

src/tir/transforms/lower_intrin.cc

@@ -118,9 +118,8 @@ class IntrinInjecter : public tvm::arith::IRMutatorWithAnalyzer {
       // If the numerator's lower bound is known, express the floordiv
       // in terms of truncdiv using only positive operands.
       arith::ConstIntBound const_int_bound = analyzer_->const_int_bound(op->a);
-      if (const_int_bound->min_value != arith::ConstIntBound::kNegInf &&
-          const_int_bound->min_value < 0 &&
-          const_int_bound->min_value > Downcast<IntImm>(tvm::min_value(op->a->dtype))->value) {
+      if (const_int_bound->min_value < 0 &&
+          const_int_bound->min_value > -(Downcast<IntImm>(tvm::max_value(op->a->dtype))->value)) {
         // The goal is to write floordiv(a,b) in terms of truncdiv, without using
         // negative operands.
         //
@@ -214,9 +213,8 @@ class IntrinInjecter : public tvm::arith::IRMutatorWithAnalyzer {
       // If the numerator's lower bound is known, express the floormod
       // in terms of truncmod using only positive operands.
       arith::ConstIntBound const_int_bound = analyzer_->const_int_bound(op->a);
-      if (const_int_bound->min_value != arith::ConstIntBound::kNegInf &&
-          const_int_bound->min_value < 0 &&
-          const_int_bound->min_value > Downcast<IntImm>(tvm::min_value(op->a->dtype))->value) {
+      if (const_int_bound->min_value < 0 &&
+          const_int_bound->min_value > -(Downcast<IntImm>(tvm::max_value(op->a->dtype))->value)) {
         // The goal is to write floormod(a,b) in terms of truncdiv and truncmod,
         // without using negative operands.
         //

tests/python/relay/test_op_floordiv.py

@@ -0,0 +1,117 @@
+# 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.
+import numpy as np
+import pytest
+import tvm
+from tvm import te
+import tvm.testing
+from tvm.script import tir
+
+
+def test_floor_div_op():
+    target = "llvm"
+    dev = tvm.device(target)
+    N = 100
+    divisor = 5
+
+    @tir.prim_func
+    def func_64(
+        A: tir.Buffer((N + 100, 2), "int64"),
+        B: tir.Buffer((N), "int64"),
+        C: tir.Buffer((N), "int64"),
+    ):
+        for i in tir.serial(N):
+            with tir.block("A"):
+                v_i = tir.axis.spatial(N, i)
+                A[v_i, 0] = tir.floordiv(C[v_i] - tir.max_value("int64"), divisor)
+                A[v_i, 1] = tir.floormod(C[v_i] - tir.max_value("int64"), divisor)
+                A[v_i + 100, 0] = tir.floordiv(B[v_i], divisor)
+                A[v_i + 100, 1] = tir.floormod(B[v_i], divisor)
+
+    @tir.prim_func
+    def func_32(
+        A: tir.Buffer((N + 100, 2), "int32"),
+        B: tir.Buffer((N), "int32"),
+        C: tir.Buffer((N), "int32"),
+    ):
+        for i in tir.serial(N):
+            with tir.block("A"):
+                v_i = tir.axis.spatial(N, i)
+                A[v_i, 0] = tir.floordiv(C[v_i] - tir.max_value("int32"), divisor)
+                A[v_i, 1] = tir.floormod(C[v_i] - tir.max_value("int32"), divisor)
+                A[v_i + 100, 0] = tir.floordiv(B[v_i], divisor)
+                A[v_i + 100, 1] = tir.floormod(B[v_i], divisor)
+
+    @tir.prim_func
+    def func_16(
+        A: tir.Buffer((N + 100, 2), "int16"),
+        B: tir.Buffer((N), "int16"),
+        C: tir.Buffer((N), "int16"),
+    ):
+        for i in tir.serial(N):
+            with tir.block("A"):
+                v_i = tir.axis.spatial(N, i)
+                A[v_i, 0] = tir.floordiv(C[v_i] - tir.max_value("int16"), divisor)
+                A[v_i, 1] = tir.floormod(C[v_i] - tir.max_value("int16"), divisor)
+                A[v_i + 100, 0] = tir.floordiv(B[v_i], divisor)
+                A[v_i + 100, 1] = tir.floormod(B[v_i], divisor)
+
+    @tir.prim_func
+    def func_8(
+        A: tir.Buffer((N + 100, 2), "int8"), B: tir.Buffer((N), "int8"), C: tir.Buffer((N), "int8")
+    ):
+        for i in tir.serial(N):
+            with tir.block("A"):
+                v_i = tir.axis.spatial(N, i)
+                A[v_i, 0] = tir.floordiv(C[v_i] - tir.max_value("int8"), divisor)
+                A[v_i, 1] = tir.floormod(C[v_i] - tir.max_value("int8"), divisor)
+                A[v_i + 100, 0] = tir.floordiv(B[v_i], divisor)
+                A[v_i + 100, 1] = tir.floormod(B[v_i], divisor)
+
+    for opfunc, type in [
+        (func_8, "int8"),
+        (func_16, "int16"),
+        (func_32, "int32"),
+        (func_64, "int64"),
+    ]:
+        built = tvm.build(opfunc, target=target)
+        x_data = np.random.randint(te.min_value(type), te.max_value(type), size=(100), dtype=type)
+        y_data = np.asarray([i for i in range(N)], dtype=type)
+
+        a_dev = tvm.nd.empty([N + 100, 2], type, dev)
+        b_dev = tvm.nd.array(x_data, dev)
+        c_dev = tvm.nd.array(y_data, dev)
+
+        built(a_dev, b_dev, c_dev)
+
+        a = a_dev.numpy()
+        b = b_dev.numpy()
+        c = c_dev.numpy()
+
+        # python modulo behaves a bit different to tvm floormod for negative numbers
+        for i in range(N + 100):
+            if a[i, 1] < 0:
+                a[i, 1] = divisor + a[i, 1]
+
+        np.testing.assert_array_equal(a[:100, 0], (c - te.max_value(type)) // divisor)
+        np.testing.assert_array_equal(a[:100, 1], (c - te.max_value(type)) % divisor)
+        np.testing.assert_array_equal(a[100 : N + 100, 0], b // divisor)
+        np.testing.assert_array_equal(a[100 : N + 100, 1], b % divisor)
+
+
+if __name__ == "__main__":
+    tvm.testing.main()

tests/python/topi/python/test_topi_transform.py

@@ -859,7 +859,6 @@ def test_dynamic_strided_slice():
     verify_dynamic_strided_slice((3, 4, 3), [0, 2, 0], [1, 2, 3])
 
 
-@tvm.testing.requires_gpu
 @tvm.testing.uses_gpu
 def test_strided_set():
     verify_strided_set((3, 4, 3), (3, 2, 2), [0, 3, 0], [4, 1, 4], [1, -1, 2])