chore: import upstream snapshot with attribution

2026-07-13 13:36:25 +08:00
commit 26446540fa
3151 changed files with 974126 additions and 0 deletions
@@ -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.
+"""External function interface to random library."""
+
+import tvm_ffi
+
+import tvm
+from tvm import te
+
+
+def randint(low, high, size, dtype="int32"):
+    """Return random integers from low (inclusive) to high (exclusive).
+    Return random integers from the "discrete uniform" distribution of the
+    specified dtype in the "half-open" interval [low, high).
+
+    Parameters
+    ----------
+    low : int
+        Lowest (signed) integer to be drawn from the distribution
+    high : int
+        One above the largest (signed) integer to be drawn from the distribution
+
+    Returns
+    -------
+    out : Tensor
+        A tensor with specified size and dtype
+    """
+    assert "int" in dtype, "the type of randint output must be int or uint"
+    return te.extern(
+        size,
+        [],
+        lambda ins, outs: tvm.tirx.call_packed(
+            "tvm.contrib.random.randint", int(low), int(high), outs[0]
+        ),
+        dtype=dtype,
+    )
+
+
+def uniform(low, high, size):
+    """Draw samples from a uniform distribution.
+
+    Samples are uniformly distributed over the half-open interval [low, high)
+    (includes low, but excludes high). In other words, any value within the
+    given interval is equally likely to be drawn by uniform.
+
+    Parameters
+    ----------
+    low : float
+        Lower boundary of the output interval. All values generated will be
+        greater than or equal to low.
+    high : float
+        Upper boundary of the output interval. All values generated will be
+        less than high.
+    size : tuple of ints
+        Output shape. If the given shape is, e.g., (m, n, k), then m * n * k
+        samples are drawn.
+
+    Returns
+    -------
+    out : Tensor
+        A tensor with specified size and dtype.
+    """
+    return te.extern(
+        size,
+        [],
+        lambda ins, outs: tvm.tirx.call_packed(
+            "tvm.contrib.random.uniform", float(low), float(high), outs[0]
+        ),
+        dtype="float32",
+    )
+
+
+def normal(loc, scale, size):
+    """Draw samples from a normal distribution.
+
+    Return random samples from a normal distribution.
+
+    Parameters
+    ----------
+    loc : float
+        loc of the distribution.
+    scale : float
+        Standard deviation of the distribution.
+    size : tuple of ints
+        Output shape. If the given shape is, e.g., (m, n, k), then m * n * k
+        samples are drawn.
+
+    Returns
+    ------
+    out : Tensor
+        A tensor with specified size and dtype
+    """
+    return te.extern(
+        size,
+        [],
+        lambda ins, outs: tvm.tirx.call_packed(
+            "tvm.contrib.random.normal", float(loc), float(scale), outs[0]
+        ),
+        dtype="float32",
+    )
+
+
+tvm_ffi.init_ffi_api("tvm.contrib.random")