apache--tvm/tests/python/relax/test_op_misc.py

# 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.
# ruff: noqa: F841
import tvm_ffi

import tvm
import tvm.testing
from tvm import relax as rx
from tvm.script import relax as R
from tvm.script import tirx as T


@tvm.register_global_func("test.op.identity", override=True)
def identity_packed(a):
    return tvm.runtime.tensor(a.numpy())


@T.prim_func(s_tir=True)
def identity_tir(a: T.handle, b: T.handle) -> None:
    A = T.match_buffer(a, [54, 96])
    B = T.match_buffer(b, [54, 96])

    for i, j in T.grid(54, 96):
        with T.sblock("compute"):
            vi, vj = T.axis.remap("SS", [i, j])
            B[vi, vj] = A[vi, vj]


def test_call_tir() -> None:
    v0 = rx.Var("v0", R.Tensor([54, 96], "float32"))
    v1 = rx.call_dps_packed(rx.extern("test.op.identity"), [v0], R.Tensor((54, 96), "float32"))
    v1 = rx.call_tir(identity_tir, [v0], R.Tensor((54, 96), "float32"))


def test_call_tir_with_grad():
    v0 = rx.Var("v0", R.Tensor([54, 96], "float32"))
    v1 = rx.call_tir_with_grad(
        identity_tir, (v0,), R.Tensor((54, 96), "float32"), te_grad_name="identity_grad"
    )
    assert v1.attrs.te_grad_name == "identity_grad"
    v2 = rx.call_tir_with_grad(
        identity_tir,
        (v0,),
        R.Tensor((54, 96), "float32"),
        te_grad_name="identity_k_grad",
        te_grad_kwargs={"k": 1.0},
    )
    assert v2.attrs.te_grad_name == "identity_k_grad"
    assert isinstance(v2.attrs.te_grad_kwargs, tvm_ffi.Map)
    val = next(iter(v2.attrs.te_grad_kwargs.items()))
    assert val[0] == "k" and float(val[1]) == 1.0


def test_implicit_op():
    m, n = tvm.tirx.Var("m", "int64"), tvm.tirx.Var("n", "int64")
    x = rx.Var("x", R.Tensor([m, n], "float32"))
    y = rx.Var("y", R.Tensor([m, n], "float32"))
    func = rx.Var(
        "func",
        R.Callable(
            [R.Tensor([m, n], "float32")],
            R.Callable(
                [R.Tensor([m, n], "float32")],
                R.Tuple,
            ),
        ),
    )

    def _check_call(expr, op_name: str):
        assert isinstance(expr, rx.Call)
        if not op_name.startswith("relax."):
            op_name = "relax." + op_name
        op = tvm.ir.Op.get(op_name)
        assert expr.op == op

    # Comparison operators
    _check_call(x > y, "greater")
    _check_call(x >= y, "greater_equal")
    _check_call(x < y, "less")
    _check_call(x <= y, "less_equal")

    # Arithmetic operators
    _check_call(-x, "negative")
    _check_call(x + y, "add")
    _check_call(x - y, "subtract")
    _check_call(x * y, "multiply")
    _check_call(x / y, "divide")
    _check_call(x // y, "floor_divide")
    _check_call(x**y, "power")
    # _check_call(x % y, "mod") <= relax.mod is not implemented yet

    # Cast
    _check_call(x.astype("float32"), "astype")

    # Call
    call_expr = func(y)(y)
    assert isinstance(call_expr.op, rx.Call)
    assert call_expr.op.op == func

    # GetTupleItem
    ## Eager get item for tuple
    tuple_expr = rx.Tuple((x, y))
    assert tuple_expr[0] == x
    assert tuple_expr[1] == y

    ## Eager get item for ShapeExpr
    shape_expr = rx.ShapeExpr((1, 2))
    assert shape_expr[0] == 1
    assert shape_expr[1] == 2

    ## Create TupleGetItem for other expr
    assert isinstance(x[0], rx.TupleGetItem)
    assert isinstance(x[1][0], rx.TupleGetItem)


def test_vm_alloc_tensor():
    bb = rx.BlockBuilder()
    storage = rx.Var("storage", rx.TensorType(dtype="float32"))
    alloc = rx.op.vm.alloc_tensor(storage, offset=0, shape=rx.ShapeExpr([4, 5]), dtype="float32")
    alloc = bb.normalize(alloc)
    tvm.ir.assert_structural_equal(alloc.ty, R.Tensor([4, 5], "float32"))


def test_vm_alloc_tensor_infer_ty():
    bb = rx.BlockBuilder()
    s1 = rx.Var("s", R.Shape(ndim=3))
    storage = rx.Var("storage", rx.TensorType(dtype="float32"))
    alloc = rx.op.vm.alloc_tensor(storage, offset=0, shape=s1, dtype="float32")
    ret = bb.normalize(alloc)
    tvm.ir.assert_structural_equal(ret.ty, R.Tensor(dtype="float32", ndim=3))


def test_vm_kill_object():
    bb = rx.BlockBuilder()
    storage = rx.Var("storage", rx.TensorType(dtype="float32"))
    kill = rx.op.vm.kill_object(storage)
    ret = bb.normalize(kill)
    tvm.ir.assert_structural_equal(ret.ty, R.Tuple([]))


def test_builtin_stop_lift_params():
    bb = rx.BlockBuilder()
    x = rx.Var("x", rx.TensorType(shape=[4, 5], dtype="float32"))
    x1 = rx.op.builtin.stop_lift_params(x)
    x1 = bb.normalize(x1)
    tvm.ir.assert_structural_equal(x1.ty, R.Tensor([4, 5], "float32"))


if __name__ == "__main__":
    tvm.testing.main()