tests/python/relax/test_transform_bind_params.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.

import numpy as np
import tvm
import tvm.script
import tvm.testing
from tvm import relax
from tvm.script import relax as R
from tvm.script import tir as T

use_np_array = tvm.testing.parameter(False, True)


def test_bind_params(use_np_array):
    @tvm.script.ir_module
    class InputModule:
        @T.prim_func
        def tir_matmul(x: T.handle, y: T.handle, z: T.handle) -> None:
            T.func_attr({"global_symbol": "tir_matmul"})
            A = T.match_buffer(x, (16, 16))
            B = T.match_buffer(y, (16, 16))
            C = T.match_buffer(z, (16, 16))
            for i0, j, k0, i1, k1 in T.grid(4, 16, 4, 4, 4):
                with T.block("matmul"):
                    vi = T.axis.S(16, i0 * 4 + i1)
                    vj = T.axis.S(16, j)
                    vk = T.axis.R(16, k0 * 4 + k1)
                    with T.init():
                        C[vi, vj] = T.float32(0)
                    C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]

        @R.function
        def main(
            x: R.Tensor((16, 16), "float32"), w: R.Tensor((16, 16), "float32")
        ) -> R.Tensor((16, 16), "float32"):
            gv0 = R.call_tir(InputModule.tir_matmul, (x, w), R.Tensor((16, 16), dtype="float32"))
            return gv0

    x_np = np.random.rand(16, 16).astype(np.float32)
    w_np = np.random.rand(16, 16).astype(np.float32)
    x_tvm = tvm.nd.array(x_np)
    w_tvm = tvm.nd.array(w_np)
    params_dict = {"w": w_np if use_np_array else w_tvm}
    mod = relax.transform.BindParams("main", params_dict)(InputModule)
    assert len(mod["main"].params) == 1

    target = tvm.target.Target("llvm")
    ex_after = relax.build(mod, target)
    vm_after = relax.VirtualMachine(ex_after, tvm.cpu())
    res_after = vm_after["main"](x_tvm)

    ex_before = relax.build(InputModule, target)
    vm_before = relax.VirtualMachine(ex_before, tvm.cpu())
    res_before = vm_before["main"](x_tvm, w_tvm)

    tvm.testing.assert_allclose(res_before.numpy(), res_after.numpy())


def test_bind_params_symbolic_vars():
    @tvm.script.ir_module
    class Before:
        @R.function
        def main(
            x: R.Tensor(("batch", "m"), dtype="float32"),
            w0: R.Tensor(("n", "m"), dtype="float32"),
            b0: R.Tensor(("n",), dtype="float32"),
            w1: R.Tensor(("k", "n"), dtype="float32"),
            b1: R.Tensor(("k",), dtype="float32"),
        ) -> R.Tensor(("batch", "k"), dtype="float32"):
            batch = T.Var("batch", "int64")
            k = T.Var("k", "int64")
            m = T.Var("m", "int64")
            n = T.Var("n", "int64")
            with R.dataflow():
                lv0 = R.call_dps_packed(
                    "linear0", (x, w0, b0), out_sinfo=R.Tensor((batch, n), dtype="float32")
                )
                out = R.call_dps_packed(
                    "linear1", (lv0, w1, b1), out_sinfo=R.Tensor((batch, k), dtype="float32")
                )
                R.output(out)
            return out

    m, n, k = 4, 6, 8
    w0_tvm = tvm.nd.array(np.random.rand(n, m).astype(np.float32))
    b0_tvm = tvm.nd.array(np.random.rand(n).astype(np.float32))
    w1_tvm = tvm.nd.array(np.random.rand(k, n).astype(np.float32))
    b1_tvm = tvm.nd.array(np.random.rand(k).astype(np.float32))
    params_dict = {"w0": w0_tvm, "b0": b0_tvm, "w1": w1_tvm, "b1": b1_tvm}
    mod = relax.transform.BindParams("main", params_dict)(Before)

    # Since it contains ConstantNode, it's hard to check with structural equality.
    func = mod["main"]
    assert len(func.params) == 1
    batch = func.params[0].struct_info.shape[0]
    tvm.ir.assert_structural_equal(
        func.params[0].struct_info, relax.TensorStructInfo((batch, 4), "float32")
    )
    tvm.ir.assert_structural_equal(
        func.ret_struct_info, relax.TensorStructInfo((batch, 8), "float32")
    )
    bindings = func.body.blocks[0].bindings
    tvm.ir.assert_structural_equal(
        bindings[0].var.struct_info, relax.TensorStructInfo((batch, 6), "float32")
    )
    tvm.ir.assert_structural_equal(
        bindings[1].var.struct_info, relax.TensorStructInfo((batch, 8), "float32")
    )


param_specification = tvm.testing.parameter("by_string", "by_var")


def test_bind_params_by_var_obj(param_specification):
    @tvm.script.ir_module
    class Before:
        @R.function
        def main(A: R.Tensor([16], "float32")):
            return A

    np_data = np.arange(16).astype("float32")
    inlined_relax_const = relax.const(np_data)

    @tvm.script.ir_module
    class Expected:
        @R.function
        def main():
            return inlined_relax_const

    if param_specification == "by_string":
        var = "A"
    elif param_specification == "by_var":
        var = Before["main"].params[0]
    else:
        raise ValueError("Unknown param_specification: {param_specification}")

    After = relax.transform.BindParams("main", {var: np_data})(Before)

    tvm.ir.assert_structural_equal(Expected, After)


def test_bind_params_by_var_name():
    @tvm.script.ir_module
    class Before:
        @R.function
        def main(A: R.Tensor([16], "float32")):
            return A

    np_data = np.arange(16).astype("float32")
    inlined_relax_const = relax.const(np_data)

    @tvm.script.ir_module
    class Expected:
        @R.function
        def main():
            return inlined_relax_const

    After = relax.transform.BindParams("main", {"A": np_data})(Before)

    tvm.ir.assert_structural_equal(Expected, After)


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