tests/python/relax/texture/adreno_utils.py

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import os
import tvm
import numpy as np
from tvm import relax
from tvm.contrib import utils, ndk
from tvm.script.parser import ir as I, relax as R, tir as T
from tvm.relax.transform.legalize_ops import adreno as legalize_adreno
from tvm.contrib import dlpack as dl
import tvm.testing
from tvm.rpc import connect_tracker


def get_target(backend, is_adreno=False):
    """
    Get the target for the Adreno GPU.

    Returns
    -------
    tvm.target.Target
        The target for the Adreno GPU.
    """
    target = tvm.target.adreno(backend=backend)
    if is_adreno:
        target = tvm.target.adreno(cfg="texture", backend=backend)
    return target


def get_rpc():
    """
    Establish an RPC connection to the remote device.

    Returns
    -------
    tvm.rpc.RPCSession or None
        The RPC session object if RPC_TARGET is set; otherwise, None.
    """
    rpc_target = os.getenv("RPC_TARGET", None)
    if rpc_target:
        host = os.getenv("TVM_TRACKER_HOST", "localhost")
        port = int(os.getenv("TVM_TRACKER_PORT", 9090))
        device_key = os.getenv("RPC_DEVICE_KEY", "android")
        tracker = connect_tracker(host, port)
        return tracker.request(device_key, priority=1, session_timeout=1000)
    else:
        return None


def get_unique_dso_lib():
    """
    Generate a unique shared library filename based on environment variables.

    Returns
    -------
    str
        The unique shared library filename.
    """
    rpc_tracker_port = os.getenv("TVM_TRACKER_PORT", "")
    device_port = os.getenv("DEVICE_LISTEN_PORT", "")
    return f"dev_lib_cl-{rpc_tracker_port}-{device_port}.so"


def run_cpu(mod, inputs, save_lib=False):
    """
    Run the Relax module on the local CPU for verification.

    Parameters
    ----------
    mod : tvm.IRModule
        The Relax IRModule to execute.
    inputs : list of numpy.ndarray
        The input data for the module.
    save_lib : bool, optional
        Whether to save the compiled library. Default is False.

    Returns
    -------
    tvm.runtime.NDArray or tuple of tvm.runtime.NDArray
        The output from the module execution.
    """
    print("Running on local CPU for verification")
    target = tvm.target.Target("llvm")
    ex = relax.build(mod, target)
    if save_lib:
        ex.export_library("mod.so")
    dev = tvm.cpu()
    vm = relax.VirtualMachine(ex, dev)
    inputs = [tvm.nd.array(inp, dev) for inp in inputs]
    vm.set_input("main", *inputs)
    vm.invoke_stateful("main")
    tvm_output = vm.get_outputs("main")
    return tvm_output


def build_run(mod, inputs, backend, is_adreno=False):
    remote = get_rpc()
    target = get_target(backend, is_adreno)
    if remote is None:
        tgt = tvm.target.Target(target, host="llvm")
    else:
        tgt = tvm.target.Target(target, host="llvm -mtriple=aarch64-linux-gnu")
    relax_pipeline = relax.pipeline.get_default_pipeline(tgt)
    tir_pipeline = tvm.tir.get_default_tir_pipeline(tgt)
    mod = relax_pipeline(mod)
    ex = tvm.compile(mod, tgt, tir_pipeline=tir_pipeline)

    if remote is None:
        # local execution
        if "opencl" in backend:
            dev = tvm.opencl(0)
        elif "vulkan" in backend:
            dev = tvm.vulkan(0)
        else:
            raise RuntimeError("Unsupported backend")

        if "vdevice" in mod.global_infos:
            device_arr = [dev for ii in range(len(mod.global_infos["vdevice"]))]
        else:
            device_arr = [dev]
        vm = relax.VirtualMachine(ex, device_arr)
    else:
        # remote execution
        temp = utils.tempdir()
        filename = get_unique_dso_lib()
        file_path = temp.relpath(filename)
        ex.export_library(
            file_path, fcompile=ndk.create_shared, options=["-shared", "-fPIC", "-lm"]
        )

        remote.upload(file_path)
        rexec = remote.load_module(filename)

        if "opencl" in backend:
            dev = remote.cl(0)
        elif "vulkan" in backend:
            dev = remote.vulkan(0)
        else:
            raise RuntimeError("Unsupported backend")

        if "vdevice" in mod.global_infos:
            device_arr = [dev for ii in range(len(mod.global_infos["vdevice"]))]
        else:
            device_arr = [dev]

        vm = relax.VirtualMachine(rexec, device_arr)

    inputs = [tvm.runtime.tensor(inp, dev) for inp in inputs]
    vm.set_input("main", *inputs)
    vm.invoke_stateful("main")
    tvm_output = vm.get_outputs("main")

    if isinstance(tvm_output, tuple):
        tvm_output = (out.numpy() for out in tvm_output)
    else:
        tvm_output = tvm_output.numpy()

    if remote:
        remote.get_function("CloseRPCConnection")()
    return tvm_output


def verify(mod, backend):

    if backend not in ["opencl", "vulkan"]:
        raise ValueError(f"Unsupported API: {backend}. Must be 'opencl' or 'vulkan'.")

    inputs = []
    for arg in mod["main"].params:
        shape = tuple(shape_val.value for shape_val in arg.struct_info.shape.values)
        inputs.append(np.random.uniform(0, 1, size=shape).astype(arg.struct_info.dtype))

    ret1 = build_run(mod, inputs, backend, True)
    ret2 = build_run(mod, inputs, backend)

    if isinstance(ret1, tuple):
        for val1, val2 in zip(ret1, ret2):
            tvm.testing.assert_allclose(val1, ret2, rtol=1e-3, atol=1e-3)
    else:
        tvm.testing.assert_allclose(ret1, ret2, rtol=1e-3, atol=1e-3)