python/tvm/relay/op/contrib/te_target.py

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"""Support a Relay partitioning target using Tensor Expressions."""
from typing import Callable, List, Dict

import tvm
import tvm.ir
from tvm import relay
from tvm import te


_LowerFunc = Callable[[relay.Call, List[te.Tensor]], te.Tensor]
_LOWER_MAP: Dict[str, _LowerFunc] = {}


def lower_composite(comp_name: str) -> Callable[[_LowerFunc], _LowerFunc]:
    """Register a lowering function for a given composite function name."""

    def _register(f: _LowerFunc) -> _LowerFunc:
        _LOWER_MAP[comp_name] = f
        return f

    return _register


def relay_to_runtime(target: tvm.target.Target) -> Callable[[relay.Function], tvm.runtime.Module]:
    """Create a Relay to runtime module lowering function using Tensor Expressions for lowering."""

    def _relay_to_runtime(partition: relay.Function) -> tvm.runtime.Module:
        """Compile Relay functions to a runtime module using Tensor Expressions."""
        assert isinstance(partition, relay.Function)
        assert isinstance(partition.body, relay.Call)
        assert isinstance(partition.body.op, relay.Function)

        global_name = str(partition.attrs.global_symbol)
        comp_func = partition.body.op
        comp_name = comp_func.attrs["Composite"]
        assert comp_name in _LOWER_MAP
        assert isinstance(comp_func.body, relay.Call)

        op = comp_func.body
        inputs = []
        for i, param in enumerate(comp_func.params):
            inputs.append(
                te.placeholder(
                    param.checked_type.shape,
                    name=f"input_{i}",
                    dtype=param.checked_type.dtype,
                )
            )

        output = _LOWER_MAP[comp_name](op, inputs)
        prim_func = te.create_prim_func(inputs + [output])
        return tvm.build(prim_func, target=target, name=global_name)

    return _relay_to_runtime