[microNPU] Add unary elementwise operator infrastructure with ABS

python/tvm/relay/backend/contrib/ethosu/legalize.py

@@ -741,6 +741,96 @@ def __call__(self, *args, **kwargs):
         pass
 
 
+class UnaryElementwiseRewriter(DFPatternCallback):
+    """
+    Convert ethosu unary elementwise composite function to
+    ethosu_unary_elementwise operators
+    """
+
+    def __init__(self, params_class: Type, pattern: CallPattern):
+        super().__init__(require_type=True)
+        self.params_class = params_class
+        self.pattern = pattern
+
+    def callback(
+        self, pre: tvm.relay.Expr, post: tvm.relay.Expr, node_map: tvm.ir.container.Map
+    ) -> tvm.relay.Expr:
+        params = self.params_class(post.op.body)
+        params.ifm.tensor = post.args[0]
+
+        if str(params.ofm.layout) != "NHWC":
+            raise UnsupportedLayout(str(params.ofm.layout))
+
+        activation_map = {"clip": "CLIP"}
+        if params.activation:
+            activation = activation_map[params.activation.op.name]
+            clip_min = int(params.activation.attrs.a_min)
+            clip_max = int(params.activation.attrs.a_max)
+        else:
+            activation = "NONE"
+            clip_min = 0
+            clip_max = 0
+
+        # We don't yet support activation functions that use LUT.
+        lut = relay.const([], dtype="int8")
+
+        unary_input_shape = params.ifm.shape
+        # If the input tensor is not 4D, enter reshapes before and after the unary operator
+        if len(params.ifm.shape) == 4:
+            unary_input = params.ifm.tensor
+        else:
+            pad_size = 4 - len(unary_input_shape)
+            unary_input_shape = ([1] * pad_size) + unary_input_shape
+            unary_input = relay.op.reshape(params.ifm.tensor, newshape=unary_input_shape)
+
+        ethosu_unary_elementwise = ethosu_ops.ethosu_unary_elementwise(
+            ifm=unary_input,
+            lut=lut,
+            operator_type=params.operator_type,
+            ifm_scale=float(params.ifm.q_params.scale_f32),
+            ifm_zero_point=int(params.ifm.q_params.zero_point),
+            ofm_scale=float(params.ofm.q_params.scale_f32),
+            ofm_zero_point=int(params.ofm.q_params.zero_point),
+            ofm_channels=unary_input_shape[3],
+            activation=activation,
+            clip_min=clip_min,
+            clip_max=clip_max,
+            ifm_layout=str(params.ifm.layout),
+            ofm_layout=str(params.ofm.layout),
+        )
+        if len(params.ifm.shape) == 4:
+            op = ethosu_unary_elementwise
+        else:
+            op = relay.op.reshape(ethosu_unary_elementwise, newshape=params.ifm.shape)
+        return op
+
+
+class AbsRewriter(UnaryElementwiseRewriter):
+    def __init__(self):
+        super().__init__(
+            params_class=ethosu_patterns.AbsParams,
+            pattern=(wildcard().has_attr({"Composite": ethosu_patterns.AbsParams.composite_name}))(
+                wildcard()
+            ),
+        )
+
+
+@ir.transform.module_pass(opt_level=1)
+class LegalizeAbs:
+    """This is the pass that wraps the AbsRewriter"""
+
+    def transform_module(
+        self, mod: tvm.ir.IRModule, ctx: tvm.ir.transform.PassContext
+    ) -> tvm.ir.IRModule:
+        for global_var, func in mod.functions.items():
+            func = rewrite(AbsRewriter(), func)
+            mod.update_func(global_var, func)
+        return mod
+
+    def __call__(self, *args, **kwargs):
+        pass
+
+
 @ir.transform.module_pass(opt_level=1)
 class LegalizeEthosU:
     """This is the pass to call graph-rewrites to perform graph transformation
@@ -765,6 +855,7 @@ def transform_module(
         mod = LegalizeMin()(mod)
         mod = LegalizeMax()(mod)
         mod = LegalizeShl()(mod)
+        mod = LegalizeAbs()(mod)
         mod = LegalizeReshape()(mod)
         mod = LegalizeStridedSlice()(mod)
         mod = LegalizeNoOps()(mod)

python/tvm/relay/backend/contrib/ethosu/op/__init__.py

@@ -21,3 +21,4 @@
 from .pooling import ethosu_pooling
 from .binary_elementwise import ethosu_binary_elementwise
 from .identity import ethosu_identity
+from .unary_elementwise import ethosu_unary_elementwise

python/tvm/relay/backend/contrib/ethosu/op/unary_elementwise.py

@@ -0,0 +1,163 @@
+# 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.
+# pylint: disable=unused-argument
+"""Relay operator for unary elementwise operations for Arm(R) Ethos(TM)-U NPU"""
+from typing import Optional
+import tvm
+from tvm.relay.op import _make
+from tvm.topi.generic import schedule_injective
+from tvm.relay.op.op import OpStrategy
+from tvm.relay.op import strategy as _strategy
+
+from ..te import unary_elementwise_compute
+
+
+def _extract_ethosu_unary_elementwise_params(attrs, args):
+    """Get the parameters necessary to construct a ethosu_unary_elementwise compute TE
+    from a ethosu_unary_elementwise Relay call."""
+    ifm = args[0]
+    lut = args[1]
+    operator_type = attrs.operator_type
+    ifm_scale = attrs.ifm_scale
+    ifm_zero_point = attrs.ifm_zero_point
+    ofm_scale = attrs.ofm_scale
+    ofm_zero_point = attrs.ofm_zero_point
+    ofm_channels = attrs.ofm_channels
+    activation = attrs.activation
+    clip_min = attrs.clip_min
+    clip_max = attrs.clip_max
+    rounding_mode = attrs.rounding_mode
+    ifm_layout = attrs.ifm_layout
+    ofm_layout = attrs.ofm_layout
+
+    return (
+        ifm,
+        lut,
+        operator_type,
+        ifm_scale,
+        ifm_zero_point,
+        ofm_scale,
+        ofm_zero_point,
+        ofm_channels,
+        activation,
+        clip_min,
+        clip_max,
+        rounding_mode,
+        ifm_layout,
+        ofm_layout,
+    )
+
+
+@tvm.ir.register_op_attr("contrib.ethosu.unary_elementwise", "FTVMCompute")
+def create_ethosu_unary_elementwise_compute(attrs, args, out_type):
+    """Create an ethosu_unary_elementwise compute op."""
+    params = _extract_ethosu_unary_elementwise_params(attrs, args)
+    op = unary_elementwise_compute(*params)
+    return [op]
+
+
+@tvm.ir.register_op_attr("contrib.ethosu.unary_elementwise", "FTVMStrategy")
+def unary_elementwise_strategy_ethosu(attrs, inputs, out_type, target):
+    strategy = OpStrategy()
+    strategy.add_implementation(
+        create_ethosu_unary_elementwise_compute,
+        _strategy.wrap_topi_schedule(schedule_injective),
+        name="ethosu_unary_elementwise",
+    )
+    return strategy
+
+
+def ethosu_unary_elementwise(
+    ifm: tvm.relay.Expr,
+    lut: tvm.relay.Expr,
+    operator_type: str,
+    ifm_scale: float,
+    ifm_zero_point: int,
+    ofm_scale: float,
+    ofm_zero_point: int,
+    ofm_channels: int,
+    activation: Optional[str] = "NONE",
+    clip_min: Optional[int] = 0,
+    clip_max: Optional[int] = 0,
+    rounding_mode: Optional[str] = "TFL",
+    ifm_layout: Optional[str] = "NHWC",
+    ofm_layout: Optional[str] = "NHWC",
+) -> tvm.relay.Call:
+    """This is a quantized unary elementwise operation as supported by the
+    NPU. It accepts either NHWC or NHCWB16 format for the input data.
+
+    Parameters
+    ----------
+    ifm : tvm.relay.Expr
+        The Input Feature Map tensor (IFM).
+    lut : tvm.relay.Expr
+        The look-up table values to use if activation = "LUT".
+    operator_type: str
+        The type of the unary elementwise operator.
+            "ABS"
+    ifm_scale : float
+        The quantization scale for the Input Feature Map tensor.
+    ifm_zero_point : int
+        The quantization zero point for the Input Feature Map tensor.
+    ofm_scale : float
+        The quantization scale for the Output Feature Map tensor.
+    ofm_zero_point : int
+       The quantization zero point for the Output Feature Map tensor.
+    ofm_channels : int
+        The number of OFM channels.
+    activation : str, optional
+        The activation function to use.
+            "NONE" - no activation function.
+            "CLIP" - clip the output between clip_min and clip_max.
+            "TANH" - tanh activation function.
+            "SIGMOID" - sigmoid activation function.
+            "LUT" - use a look-up table to perform the activation function.
+    clip_min : int, optional
+        The minimum clipping value if activation = "CLIP".
+    clip_max : int, optional
+        The maximum clipping value if activation = "CLIP".
+    rounding_mode : str, optional
+        The rounding mode to apply to the Output Feature Map tensor.
+            "TFL" - Tensorflow Lite rounding scheme.
+            "TRUNCATE" - Truncate towards zero.
+            "NATURAL" - Round to nearest value, with x.5 rounded up towards +infinity.
+    ifm_layout : str, optional
+        The layout of the Input Feature Map tensor. Can be "NHWC" or "NHCWB16".
+    ofm_layout : str, optional
+        The layout of the Output Feature Map tensor. Can be "NHWC" or "NHCWB16".
+
+    Returns
+    -------
+    out : tvm.relay.Call
+        A call to the ethosu_binary_elementwise op.
+    """
+    return _make.ethosu_unary_elementwise(
+        ifm,
+        lut,
+        operator_type,
+        ifm_scale,
+        ifm_zero_point,
+        ofm_scale,
+        ofm_zero_point,
+        ofm_channels,
+        activation,
+        clip_min,
+        clip_max,
+        rounding_mode,
+        ifm_layout,
+        ofm_layout,
+    )

python/tvm/relay/backend/contrib/ethosu/te/__init__.py

@@ -21,3 +21,4 @@
 from .pooling import *
 from .binary_elementwise import *
 from .identity import *
+from .unary_elementwise import *