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convolution.py
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convolution.py
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# 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 operators for convolutions for Arm(R) Ethos(TM)-U NPU"""
from typing import Tuple
import tvm # type: ignore
from tvm.relay.op import _make # type: ignore
from tvm.topi.generic import schedule_injective # type: ignore
from tvm.relay.op.op import OpStrategy # type: ignore
from tvm.relay.op import strategy as _strategy
from ..te import conv2d_compute
def _extract_ethosu_conv2d_params(attrs, args):
"""Get the parameters necessary to construct a compute TE
from a ethosu_conv2d Relay call."""
ifm = args[0]
weight = args[1]
scale_bias = args[2]
lut = args[3]
ifm_scale = attrs.ifm_scale
ifm_zero_point = attrs.ifm_zero_point
weight_zero_point = attrs.weight_zero_point
ofm_scale = attrs.ofm_scale
ofm_zero_point = attrs.ofm_zero_point
strides = attrs.strides
padding = attrs.padding
dilation = attrs.dilation
activation = attrs.activation
clip_min = attrs.clip_min
clip_max = attrs.clip_max
rounding_mode = attrs.rounding_mode
upscale = attrs.upscale
ifm_layout = attrs.ifm_layout
ofm_layout = attrs.ofm_layout
return (
ifm,
weight,
scale_bias,
lut,
ifm_scale,
ifm_zero_point,
weight_zero_point,
ofm_scale,
ofm_zero_point,
strides,
padding,
dilation,
activation,
clip_min,
clip_max,
rounding_mode,
upscale,
ifm_layout,
ofm_layout,
)
@tvm.ir.register_op_attr("contrib.ethosu.conv2d", "FTVMCompute")
def create_ethosu_conv2d_compute(attrs, args, out_type):
"""Create an ethosu_conv2d compute op."""
params = _extract_ethosu_conv2d_params(attrs, args)
op = conv2d_compute(*params)
return [op]
@tvm.ir.register_op_attr("contrib.ethosu.conv2d", "FTVMStrategy")
def conv2d_strategy_ethosu(attrs, inputs, out_type, target):
strategy = OpStrategy()
strategy.add_implementation(
create_ethosu_conv2d_compute,
_strategy.wrap_topi_schedule(schedule_injective),
name="ethosu_conv2d",
)
return strategy
def ethosu_conv2d(
ifm: tvm.relay.Expr,
weight: tvm.relay.Expr,
scale_bias: tvm.relay.Expr,
lut: tvm.relay.Expr,
ifm_scale: float,
ifm_zero_point: int,
weight_zero_point: int,
ofm_scale: float,
ofm_zero_point: int,
kernel_shape: Tuple[int, int],
ofm_channels: int,
strides: Tuple[int, int] = (1, 1),
padding: Tuple[int, int, int, int] = (0, 0, 0, 0),
dilation: Tuple[int, int] = (1, 1),
activation: str = "NONE",
clip_min: int = 0,
clip_max: int = 0,
rounding_mode: str = "TFL",
upscale: str = "NONE",
ifm_layout: str = "NHWC",
ofm_layout: str = "NHWC",
) -> tvm.relay.Call:
"""This is a quantized 2D convolution operation as supported by
the NPU. It accepts either NHWC or NHCWB16 format
for the input data and OHWI format for the kernel weights.
Reference: https://developer.arm.com/documentation/102420/0200/
Note that the per-channel weight scale and bias tensor must be
packed together into a combined tensor of uint80s. This is represented
in TVM by a (channels, 10) tensor of type uint8. For more detail,
refer to the Technical Reference Manual linked above.
Parameters
----------
ifm : tvm.relay.Expr
The Input Feature Map tensor (IFM).
weight : tvm.relay.Expr
The weight tensor.
scale_bias : tvm.relay.Expr
The packed per-channel weight scale and bias tensor.
lut : tvm.relay.Expr
The look-up table of values to use if activation = "LUT".
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.
weight_zero_point : int
The quantization zero point for the weight tensor.
ofm_scale : int
The quantization scale for the Output Feature Map tensor.
ofm_zero_point : int
The quantization zero point for the Output Feature Map tensor.
kernel_shape : tuple of int
The 2 dimensional kernel shape as (kernel_height, kernel_width).
ofm_channels : int
The number of the Output Feature Map channels.
strides : tuple of int, optional
The 2 dimensional strides as (stride_height, stride_width).
padding : tuple of int, optional
The 4 dimensional padding as (pad_top, pad_left, pad_bottom, pad_right).
dilation : tuple of int, optional
The 2 dimensional dilation as (dilation_height, dilation_width).
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.
upscale : str, optional
The 2x2 upscaling mode to apply to the Input Feature Map tensor.
"NONE" - no upscaling.
"NEAREST" - upscale using nearest neighbour.
"ZEROS" - upscale using zeros.
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
-------
tvm.relay.Call
A call to the ethosu_conv2d op.
"""
return _make.ethosu_conv2d(
ifm,
weight,
scale_bias,
lut,
ifm_scale,
ifm_zero_point,
weight_zero_point,
ofm_scale,
ofm_zero_point,
kernel_shape,
ofm_channels,
strides,
padding,
dilation,
activation,
clip_min,
clip_max,
rounding_mode,
upscale,
ifm_layout,
ofm_layout,
)