Lunderberg commented on code in PR #11489: URL: https://github.com/apache/tvm/pull/11489#discussion_r906048768
########## python/tvm/topi/hexagon/slice_ops/conv2d.py: ########## @@ -0,0 +1,223 @@ +# 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=line-too-long + +"""Hexagon slice conv2d compute and schedule""" +import typing + +import tvm +from tvm import te +from ..utils import get_layout_transform_fn + + +def conv2d_compute( + activations: te.Tensor, + weights: te.Tensor, + out_shape: typing.Tuple, + stride: typing.Tuple, + dilation: typing.Tuple, + dtype: str, + output_name: str, +) -> te.Tensor: + """Compute for slice conv2d op for hexagon. + + This op makes the following assumptions: + 1. This op is written for a sliced convolution with 2d physical buffers + 2. The input activations is assumed to be in NHWC layout and filter is in HWIO layout + 3. Grouped convolutions are not supported. and there will be a separate compute definition for depthwise convolution + 4. In order to get grouped convolutions, it is assumed that the op will be sliced according to the groups and multiple calls to this compute would be placed. + + Parameters + ---------- + activations : te.Tensor + Input activations padded for inner dimension size + weights : te.Tensor + Weights without dilation + out_shape : typing.Tuple + The logical output shape without considering input padding + stride : typing.Tuple + stride + dilation : typing.Tuple + dilation + dtype : str + dtype + output_name : str + The name to be given to output. This would become the block name for the corresponding STIR compute + + Returns + ------- + output : te.Tensor + Output of applying 2D convolution of Weights on Input + """ + + filt_shape = weights.shape + + reduce_channel = tvm.te.reduce_axis((0, filt_shape[2]), name="reduce_channel") + reduce_height = tvm.te.reduce_axis((0, filt_shape[0]), name="reduce_height") + reduce_width = tvm.te.reduce_axis((0, filt_shape[1]), name="reduce_width") + stride_height, stride_width = stride + dilation_height, dilation_width = dilation + output = tvm.te.compute( + out_shape, + lambda n, h, w, c: tvm.te.sum( + ( + activations[ + n, + h * stride_height + reduce_height * dilation_height, + w * stride_width + reduce_width * dilation_width, + reduce_channel, + ] + * weights[reduce_height, reduce_width, reduce_channel, c] + ).astype(dtype), + axis=[reduce_channel, reduce_height, reduce_width], + ), + name=output_name, + ) + return output + + +def conv2d_te_schedule( + out: te.Tensor, + ins: typing.List[te.Tensor], + transform_activation_layout: str, Review Comment: This seems a bit odd to have one layout as a string and another as a callable. Can we use the same convention for both? -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
