masahi commented on a change in pull request #7334: URL: https://github.com/apache/tvm/pull/7334#discussion_r564164437
########## File path: python/tvm/topi/cumsum.py ########## @@ -0,0 +1,105 @@ +# 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=invalid-name +"""Cumsum operator""" +from ..tir import decl_buffer, ir_builder +from ..te import extern +from .utils import prod, get_const_int +from .math import cast + + +def cumsum(data, axis=None, dtype=None): + """Numpy style cumsum op. Return the cumulative sum of the elements along a given axis. + + Parameters + ---------- + data : tvm.te.Tensor + The input data to the operator. + + axis : int, optional + Axis along which the cumulative sum is computed. The default (None) is to compute + the cumsum over the flattened array. + + dtype : string, optional + Type of the returned array and of the accumulator in which the elements are summed. + If dtype is not specified, it defaults to the dtype of data. + + Returns + ------- + result : tvm.te.Tensor + The result has the same size as data, and the same shape as data if axis is not None. + If axis is None, the result is a 1-d array. + """ + if dtype is None or dtype == "": + dtype = data.dtype + + def maybe_cast(x): + if dtype != data.dtype: + return cast(x, dtype) + return x + + axis_mul_before = 1 + axis_mul_after = 1 + + if axis is None: + axis = 0 + cumsum_axis_len = prod(data.shape) + shape = (cumsum_axis_len,) + else: + if not isinstance(axis, int): + axis = get_const_int(axis) + + shape = data.shape + cumsum_axis_len = shape[axis] + + if axis < 0: + axis = len(shape) + axis + + for i, value in enumerate(shape, 0): + if i < axis: + axis_mul_before *= value + elif i > axis: + axis_mul_after *= value + + def gen_ir(data_buf, out_buf): + ib = ir_builder.create() + data_buf = ib.buffer_ptr(data_buf) + out_buf = ib.buffer_ptr(out_buf) + + with ib.for_range(0, axis_mul_before, "i") as i: Review comment: Done. Fused `i` and `j` loop into a single parallel loop and do some math to recover `i` and `j`. Parallelizing `i` loop alone doesn't help when the scan axis is 0. ---------------------------------------------------------------- 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. For queries about this service, please contact Infrastructure at: [email protected]
