vvchernov commented on code in PR #13894: URL: https://github.com/apache/tvm/pull/13894#discussion_r1108216622
########## python/tvm/topi/cuda/scatter_elements.py: ########## @@ -0,0 +1,181 @@ +# 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 +"""Scatter operator """ +import tvm +from tvm import te, tir +from ..utils import ceil_div, get_const_int +from ..math import cast + + +def scatter_elements(data, indices, updates, axis=0, reduction="update"): + """Scatter elements from updates to corresponding indices of copied data. + + Data, indices, updates and output have the same shape. + Indices can not have duplicates (if idx1 != idx2, then indices[idx1] != indices[idx2]) + if reduction == "update". + + .. code-block:: + + output[indices[i][j]][j] = f(output[indices[i][j]][j], updates[i][j]) if axis = 0 + output[i][indices[i][j]] = f(output[i][indices[i][j]], updates[i][j]) if axis = 1 + + where the update function f is determinted by the reduction. + Five types of the function are supported: "update", "add", "mul", "min" and "max" (see below) + + Parameters + ---------- + data : tvm.te.Tensor + The source array. + + indices : tvm.te.Tensor + The indices of the values to extract. + + updates : tvm.te.Tensor + The updates to apply at the Indices + + axis : optional, int + The axis to scatter on. It is zero by default. + + reduction : optional, string + The update mode for the algorithm, either "update", "add", "mul", "min" or "max" + If update, the update values will replace the input data + If add, the update values will be added to the input data + If mul, the update values will be multiply to the input data + If min, there is choice of minimal between the update values and the input data + If max, there is choice of maximal between the update values and the input data + It is "update" by default + + Returns + ------- + ret : tvm.te.Tensor + """ + if not isinstance(axis, int): + axis = get_const_int(axis) + + def gen_ir(data, indices, updates, out, axis): + ib = tir.ir_builder.create() + + data_ptr = ib.buffer_ptr(data) + indices_ptr = ib.buffer_ptr(indices) + updates_ptr = ib.buffer_ptr(updates) + out_ptr = ib.buffer_ptr(out) + + # Prepare ranges and strides + shape = data.shape + if axis < 0: + axis = len(shape) + axis + axis_range = cast(shape[axis], indices.dtype) + + before_axis_range = 1 + after_axis_range = 1 + for i, value in enumerate(shape, 0): + if i < axis: + before_axis_range *= value + elif i > axis: + after_axis_range *= value + before_axis_stride = axis_range * after_axis_range + full_range = before_axis_range * before_axis_stride + + ind_shape = indices.shape + ind_axis_range = ind_shape[axis] + + ind_before_axis_range = 1 + ind_after_axis_range = 1 + for i, value in enumerate(ind_shape, 0): + if i < axis: + ind_before_axis_range *= value + elif i > axis: + ind_after_axis_range *= value + ind_before_axis_stride = ind_axis_range * ind_after_axis_range + ind_full_range = ind_before_axis_range * ind_before_axis_stride + ind_full_range_excl_axis = ind_before_axis_range * ind_after_axis_range + + max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads) + # Copy initial input data to output + with ib.new_scope(): + num_blocks = ceil_div(full_range, max_threads) + bx = te.thread_axis("blockIdx.x") + tx = te.thread_axis("threadIdx.x") + ib.scope_attr(bx, "thread_extent", num_blocks) + ib.scope_attr(tx, "thread_extent", max_threads) + + index = bx * max_threads + tx + with ib.if_scope(index < full_range): + out_ptr[index] = data_ptr[index] + + # Check indices and shift to positive side if need + with ib.new_scope(): + num_blocks_1 = ceil_div(ind_full_range, max_threads) + bx1 = te.thread_axis("blockIdx.x") + tx1 = te.thread_axis("threadIdx.x") + ib.scope_attr(bx1, "thread_extent", num_blocks_1) + ib.scope_attr(tx1, "thread_extent", max_threads) + + ind_fused = bx1 * max_threads + tx1 + with ib.if_scope(ind_fused < ind_full_range): + index_check = tir.LT(indices_ptr[ind_fused], tir.const(0, indices.dtype)) Review Comment: Ok. I have fused block related to index shifting with general one and use your expression. Code related to scatter_add was refactored in another branch and PR was prepared (see below), the latter is waiting for merging of this PR. -- 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: commits-unsubscr...@tvm.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
