chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""ScatterElements operator"""
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from tvm import te, tirx
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from tvm.script.ir_builder import IRBuilder
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from tvm.script.ir_builder import tirx as T
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from . import utils
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from .math import cast
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def scatter_elements(data, indices, updates, axis=0, reduction="update"):
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"""Scatter elements from updates to corresponding indices of copied data.
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Data, indices, updates and output have the same shape.
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Indices can not have duplicates (if idx1 != idx2, then indices[idx1] != indices[idx2])
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if reduction == "update".
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.. code-block::
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output[indices[i][j]][j] = f(output[indices[i][j]][j], updates[i][j]) if axis = 0
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output[i][indices[i][j]] = f(output[i][indices[i][j]], updates[i][j]) if axis = 1
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where the update function f is determined by the reduction.
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Five types of the function are supported: "update", "add", "mul", "min" and "max" (see below)
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Parameters
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----------
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data : tvm.te.Tensor
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The source array.
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indices : tvm.te.Tensor
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The indices of the values to extract.
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updates : tvm.te.Tensor
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The updates to apply at the Indices
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axis : optional, int
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The axis to scatter on. It is zero by default.
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reduction : optional, string
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The update mode for the algorithm, either "update", "add", "mul", "min" or "max"
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If update, the update values will replace the input data
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If add, the update values will be added to the input data
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If mul, the input data will be multiplied on the update values
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If mean, the input data will be mean between the update values and the input data
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If min, there is choice of minimal between the update values and the input data
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If max, there is choice of maximal between the update values and the input data
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It is "update" by default
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Returns
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-------
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ret : tvm.te.Tensor
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"""
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if not isinstance(axis, int):
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axis = utils.get_const_int(axis)
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# Prepare ranges and strides
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shape = data.shape
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if axis < 0:
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axis = len(shape) + axis
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axis_range = cast(shape[axis], indices.dtype)
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full_range = 1
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after_axis_range = 1
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for i, value in enumerate(shape, 0):
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full_range *= value
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if i > axis:
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after_axis_range *= value
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before_axis_stride = axis_range * after_axis_range
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ind_shape = indices.shape
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ind_axis_range = ind_shape[axis]
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ind_before_axis_range = 1
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ind_after_axis_range = 1
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for i, value in enumerate(ind_shape, 0):
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if i < axis:
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ind_before_axis_range *= value
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elif i > axis:
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ind_after_axis_range *= value
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ind_before_axis_stride = ind_axis_range * ind_after_axis_range
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def gen_ir(data_ptr, indices_ptr, updates_ptr, out_ptr, reduce_func):
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# pylint: disable=invalid-name
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data = T.buffer_proxy(data_ptr)
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indices = T.buffer_proxy(indices_ptr)
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updates = T.buffer_proxy(updates_ptr)
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out = T.buffer_proxy(out_ptr)
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# Copy initial input data to output
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with IRBuilder() as ib:
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with T.seq_scope():
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with T.parallel(0, full_range) as i:
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out[i] = data[i]
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with T.parallel(0, ind_before_axis_range * ind_after_axis_range) as fused:
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i = fused // ind_after_axis_range
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j = fused % ind_after_axis_range
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pre_index1 = i * ind_before_axis_stride + j
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pre_index2 = i * before_axis_stride + j
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with T.serial(0, ind_axis_range) as k:
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# Offset along indices or updates
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index1 = pre_index1 + k * ind_after_axis_range
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# Get index and shift to positive side if need
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k_new = indices[index1]
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shifted_index = k_new + (k_new < 0) * axis_range
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# Offset along data
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index2 = pre_index2 + shifted_index * after_axis_range
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reduce_func(out, index2, updates[index1])
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return ib.get()
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def update_func(dst_ptr, dst_index, update):
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dst_ptr[dst_index] = update
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def add_func(dst_ptr, dst_index, update):
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dst_ptr[dst_index] += update
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def mul_func(dst_ptr, dst_index, update):
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dst_ptr[dst_index] *= update
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def mean_func(dst_ptr, dst_index, update):
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dst_ptr[dst_index] = (dst_ptr[dst_index] + update) / 2
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def min_func(dst_ptr, dst_index, update):
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dst_ptr[dst_index] = tirx.min(dst_ptr[dst_index], update)
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def max_func(dst_ptr, dst_index, update):
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dst_ptr[dst_index] = tirx.max(dst_ptr[dst_index], update)
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reduce_func = None
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if reduction == "update":
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reduce_func = update_func
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elif reduction == "add":
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reduce_func = add_func
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elif reduction == "mul":
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reduce_func = mul_func
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elif reduction == "mean":
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reduce_func = mean_func
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elif reduction == "min":
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reduce_func = min_func
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elif reduction == "max":
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reduce_func = max_func
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else:
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raise NotImplementedError(
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"scatter_elements reduction not in [update, add, mul, mean, min, max]:", reduction
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)
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out_buf = tirx.decl_buffer(data.shape, data.dtype, "out_buf", layout=None)
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return te.extern(
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[data.shape],
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[data, indices, updates],
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lambda ins, outs: gen_ir(ins[0], ins[1], ins[2], outs[0], reduce_func),
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dtype=data.dtype,
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out_buffers=[out_buf],
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name="scatter_elements.generic",
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tag="scatter_elements.generic",
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)
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