wehub-resource-sync
175 lines
6.3 KiB
Python
175 lines
6.3 KiB
Python
# 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.
|
|
"""ScatterElements operator"""
|
|
|
|
from tvm import te, tirx
|
|
from tvm.script.ir_builder import IRBuilder
|
|
from tvm.script.ir_builder import tirx as T
|
|
|
|
from . import utils
|
|
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 determined 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 input data will be multiplied on the update values
|
|
If mean, the input data will be mean between the update values and 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 = utils.get_const_int(axis)
|
|
|
|
# Prepare ranges and strides
|
|
shape = data.shape
|
|
if axis < 0:
|
|
axis = len(shape) + axis
|
|
axis_range = cast(shape[axis], indices.dtype)
|
|
|
|
full_range = 1
|
|
after_axis_range = 1
|
|
for i, value in enumerate(shape, 0):
|
|
full_range *= value
|
|
if i > axis:
|
|
after_axis_range *= value
|
|
before_axis_stride = axis_range * after_axis_range
|
|
|
|
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
|
|
|
|
def gen_ir(data_ptr, indices_ptr, updates_ptr, out_ptr, reduce_func):
|
|
# pylint: disable=invalid-name
|
|
data = T.buffer_proxy(data_ptr)
|
|
indices = T.buffer_proxy(indices_ptr)
|
|
updates = T.buffer_proxy(updates_ptr)
|
|
out = T.buffer_proxy(out_ptr)
|
|
|
|
# Copy initial input data to output
|
|
with IRBuilder() as ib:
|
|
with T.seq_scope():
|
|
with T.parallel(0, full_range) as i:
|
|
out[i] = data[i]
|
|
|
|
with T.parallel(0, ind_before_axis_range * ind_after_axis_range) as fused:
|
|
i = fused // ind_after_axis_range
|
|
j = fused % ind_after_axis_range
|
|
pre_index1 = i * ind_before_axis_stride + j
|
|
pre_index2 = i * before_axis_stride + j
|
|
with T.serial(0, ind_axis_range) as k:
|
|
# Offset along indices or updates
|
|
index1 = pre_index1 + k * ind_after_axis_range
|
|
# Get index and shift to positive side if need
|
|
k_new = indices[index1]
|
|
shifted_index = k_new + (k_new < 0) * axis_range
|
|
# Offset along data
|
|
index2 = pre_index2 + shifted_index * after_axis_range
|
|
reduce_func(out, index2, updates[index1])
|
|
|
|
return ib.get()
|
|
|
|
def update_func(dst_ptr, dst_index, update):
|
|
dst_ptr[dst_index] = update
|
|
|
|
def add_func(dst_ptr, dst_index, update):
|
|
dst_ptr[dst_index] += update
|
|
|
|
def mul_func(dst_ptr, dst_index, update):
|
|
dst_ptr[dst_index] *= update
|
|
|
|
def mean_func(dst_ptr, dst_index, update):
|
|
dst_ptr[dst_index] = (dst_ptr[dst_index] + update) / 2
|
|
|
|
def min_func(dst_ptr, dst_index, update):
|
|
dst_ptr[dst_index] = tirx.min(dst_ptr[dst_index], update)
|
|
|
|
def max_func(dst_ptr, dst_index, update):
|
|
dst_ptr[dst_index] = tirx.max(dst_ptr[dst_index], update)
|
|
|
|
reduce_func = None
|
|
if reduction == "update":
|
|
reduce_func = update_func
|
|
elif reduction == "add":
|
|
reduce_func = add_func
|
|
elif reduction == "mul":
|
|
reduce_func = mul_func
|
|
elif reduction == "mean":
|
|
reduce_func = mean_func
|
|
elif reduction == "min":
|
|
reduce_func = min_func
|
|
elif reduction == "max":
|
|
reduce_func = max_func
|
|
else:
|
|
raise NotImplementedError(
|
|
"scatter_elements reduction not in [update, add, mul, mean, min, max]:", reduction
|
|
)
|
|
|
|
out_buf = tirx.decl_buffer(data.shape, data.dtype, "out_buf", layout=None)
|
|
return te.extern(
|
|
[data.shape],
|
|
[data, indices, updates],
|
|
lambda ins, outs: gen_ir(ins[0], ins[1], ins[2], outs[0], reduce_func),
|
|
dtype=data.dtype,
|
|
out_buffers=[out_buf],
|
|
name="scatter_elements.generic",
|
|
tag="scatter_elements.generic",
|
|
)
|