wehub-resource-sync
195 lines
7.9 KiB
Python
195 lines
7.9 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.
|
|
# pylint: disable=invalid-name, too-many-arguments, too-many-nested-blocks
|
|
"""Sparse_Reshape operator"""
|
|
|
|
from tvm.script.ir_builder import IRBuilder
|
|
from tvm.script.ir_builder import tirx as T
|
|
from tvm.te import div, extern, floordiv, floormod
|
|
from tvm.tirx import Cast, decl_buffer
|
|
|
|
|
|
def sparse_reshape(
|
|
sparse_indices,
|
|
prev_shape,
|
|
new_shape,
|
|
new_sparse_indices_shape,
|
|
new_shape_shape,
|
|
):
|
|
"""
|
|
Reshape a Sparse Tensor
|
|
|
|
Parameters
|
|
----------
|
|
sparse_indices : te.Expr
|
|
A 2-D tensor[N, n_dim] of integers containing location of sparse values, where N is the
|
|
number of sparse values and n_dim is the number of dimensions of the dense_shape
|
|
|
|
prev_shape : te.Expr
|
|
A 1-D tensor containing the previous shape of the dense tensor
|
|
|
|
new_shape : te.Expr
|
|
A 1-D tensor containing the new shape of the dense tensor
|
|
|
|
Returns
|
|
-------
|
|
result: te.Expr
|
|
Output tensor.
|
|
|
|
Examples
|
|
--------
|
|
.. code-block:: python
|
|
|
|
sparse_indices = [[0, 0, 0],
|
|
[0, 0, 1],
|
|
[0, 1, 0],
|
|
[1, 0, 0],
|
|
[1, 2, 3]]
|
|
prev_shape = [2, 3, 4]
|
|
new_shape = [9, -1]
|
|
new_sparse_indices, new_shape = topi.sparse_reshape(
|
|
sparse_indices, prev_shape, new_shape)
|
|
new_sparse_indices = [[0, 0],
|
|
[0, 1],
|
|
[1, 2],
|
|
[4, 2],
|
|
[8, 1]]
|
|
new_shape = [9, 4]
|
|
"""
|
|
|
|
def gen_ir(
|
|
sparse_indices_ptr,
|
|
prev_shape_ptr,
|
|
new_shape_ptr,
|
|
new_sparse_indices_ptr,
|
|
out_new_shape_ptr,
|
|
):
|
|
with IRBuilder() as ib:
|
|
sparse_indices = T.buffer_proxy(sparse_indices_ptr)
|
|
prev_shape = T.buffer_proxy(prev_shape_ptr)
|
|
|
|
new_shape = T.buffer_proxy(new_shape_ptr)
|
|
out_new_shape = T.buffer_proxy(out_new_shape_ptr)
|
|
new_sparse_indices = T.buffer_proxy(new_sparse_indices_ptr)
|
|
|
|
prev_shape_size = prev_shape_ptr.shape[0]
|
|
new_shape_size = new_shape_ptr.shape[0]
|
|
|
|
multipliers_buf = T.alloc_buffer([prev_shape_size], new_shape_ptr.dtype, scope="local")
|
|
multipliers = T.buffer_proxy(multipliers_buf)
|
|
dividers_buf = T.alloc_buffer([new_shape_size], new_shape_ptr.dtype, scope="local")
|
|
dividers = T.buffer_proxy(dividers_buf)
|
|
flattened_indices_buf = T.alloc_buffer(
|
|
[sparse_indices_ptr.shape[0]], new_shape_ptr.dtype, scope="local"
|
|
)
|
|
flattened_indices = T.buffer_proxy(flattened_indices_buf)
|
|
total_ele_buf = T.alloc_buffer([1], new_shape_ptr.dtype, scope="local")
|
|
total_ele = T.buffer_proxy(total_ele_buf)
|
|
division_total_ele_buf = T.alloc_buffer([1], new_shape_ptr.dtype, scope="local")
|
|
division_total_ele = T.buffer_proxy(division_total_ele_buf)
|
|
equal_shape_buf = T.alloc_buffer([1], "bool", scope="local")
|
|
equal_shape = T.buffer_proxy(equal_shape_buf)
|
|
|
|
total_ele[0] = prev_shape[0]
|
|
|
|
# Cumulative Reverse Exclusive Multiply
|
|
multipliers[prev_shape_size - 1] = Cast(new_shape_ptr.dtype, 1)
|
|
with T.serial(0, prev_shape_size - 1) as i_:
|
|
i = i_ + 1
|
|
multipliers[prev_shape_size - 1 - i] = (
|
|
prev_shape[prev_shape_size - i] * multipliers[prev_shape_size - i]
|
|
)
|
|
total_ele[0] *= prev_shape[prev_shape_size - i]
|
|
|
|
division_total_ele[0] = Cast(new_shape_ptr.dtype, 1)
|
|
with T.serial(0, new_shape_size) as i:
|
|
with T.If(new_shape[i] != -1):
|
|
with T.Then():
|
|
division_total_ele[0] *= new_shape[i]
|
|
|
|
# Compute true output shape (replace negative ones)
|
|
with T.serial(0, new_shape_size) as i:
|
|
with T.If(new_shape[i] == -1):
|
|
with T.Then():
|
|
out_new_shape[i] = Cast(
|
|
new_shape_ptr.dtype, div(total_ele[0], division_total_ele[0])
|
|
)
|
|
with T.Else():
|
|
out_new_shape[i] = new_shape[i]
|
|
|
|
# Check if prev_shape and new_shape are equal
|
|
equal_shape[0] = True
|
|
with T.If(prev_shape_size == new_shape_size):
|
|
with T.Then():
|
|
with T.serial(0, prev_shape_size) as i:
|
|
with T.If(prev_shape[i] != out_new_shape[i]):
|
|
with T.Then():
|
|
equal_shape[0] = False
|
|
with T.Else():
|
|
equal_shape[0] = False
|
|
|
|
# Return same inputs if shapes are equal
|
|
with T.If(equal_shape[0]):
|
|
with T.Then():
|
|
with T.parallel(0, sparse_indices_ptr.shape[0]) as i:
|
|
with T.serial(0, sparse_indices_ptr.shape[1]) as j:
|
|
new_sparse_indices[i, j] = sparse_indices[i, j]
|
|
|
|
# Else compute new_sparse_indices
|
|
with T.Else():
|
|
dividers[new_shape_size - 1] = Cast(new_shape_ptr.dtype, 1)
|
|
with T.serial(0, new_shape_size - 1) as i_:
|
|
i = i_ + 1
|
|
dividers[new_shape_size - 1 - i] = (
|
|
dividers[new_shape_size - i] * out_new_shape[new_shape_size - i]
|
|
)
|
|
|
|
with T.parallel(0, sparse_indices_ptr.shape[0]) as i:
|
|
flattened_indices[i] = Cast(new_shape_ptr.dtype, 0)
|
|
with T.serial(0, sparse_indices_ptr.shape[1]) as j:
|
|
flattened_indices[i] += sparse_indices[i, j] * multipliers[j]
|
|
|
|
with T.parallel(0, new_sparse_indices_ptr.shape[0]) as i:
|
|
current_element_buf = T.alloc_buffer(
|
|
[1], new_shape_ptr.dtype, scope="local"
|
|
)
|
|
current_element = T.buffer_proxy(current_element_buf)
|
|
current_element[0] = flattened_indices[i]
|
|
|
|
with T.serial(0, new_sparse_indices_ptr.shape[1]) as j:
|
|
new_sparse_indices[i, j] = Cast(
|
|
sparse_indices_ptr.dtype,
|
|
floordiv(current_element[0], dividers[j]),
|
|
)
|
|
current_element[0] = floormod(current_element[0], dividers[j])
|
|
|
|
return ib.get()
|
|
|
|
new_sparse_indices_buf = decl_buffer(
|
|
new_sparse_indices_shape, sparse_indices.dtype, "new_sparse_indices_buf"
|
|
)
|
|
new_shape_buf = decl_buffer(new_shape_shape, prev_shape.dtype, "new_shape_buf")
|
|
|
|
return extern(
|
|
[new_sparse_indices_shape, new_shape_shape],
|
|
[sparse_indices, prev_shape, new_shape],
|
|
lambda ins, outs: gen_ir(ins[0], ins[1], ins[2], outs[0], outs[1]),
|
|
out_buffers=[new_sparse_indices_buf, new_shape_buf],
|
|
name="sparse_reshape_cpu",
|
|
tag="sparse_reshape_cpu",
|
|
)
|