mbrookhart commented on a change in pull request #7477:
URL: https://github.com/apache/tvm/pull/7477#discussion_r583167241
##########
File path: tests/python/relay/test_op_level3.py
##########
@@ -1311,6 +1311,232 @@ def verify_sparse_to_dense(sparse_indices,
sparse_values, default_value, output_
# verify_sparse_to_dense([[[[0, 1, 4], [0, 2, 4]]]], [[[[3.1, 3.1,
3.1]]]], 3.5, [5], [3.1, 3.1, 3.5, 3.5, 3.1])
[email protected]_gpu
[email protected](
+ "sparse_indices_np, sparse_values_np, prev_shape_np, new_shape_np",
+ [
+ (
+ np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 2, 3]],
dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([2, 3, 6], dtype=np.int64),
+ np.array([9, -1], dtype=np.int64),
+ ),
+ (
+ np.array(
+ [[0, 0, 0, 0], [0, 0, 1, 2], [0, 1, 0, 3], [1, 0, 0, 4], [1,
2, 3, 6]],
+ dtype=np.int64,
+ ),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([2, 3, 6, 7], dtype=np.int64),
+ np.array([9, -1, 7], dtype=np.int64),
+ ),
+ (
+ np.array(
+ [
+ [0, 0, 0, 0, 0],
+ [0, 0, 1, 2, 3],
+ [0, 1, 0, 3, 5],
+ [1, 0, 0, 4, 6],
+ [1, 2, 3, 6, 8],
+ ],
+ dtype=np.int64,
+ ),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([2, 3, 6, 7, 9], dtype=np.int64),
+ np.array([9, -1, 7], dtype=np.int64),
+ ),
+ (
+ np.array([[0, 0], [0, 1], [3, 4], [4, 3], [7, 3]], dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([9, 4], dtype=np.int64),
+ np.array([2, -1, 6], dtype=np.int64),
+ ),
+ (
+ np.array([[0, 0], [0, 1], [3, 4], [4, 3], [7, 3]], dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([9, 4], dtype=np.int64),
+ np.array([-1], dtype=np.int64),
+ ),
+ (
+ np.array([[0], [5], [10], [20], [24]], dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([25], dtype=np.int64),
+ np.array([5, 5], dtype=np.int64),
+ ),
+ (
+ np.array([[0, 100], [200, 100], [300, 400], [50, 20], [400, 50]],
dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([500, 20], dtype=np.int64),
+ np.array([500, 20], dtype=np.int64),
+ ),
+ (
+ np.array([[0, 100], [200, 100], [300, 400], [50, 20], [400, 50]],
dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([500, 20], dtype=np.int64),
+ np.array([500, -1], dtype=np.int64),
+ ),
+ (
+ np.array([[0, 100], [200, 100], [300, 400], [50, 20], [400, 50]],
dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([500, 20], dtype=np.int64),
+ np.array([250, 40], dtype=np.int64),
+ ),
+ (
+ np.ones((0, 1), dtype=np.int64),
+ np.array([], dtype=np.int64),
+ np.array([4], dtype=np.int64),
+ np.array([2, -1], dtype=np.int64),
+ ),
+ (
+ np.ones((0, 1), dtype=np.int64),
+ np.array([], dtype=np.int64),
+ np.array([4], dtype=np.int64),
+ np.array([2, 2], dtype=np.int64),
+ ),
+ (
+ np.ones((0, 2), dtype=np.int64),
+ np.array([], dtype=np.int64),
+ np.array([3, 6], dtype=np.int64),
+ np.array([-1, 2], dtype=np.int64),
+ ),
+ ],
+)
[email protected]("dtype", [np.int32, np.int64])
[email protected]("use_dyn", [True, False])
+def test_sparse_reshape(
+ sparse_indices_np, sparse_values_np, prev_shape_np, new_shape_np, dtype,
use_dyn
+):
+ def ref_sparse_reshape(
Review comment:
Probably not worth it to duplicate the tests between TF and topi? Maybe
run a subset?
##########
File path: python/tvm/topi/sparse_reshape.py
##########
@@ -0,0 +1,185 @@
+# 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 ..tir import decl_buffer, ir_builder, Cast
+from ..te import extern, div, floordiv, floormod
+
+
+def sparse_reshape(
+ sparse_indices,
+ prev_shape,
+ new_shape,
+ new_sparse_indices_shape,
+ new_shape_shape,
+):
+ """
+ Reshape a Sparse Tensor
+ Parameters
+ ----------
+ sparse_indices : relay.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 : relay.Expr
+ A 1-D tensor containing the previous shape of the dense tensor
+ new_shape : relay.Expr
+ A 1-D tensor containing the new shape of the dense tensor
+ Returns
+ -------
+ result: relay.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 = relay.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(
Review comment:
I'm wondering if it would help to parallelize some of the for loops on
CPU like we do on GPU? On CPU you can use the parallel tag in
ir_builder.for_range. Not sure if this is enough of a performance improvement
to justify it.
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