wehub-resource-sync
198 lines
7.5 KiB
Python
198 lines
7.5 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# 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, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from collections import OrderedDict
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from paddle.distributed.auto_parallel.static.dist_attribute import (
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DistTensorSpec,
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TensorDistAttr,
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)
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from paddle.distributed.fleet import auto
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from paddle.framework import core
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class TestSoftmaxSPMDRule(unittest.TestCase):
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def setUp(self):
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self.rule1 = core.get_phi_spmd_rule("softmax")
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self.rule2 = core.get_phi_spmd_rule("log_softmax")
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x_shape = [8, 16, 48]
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process_mesh = auto.ProcessMesh(mesh=[[0, 1, 2, 3], [4, 5, 6, 7]])
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x_tensor_dist_attr = TensorDistAttr()
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x_tensor_dist_attr.process_mesh = process_mesh
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self.x_dist_tensor_spec = DistTensorSpec(x_shape, x_tensor_dist_attr)
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self.out_dist_tensor_spec = DistTensorSpec(self.x_dist_tensor_spec)
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self.attrs = OrderedDict([('axis', -1)])
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def test_softmax_infer_forward(self):
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# sharding on batch axis I
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self.x_dist_tensor_spec.set_dims_mapping([1, -1, -1])
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result_dist_attrs = self.rule1.infer_forward(
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self.x_dist_tensor_spec, self.attrs['axis']
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)
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self.assertEqual(len(result_dist_attrs), 2)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(len(inferred_input_dist_attrs), 1)
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self.assertEqual(len(inferred_output_dist_attrs), 1)
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [1, -1, -1])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [1, -1, -1]
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)
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# sharding on batch axis II
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self.x_dist_tensor_spec.set_dims_mapping([-1, 1, -1])
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result_dist_attrs = self.rule1.infer_forward(
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self.x_dist_tensor_spec, self.attrs['axis']
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [-1, 1, -1])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [-1, 1, -1]
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)
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# sharding on softmax_axis
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self.x_dist_tensor_spec.set_dims_mapping([1, -1, 0])
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result_dist_attrs = self.rule1.infer_forward(
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self.x_dist_tensor_spec, self.attrs['axis']
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [1, -1, -1])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [1, -1, -1]
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)
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# sharding on softmax_axis + axis = 1
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self.attrs = {
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'axis': 1,
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}
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self.x_dist_tensor_spec.set_dims_mapping([-1, 1, 0])
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result_dist_attrs = self.rule1.infer_forward(
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self.x_dist_tensor_spec, self.attrs['axis']
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [-1, -1, 0])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [-1, -1, 0]
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)
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# sharding on softmax_axis + axis = -2
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self.attrs = {
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'axis': -2,
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}
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self.x_dist_tensor_spec.set_dims_mapping([-1, 1, 0])
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result_dist_attrs = self.rule1.infer_forward(
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self.x_dist_tensor_spec, self.attrs['axis']
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [-1, -1, 0])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [-1, -1, 0]
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)
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def test_softmax_infer_backward(self):
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# sharding on batch axis I
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self.out_dist_tensor_spec.set_dims_mapping([1, -1, -1])
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result_dist_attrs = self.rule1.infer_backward(
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self.x_dist_tensor_spec,
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self.out_dist_tensor_spec,
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self.attrs['axis'],
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)
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self.assertEqual(len(result_dist_attrs), 2)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(len(inferred_input_dist_attrs), 1)
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self.assertEqual(len(inferred_output_dist_attrs), 1)
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [1, -1, -1])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [1, -1, -1]
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)
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# sharding on batch axis II
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self.out_dist_tensor_spec.set_dims_mapping([-1, 1, -1])
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result_dist_attrs = self.rule1.infer_backward(
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self.x_dist_tensor_spec,
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self.out_dist_tensor_spec,
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self.attrs['axis'],
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [-1, 1, -1])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [-1, 1, -1]
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)
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# sharding on softmax_axis
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self.out_dist_tensor_spec.set_dims_mapping([1, -1, 0])
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result_dist_attrs = self.rule1.infer_backward(
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self.x_dist_tensor_spec,
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self.out_dist_tensor_spec,
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self.attrs['axis'],
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [1, -1, -1])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [1, -1, -1]
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)
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# sharding on softmax_axis + axis = 1
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self.attrs = {
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'axis': 1,
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}
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self.out_dist_tensor_spec.set_dims_mapping([-1, 1, 0])
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result_dist_attrs = self.rule1.infer_backward(
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self.x_dist_tensor_spec,
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self.out_dist_tensor_spec,
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self.attrs['axis'],
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [-1, -1, 0])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [-1, -1, 0]
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)
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# sharding on softmax_axis + axis = -2
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self.attrs = {
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'axis': -2,
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}
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self.out_dist_tensor_spec.set_dims_mapping([-1, 1, 0])
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result_dist_attrs = self.rule1.infer_backward(
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self.x_dist_tensor_spec,
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self.out_dist_tensor_spec,
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self.attrs['axis'],
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [-1, -1, 0])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [-1, -1, 0]
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)
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if __name__ == "__main__":
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unittest.main()
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