wehub-resource-sync
431 lines
19 KiB
Python
431 lines
19 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 TestMatmulSPMDRule(unittest.TestCase):
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def setUp(self):
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# After replaced all spmd rules by phi impl, we can recover the
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# api name to `get_spmd_rule`
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self.rule = core.get_phi_spmd_rule("matmul")
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self.attrs = OrderedDict([('trans_x', False), ('trans_y', False)])
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def test_matmul_infer_forward(self):
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# forward setup
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x_shape = [64, 32]
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y_shape = [32, 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.dims_mapping = [1, 0]
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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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y_tensor_dist_attr = TensorDistAttr()
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y_tensor_dist_attr.dims_mapping = [0, -1]
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y_tensor_dist_attr.process_mesh = process_mesh
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self.y_dist_tensor_spec = DistTensorSpec(y_shape, y_tensor_dist_attr)
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# TODO test partial: mk[1, 0],kn[0, -1] --> mk[1, 0],kn[0, -1] = nm[1, -1] partial[0]
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec, self.y_dist_tensor_spec, False, False
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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(len(result_dist_attrs), 2)
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self.assertEqual(len(inferred_input_dist_attrs), 2)
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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, 0])
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [0, -1])
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self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [1, -1])
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self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), True)
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self.assertEqual(inferred_output_dist_attrs[0]._partial_dims(), {0})
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# test row parallel: mk[1, -1],kn[-1, -1] --> mk[1, -1],kn[-1, -1] = nm[1, -1] partial[]
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self.x_dist_tensor_spec.set_dims_mapping([1, -1])
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self.y_dist_tensor_spec.set_dims_mapping([-1, -1])
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec, self.y_dist_tensor_spec, False, False
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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])
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [-1, -1])
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self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [1, -1])
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self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), False)
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# test row parallel: mk[1, -1],kn[-1, -1] --> mk[1, -1],kn[-1, -1] = nm[1, -1] partial[]
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self.x_dist_tensor_spec.set_dims_mapping([1, -1])
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self.y_dist_tensor_spec.set_dims_mapping([-1, -1])
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec, self.y_dist_tensor_spec, False, False
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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])
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [-1, -1])
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self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [1, -1])
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self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), False)
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# test n parallel: mk[-1, -1],kn[-1, 0] --> mk[-1, -1],kn[-1, 0] = nm[-1, 0] partial[]
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self.x_dist_tensor_spec.set_dims_mapping([-1, -1])
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self.y_dist_tensor_spec.set_dims_mapping([-1, 0])
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec, self.y_dist_tensor_spec, False, False
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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])
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [-1, 0])
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self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [-1, 0])
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self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), False)
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# test partial with propagation: mk[1, 0],kn[-1,-1] --> mk[1, 0],kn[0, -1] = nm[1, -1] partial[0]
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self.x_dist_tensor_spec.set_dims_mapping([1, 0])
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self.y_dist_tensor_spec.set_dims_mapping([-1, -1])
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec, self.y_dist_tensor_spec, False, False
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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, 0])
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [0, -1])
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self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [1, -1])
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self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), True)
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self.assertEqual(inferred_output_dist_attrs[0]._partial_dims(), {0})
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# mk[-1,-1],kn[1,0] --> mk[-1, 1],kn[1, 0] = nm[-1, 0] partial[1]:
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self.x_dist_tensor_spec.set_dims_mapping([-1, -1])
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self.y_dist_tensor_spec.set_dims_mapping([1, 0])
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec, self.y_dist_tensor_spec, False, False
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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])
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [1, 0])
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self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [-1, 0])
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self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), True)
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self.assertEqual(inferred_output_dist_attrs[0]._partial_dims(), {1})
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# abcmk[1, 0, -1, -1],kn[-1, -1] --> abcmk[1, 0, -1, -1],kn[-1, -1] = abcmn[1, 0, -1, -1] partial[]: done
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self.x_dist_tensor_spec.shape = [512, 48, 64, 32]
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self.x_dist_tensor_spec.set_dims_mapping([0, 1, -1, -1])
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self.y_dist_tensor_spec.set_dims_mapping([-1, -1])
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec, self.y_dist_tensor_spec, False, False
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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(
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inferred_input_dist_attrs[0].dims_mapping, [0, 1, -1, -1]
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)
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [-1, -1])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [0, 1, -1, -1]
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)
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self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), False)
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# abcmk[1, -1, -1, 0],kn[-1, -1] --> abcmk[1, -1, -1, 0],kn[0, -1] = abcmn[1,-1, -1, -1] partial[0]
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self.x_dist_tensor_spec.set_dims_mapping([1, -1, -1, 0])
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self.y_dist_tensor_spec.set_dims_mapping([-1, -1])
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec, self.y_dist_tensor_spec, False, False
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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(
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inferred_input_dist_attrs[0].dims_mapping, [1, -1, -1, 0]
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)
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [0, -1])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [1, -1, -1, -1]
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)
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self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), True)
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self.assertEqual(inferred_output_dist_attrs[0]._partial_dims(), {0})
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# trans_x = True, abcmk[1, -1, -1, 0], kn[-1, -1] --> abcmk[1, -1, -1, 0],kn[-1, -1] = abcmn[1, -1, 0, -1] partial[]
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self.x_dist_tensor_spec.shape = [512, 48, 32, 64]
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self.x_dist_tensor_spec.set_dims_mapping([1, -1, -1, 0])
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self.y_dist_tensor_spec.set_dims_mapping([-1, -1])
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec, self.y_dist_tensor_spec, True, False
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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(
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inferred_input_dist_attrs[0].dims_mapping, [1, -1, -1, 0]
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)
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [-1, -1])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [1, -1, 0, -1]
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)
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self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), False)
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# trans_y = True, abcmk[-1, -1, -1, -1], kn[1, 0] --> abcmk[-1, -1, -1, 0],kn[1, 0] = abcmn[-1, -1, -1, 1] partial[0]: done
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self.x_dist_tensor_spec.shape = [512, 48, 64, 32]
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self.y_dist_tensor_spec.shape = [48, 32]
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self.x_dist_tensor_spec.set_dims_mapping([-1, -1, -1, -1])
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self.y_dist_tensor_spec.set_dims_mapping([1, 0])
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec, self.y_dist_tensor_spec, False, True
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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(
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inferred_input_dist_attrs[0].dims_mapping, [-1, -1, -1, 0]
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)
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [1, 0])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [-1, -1, -1, 1]
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)
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self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), True)
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self.assertEqual(inferred_output_dist_attrs[0]._partial_dims(), {0})
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inferred_output_dist_attrs[0]._clean_partial_dims([0])
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self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), False)
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# trans_y = True, trans_x = True, abcmk[-1, -1, 0, 1], kn[1, 0] --> abcmk[-1, -1, 0, 1]],kn[-1, 0] = abcmn[-1, -1, 1, -1] partial[0]
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# multiple mesh dim shard same tensor axis
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self.x_dist_tensor_spec.shape = [512, 48, 32, 64]
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self.y_dist_tensor_spec.shape = [48, 32]
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self.x_dist_tensor_spec.set_dims_mapping([-1, -1, 0, 1])
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self.y_dist_tensor_spec.set_dims_mapping([1, 0])
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec, self.y_dist_tensor_spec, True, True
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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(
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inferred_input_dist_attrs[0].dims_mapping, [-1, -1, 0, 1]
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)
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [-1, 0])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [-1, -1, 1, -1]
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)
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self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), True)
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self.assertEqual(inferred_output_dist_attrs[0]._partial_dims(), {0})
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inferred_output_dist_attrs[0]._clean_partial_status()
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self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), False)
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# trans_y = True, trans_x = True, abcmk[-1, -1, 1, 0], kn[1, 0] --> error:
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# one tensor axis shard multiple mesh dim
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self.x_dist_tensor_spec.set_dims_mapping([-1, -1, 1, -1])
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self.y_dist_tensor_spec.set_dims_mapping([-1, 0])
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self.attrs['trans_x'] = True
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self.attrs['trans_y'] = True
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec,
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self.y_dist_tensor_spec,
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self.attrs['trans_x'],
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self.attrs['trans_y'],
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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(
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inferred_input_dist_attrs[0].multi_dims_mapping,
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[[], [], [1, 0], []],
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)
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self.assertEqual(
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inferred_input_dist_attrs[1].multi_dims_mapping, [[], [1, 0]]
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)
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self.assertEqual(
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inferred_output_dist_attrs[0].multi_dims_mapping, [[], [], [], []]
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)
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def test_matmul_infer_backward(self):
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# backward setup
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x_shape = [64, 32]
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y_shape = [32, 48]
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out_shape = [64, 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.dims_mapping = [-1, -1]
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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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y_tensor_dist_attr = TensorDistAttr()
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y_tensor_dist_attr.dims_mapping = [-1, -1]
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y_tensor_dist_attr.process_mesh = process_mesh
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self.y_dist_tensor_spec = DistTensorSpec(y_shape, y_tensor_dist_attr)
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out_tensor_dist_attr = TensorDistAttr()
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out_tensor_dist_attr.dims_mapping = [1, 0]
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out_tensor_dist_attr.process_mesh = process_mesh
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self.out_dist_tensor_spec = DistTensorSpec(
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out_shape, out_tensor_dist_attr
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)
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# mn[1, 0] --> mk[1, -1],kn[-1, 0]
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result_dist_attrs = self.rule.infer_backward(
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self.x_dist_tensor_spec,
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self.y_dist_tensor_spec,
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self.out_dist_tensor_spec,
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self.attrs['trans_x'],
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self.attrs['trans_y'],
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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(len(result_dist_attrs), 2)
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self.assertEqual(len(inferred_input_dist_attrs), 2)
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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])
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [-1, 0])
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self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [1, 0])
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self.assertEqual(inferred_input_dist_attrs[0]._is_partial(), False)
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self.assertEqual(inferred_input_dist_attrs[1]._is_partial(), False)
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self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), False)
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# test on broadcast axes propagation
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# abmn[1, 0, -1, -1] --> 1mk[-1, -1, -1], abkn[1, 0, -1, -1]
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self.out_dist_tensor_spec.shape = [512, 48, 64, 48]
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self.x_dist_tensor_spec.shape = [1, 64, 32]
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self.y_dist_tensor_spec.shape = [512, 48, 32, 48]
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self.x_dist_tensor_spec.set_dims_mapping(
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[0, -1, 1]
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) # dims mapping of input should not influence inferbackward
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self.y_dist_tensor_spec.set_dims_mapping(
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[
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-1,
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-1,
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1,
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0,
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]
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) # dims mapping of input should not influence inferbackward
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self.out_dist_tensor_spec.set_dims_mapping([1, 0, -1, -1])
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result_dist_attrs = self.rule.infer_backward(
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self.x_dist_tensor_spec,
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self.y_dist_tensor_spec,
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self.out_dist_tensor_spec,
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self.attrs['trans_x'],
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self.attrs['trans_y'],
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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(
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inferred_input_dist_attrs[0].dims_mapping, [-1, -1, -1]
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)
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self.assertEqual(
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inferred_input_dist_attrs[1].dims_mapping, [1, 0, -1, -1]
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)
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [1, 0, -1, -1]
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)
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# abmn[-1, 0, -1, 1] --> abmk[-1, 0, -1, -1], a1kn[-1, -1, -1, 1]
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self.out_dist_tensor_spec.shape = [512, 48, 64, 48]
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self.x_dist_tensor_spec.shape = [512, 48, 64, 32]
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self.y_dist_tensor_spec.shape = [512, 1, 32, 48]
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self.out_dist_tensor_spec.set_dims_mapping([-1, 0, -1, 1])
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result_dist_attrs = self.rule.infer_backward(
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self.x_dist_tensor_spec,
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|
self.y_dist_tensor_spec,
|
|
self.out_dist_tensor_spec,
|
|
self.attrs['trans_x'],
|
|
self.attrs['trans_y'],
|
|
)
|
|
|
|
inferred_input_dist_attrs = result_dist_attrs[0]
|
|
inferred_output_dist_attrs = result_dist_attrs[1]
|
|
|
|
self.assertEqual(
|
|
inferred_input_dist_attrs[0].dims_mapping, [-1, 0, -1, -1]
|
|
)
|
|
self.assertEqual(
|
|
inferred_input_dist_attrs[1].dims_mapping, [-1, -1, -1, 1]
|
|
)
|
|
self.assertEqual(
|
|
inferred_output_dist_attrs[0].dims_mapping, [-1, 0, -1, 1]
|
|
)
|
|
|
|
# trans_x = true, trans_y = true, abmn[-1, -1, 0, 1] --> abmk[-1, -1, -1, 0], a1kn[-1, -1, 1, -1]
|
|
self.out_dist_tensor_spec.shape = [512, 48, 64, 48]
|
|
self.x_dist_tensor_spec.shape = [512, 48, 32, 64]
|
|
self.y_dist_tensor_spec.shape = [512, 1, 48, 32]
|
|
self.out_dist_tensor_spec.set_dims_mapping([-1, -1, 0, 1])
|
|
self.attrs['trans_x'] = True
|
|
self.attrs['trans_y'] = True
|
|
|
|
result_dist_attrs = self.rule.infer_backward(
|
|
self.x_dist_tensor_spec,
|
|
self.y_dist_tensor_spec,
|
|
self.out_dist_tensor_spec,
|
|
self.attrs['trans_x'],
|
|
self.attrs['trans_y'],
|
|
)
|
|
|
|
inferred_input_dist_attrs = result_dist_attrs[0]
|
|
inferred_output_dist_attrs = result_dist_attrs[1]
|
|
|
|
self.assertEqual(
|
|
inferred_input_dist_attrs[0].dims_mapping, [-1, -1, -1, 0]
|
|
)
|
|
self.assertEqual(
|
|
inferred_input_dist_attrs[1].dims_mapping, [-1, -1, 1, -1]
|
|
)
|
|
self.assertEqual(
|
|
inferred_output_dist_attrs[0].dims_mapping, [-1, -1, 0, 1]
|
|
)
|
|
|
|
# # trans_x = true, trans_y = true, abmn[-1, 1, 0, 1] --> error:
|
|
# one mesh dim shard multiple tensor axes
|
|
self.out_dist_tensor_spec.set_dims_mapping([-1, 1, 0, 1])
|
|
with self.assertRaises(RuntimeError):
|
|
result_dist_attrs = self.rule.infer_backward(
|
|
self.x_dist_tensor_spec,
|
|
self.y_dist_tensor_spec,
|
|
self.out_dist_tensor_spec,
|
|
self.attrs['trans_x'],
|
|
self.attrs['trans_y'],
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|