wehub-resource-sync
296 lines
12 KiB
Python
296 lines
12 KiB
Python
# Copyright (c) 2024 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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import paddle
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import paddle.distributed as dist
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from paddle.base.libpaddle.pir import apply_dist2dense_pass
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from paddle.distributed.auto_parallel.static.mix_to_dist_pass import (
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apply_mix2dist_pass,
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)
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BATCH_SIZE = 2
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SEQ_LEN = 4
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HIDDEN_SIZE = 8
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MP_SIZE = 2
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class TestBuildFakeProgram(unittest.TestCase):
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def test_build_with_shard_tensor(self):
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paddle.enable_static()
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with paddle.pir_utils.IrGuard():
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main_program = paddle.base.Program()
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with paddle.base.program_guard(main_program):
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mesh = dist.ProcessMesh([0, 1], dim_names=['mp'])
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input = paddle.static.data(
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name='input',
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shape=[BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE],
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)
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w0 = paddle.create_parameter(
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dtype="float32",
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shape=[HIDDEN_SIZE, HIDDEN_SIZE],
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name="w0",
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default_initializer=paddle.nn.initializer.Uniform(),
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)
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w1 = paddle.create_parameter(
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dtype="float32",
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shape=[HIDDEN_SIZE, HIDDEN_SIZE],
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name="w1",
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default_initializer=paddle.nn.initializer.Uniform(),
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)
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self.assertTrue(input.is_dense_tensor_type())
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self.assertTrue(w0.is_dense_tensor_type())
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dist_input = dist.shard_tensor(input, mesh, [dist.Replicate()])
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dist_w0 = dist.shard_tensor(w0, mesh, [dist.Shard(0)])
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dist_w1 = dist.shard_tensor(w1, mesh, [dist.Shard(1)])
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self.assertTrue(main_program.num_ops() == 6)
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self.assertFalse(input.use_empty())
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self.assertFalse(w0.use_empty())
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self.assertFalse(w1.use_empty())
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self.assertTrue(dist_input.use_empty())
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self.assertTrue(dist_w0.use_empty())
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self.assertTrue(dist_w1.use_empty())
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self.assertTrue(w0.is_dense_tensor_type())
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self.assertTrue(w1.is_dense_tensor_type())
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self.assertTrue(input.is_dense_tensor_type())
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# check dist type
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self.assertTrue(dist_input.is_dist_dense_tensor_type())
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self.assertTrue(dist_w0.is_dist_dense_tensor_type())
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self.assertTrue(dist_w1.is_dist_dense_tensor_type())
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# check global shape
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self.assertEqual(dist_input.shape, [BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE])
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self.assertEqual(dist_w0.shape, [HIDDEN_SIZE, HIDDEN_SIZE])
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self.assertEqual(dist_w1.shape, [HIDDEN_SIZE, HIDDEN_SIZE])
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# check local shape
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self.assertTrue(
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dist_input._local_shape == dist_input.shape
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) # replicated, local = global
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self.assertTrue(
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dist_w0._local_shape == [HIDDEN_SIZE // MP_SIZE, HIDDEN_SIZE]
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) # sharded, local != global, sharded by mesh size
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self.assertTrue(
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dist_w1._local_shape == [HIDDEN_SIZE, HIDDEN_SIZE // MP_SIZE]
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) # sharded, local != global, sharded by mesh size
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# check op dist_attr
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self.assertFalse(input.get_defining_op().has_attr("op_dist_attr"))
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self.assertFalse(w0.get_defining_op().has_attr("op_dist_attr"))
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self.assertFalse(w1.get_defining_op().has_attr("op_dist_attr"))
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dist_input_op_dist_attr = dist_input.get_defining_op().dist_attr
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# #check attrs
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self.assertEqual(dist_input_op_dist_attr.process_mesh, mesh)
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self.assertEqual(dist_input_op_dist_attr.num_operands(), 0)
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self.assertEqual(dist_input_op_dist_attr.num_results(), 1)
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dist_w0_op_dist_attr = dist_w0.get_defining_op().dist_attr
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self.assertEqual(dist_w0_op_dist_attr.process_mesh, mesh)
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self.assertEqual(dist_w0_op_dist_attr.num_operands(), 0)
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self.assertEqual(dist_w0_op_dist_attr.num_results(), 1)
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dist_w1_op_dist_attr = dist_w1.get_defining_op().dist_attr
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self.assertEqual(dist_w1_op_dist_attr.process_mesh, mesh)
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self.assertEqual(dist_w1_op_dist_attr.num_operands(), 0)
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self.assertEqual(dist_w1_op_dist_attr.num_results(), 1)
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attrs_op_dist_attr = (
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dist_input.get_defining_op().attrs().get("op_dist_attr")
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)
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self.assertEqual(attrs_op_dist_attr.process_mesh, mesh)
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# check op result dist_attr
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tensor_dist_attr = dist_input_op_dist_attr.result(
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0
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).as_tensor_dist_attr()
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self.assertEqual(tensor_dist_attr.process_mesh, mesh)
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self.assertEqual(
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tensor_dist_attr.dims_mapping,
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[-1, -1, -1],
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)
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tensor_dist_attr = dist_w0_op_dist_attr.result(0).as_tensor_dist_attr()
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self.assertEqual(tensor_dist_attr.process_mesh, mesh)
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self.assertEqual(tensor_dist_attr.dims_mapping, [0, -1])
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tensor_dist_attr = dist_w1_op_dist_attr.result(0).as_tensor_dist_attr()
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self.assertEqual(tensor_dist_attr.process_mesh, mesh)
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self.assertEqual(tensor_dist_attr.dims_mapping, [-1, 0])
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# check value dist_attr
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self.assertEqual(dist_input.dist_attr().process_mesh, mesh)
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self.assertEqual(dist_input.dist_attr().dims_mapping, [-1, -1, -1])
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self.assertEqual(dist_w0.dist_attr().process_mesh, mesh)
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self.assertEqual(dist_w0.dist_attr().dims_mapping, [0, -1])
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self.assertEqual(dist_w1.dist_attr().process_mesh, mesh)
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self.assertEqual(dist_w1.dist_attr().dims_mapping, [-1, 0])
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def test_build_with_apply_mix2dist_pass(self):
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paddle.enable_static()
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with paddle.pir_utils.IrGuard():
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main_program = paddle.base.Program()
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with paddle.base.program_guard(main_program):
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mesh = dist.ProcessMesh([0, 1], dim_names=['dp'])
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input1 = paddle.randint(low=0, high=1000, shape=[8, 4])
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output1 = dist.shard_tensor(input1, mesh, [dist.Shard(0)])
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input2 = paddle.randn([4, 8])
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output2 = dist.shard_tensor(input2, mesh, [dist.Shard(1)])
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self.assertTrue(input1.is_dense_tensor_type())
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self.assertTrue(input2.is_dense_tensor_type())
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self.assertTrue(main_program.num_ops() == 6)
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self.assertFalse(input1.use_empty())
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self.assertFalse(input2.use_empty())
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self.assertTrue(output1.use_empty())
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self.assertTrue(output2.use_empty())
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self.assertFalse(input1.get_defining_op().has_attr("op_dist_attr"))
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self.assertFalse(input2.get_defining_op().has_attr("op_dist_attr"))
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# check dist type
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self.assertTrue(output1.is_dist_dense_tensor_type())
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self.assertTrue(output2.is_dist_dense_tensor_type())
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# run apply_mix2dist_pass
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apply_mix2dist_pass(main_program)
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# after apply_mix2dist_pass, the program changed
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self.assertTrue(main_program.num_ops() == 4)
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self.assertTrue(input1.is_dist_dense_tensor_type())
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self.assertTrue(input2.is_dist_dense_tensor_type())
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self.assertTrue(input1.get_defining_op().has_attr("op_dist_attr"))
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self.assertTrue(input2.get_defining_op().has_attr("op_dist_attr"))
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# check op result dist_attr
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input1_op_dist_attr = input1.get_defining_op().dist_attr
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tensor_dist_attr = input1_op_dist_attr.result(0).as_tensor_dist_attr()
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self.assertEqual(tensor_dist_attr.process_mesh, mesh)
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self.assertEqual(tensor_dist_attr.dims_mapping, [0, -1])
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input2_op_dist_attr = input2.get_defining_op().dist_attr
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tensor_dist_attr = input2_op_dist_attr.result(0).as_tensor_dist_attr()
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self.assertEqual(tensor_dist_attr.process_mesh, mesh)
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self.assertEqual(tensor_dist_attr.dims_mapping, [-1, 0])
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# check value dist_attr
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self.assertEqual(input1.dist_attr().process_mesh, mesh)
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self.assertEqual(input1.dist_attr().dims_mapping, [0, -1])
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self.assertEqual(input2.dist_attr().process_mesh, mesh)
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self.assertEqual(input2.dist_attr().dims_mapping, [-1, 0])
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# check full_int_array op result dist_attr
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input1_shape = input1.get_defining_op().operand_source(0)
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input1_shape_op_dist_attr = input1_shape.get_defining_op().dist_attr
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tensor_dist_attr = input1_shape_op_dist_attr.result(
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0
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).as_tensor_dist_attr()
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self.assertEqual(tensor_dist_attr.process_mesh, mesh)
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self.assertEqual(tensor_dist_attr.dims_mapping, [-1])
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input2_shape = input2.get_defining_op().operand_source(0)
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input2_shape_op_dist_attr = input2_shape.get_defining_op().dist_attr
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tensor_dist_attr = input2_shape_op_dist_attr.result(
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0
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).as_tensor_dist_attr()
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self.assertEqual(tensor_dist_attr.process_mesh, mesh)
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self.assertEqual(tensor_dist_attr.dims_mapping, [-1])
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# check shape value dist_attr
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self.assertEqual(input1_shape.dist_attr().process_mesh, mesh)
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self.assertEqual(input1_shape.dist_attr().dims_mapping, [-1])
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self.assertEqual(input2_shape.dist_attr().process_mesh, mesh)
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self.assertEqual(input2_shape.dist_attr().dims_mapping, [-1])
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def test_build_with_apply_dist2dense_pass(self):
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paddle.enable_static()
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with paddle.pir_utils.IrGuard():
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main_program = paddle.base.Program()
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with paddle.base.program_guard(main_program):
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mesh = dist.ProcessMesh([0, 1], dim_names=['dp'])
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input1 = paddle.randint(low=0, high=1000, shape=[8, 4])
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output1 = dist.shard_tensor(input1, mesh, [dist.Shard(0)])
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input2 = paddle.randn([4, 8])
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output2 = dist.shard_tensor(input2, mesh, [dist.Shard(1)])
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self.assertTrue(input1.is_dense_tensor_type())
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self.assertTrue(input2.is_dense_tensor_type())
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self.assertTrue(main_program.num_ops() == 6)
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self.assertFalse(input1.use_empty())
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self.assertFalse(input2.use_empty())
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self.assertTrue(output1.use_empty())
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self.assertTrue(output2.use_empty())
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self.assertFalse(input1.get_defining_op().has_attr("op_dist_attr"))
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self.assertFalse(input2.get_defining_op().has_attr("op_dist_attr"))
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# check dist type
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self.assertTrue(output1.is_dist_dense_tensor_type())
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self.assertTrue(output2.is_dist_dense_tensor_type())
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# run apply_mix2dist_pass and apply_dist2dense_pass
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apply_mix2dist_pass(main_program)
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apply_dist2dense_pass(main_program)
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# after apply_mix2dist_pass, the program changed
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# and after apply_dist2dense_pass, the operator in program do not have dist_attr
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self.assertTrue(main_program.num_ops() == 4)
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self.assertTrue(input1.is_dense_tensor_type())
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self.assertTrue(input2.is_dense_tensor_type())
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self.assertFalse(input1.get_defining_op().has_attr("op_dist_attr"))
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self.assertFalse(input2.get_defining_op().has_attr("op_dist_attr"))
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# check shape attribute of full_int_array op
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input1_shape = input1.get_defining_op().operand_source(0)
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input1_shape_op = input1_shape.get_defining_op()
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self.assertFalse(input1_shape_op.has_attr("op_dist_attr"))
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input1_shape_op_attr = input1_shape_op.attrs()
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self.assertEqual(input1_shape_op_attr['value'], [4, 4])
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input2_shape = input2.get_defining_op().operand_source(0)
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input2_shape_op = input2_shape.get_defining_op()
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self.assertFalse(input2_shape_op.has_attr("op_dist_attr"))
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input2_shape_op_attr = input2_shape_op.attrs()
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self.assertEqual(input2_shape_op_attr['value'], [4, 4])
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# check shape of input1 and input2
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self.assertEqual(input1.shape, [4, 4])
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self.assertEqual(input2.shape, [4, 4])
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if __name__ == "__main__":
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unittest.main()
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