wehub-resource-sync
147 lines
5.0 KiB
Python
147 lines
5.0 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 os
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import numpy as np
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import paddle
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import paddle.distributed as dist
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from paddle.distributed.auto_parallel.placement_type import (
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dims_mapping_to_placements,
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)
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class SemiAutoParallelTestBase:
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def __init__(self):
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self._dtype = os.getenv("dtype")
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self._backend = os.getenv("backend")
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self._seed = eval(os.getenv("seed"))
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self._mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
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def check_tensor_eq(self, a, b):
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if a is None:
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assert b is None
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return
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np1 = a.numpy()
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np2 = b.numpy()
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np.testing.assert_allclose(np1, np2, rtol=1e-05, verbose=True)
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def flatten(self, inputs, terminal_cond):
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"""
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inputs may be single tensor、tuple
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"""
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if terminal_cond(inputs):
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return [inputs], "i"
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assert isinstance(inputs, (tuple, list))
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flattened = []
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structure = []
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for i in range(len(inputs)):
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tmp, tmp_structure = self.flatten(inputs[i], terminal_cond)
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flattened.extend(tmp)
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structure.append(tmp_structure)
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if isinstance(inputs, tuple):
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structure = tuple(structure)
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return flattened, structure
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def unflatten(self, inputs, structure, offset=0):
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"""
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inputs may be single tensor
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"""
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assert isinstance(inputs, list)
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assert offset < len(inputs)
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if structure == "i":
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offset = offset + 1
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# return a list
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return inputs[offset - 1], offset
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assert isinstance(structure, (tuple, list))
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unflattened = []
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for i in range(len(structure)):
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tmp, offset = self.unflatten(inputs, structure[i], offset)
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unflattened.append(tmp)
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if isinstance(structure, tuple):
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unflattened = tuple(unflattened)
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return unflattened, offset
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def runfunc_and_check(
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self, inputs_shape, inputs_specs, op_func, with_backward, **kwargs
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):
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paddle.seed(self._seed)
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np.random.seed(self._seed)
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flat_inputs = []
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flat_dist_inputs = []
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def terminal_cond(x):
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return isinstance(x, list) and all(
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not isinstance(e, (list, tuple)) for e in x
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)
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flat_inputs_specs, inputs_structure = self.flatten(
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inputs_specs, terminal_cond
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)
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flat_inputs_shape, _ = self.flatten(inputs_shape, terminal_cond)
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assert len(flat_inputs_specs) == len(flat_inputs_shape)
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for shape, spec in zip(flat_inputs_shape, flat_inputs_specs):
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input_np = np.random.random(size=shape).astype(self._dtype)
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input = paddle.to_tensor(input_np)
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input.stop_gradient = not with_backward
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# retain dist_attr here.
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input_dist_attr = dist.DistAttr(
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mesh=self._mesh, sharding_specs=spec
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)
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# for dygraph auto_parallel, get placements by using to_placements
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placements = dims_mapping_to_placements(
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input_dist_attr.multi_dims_mapping, self._mesh
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)
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dist_input = dist.shard_tensor(input, self._mesh, placements)
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dist_input.stop_gradient = not with_backward
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flat_inputs.append(input)
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flat_dist_inputs.append(dist_input)
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inputs, _ = self.unflatten(flat_inputs, inputs_structure)
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dist_inputs, _ = self.unflatten(flat_dist_inputs, inputs_structure)
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def wrap_tuple(e):
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return e if isinstance(e, tuple) else (e,)
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op_inputs = wrap_tuple(inputs)
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op_dist_input = wrap_tuple(dist_inputs)
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out = op_func(*op_inputs, **kwargs)
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dist_out = op_func(*op_dist_input, **kwargs)
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if with_backward:
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def terminal_cond2(x):
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return not isinstance(x, (list, tuple))
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flat_out, _ = self.flatten(out, terminal_cond2)
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flat_dist_out, _ = self.flatten(dist_out, terminal_cond2)
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assert len(flat_out) == len(flat_dist_out)
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for output, dist_output in zip(flat_out, flat_dist_out):
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self.check_tensor_eq(output, dist_output)
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if output is not None:
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output.backward()
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dist_output.backward()
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for x, dist_x in zip(flat_inputs, flat_dist_inputs):
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self.check_tensor_eq(x.grad, dist_x.grad)
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return dist_inputs, dist_out
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