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97 lines
3.5 KiB
Python
97 lines
3.5 KiB
Python
"""
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# Copyright (c) 2025 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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"""
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import unittest
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import numpy as np
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import paddle
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from fastdeploy.model_executor.ops.gpu import moe_redundant_topk_select
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class TestMoERedundantTopKSelect(unittest.TestCase):
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def setUp(self):
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paddle.set_device("gpu")
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np.random.seed(42)
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def _run_and_check(
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self,
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gating_shape,
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expert_num,
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moe_topk,
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apply_norm_weight=False,
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enable_softmax_top_k_fused=False,
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use_bias=False,
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):
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"""Helper function to run the operator and check."""
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gating_logits = paddle.to_tensor(np.random.rand(*gating_shape).astype("float32"))
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expert_id_to_ep_rank_array = paddle.to_tensor(
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np.random.randint(0, expert_num, size=(expert_num,)).astype("int32")
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)
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expert_in_rank_num_list = paddle.to_tensor(np.random.randint(1, 4, size=(expert_num,)).astype("int32"))
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tokens_per_expert_stats_list = paddle.zeros([expert_num], dtype="int32")
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bias = None
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if use_bias:
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bias = paddle.to_tensor(np.random.rand(*gating_shape[:-1], expert_num).astype("float32"))
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outputs = moe_redundant_topk_select(
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gating_logits=gating_logits,
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expert_id_to_ep_rank_array=expert_id_to_ep_rank_array,
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expert_in_rank_num_list=expert_in_rank_num_list,
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tokens_per_expert_stats_list=tokens_per_expert_stats_list,
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bias=bias,
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moe_topk=moe_topk,
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apply_norm_weight=apply_norm_weight,
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enable_softmax_top_k_fused=enable_softmax_top_k_fused,
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redundant_ep_rank_num_plus_one=2,
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)
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topk_ids, topk_weights = outputs
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# Check shapes are correct
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expected_shape = [int(np.prod(gating_shape[:-1])), moe_topk]
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self.assertEqual(topk_ids.shape, expected_shape)
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self.assertEqual(topk_weights.shape, expected_shape)
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# Check topk_ids are non-negative
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self.assertTrue(np.all(topk_ids.numpy() >= 0))
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# Check topk weights are non-negative
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self.assertTrue(np.all(topk_weights.numpy() >= -1e-6))
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# Check tokens_per_expert_stats_list has valid values
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self.assertEqual(tokens_per_expert_stats_list.shape[0], expert_num)
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self.assertTrue(np.all(tokens_per_expert_stats_list.numpy() >= 0))
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def test_basic_case(self):
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self._run_and_check(gating_shape=(4, 16), expert_num=8, moe_topk=2)
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def test_3d_input_case(self):
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self._run_and_check(gating_shape=(2, 3, 8), expert_num=8, moe_topk=2)
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def test_with_bias(self):
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self._run_and_check(gating_shape=(3, 12), expert_num=4, moe_topk=2, use_bias=True)
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def test_with_norm_weight(self):
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self._run_and_check(gating_shape=(5, 10), expert_num=4, moe_topk=2, apply_norm_weight=True)
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def test_softmax_topk_fused(self):
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self._run_and_check(gating_shape=(6, 8), expert_num=8, moe_topk=2, enable_softmax_top_k_fused=True)
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if __name__ == "__main__":
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unittest.main()
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