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https://github.com/PaddlePaddle/FastDeploy.git
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3a6058e445
* add stable ci * fix * update * fix * rename tests dir;fix stable ci bug * add timeout limit * update
89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
# 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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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_topk_select
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class Test(unittest.TestCase):
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def setUp(self):
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"""
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Initialize.
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"""
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paddle.seed(2024)
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print(paddle.device.cuda.get_device_properties())
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print(paddle.__git_commit__)
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self.batch_size = 1500
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self.num_experts = 128
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self.top_k = 8
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def moe_topk_select_ref(self, gate_out: paddle.Tensor, bias: paddle.Tensor, top_k: int, apply_norm_weight: bool):
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gate_out_after_softmax = paddle.nn.functional.softmax(gate_out, axis=-1)
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topk_weights_ref, topk_ids_ref = paddle.topk(gate_out_after_softmax, k=top_k, axis=-1)
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if bias is not None:
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gate_out_after_softmax_bias = gate_out_after_softmax + bias
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_, topk_ids_ref = paddle.topk(gate_out_after_softmax_bias, k=top_k, axis=-1)
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batch_indices = paddle.arange(gate_out.shape[0]).unsqueeze(-1).expand_as(topk_ids_ref)
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topk_weights_ref = gate_out_after_softmax.gather_nd(paddle.stack([batch_indices, topk_ids_ref], axis=-1))
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if apply_norm_weight:
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topk_weights_ref = topk_weights_ref / topk_weights_ref.sum(axis=-1, keepdim=True)
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return topk_ids_ref, topk_weights_ref
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def test_moe_topk_select(self):
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"""
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Check moe_topk_select.
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"""
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gate_out = paddle.rand([self.batch_size, self.num_experts], dtype="float32")
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gate_correction_bias = paddle.rand([1, self.num_experts], dtype="float32")
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gate_correction_bias = gate_correction_bias / 10.0
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for apply_norm_weight in [True, False]:
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for bias in [None, gate_correction_bias]:
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topk_ids_ref, topk_weights_ref = self.moe_topk_select_ref(
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gate_out, bias, self.top_k, apply_norm_weight
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)
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for fused in [True, False]:
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topk_ids, topk_weights = moe_topk_select(
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gate_out,
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bias,
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self.top_k,
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apply_norm_weight,
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fused,
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)
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np.testing.assert_allclose(
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topk_ids_ref,
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topk_ids,
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rtol=1e-05,
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atol=1e-05,
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)
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np.testing.assert_allclose(
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topk_weights_ref,
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topk_weights,
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rtol=1e-05,
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atol=1e-05,
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)
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if __name__ == "__main__":
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unittest.main()
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