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68 lines
2.3 KiB
Python
68 lines
2.3 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 numpy as np
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import paddle
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from fastdeploy.model_executor.ops.xpu import f_moe_topk_select
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np.random.seed(2025)
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token_num = 15
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expert_num = 256
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moe_topk = 8
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apply_norm_weight = True
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gating_logits = np.random.random([token_num, expert_num]).astype("float32")
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bias = np.random.random([expert_num]).astype("float32")
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def ref_moe_topk_select(gating_logits, bias, moe_topk, apply_norm_weight):
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assert apply_norm_weight is True
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def _softmax(x):
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axis = 1
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x_max = np.max(x, axis=axis, keepdims=True)
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e_x = np.exp(x - x_max)
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return e_x / np.sum(e_x, axis=axis, keepdims=True)
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softmax_logits = _softmax(gating_logits)
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softmax_logits_with_bias = np.copy(softmax_logits)
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if bias is not None:
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softmax_logits_with_bias += bias.reshape([1, -1])
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sorted_indices = np.argsort(softmax_logits_with_bias, axis=1, kind="stable")[:, ::-1]
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topk_ids = sorted_indices[:, :moe_topk]
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topk_weights = np.take_along_axis(softmax_logits, topk_ids, axis=1)
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topk_weights = topk_weights[:, :moe_topk]
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topk_weights /= np.sum(topk_weights, axis=1, keepdims=True)
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return topk_ids, topk_weights
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ref_topk_ids, ref_topk_weights = ref_moe_topk_select(gating_logits, bias, moe_topk, apply_norm_weight)
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gating_logits = paddle.to_tensor(gating_logits)
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if bias is not None:
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bias = paddle.to_tensor(bias)
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topk_ids, topk_weights = f_moe_topk_select(gating_logits, bias, moe_topk, apply_norm_weight)
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assert np.array_equal(
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topk_ids.numpy(), ref_topk_ids
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), f"\ntopk_ids:\n{topk_ids.numpy()}\nref_topk_ids:\n{ref_topk_ids}"
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assert np.allclose(
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topk_weights.numpy(), ref_topk_weights
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), f"\ntopk_weights:\n{topk_weights.numpy()}\nref_topk_weights:\n{ref_topk_weights}"
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print("Passed all tests.")
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