34eecacace
### What this PR does / why we need it? 1. Currently, eplb registers different attributes for different models, but these attributes are not actually used. Now, these attributes are directly deleted. 2. Add some log about eplb. ### Does this PR introduce _any_ user-facing change? ### How was this patch tested? #### Deepseek v3.1 chat Of course! Here is a comprehensive explanation of deep learning, broken down for clarity.\n\n### The Simple Analogy: A Child Learning to Recognize a Cat\n\nImagine teaching a child what a cat is. You don't give them a rulebook with instructions like \"has pointy ears, whiskers, and a tail.\" Instead, you show them many pictures, saying \"this is a cat\" or \"this is not a cat.\" The child's brain gradually learns to identify the complex patterns—the combination of shapes, colors, and textures—that define \"cat-ness.\"\n\n**Deep learning is essentially this, but for computers.** It's a method for teaching computers to learn from examples and recognize patterns directly from data (like images, sound, or text) without being explicitly programmed with rigid rules.\n\n---\n\n### The Technical Definition\n\n**Deep Learning is a subfield of machine learning, which itself is a subfield of artificial intelligence (AI).** It uses artificial **neural networks** with many layers (\"deep\" networks) to model and understand complex patterns in data.\n\nHere are the key concepts in that definition:\n\n1. **Artificial Intelligence (AI):** The broad science of making machines smart and capable of performing tasks that typically require human intelligence.\n2. **Machine Learning (ML):** A subset of AI that gives computers the ability to learn from data *without* being explicitly programmed for every single rule.\n3. **Deep Learning (DL):** A specific, powerful - vLLM version: v0.15.0 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0 Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. 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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# This file is a part of the vllm-ascend project.
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#
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# Todo: Once https://github.com/vllm-project/vllm/pull/23553 is merged in vllm. Remove this model register.
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import types
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import torch
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def get_expert_map(self, layer_id):
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return self.model.layers[layer_id].mlp.experts.expert_map
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def get_log2phy_map(self, layer_id):
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return self.model.layers[layer_id].mlp.experts.get_log2phy_map()
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def get_all_moe_loads(self):
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num_dense_layers = getattr(self.model.config, "first_k_dense_replace", 0)
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num_layers = self.model.config.num_hidden_layers
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all_moe_loads = torch.stack(
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[self.model.layers[layer_id].mlp.experts.moe_load for layer_id in range(num_dense_layers, num_layers)],
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dim=0,
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)
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return all_moe_loads
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def clear_all_moe_loads(self):
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num_dense_layers = getattr(self.model.config, "first_k_dense_replace", 0)
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num_layers = self.model.config.num_hidden_layers
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for layer_id in range(num_dense_layers, num_layers):
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self.model.layers[layer_id].mlp.experts.clear_moe_load()
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def model_register(model):
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model.get_expert_map = types.MethodType(get_expert_map, model)
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model.get_log2phy_map = types.MethodType(get_log2phy_map, model)
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model.get_all_moe_loads = types.MethodType(get_all_moe_loads, model)
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model.clear_all_moe_loads = types.MethodType(clear_all_moe_loads, model)
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