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xc-llm-ascend/tests
LI SHENGYONG ff29e029de [EPLB][Bugfix] Bugfix for ineffective dynamic eplb (#6653)
### What this PR does / why we need it?
#6043 deleted the forward_before phase of the dynamic eplb. Currently,
the end-to-end precision is monitored in the UT, and the log is not
printed in the key place. As a result, the eplb does not take effect and
is not intercepted.
1. The forward_before function is added back.
2. Delete unnecessary logs and add key logs.
3. Warm-up of algorithm 3 is added.

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?


![Snipaste_2026-02-10_15-57-31](https://github.com/user-attachments/assets/03813e5f-3d19-42d8-8118-76223afe8298)

#### The conversation is normal.
Okay, the user is asking, \"What is deep learning?\" I need to explain
this in a clear and concise way. Let me start by recalling what I know
about deep learning. It's a subset of machine learning, right? So first,
I should mention that it's part of machine learning, which itself is a
branch of AI. Then, the key aspect of deep learning is the use of neural
networks with multiple layers. These are called deep neural
networks.\n\nWait, I should define neural networks first. Maybe start
with the basics. A neural network is inspired by the human brain, with
layers of nodes (neurons) that process data. But deep learning
specifically refers to networks with many layers—hence \"deep.\" So the
term \"deep\" comes from the number of layers. \n\nI should explain how
deep learning works. It involves training these networks on large
datasets, allowing them to automatically learn features from the data.
Unlike traditional machine learning, where you might have to manually
extract features, deep learning models can do this automatically. That's
a key point. For example, in image recognition, a deep learning model
can learn to detect edges, shapes, and then more complex patterns
without human intervention.\n\nApplications are important too. The user
might want to know where deep learning is used. Common examples include
image and speech recognition, natural language processing, autonomous
vehicles, and recommendation systems. Maybe mention specific
technologies like self-driving cars using computer vision or virtual
assistants like Siri or Alexa

- vLLM version: v0.15.0
- vLLM main:
13397841ab

Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
2026-02-24 14:43:04 +08:00
..