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Storing exception frame is extremely prone to circular refernece because it contains the reference to objects.

When tensorizer is not installed, it leaks llm instance because error frame has references to various modules which cause circular reference problem.

I also found spec decoding has a circular reference issue, and I solved it using weakref.proxy.

Fix the docs: https://docs.vllm.ai/en/latest/models/performance.html

Co-authored-by: sang <rkooo567@gmail.com>

[Core][Distributed] refactor pynccl to hold multiple communicators (#4591)

This PR improves the FP8 performance of linear layers, which had been lacking before (#4118 (comment) and #4118 (comment)).

We noticed that CUBLASLt can find a better algorithm if the first dimension of the matrix is greater than 16. So this PR enlarges matrices appropriately during quantization. This improves FP8 performance and removes the performance regression vs. FP16, in many cases exceeding FP16 performance.

Here are benchmarks on llama3 70b (ITL numbers for 1000 input and 50 output tokens at fixed qps and at TP 4), all FP8 measurements are for dynamic quantization:

qps = 1: 24 ms (FP8, this PR), 32 ms (FP8, previous main), 26 ms (FP16)
qps = 2: 26 ms (FP8, this PR), 34ms (FP8, previous main), 28 ms (FP16) 
qps = 4: 33 ms (FP8, this PR), 44 ms (FP8, previous main), 36 ms (FP16)
qps = 6: 46 ms (FP8, this PR), 56 ms (FP8, previous main), 54 ms (FP16)
qps = 8: 85 ms (FP8, this PR), 85 ms (FP8, previous main), 138 ms (FP16)

Co-authored-by: Dash Desai <1723932+iamontheinet@users.noreply.github.com>
Co-authored-by: Aurick Qiao <qiao@aurick.net>
Co-authored-by: Aurick Qiao <aurick.qiao@snowflake.com>
Co-authored-by: Aurick Qiao <aurickq@users.noreply.github.com>
Co-authored-by: Cody Yu <hao.yu.cody@gmail.com>

Co-authored-by: miloice <jeffaw99@hotmail.com>

Co-authored-by: Michael Goin <michael@neuralmagic.com>

Co-authored-by: Cade Daniel <edacih@gmail.com>

[Core][Distributed] support both cpu and device tensor in broadcast tensor dict (#4660)

Co-authored-by: Lei Wen <wenlei03@qiyi.com>
Co-authored-by: Cade Daniel <edacih@gmail.com>
Co-authored-by: Cody Yu <hao.yu.cody@gmail.com>

Previously FP8 static scaling works if the scales are overestimating the maxima of all activation tensors during computation. However this will not always be the case even if the scales were calibrated very carefully. For example, with the activations in my checkpoint

https://huggingface.co/pcmoritz/Mixtral-8x7B-v0.1-fp8-act-scale

(which was calibrated on https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k), I'm getting the following mostly random performance on MMLU:

|      Groups      |Version|Filter|n-shot|Metric|Value |   |Stderr|
|------------------|-------|------|-----:|------|-----:|---|-----:|
|mmlu              |N/A    |none  |     0|acc   |0.2295|±  |0.0035|
| - humanities     |N/A    |none  |     5|acc   |0.2421|±  |0.0062|
| - other          |N/A    |none  |     5|acc   |0.2398|±  |0.0076|
| - social_sciences|N/A    |none  |     5|acc   |0.2171|±  |0.0074|
| - stem           |N/A    |none  |     5|acc   |0.2125|±  |0.0073|
With the fix in this PR where the scaled activations are clamped between [-std::numeric_limits<c10::Float8_e4m3fn>::max(), std::numeric_limits<c10::Float8_e4m3fn>::max()] to make sure there are no NaNs, the performance is

|      Groups      |Version|Filter|n-shot|Metric|Value |   |Stderr|
|------------------|-------|------|-----:|------|-----:|---|-----:|
|mmlu              |N/A    |none  |     0|acc   |0.7008|±  |0.0036|
| - humanities     |N/A    |none  |     5|acc   |0.6453|±  |0.0065|
| - other          |N/A    |none  |     5|acc   |0.7692|±  |0.0072|
| - social_sciences|N/A    |none  |     5|acc   |0.8083|±  |0.0070|
| - stem           |N/A    |none  |     5|acc   |0.6115|±  |0.0083|
This is not perfect yet but is getting very close to the FP16 / dynamic activation scale performance.