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[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.

[Kernel] Support MoE Fp8 Checkpoints for Mixtral (Static Weights with Dynamic/Static Activations) (#4527)

Follow on to #4332 to enable FP8 checkpoint loading for Mixtral and supersedes #4436.

This PR enables the following checkpoint loading features for Mixtral:

Supports loading fp8 checkpoints for Mixtral, such as this "nm-testing/Mixtral-8x7B-Instruct-v0.1-FP8" test model
Supports static or dynamic activation quantization with static weight quantization (all per tensor)
Supports different scales for each expert weight
Supports Fp8 in QKV layer
Notes:

The Expert Gate/Router always runs at half / full precision for now.
If there are different weight scales between QKV layer (for separate QKV weights), they are re-quantized using layer.weight_scale.max() so we can have a single gemm for performance.