From 3ad12751cf17edb6e2f4fa7d5e3afe29b080a816 Mon Sep 17 00:00:00 2001 From: liam Date: Wed, 26 Feb 2025 00:17:02 +0800 Subject: [PATCH] :memo: update more detail and fix typo --- doc/en/benchmark.md | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/doc/en/benchmark.md b/doc/en/benchmark.md index c9e152f..599aef8 100644 --- a/doc/en/benchmark.md +++ b/doc/en/benchmark.md @@ -12,7 +12,7 @@ We set the argument `temperature=0.6`, and to simplify the test process, we skip Given that we have only tested 1,000 cases, which provides only a preliminary judgment, some fluctuations in the results are reasonable. We selected all datasets and shuffled them with a fixed random seed to ensure consistency. -## Some Detail +## Some Details - The bf16 model of DeepSeek-V3 is available [here](https://huggingface.co/opensourcerelease/DeepSeek-V3-bf16/tree/main) (you may convert it to gguf by llama.cpp). The q4km model can be found [here](https://huggingface.co/unsloth/DeepSeek-V3-GGUF/tree/main/DeepSeek-V3-Q4_K_M). @@ -43,14 +43,17 @@ Given that we have only tested 1,000 cases, which provides only a preliminary ju | HumanEval | tbd | tbd | tbd | tbd | tbd | tbd | tbd | | GSM8K | tbd | tbd | tbd | tbd | tbd | tbd | tbd | -**the yaml files used for each case are listed below**: +**The details for each case are listed below**: + +By default, The MLA kernel uses triton in linux and torch in windows. But we need to test torch in linux, so we manually modify the [file](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/operators/attention.py#L592). Just get rid of all the if branch and force it to use `self.forward_windows` + - MMLU test 1. [v3-chat_yaml](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml) change all the `KLinearMarlin` to `KLinearTorch` (just find all the usage in this file). The source weight comes from [there](https://huggingface.co/opensourcerelease/DeepSeek-V3-bf16) (you need to use llama.cpp to convert it to gguf) - 2. [v3-chat_yaml](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml). You need to modify the code to seperately load cpu's expert weight. We leave this as comment in these places: [1](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/operators/experts.py#L122), [2](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/operators/experts.py#L136), [3](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/operators/experts.py#L137) (note in 3, change the path to your local weight file path). The weight file for q8_0 is [here](https://huggingface.co/unsloth/DeepSeek-V3-GGUF/tree/main/DeepSeek-V3-Q8_0) - 3. [v3-chat_yaml](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml). You need to modify the code to seperately load cpu's expert weight. We leave this as comment in these places: [1](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/operators/experts.py#L122), [2](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/operators/experts.py#L136), [3](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/operators/experts.py#L137) (note in 3, change the path to your local weight file path). The weight file for q4km is [here](https://huggingface.co/unsloth/DeepSeek-V3-GGUF/tree/main/DeepSeek-V3-Q4_K_M) + 2. [v3-chat_yaml](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml). You need to modify the code to separately load cpu's expert weight. We leave this as comment in these places: [1](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/operators/experts.py#L122), [2](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/operators/experts.py#L136), [3](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/operators/experts.py#L137) (note in 3, change the path to your local weight file path). The weight file for q8_0 is [here](https://huggingface.co/unsloth/DeepSeek-V3-GGUF/tree/main/DeepSeek-V3-Q8_0) + 3. [v3-chat_yaml](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml). You need to modify the code to separately load cpu's expert weight. We leave this as comment in these places: [1](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/operators/experts.py#L122), [2](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/operators/experts.py#L136), [3](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/operators/experts.py#L137) (note in 3, change the path to your local weight file path). The weight file for q4km is [here](https://huggingface.co/unsloth/DeepSeek-V3-GGUF/tree/main/DeepSeek-V3-Q4_K_M) 4. [v3-chat_yaml](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml). You don't need to change the source code as they both use q4km. But note the yaml file [here](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml#L29) and [here](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml#L18), below these lines you need to add `num_bits: 8` (in other words: add this kwargs to all that use `KLinearMarlin`). The weight file for q4km is [here](https://huggingface.co/unsloth/DeepSeek-V3-GGUF/tree/main/DeepSeek-V3-Q4_K_M) 5. [v3-chat_yaml](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml). No need to change yaml, just use the default. The weight file for q4km is [here](https://huggingface.co/unsloth/DeepSeek-V3-GGUF/tree/main/DeepSeek-V3-Q4_K_M) 6. You should check the [doc](./fp8_kernel.md) to learn how to test this case. This is a mixture tensor case. - MMLU-pro test 1. You should check the [doc](./fp8_kernel.md) to learn how to test this case. This is a mixture tensor case. - 2. [v3-chat_yaml](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml). No need to change yaml, just use the default. The weight file for q4km is [here](https://huggingface.co/unsloth/DeepSeek-V3-GGUF/tree/main/DeepSeek-V3-Q4_K_M) \ No newline at end of file + 2. [v3-chat_yaml](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml). No need to change yaml, just use the default. The weight file for q4km is [here](https://huggingface.co/unsloth/DeepSeek-V3-GGUF/tree/main/DeepSeek-V3-Q4_K_M) \ No newline at end of file