mirror of
https://github.com/RYDE-WORK/ktransformers.git
synced 2026-01-26 16:43:34 +08:00
Merge pull request #122 from kvcache-ai/feat-DeepSeekV3
[Feat] add support to DeepSeekV3
This commit is contained in:
UnicornChan
GitHub
commit
7527619f53
4
.github/workflows/package_wheel_release.yml
vendored
4
.github/workflows/package_wheel_release.yml
vendored
@ -142,11 +142,11 @@ jobs:
|
||||
|
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- name: Setup Mamba
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if: matrix.cuda != ''
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uses: conda-incubator/setup-miniconda@v2.3.0
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uses: conda-incubator/setup-miniconda@v3
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with:
|
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activate-environment: "ktransformers"
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python-version: ${{ matrix.pyver }}
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miniforge-variant: Mambaforge
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miniforge-variant: Miniforge3
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miniforge-version: latest
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use-mamba: true
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add-pip-as-python-dependency: true
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|
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4
.github/workflows/package_wheel_test.yml
vendored
4
.github/workflows/package_wheel_test.yml
vendored
@ -54,11 +54,11 @@ jobs:
|
||||
|
||||
- name: Setup Mamba
|
||||
if: matrix.cuda != ''
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uses: conda-incubator/setup-miniconda@v2.3.0
|
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uses: conda-incubator/setup-miniconda@v3
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with:
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activate-environment: "ktransformers"
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python-version: ${{ matrix.pyver }}
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miniforge-variant: Mambaforge
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miniforge-variant: Miniforge3
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miniforge-version: latest
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use-mamba: true
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add-pip-as-python-dependency: true
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5
.gitignore
vendored
5
.gitignore
vendored
@ -18,4 +18,7 @@ compile_commands.json
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ktransformers/server/local_store/
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ktransformers/server_test1.db
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*.patch
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img/
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img/
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tmp1.txt
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test_65_300_1536.txt
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test.txt
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2
Makefile
2
Makefile
@ -17,5 +17,5 @@ dev_install:
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pip install -r requirements-local_chat.txt
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echo "Installing ktransformers"
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KTRANSFORMERS_FORCE_BUILD=TRUE pip install -e . --no-build-isolation
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KTRANSFORMERS_FORCE_BUILD=TRUE pip install -e . -v --no-build-isolation
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echo "Installation completed successfully"
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63
README.md
63
README.md
@ -23,6 +23,7 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin
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<h2 id="Updates">🔥 Updates</h2>
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* **Fed 10, 2025**: Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~64x speedup. The detailed tutorial is [here](./doc/en/DeepseekR1_V3_tutorial.md)
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* **Aug 28, 2024**: Support 1M context under the InternLM2.5-7B-Chat-1M model, utilizing 24GB of VRAM and 150GB of DRAM. The detailed tutorial is [here](./doc/en/long_context_tutorial.md).
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* **Aug 28, 2024**: Decrease DeepseekV2's required VRAM from 21G to 11G.
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* **Aug 15, 2024**: Update detailed [TUTORIAL](doc/en/injection_tutorial.md) for injection and multi-GPU.
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@ -30,7 +31,44 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin
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* **Aug 12, 2024**: Support multiple GPU; Support new model: mixtral 8\*7B and 8\*22B; Support q2k, q3k, q5k dequant on gpu.
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* **Aug 9, 2024**: Support windows native.
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<h2 id="show-cases">🔥 Show Cases</h2>
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<h2 id="show-cases">🌟 Show Cases</h2>
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<div>
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<h3>GPT-4/o1-level Local VSCode Copilot on a Desktop with only 24GB VRAM</h3>
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</div>
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https://github.com/user-attachments/assets/ebd70bfa-b2c1-4abb-ae3b-296ed38aa285
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</p>
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- **[NEW!!!] Local 671B DeepSeek-Coder-V3/R1:** Running its Q4_K_M version using only 14GB VRAM and 382GB DRAM.
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- Prefill Speed (tokens/s):
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- KTransfermor: 54.21 (32 cores) → 74.362 (dual-socket, 2×32 cores) → 255.26 (optimized AMX-based MoE kernel, V0.3 only) → 286.55 (selectively using 6 experts, V0.3 only)
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- Compared to 4.51 tokens/s in llama.cpp with 2×32 cores, achieving up to **63.53× speedup**.
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- Decode Speed (tokens/s):
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- KTransfermor: 8.73 (32 cores) → 11.26 (dual-socket, 2×32 cores) → 13.69 (selectively using 6 experts, V0.3 only)
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- Compared to 4.51 tokens/s in llama.cpp with 2×32 cores, achieving up to **3.03× speedup**.
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- Upcoming Open Source Release:
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- AMX optimizations and selective expert activation will be open-sourced in V0.3.
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- Currently available only in preview binary distribution, which can be downloaded [here](https://github.com/kvcache-ai/ktransformers/releases/download/v0.1.4/ktransformers-0.3.0rc0+cu126torch26fancy-cp311-cp311-linux_x86_64.whl).
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- **Local 236B DeepSeek-Coder-V2:** Running its Q4_K_M version using only 21GB VRAM and 136GB DRAM, attainable on a local desktop machine, which scores even better than GPT4-0613 in [BigCodeBench](https://huggingface.co/blog/leaderboard-bigcodebench).
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<p align="center">
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<picture>
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<img alt="DeepSeek-Coder-V2 Score" src="https://github.com/user-attachments/assets/d052924e-8631-44de-aad2-97c54b965693" width=100%>
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</picture>
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</p>
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- **Faster Speed:** Achieving 126 tokens/s for 2K prompt prefill and 13.6 tokens/s for generation through MoE offloading and injecting advanced kernels from [Llamafile](https://github.com/Mozilla-Ocho/llamafile/tree/main) and [Marlin](https://github.com/IST-DASLab/marlin).
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- **VSCode Integration:** Wrapped into an OpenAI and Ollama compatible API for seamless integration as a backend for [Tabby](https://github.com/TabbyML/tabby) and various other frontends.
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<p align="center">
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|
||||
https://github.com/user-attachments/assets/4c6a8a38-05aa-497d-8eb1-3a5b3918429c
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</p>
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<h3>1M Context Local Inference on a Desktop with Only 24GB VRAM</h3>
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<p align="center">
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@ -54,30 +92,7 @@ https://github.com/user-attachments/assets/a865e5e4-bca3-401e-94b8-af3c080e6c12
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* **Flexible Sparse Attention Framework**: Offers a flexible block sparse attention framework for CPU offloaded decoding. Compatible with SnapKV, Quest, and InfLLm. Further information is available [here](./doc/en/long_context_introduction.md).
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<div>
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<h3>GPT-4-level Local VSCode Copilot on a Desktop with only 24GB VRAM</h3>
|
||||
</div>
|
||||
|
||||
https://github.com/user-attachments/assets/0b9fa2da-66f0-48eb-b4b9-f0e1f06f8927
|
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|
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</p>
|
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|
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- **Local 236B DeepSeek-Coder-V2:** Running its Q4_K_M version using only 21GB VRAM and 136GB DRAM, attainable on a local desktop machine, which scores even better than GPT4-0613 in [BigCodeBench](https://huggingface.co/blog/leaderboard-bigcodebench).
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|
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<p align="center">
|
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<picture>
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<img alt="DeepSeek-Coder-V2 Score" src="https://github.com/user-attachments/assets/d052924e-8631-44de-aad2-97c54b965693" width=100%>
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</picture>
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</p>
|
||||
|
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- **Faster Speed:** Achieving 126 tokens/s for 2K prompt prefill and 13.6 tokens/s for generation through MoE offloading and injecting advanced kernels from [Llamafile](https://github.com/Mozilla-Ocho/llamafile/tree/main) and [Marlin](https://github.com/IST-DASLab/marlin).
|
||||
- **VSCode Integration:** Wrapped into an OpenAI and Ollama compatible API for seamless integration as a backend for [Tabby](https://github.com/TabbyML/tabby) and various other frontends.
|
||||
|
||||
<p align="center">
|
||||
|
||||
https://github.com/user-attachments/assets/4c6a8a38-05aa-497d-8eb1-3a5b3918429c
|
||||
|
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</p>
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<strong>More advanced features will coming soon, so stay tuned!</strong>
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139
doc/en/DeepseekR1_V3_tutorial.md
Normal file
139
doc/en/DeepseekR1_V3_tutorial.md
Normal file
@ -0,0 +1,139 @@
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# GPT-4/o1-level Local VSCode Copilot on a Desktop with only 24GB VRAM
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# SUMMARY
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> **Fed 10, 2025**: Support DeepseekR1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~64x speedup.<br>
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Hi, we're the KTransformers team (formerly known for our local CPU/GPU hybrid inference open source project with DeepSeek-V2).
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We've heard your requests for DeepSeek-R1/V3 support—and we're excited to finally deliver!
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Apologies for the wait, but we've been cooking up something truly amazing!
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Today, we're proud to announce that we not only support DeepSeek-R1/V3, as showcased in the video below:
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|
||||
https://github.com/user-attachments/assets/ebd70bfa-b2c1-4abb-ae3b-296ed38aa285
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||||
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</p>
|
||||
|
||||
- **[NEW!!!] Local 671B DeepSeek-Coder-V3/R1:** Running its Q4_K_M version using only 14GB VRAM and 382GB DRAM.
|
||||
- Prefill Speed (tokens/s):
|
||||
- KTransfermor: 54.21 (32 cores) → 74.362 (dual-socket, 2×32 cores) → 255.26 (optimized AMX-based MoE kernel, V0.3 only) → 286.55 (selectively using 6 experts, V0.3 only)
|
||||
- Compared to 4.51 tokens/s in llama.cpp with 2×32 cores, achieving up to **63.53× speedup**.
|
||||
- Decode Speed (tokens/s):
|
||||
- KTransfermor: 8.73 (32 cores) → 11.26 (dual-socket, 2×32 cores) → 13.69 (selectively using 6 experts, V0.3 only)
|
||||
- Compared to 4.51 tokens/s in llama.cpp with 2×32 cores, achieving up to **3.03× speedup**.
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We also give our upcoming optimizations previews, including an Intel AMX-accelerated kernel and a selective expert activation method, which will significantly enhance performance. With V0.3-preview, we achieve up to 286 tokens/s for prefill, making it up to **64× faster than llama.cpp** for local inference.
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The binary distribution is available now and the source code will come ASAP! Check out the wheel package [here](https://github.com/kvcache-ai/ktransformers/releases/download/v0.1.4/ktransformers-0.3.0rc0+cu126torch26fancy-cp311-cp311-linux_x86_64.whl)
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## Prerequisites
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We run our best performance tests (V0.2) on <br>
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CPU: Intel (R) Xeon (R) Gold 6454S 1T DRAM (2 NUMA nodes) <br>
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GPU: 4090D 24G VRAM <br>
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## Bench Result
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### V0.2
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#### Settings
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- Model: DeepseekV3-q4km (int4)<br>
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- CPU: cpu_model_name: Intel (R) Xeon (R) Gold 6454S, 32 cores per socket, 2 sockets, 2 numa nodes
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- GPU: 4090D 24G VRAM
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- We test after enough warm up
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#### Memory consumption:
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- Single socket: 382G DRAM, at least 14GB VRAM
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- Dual socket: 1T DRAM, at least 14GB VRAM
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#### Benchmark Results
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"6 experts" case is part of V0.3's preview
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| Prompt<br>(500 tokens) | Dual socket Ktrans (6 experts) | Dual socket Ktrans (8 experts) | Single socket Ktrans (6 experts) | Single socket Ktrans (8 experts)| llama.cpp (8 experts) |
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| --- | --- | --- | --- | --- | --- |
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| Prefill token/s | 97.32 | 82.94 | 65.14 | 54.21 | 10.31 |
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| Decode token/s | 13.69 | 12.208 | 10.303 | 8.73 |4.51 |
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**The highest speedup reaches up to <u>3.03x</u> in decoding and <u>9.44x</u> in prefill.**
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### V0.3-Preview
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#### Settings
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- Model: DeepseekV3-BF16 (online quant into int8 for CPU and int4 for GPU)
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- CPU: cpu_model_name: Intel (R) Xeon (R) Gold 6454S, 32 cores per socket, 2 socket, 2 numa nodes
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- GPU: (1~4)x 4090D 24GVRAM (requires more VRAM for longer prompt)
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#### Memory consumptions:
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- 644GB DRAM, at least 14GB VRAM
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#### Benchmark results
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| Prompt length | 1K | 2K | 4K | 8K |
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|---------------|-----|-----|-----|-----|
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| KTrans (8 experts) Prefill token/s | 185.96 | 255.26 | 252.58 | 195.62 |
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| KTrans (6 experts) Prefill token/s | 203.70 | 286.55 | 271.08 | 207.20 |
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**The prefill of KTrans V0.3 is up to <u>3.45x</u> times faster than KTrans V0.2, and is up to <u>63.53x</u> times faster than llama.cpp.**
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**The decoding speed is the same as KTrans V0.2 (6 experts version) so it is omitted**
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The main acceleration comes from
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- Intel AMX instruction set and our specially designed cache friendly memory layout
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- Expert selection strategy that selects fewer experts based on offline profile results of out of domain data
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*From our research on DeepSeekV2, DeepSeekV3 and DeepSeekR1,
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when we slightly decrease the activation experts num in inference,
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the output quality doesn't change. But the speed of decoding and prefill
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is speed up which is inspiring. So our showcase makes use of this finding*
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## How to Run
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### V0.2 Showcase
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#### Single socket version (32 cores)
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Our local_chat test command is:
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``` shell
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git clone https://github.com/kvcache-ai/ktransformers.git
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cd ktransformers
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numactl -N 1 -m 1 python ./ktransformers/local_chat.py --model_path <your model path> --gguf_path <your gguf path> --prompt_file <your prompt txt file> --cpu_infer 33 --cache_lens 1536
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<when you see chat, then press enter to load the text prompt_file>
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```
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\<your model path\> can be local or set from online hugging face like deepseek-ai/DeepSeek-V3. If online encounters connection problem, try use mirror (hf-mirror.com) <br>
|
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\<your gguf path\> can also be online, but as its large we recommend you download it and quantize the model to what you want <br>
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The command numactl -N 1 -m 1 aims to advoid data transfer between numa nodes
|
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#### Dual socket version (64 cores)
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||||
Make suer before you install (use install.sh or `make dev_install`), setting the env var `USE_NUMA=1` by `export USE_NUMA=1` (if already installed, reinstall it with this env var set) <br>
|
||||
Our local_chat test command is:
|
||||
``` shell
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git clone https://github.com/kvcache-ai/ktransformers.git
|
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cd ktransformers
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export USE_NUMA=1
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make dev_install # or sh ./install.sh
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python ./ktransformers/local_chat.py --model_path <your model path> --gguf_path <your gguf path> --prompt_file <your prompt txt file> --cpu_infer 65 --cache_lens 1536
|
||||
<when you see chat, then press enter to load the text prompt_file>
|
||||
```
|
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The parameters' meaning is the same. But As we use dual socket, we set cpu_infer to 65
|
||||
|
||||
### V0.3 Showcase
|
||||
#### Dual socket version (64 cores)
|
||||
Our local_chat test command is:
|
||||
``` shell
|
||||
wget https://github.com/kvcache-ai/ktransformers/releases/download/v0.1.4/ktransformers-0.3.0rc0+cu126torch26fancy-cp311-cp311-linux_x86_64.whl
|
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pip install ./ktransformers-0.3.0rc0+cu126torch26fancy-cp311-cp311-linux_x86_64.whl
|
||||
python -m ktransformers.local_chat --model_path <your model path> --gguf_path <your gguf path> --prompt_file <your prompt txt file> --cpu_infer 65 --cache_lens 1536
|
||||
<when you see chat, then press enter to load the text prompt_file>
|
||||
```
|
||||
The parameters' meaning is the same with V0.2. But As we use dual socket, we set cpu_infer to 65
|
||||
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## Some Explanations
|
||||
1. Also we want to make further use of our two NUMA nodes on Xeon Gold cpu.
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To avoid the cost of data transfer between nodes, we "copy" the critical matrix on
|
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both nodes which takes more memory consumption but accelerates the prefill and decoding process.
|
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But this method takes huge memory and slow when loading weights, So be patient when loading
|
||||
and monitor the memory usage. We are going to optimize this huge memory overhead. Stay tuned~ <br>
|
||||
2. The command args `--cpu_infer 65` specifies how many cores to use (it's ok that it exceeds the physical number,
|
||||
but it's not the more the better. Adjust it slightly lower to your actual number of cores)<br>
|
||||
|
||||
3. Why CPU/GPU Hybrid Inference?
|
||||
DeepSeek's MLA operators are highly computationally intensive. While running everything on CPU is possible, offloading the heavy computations to the GPU results in a massive performance boost.
|
||||
|
||||
4. Where Does the Speedup Come From?
|
||||
|
||||
- Expert Offload: Unlike traditional layer-based or KVCache offloading (as seen in llama.cpp), we offload the expert computation to the CPU and MLA/KVCache to GPU, aligning perfectly with DeepSeek’s architecture for optimal efficiency.
|
||||
- Intel AMX Optimization – Our AMX-accelerated kernel is meticulously tuned, running several times faster than existing llama.cpp implementations. We plan to open-source this kernel after cleansing and are considering upstream contributions to llama.cpp.
|
||||
|
||||
5. Why Intel CPUs?
|
||||
Intel is currently the only CPU vendor that supports AMX-like instructions, which delivers significantly better performance compared to AVX-only alternatives.
|
||||
@ -5,7 +5,7 @@ Description :
|
||||
Author : kkk1nak0
|
||||
Date : 2024-08-15 07:34:46
|
||||
Version : 1.0.0
|
||||
LastEditors : Azure-Tang
|
||||
LastEditTime : 2024-08-29 22:35:51
|
||||
LastEditors : unicornchan
|
||||
LastEditTime : 2025-02-10 00:59:53
|
||||
'''
|
||||
__version__ = "0.1.4"
|
||||
__version__ = "0.2.0"
|
||||
@ -54,4 +54,4 @@ long_context:
|
||||
token_step:
|
||||
|
||||
local_chat:
|
||||
prompt_file: "./ktransformers/p.txt"
|
||||
prompt_file: ""
|
||||
@ -230,3 +230,24 @@ elseif(UNIX)
|
||||
endif()
|
||||
target_link_libraries(${PROJECT_NAME} PRIVATE "$ENV{CUDA_HOME}/lib64/libcudart.so")
|
||||
endif()
|
||||
|
||||
# Define the USE_NUMA option
|
||||
option(USE_NUMA "Disable NUMA support" OFF)
|
||||
# Check if the USE_NUMA environment variable is set
|
||||
if(DEFINED ENV{USE_NUMA})
|
||||
set(USE_NUMA ON)
|
||||
endif()
|
||||
if (USE_NUMA)
|
||||
message(STATUS "NUMA support is enabled")
|
||||
else()
|
||||
message(STATUS "NUMA support is disabled")
|
||||
endif()
|
||||
|
||||
find_library(NUMA_LIBRARY NAMES numa)
|
||||
if (NUMA_LIBRARY AND USE_NUMA)
|
||||
message(STATUS "NUMA library found: ${NUMA_LIBRARY} - enabling NUMA support")
|
||||
target_link_libraries(${PROJECT_NAME} PRIVATE ${NUMA_LIBRARY})
|
||||
target_compile_definitions(${PROJECT_NAME} PRIVATE USE_NUMA)
|
||||
else()
|
||||
message(STATUS "NUMA library not found or user not set USE_NUMA - disabling NUMA support")
|
||||
endif()
|
||||
|
||||
@ -10,6 +10,13 @@
|
||||
|
||||
#include "backend.h"
|
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|
||||
#ifdef USE_NUMA
|
||||
#include <numa.h>
|
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#include <numaif.h>
|
||||
|
||||
thread_local int Backend::numa_node = -1;
|
||||
#endif
|
||||
|
||||
thread_local int Backend::thread_local_id = -1;
|
||||
|
||||
Backend::Backend(int max_thread_num) {
|
||||
@ -74,6 +81,16 @@ void Backend::do_work_stealing_job(int task_num,
|
||||
}
|
||||
|
||||
void Backend::process_tasks(int thread_id) {
|
||||
|
||||
#ifdef USE_NUMA
|
||||
if(numa_node == -1){
|
||||
numa_node = thread_id * numa_num_configured_nodes() / thread_num_;
|
||||
struct bitmask* mask = numa_bitmask_alloc(numa_num_configured_nodes());
|
||||
numa_bitmask_setbit(mask, numa_node);
|
||||
numa_bind(mask);
|
||||
}
|
||||
#endif
|
||||
|
||||
if (init_func_ != nullptr) {
|
||||
init_func_(thread_id);
|
||||
}
|
||||
|
||||
@ -38,6 +38,9 @@ class Backend {
|
||||
void do_work_stealing_job(int, std::function<void(int)>,
|
||||
std::function<void(int)>,
|
||||
std::function<void(int)>);
|
||||
#ifdef USE_NUMA
|
||||
static thread_local int numa_node;
|
||||
#endif
|
||||
static thread_local int thread_local_id;
|
||||
|
||||
private:
|
||||
|
||||
@ -11,11 +11,41 @@
|
||||
#include <iostream>
|
||||
#include <cstdint>
|
||||
|
||||
#ifdef USE_NUMA
|
||||
#include <numa.h>
|
||||
#include <numaif.h>
|
||||
#endif
|
||||
|
||||
MOE::MOE(MOEConfig config) {
|
||||
config_ = config;
|
||||
gate_proj_ = config_.gate_proj;
|
||||
up_proj_ = config_.up_proj;
|
||||
down_proj_ = config_.down_proj;
|
||||
|
||||
#ifdef USE_NUMA
|
||||
int numa_nodes = numa_num_configured_nodes();
|
||||
gate_proj_numa_.resize(numa_nodes);
|
||||
up_proj_numa_.resize(numa_nodes);
|
||||
down_proj_numa_.resize(numa_nodes);
|
||||
size_t exp_inter_hidden_mul_ = (size_t)config.expert_num * config.intermediate_size * config.hidden_size;
|
||||
for (int i = 0; i < numa_nodes; i++) {
|
||||
gate_proj_numa_[i] = numa_alloc_onnode(exp_inter_hidden_mul_* ggml_type_size(config.gate_type) / ggml_blck_size(config.gate_type), i);
|
||||
up_proj_numa_[i] = numa_alloc_onnode(exp_inter_hidden_mul_* ggml_type_size(config.up_type) / ggml_blck_size(config.up_type), i);
|
||||
down_proj_numa_[i] = numa_alloc_onnode(exp_inter_hidden_mul_* ggml_type_size(config.down_type) / ggml_blck_size(config.down_type), i);
|
||||
if (!gate_proj_numa_[i]) {
|
||||
std::cout << "Memory allocation failed for gate_proj_numa_ on node " << i << std::endl;
|
||||
}
|
||||
if (!up_proj_numa_[i]) {
|
||||
std::cout << "Memory allocation failed for up_proj_numa_ on node " << i << std::endl;
|
||||
}
|
||||
if (!down_proj_numa_[i]) {
|
||||
std::cout << "Memory allocation failed for down_proj_numa_ on node " << i << std::endl;
|
||||
}
|
||||
memcpy(gate_proj_numa_[i], gate_proj_, exp_inter_hidden_mul_* ggml_type_size(config.gate_type) / ggml_blck_size(config.gate_type));
|
||||
memcpy(up_proj_numa_[i], up_proj_, exp_inter_hidden_mul_* ggml_type_size(config.up_type) / ggml_blck_size(config.up_type));
|
||||
memcpy(down_proj_numa_[i], down_proj_, exp_inter_hidden_mul_* ggml_type_size(config.down_type) / ggml_blck_size(config.down_type));
|
||||
}
|
||||
#endif
|
||||
|
||||
std::vector<std::pair<void**, uint64_t>> s_mem_requests;
|
||||
s_mem_requests.push_back({(void**)&s_input_fp32_, sizeof(float) * config_.hidden_size});
|
||||
@ -74,6 +104,15 @@ MOE::MOE(MOEConfig config) {
|
||||
|
||||
MOE::~MOE() {
|
||||
shared_mem_buffer.dealloc(this);
|
||||
|
||||
#ifdef USE_NUMA
|
||||
int numa_nodes = numa_num_configured_nodes();
|
||||
for (int i = 0; i < numa_nodes; i++) {
|
||||
numa_free(gate_proj_numa_[i], config_.expert_num * config_.intermediate_size * config_.hidden_size * ggml_type_size(config_.gate_type) / ggml_blck_size(config_.gate_type));
|
||||
numa_free(up_proj_numa_[i], config_.expert_num * config_.intermediate_size * config_.hidden_size * ggml_type_size(config_.up_type) / ggml_blck_size(config_.up_type));
|
||||
numa_free(down_proj_numa_[i], config_.expert_num * config_.hidden_size * config_.intermediate_size * ggml_type_size(config_.down_type) / ggml_blck_size(config_.down_type));
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
void MOE::warm_up(Backend* backend) {
|
||||
@ -125,10 +164,22 @@ void MOE::forward_one(int k, const uint64_t* expert_ids, const float* weights, c
|
||||
int expert_idx = task_id / nth;
|
||||
uint64_t expert_id = expert_ids[expert_idx];
|
||||
int ith = task_id % nth;
|
||||
|
||||
#ifdef USE_NUMA
|
||||
void* gate_proj_ptr = (uint8_t*)gate_proj_numa_[Backend::numa_node] + (expert_id * config_.intermediate_size + ith * config_.stride) * config_.hidden_size * ggml_type_size(config_.gate_type) / ggml_blck_size(config_.gate_type);
|
||||
#else
|
||||
void* gate_proj_ptr = (uint8_t*)gate_proj_ + (expert_id * config_.intermediate_size + ith * config_.stride) * config_.hidden_size * ggml_type_size(config_.gate_type) / ggml_blck_size(config_.gate_type);
|
||||
#endif
|
||||
|
||||
float* gate_output_ptr = s_gate_output_[expert_idx] + ith * config_.stride;
|
||||
llamafile_sgemm(config_.stride, 1, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_proj_ptr, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_input_ptr, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_output_ptr, config_.stride, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.gate_type, ggml_internal_get_type_traits(config_.gate_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
|
||||
|
||||
#ifdef USE_NUMA
|
||||
void* up_proj_ptr = (uint8_t*)up_proj_numa_[Backend::numa_node] + (expert_id * config_.intermediate_size + ith * config_.stride) * config_.hidden_size * ggml_type_size(config_.up_type) / ggml_blck_size(config_.up_type);
|
||||
#else
|
||||
void* up_proj_ptr = (uint8_t*)up_proj_ + (expert_id * config_.intermediate_size + ith * config_.stride) * config_.hidden_size * ggml_type_size(config_.up_type) / ggml_blck_size(config_.up_type);
|
||||
#endif
|
||||
|
||||
float* up_output_ptr = s_up_output_[expert_idx] + ith * config_.stride;
|
||||
llamafile_sgemm(config_.stride, 1, config_.hidden_size / ggml_blck_size(config_.up_type), up_proj_ptr, config_.hidden_size / ggml_blck_size(config_.up_type), up_input_ptr, config_.hidden_size / ggml_blck_size(config_.up_type), up_output_ptr, config_.stride, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.up_type, ggml_internal_get_type_traits(config_.up_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
|
||||
for (int i = ith * config_.stride; i < (ith + 1) * config_.stride; i++) {
|
||||
@ -153,7 +204,13 @@ void MOE::forward_one(int k, const uint64_t* expert_ids, const float* weights, c
|
||||
}
|
||||
for (int expert_idx = 0; expert_idx < k; expert_idx++) {
|
||||
uint64_t expert_id = expert_ids[expert_idx];
|
||||
|
||||
#ifdef USE_NUMA
|
||||
void* down_proj_ptr = (uint8_t*)down_proj_numa_[Backend::numa_node] + (expert_id * config_.hidden_size + ith * config_.stride) * config_.intermediate_size * ggml_type_size(config_.down_type) / ggml_blck_size(config_.down_type);
|
||||
#else
|
||||
void* down_proj_ptr = (uint8_t*)down_proj_ + (expert_id * config_.hidden_size + ith * config_.stride) * config_.intermediate_size * ggml_type_size(config_.down_type) / ggml_blck_size(config_.down_type);
|
||||
#endif
|
||||
|
||||
float* down_output_ptr = s_down_output_[expert_idx] + ith * config_.stride;
|
||||
llamafile_sgemm(config_.stride, 1, config_.intermediate_size / ggml_blck_size(config_.down_type), down_proj_ptr, config_.intermediate_size / ggml_blck_size(config_.down_type), s_down_input_[expert_idx], config_.intermediate_size / ggml_blck_size(config_.down_type), down_output_ptr, config_.stride, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.down_type, ggml_internal_get_type_traits(config_.down_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
|
||||
for (int i = ith * config_.stride; i < (ith + 1) * config_.stride; i++) {
|
||||
@ -224,14 +281,26 @@ void MOE::forward_many(int qlen, int k, const uint64_t* expert_ids, const float*
|
||||
int stride = QK_K;
|
||||
int nth = config_.intermediate_size / stride;
|
||||
backend->do_work_stealing_job(nth * config_.expert_num, nullptr, [&](int task_id) {
|
||||
int expert_idx = task_id / nth;
|
||||
uint64_t expert_idx = task_id / nth;
|
||||
int ith = task_id % nth;
|
||||
void* gate_input_ptr = m_local_gate_input_ptr_[expert_idx];
|
||||
|
||||
#ifdef USE_NUMA
|
||||
void* gate_proj_ptr = (uint8_t*)gate_proj_numa_[Backend::numa_node] + (expert_idx * config_.intermediate_size + ith * stride) * config_.hidden_size * ggml_type_size(config_.gate_type) / ggml_blck_size(config_.gate_type);
|
||||
#else
|
||||
void* gate_proj_ptr = (uint8_t*)gate_proj_ + (expert_idx * config_.intermediate_size + ith * stride) * config_.hidden_size * ggml_type_size(config_.gate_type) / ggml_blck_size(config_.gate_type);
|
||||
#endif
|
||||
|
||||
float* gate_output_ptr = m_local_gate_output_ptr_[expert_idx] + ith * stride;
|
||||
llamafile_sgemm(stride, m_local_num_[expert_idx], config_.hidden_size / ggml_blck_size(config_.gate_type), gate_proj_ptr, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_input_ptr, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_output_ptr, config_.intermediate_size, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.gate_type, ggml_internal_get_type_traits(config_.gate_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
|
||||
void* up_input_ptr = m_local_up_input_ptr_[expert_idx];
|
||||
|
||||
#ifdef USE_NUMA
|
||||
void* up_proj_ptr = (uint8_t*)up_proj_numa_[Backend::numa_node] + (expert_idx * config_.intermediate_size + ith * stride) * config_.hidden_size * ggml_type_size(config_.up_type) / ggml_blck_size(config_.up_type);
|
||||
#else
|
||||
void* up_proj_ptr = (uint8_t*)up_proj_ + (expert_idx * config_.intermediate_size + ith * stride) * config_.hidden_size * ggml_type_size(config_.up_type) / ggml_blck_size(config_.up_type);
|
||||
#endif
|
||||
|
||||
float* up_output_ptr = m_local_up_output_ptr_[expert_idx] + ith * stride;
|
||||
llamafile_sgemm(stride, m_local_num_[expert_idx], config_.hidden_size / ggml_blck_size(config_.up_type), up_proj_ptr, config_.hidden_size / ggml_blck_size(config_.up_type), up_input_ptr, config_.hidden_size / ggml_blck_size(config_.up_type), up_output_ptr, config_.intermediate_size, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.up_type, ggml_internal_get_type_traits(config_.up_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
|
||||
for (int i = 0; i < m_local_num_[expert_idx]; i++) {
|
||||
@ -246,10 +315,16 @@ void MOE::forward_many(int qlen, int k, const uint64_t* expert_ids, const float*
|
||||
stride = QK_K;
|
||||
nth = config_.hidden_size / stride;
|
||||
backend->do_work_stealing_job(nth * config_.expert_num, nullptr, [&](int task_id) {
|
||||
int expert_idx = task_id / nth;
|
||||
uint64_t expert_idx = task_id / nth;
|
||||
int ith = task_id % nth;
|
||||
void* down_input_ptr = m_local_down_input_ptr_[expert_idx];
|
||||
|
||||
#ifdef USE_NUMA
|
||||
void* down_proj_ptr = (uint8_t*)down_proj_numa_[Backend::numa_node] + (expert_idx * config_.hidden_size + ith * stride) * config_.intermediate_size * ggml_type_size(config_.down_type) / ggml_blck_size(config_.down_type);
|
||||
#else
|
||||
void* down_proj_ptr = (uint8_t*)down_proj_ + (expert_idx * config_.hidden_size + ith * stride) * config_.intermediate_size * ggml_type_size(config_.down_type) / ggml_blck_size(config_.down_type);
|
||||
#endif
|
||||
|
||||
float* down_output_ptr = m_local_down_output_ptr_[expert_idx] + ith * stride;
|
||||
llamafile_sgemm(stride, m_local_num_[expert_idx], config_.intermediate_size / ggml_blck_size(config_.down_type), down_proj_ptr, config_.intermediate_size / ggml_blck_size(config_.down_type), down_input_ptr, config_.intermediate_size / ggml_blck_size(config_.down_type), down_output_ptr, config_.hidden_size, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.down_type, ggml_internal_get_type_traits(config_.down_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
|
||||
}, nullptr);
|
||||
|
||||
@ -61,6 +61,12 @@ class MOE {
|
||||
void* up_proj_; // [expert_num * intermediate_size * hidden_size ( /32 if quantized)]
|
||||
void* down_proj_; // [expert_num * hidden_size * intermediate_size ( /32 if quantized)]
|
||||
|
||||
#ifdef USE_NUMA
|
||||
std::vector<void*> gate_proj_numa_; // [numa_num, expert_num * intermediate_size * hidden_size ( /32 if quantized)]
|
||||
std::vector<void*> up_proj_numa_; // [numa_num, expert_num * intermediate_size * hidden_size ( /32 if quantized)]
|
||||
std::vector<void*> down_proj_numa_; // [numa_num, expert_num * hidden_size * intermediate_size ( /32 if quantized)]
|
||||
#endif
|
||||
|
||||
float* s_input_fp32_; // [hidden_size]
|
||||
uint8_t* s_gate_input_; // [hidden_size * ggml_type_size(ggml_internal_get_type_traits(gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(gate_type).vec_dot_type)]
|
||||
uint8_t* s_up_input_; // [hidden_size * ggml_type_size(ggml_internal_get_type_traits(up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(up_type).vec_dot_type)]
|
||||
|
||||
@ -1,63 +1,241 @@
|
||||
# """
|
||||
# Description :
|
||||
# Author : Boxin Zhang, Azure-Tang
|
||||
# Version : 0.1.0
|
||||
# Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
|
||||
# """
|
||||
|
||||
# import asyncio
|
||||
# import os
|
||||
# import platform
|
||||
# import sys
|
||||
# project_dir = os.path.dirname(os.path.dirname(__file__))
|
||||
# sys.path.insert(0, project_dir)
|
||||
# from ktransformers.server.args import ArgumentParser
|
||||
|
||||
|
||||
# from ktransformers.models.modeling_deepseek import DeepseekV2ForCausalLM
|
||||
# from ktransformers.models.modeling_deepseek_v3 import DeepseekV3ForCausalLM
|
||||
# from ktransformers.models.modeling_qwen2_moe import Qwen2MoeForCausalLM
|
||||
# from ktransformers.models.modeling_llama import LlamaForCausalLM
|
||||
# from ktransformers.models.modeling_mixtral import MixtralForCausalLM
|
||||
# from ktransformers.server.config.config import Config
|
||||
|
||||
# custom_models = {
|
||||
# "DeepseekV2ForCausalLM": DeepseekV2ForCausalLM,
|
||||
# "DeepseekV3ForCausalLM": DeepseekV3ForCausalLM,
|
||||
# "Qwen2MoeForCausalLM": Qwen2MoeForCausalLM,
|
||||
# "LlamaForCausalLM": LlamaForCausalLM,
|
||||
# "MixtralForCausalLM": MixtralForCausalLM,
|
||||
# }
|
||||
|
||||
# ktransformer_rules_dir = os.path.dirname(os.path.abspath(__file__)) + "/optimize/optimize_rules/"
|
||||
# default_optimize_rules = {
|
||||
# "DeepseekV2ForCausalLM": ktransformer_rules_dir + "DeepSeek-V2-Chat.yaml",
|
||||
# "DeepseekV3ForCausalLM": ktransformer_rules_dir + "DeepSeek-V3-Chat.yaml",
|
||||
# "Qwen2MoeForCausalLM": ktransformer_rules_dir + "Qwen2-57B-A14B-Instruct.yaml",
|
||||
# "LlamaForCausalLM": ktransformer_rules_dir + "Internlm2_5-7b-Chat-1m.yaml",
|
||||
# "MixtralForCausalLM": ktransformer_rules_dir + "Mixtral.yaml",
|
||||
# }
|
||||
|
||||
|
||||
# def local_chat():
|
||||
# config = Config()
|
||||
# arg_parser = ArgumentParser(config)
|
||||
# # 初始化消息
|
||||
# arg_parser.parse_args()
|
||||
# if config.backend_type == "transformers":
|
||||
# from ktransformers.server.backend.interfaces.transformers import TransformersInterface as BackendInterface
|
||||
# elif config.backend_type == "exllamav2":
|
||||
# from ktransformers.server.backend.interfaces.exllamav2 import ExllamaInterface as BackendInterface
|
||||
# elif config.backend_type == "ktransformers":
|
||||
# from ktransformers.server.backend.interfaces.ktransformers import KTransformersInterface as BackendInterface
|
||||
# else:
|
||||
# raise NotImplementedError(f"{config.backend_type} not implemented")
|
||||
# interface = BackendInterface(config)
|
||||
|
||||
# system = platform.system()
|
||||
# if system == "Windows":
|
||||
# os.system("cls")
|
||||
# else:
|
||||
# os.system("clear")
|
||||
# # add a history chat content
|
||||
# his_content = []
|
||||
# while True:
|
||||
# content = input("Chat: ")
|
||||
# if content.startswith('"""'): # prefix """
|
||||
# # multi lines input
|
||||
# content = content[3:] + "\n"
|
||||
# while True:
|
||||
# line = input("")
|
||||
# if line.endswith('"""'):
|
||||
# # end multi lines input
|
||||
# line = line[:-3] # suffix """
|
||||
# if line:
|
||||
# content += line + "\n"
|
||||
# break
|
||||
# else:
|
||||
# content += line + "\n"
|
||||
# if content == "":
|
||||
# if not config.prompt_file:
|
||||
# content = "hi"
|
||||
# else:
|
||||
# content = open(config.prompt_file, "r").read()
|
||||
# print("User: ", content)
|
||||
# elif os.path.isfile(content):
|
||||
# content = open(content, "r").read()
|
||||
# print("User: ", content)
|
||||
# messages = his_content + [{"role": "user", "content": content}]
|
||||
|
||||
# async def async_inference(messages):
|
||||
# generated = ""
|
||||
# async for token in interface.inference(messages, "local_chat"):
|
||||
# generated += token
|
||||
# return generated
|
||||
|
||||
# generated = asyncio.run(async_inference(messages))
|
||||
# his_content += [
|
||||
# {"role": "user", "content": content},
|
||||
# {"role": "assistant", "content": generated},
|
||||
# ]
|
||||
|
||||
|
||||
# if __name__ == "__main__":
|
||||
# local_chat()
|
||||
|
||||
|
||||
"""
|
||||
Description :
|
||||
Description :
|
||||
Author : Boxin Zhang, Azure-Tang
|
||||
Version : 0.1.0
|
||||
Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
|
||||
Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
|
||||
project_dir = os.path.dirname(os.path.dirname(__file__))
|
||||
sys.path.insert(0, project_dir)
|
||||
from ktransformers.server.args import ArgumentParser
|
||||
|
||||
|
||||
import torch
|
||||
import logging
|
||||
from transformers import (
|
||||
AutoTokenizer,
|
||||
AutoConfig,
|
||||
AutoModelForCausalLM,
|
||||
GenerationConfig,
|
||||
TextStreamer,
|
||||
)
|
||||
import json
|
||||
import fire
|
||||
from ktransformers.optimize.optimize import optimize_and_load_gguf
|
||||
from ktransformers.models.modeling_deepseek import DeepseekV2ForCausalLM
|
||||
from ktransformers.models.modeling_qwen2_moe import Qwen2MoeForCausalLM
|
||||
from ktransformers.models.modeling_deepseek_v3 import DeepseekV3ForCausalLM
|
||||
from ktransformers.models.modeling_llama import LlamaForCausalLM
|
||||
from ktransformers.models.modeling_mixtral import MixtralForCausalLM
|
||||
from ktransformers.util.utils import prefill_and_generate
|
||||
from ktransformers.server.config.config import Config
|
||||
|
||||
custom_models = {
|
||||
"DeepseekV2ForCausalLM": DeepseekV2ForCausalLM,
|
||||
"DeepseekV3ForCausalLM": DeepseekV3ForCausalLM,
|
||||
"Qwen2MoeForCausalLM": Qwen2MoeForCausalLM,
|
||||
"LlamaForCausalLM": LlamaForCausalLM,
|
||||
"MixtralForCausalLM": MixtralForCausalLM,
|
||||
}
|
||||
|
||||
ktransformer_rules_dir = os.path.dirname(os.path.abspath(__file__)) + "/optimize/optimize_rules/"
|
||||
ktransformer_rules_dir = (
|
||||
os.path.dirname(os.path.abspath(__file__)) + "/optimize/optimize_rules/"
|
||||
)
|
||||
default_optimize_rules = {
|
||||
"DeepseekV2ForCausalLM": ktransformer_rules_dir + "DeepSeek-V2-Chat.yaml",
|
||||
"DeepseekV3ForCausalLM": ktransformer_rules_dir + "DeepSeek-V3-Chat.yaml",
|
||||
"Qwen2MoeForCausalLM": ktransformer_rules_dir + "Qwen2-57B-A14B-Instruct.yaml",
|
||||
"LlamaForCausalLM": ktransformer_rules_dir + "Internlm2_5-7b-Chat-1m.yaml",
|
||||
"MixtralForCausalLM": ktransformer_rules_dir + "Mixtral.yaml",
|
||||
}
|
||||
|
||||
|
||||
def local_chat():
|
||||
config = Config()
|
||||
arg_parser = ArgumentParser(config)
|
||||
# 初始化消息
|
||||
arg_parser.parse_args()
|
||||
if config.backend_type == "transformers":
|
||||
from ktransformers.server.backend.interfaces.transformers import TransformersInterface as BackendInterface
|
||||
elif config.backend_type == "exllamav2":
|
||||
from ktransformers.server.backend.interfaces.exllamav2 import ExllamaInterface as BackendInterface
|
||||
elif config.backend_type == "ktransformers":
|
||||
from ktransformers.server.backend.interfaces.ktransformers import KTransformersInterface as BackendInterface
|
||||
def local_chat(
|
||||
model_path: str | None = None,
|
||||
optimize_rule_path: str = None,
|
||||
gguf_path: str | None = None,
|
||||
max_new_tokens: int = 1000,
|
||||
cpu_infer: int = Config().cpu_infer,
|
||||
use_cuda_graph: bool = True,
|
||||
prompt_file : str | None = None,
|
||||
mode: str = "normal",
|
||||
):
|
||||
|
||||
|
||||
torch.set_grad_enabled(False)
|
||||
|
||||
Config().cpu_infer = cpu_infer
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
||||
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
|
||||
if mode == 'long_context':
|
||||
assert config.architectures[0] == "LlamaForCausalLM", "only LlamaForCausalLM support long_context mode"
|
||||
torch.set_default_dtype(torch.float16)
|
||||
else:
|
||||
raise NotImplementedError(f"{config.backend_type} not implemented")
|
||||
interface = BackendInterface(config)
|
||||
torch.set_default_dtype(config.torch_dtype)
|
||||
|
||||
with torch.device("meta"):
|
||||
if config.architectures[0] in custom_models:
|
||||
print("using custom modeling_xxx.py.")
|
||||
if (
|
||||
"Qwen2Moe" in config.architectures[0]
|
||||
): # Qwen2Moe must use flash_attention_2 to avoid overflow.
|
||||
config._attn_implementation = "flash_attention_2"
|
||||
if "Llama" in config.architectures[0]:
|
||||
config._attn_implementation = "eager"
|
||||
if "Mixtral" in config.architectures[0]:
|
||||
config._attn_implementation = "flash_attention_2"
|
||||
|
||||
model = custom_models[config.architectures[0]](config)
|
||||
else:
|
||||
model = AutoModelForCausalLM.from_config(
|
||||
config, trust_remote_code=True, attn_implementation="flash_attention_2"
|
||||
)
|
||||
|
||||
if optimize_rule_path is None:
|
||||
if config.architectures[0] in default_optimize_rules:
|
||||
print("using default_optimize_rule for", config.architectures[0])
|
||||
optimize_rule_path = default_optimize_rules[config.architectures[0]]
|
||||
else:
|
||||
optimize_rule_path = input(
|
||||
"please input the path of your rule file(yaml file containing optimize rules):"
|
||||
)
|
||||
|
||||
if gguf_path is None:
|
||||
gguf_path = input(
|
||||
"please input the path of your gguf file(gguf file in the dir containing input gguf file must all belong to current model):"
|
||||
)
|
||||
optimize_and_load_gguf(model, optimize_rule_path, gguf_path, config)
|
||||
|
||||
try:
|
||||
model.generation_config = GenerationConfig.from_pretrained(model_path)
|
||||
except:
|
||||
gen_config = GenerationConfig(
|
||||
max_length=128,
|
||||
temperature=0.7,
|
||||
top_p=0.9,
|
||||
do_sample=True
|
||||
)
|
||||
model.generation_config = gen_config
|
||||
# model.generation_config = GenerationConfig.from_pretrained(model_path)
|
||||
if model.generation_config.pad_token_id is None:
|
||||
model.generation_config.pad_token_id = model.generation_config.eos_token_id
|
||||
model.eval()
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
system = platform.system()
|
||||
if system == "Windows":
|
||||
os.system("cls")
|
||||
else:
|
||||
os.system("clear")
|
||||
# add a history chat content
|
||||
his_content = []
|
||||
|
||||
while True:
|
||||
content = input("Chat: ")
|
||||
if content.startswith('"""'): # prefix """
|
||||
@ -73,27 +251,28 @@ def local_chat():
|
||||
break
|
||||
else:
|
||||
content += line + "\n"
|
||||
|
||||
if content == "":
|
||||
if config.prompt_file == None or config.prompt_file == "":
|
||||
content = "Please write a piece of quicksort code in C++."
|
||||
if prompt_file != None:
|
||||
content = open(prompt_file, "r").read()
|
||||
else:
|
||||
content = open(config.prompt_file, "r").read()
|
||||
content = "Please write a piece of quicksort code in C++."
|
||||
elif os.path.isfile(content):
|
||||
content = open(content, "r").read()
|
||||
messages = his_content + [{"role": "user", "content": content}]
|
||||
|
||||
async def async_inference(messages):
|
||||
generated = ""
|
||||
async for token in interface.inference(messages, "local_chat"):
|
||||
generated += token
|
||||
return generated
|
||||
|
||||
generated = asyncio.run(async_inference(messages))
|
||||
his_content += [
|
||||
{"role": "user", "content": content},
|
||||
{"role": "assistant", "content": generated},
|
||||
]
|
||||
messages = [{"role": "user", "content": content}]
|
||||
input_tensor = tokenizer.apply_chat_template(
|
||||
messages, add_generation_prompt=True, return_tensors="pt"
|
||||
)
|
||||
if mode == 'long_context':
|
||||
assert Config().long_context_config['max_seq_len'] > input_tensor.shape[1] + max_new_tokens, \
|
||||
"please change max_seq_len in ~/.ktransformers/config.yaml"
|
||||
torch.set_default_dtype(
|
||||
torch.bfloat16
|
||||
) # TODO: Remove this, replace dtype using config
|
||||
generated = prefill_and_generate(
|
||||
model, tokenizer, input_tensor.cuda(), max_new_tokens, use_cuda_graph, mode
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
local_chat()
|
||||
fire.Fire(local_chat)
|
||||
235
ktransformers/models/configuration_deepseek_v3.py
Normal file
235
ktransformers/models/configuration_deepseek_v3.py
Normal file
@ -0,0 +1,235 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2025 bzantium and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on the DeepSeekV3 implementations from the DeepSeek AI team. (https://huggingface.co/deepseek-ai/DeepSeek-V3)
|
||||
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""DeepSeekV3 model configuration"""
|
||||
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from transformers.modeling_rope_utils import rope_config_validation
|
||||
|
||||
|
||||
DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
|
||||
|
||||
|
||||
class DeepseekV3Config(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek
|
||||
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
||||
defaults will yield a similar configuration to that of the DeepSeek-V3.
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
|
||||
Args:
|
||||
vocab_size (`int`, *optional*, defaults to 129280):
|
||||
Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
|
||||
`inputs_ids` passed when calling [`DeepseekV3Model`]
|
||||
hidden_size (`int`, *optional*, defaults to 7168):
|
||||
Dimension of the hidden representations.
|
||||
intermediate_size (`int`, *optional*, defaults to 18432):
|
||||
Dimension of the MLP representations.
|
||||
moe_intermediate_size (`int`, *optional*, defaults to 2048):
|
||||
Dimension of the MoE representations.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 61):
|
||||
Number of hidden layers in the Transformer decoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 128):
|
||||
Number of attention heads for each attention layer in the Transformer decoder.
|
||||
num_key_value_heads (`int`, *optional*, defaults to 128):
|
||||
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
||||
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
||||
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
||||
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
||||
by meanpooling all the original heads within that group. For more details checkout [this
|
||||
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
||||
`num_attention_heads`.
|
||||
n_shared_experts (`int`, *optional*, defaults to 1):
|
||||
Number of shared experts.
|
||||
n_routed_experts (`int`, *optional*, defaults to 256):
|
||||
Number of routed experts.
|
||||
routed_scaling_factor (`float`, *optional*, defaults to 2.5):
|
||||
Scaling factor or routed experts.
|
||||
kv_lora_rank (`int`, *optional*, defaults to 512):
|
||||
Rank of the LoRA matrices for key and value projections.
|
||||
q_lora_rank (`int`, *optional*, defaults to 1536):
|
||||
Rank of the LoRA matrices for query projections.
|
||||
qk_rope_head_dim (`int`, *optional*, defaults to 64):
|
||||
Dimension of the query/key heads that use rotary position embeddings.
|
||||
v_head_dim (`int`, *optional*, defaults to 128):
|
||||
Dimension of the value heads.
|
||||
qk_nope_head_dim (`int`, *optional*, defaults to 128):
|
||||
Dimension of the query/key heads that don't use rotary position embeddings.
|
||||
n_group (`int`, *optional*, defaults to 8):
|
||||
Number of groups for routed experts.
|
||||
topk_group (`int`, *optional*, defaults to 4):
|
||||
Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
|
||||
num_experts_per_tok (`int`, *optional*, defaults to 8):
|
||||
Number of selected experts, None means dense model.
|
||||
first_k_dense_replace (`int`, *optional*, defaults to 3):
|
||||
Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
|
||||
\--k dense layers--/
|
||||
norm_topk_prob (`bool`, *optional*, defaults to `True`):
|
||||
Whether to normalize the weights of the routed experts.
|
||||
aux_loss_alpha (`float`, *optional*, defaults to 0.001):
|
||||
Auxiliary loss weight coefficient.
|
||||
Whether to compute the auxiliary loss for each individual sample.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
||||
The non-linear activation function (function or string) in the decoder.
|
||||
max_position_embeddings (`int`, *optional*, defaults to 4096):
|
||||
The maximum sequence length that this model might ever be used with.
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
||||
The epsilon used by the rms normalization layers.
|
||||
use_cache (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
||||
relevant if `config.is_decoder=True`.
|
||||
pad_token_id (`int`, *optional*):
|
||||
Padding token id.
|
||||
bos_token_id (`int`, *optional*, defaults to 0):
|
||||
Beginning of stream token id.
|
||||
eos_token_id (`int`, *optional*, defaults to 1):
|
||||
End of stream token id.
|
||||
pretraining_tp (`int`, *optional*, defaults to 1):
|
||||
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
|
||||
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
|
||||
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
|
||||
issue](https://github.com/pytorch/pytorch/issues/76232).
|
||||
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
||||
Whether to tie weight embeddings
|
||||
rope_theta (`float`, *optional*, defaults to 10000.0):
|
||||
The base period of the RoPE embeddings.
|
||||
rope_scaling (`Dict`, *optional*):
|
||||
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
|
||||
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
|
||||
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
|
||||
`max_position_embeddings` to the expected new maximum.
|
||||
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
|
||||
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the attention probabilities.
|
||||
|
||||
```python
|
||||
>>> from transformers import DeepseekV3Model, DeepseekV3Config
|
||||
|
||||
>>> # Initializing a Deepseek-V3 style configuration
|
||||
>>> configuration = DeepseekV3Config()
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
|
||||
model_type = "deepseek_v3"
|
||||
keys_to_ignore_at_inference = ["past_key_values"]
|
||||
# Default tensor parallel plan for base model `DeepseekV3Model`
|
||||
base_model_tp_plan = {
|
||||
"layers.*.gate_proj": "colwise",
|
||||
"layers.*.up_proj": "colwise",
|
||||
"layers.*.down_proj": "rowwise",
|
||||
}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=129280,
|
||||
hidden_size=7168,
|
||||
intermediate_size=18432,
|
||||
moe_intermediate_size=2048,
|
||||
num_hidden_layers=61,
|
||||
num_attention_heads=128,
|
||||
num_key_value_heads=128,
|
||||
n_shared_experts=1,
|
||||
n_routed_experts=256,
|
||||
routed_scaling_factor=2.5,
|
||||
kv_lora_rank=512,
|
||||
q_lora_rank=1536,
|
||||
qk_rope_head_dim=64,
|
||||
v_head_dim=128,
|
||||
qk_nope_head_dim=128,
|
||||
n_group=8,
|
||||
topk_group=4,
|
||||
num_experts_per_tok=8,
|
||||
first_k_dense_replace=3,
|
||||
norm_topk_prob=True,
|
||||
aux_loss_alpha=0.001,
|
||||
hidden_act="silu",
|
||||
max_position_embeddings=4096,
|
||||
initializer_range=0.02,
|
||||
rms_norm_eps=1e-6,
|
||||
use_cache=True,
|
||||
pad_token_id=None,
|
||||
bos_token_id=0,
|
||||
eos_token_id=1,
|
||||
pretraining_tp=1,
|
||||
tie_word_embeddings=False,
|
||||
rope_theta=10000.0,
|
||||
rope_scaling=None,
|
||||
attention_bias=False,
|
||||
attention_dropout=0.0,
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.moe_intermediate_size = moe_intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.n_shared_experts = n_shared_experts
|
||||
self.n_routed_experts = n_routed_experts
|
||||
self.routed_scaling_factor = routed_scaling_factor
|
||||
self.kv_lora_rank = kv_lora_rank
|
||||
self.q_lora_rank = q_lora_rank
|
||||
self.qk_rope_head_dim = qk_rope_head_dim
|
||||
self.v_head_dim = v_head_dim
|
||||
self.qk_nope_head_dim = qk_nope_head_dim
|
||||
self.q_head_dim = qk_nope_head_dim + qk_rope_head_dim
|
||||
self.head_dim = qk_rope_head_dim
|
||||
self.n_group = n_group
|
||||
self.topk_group = topk_group
|
||||
self.num_experts_per_tok = num_experts_per_tok
|
||||
self.first_k_dense_replace = first_k_dense_replace
|
||||
self.norm_topk_prob = norm_topk_prob
|
||||
self.aux_loss_alpha = aux_loss_alpha
|
||||
|
||||
# for backward compatibility
|
||||
if num_key_value_heads is None:
|
||||
num_key_value_heads = num_attention_heads
|
||||
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
self.hidden_act = hidden_act
|
||||
self.initializer_range = initializer_range
|
||||
self.rms_norm_eps = rms_norm_eps
|
||||
self.pretraining_tp = pretraining_tp
|
||||
self.use_cache = use_cache
|
||||
self.rope_theta = rope_theta
|
||||
self.rope_scaling = rope_scaling
|
||||
self.attention_bias = attention_bias
|
||||
self.attention_dropout = attention_dropout
|
||||
# Validate the correctness of rotary position embeddings parameters
|
||||
# BC: if there is a 'type' field, copy it it to 'rope_type'.
|
||||
if self.rope_scaling is not None and "type" in self.rope_scaling:
|
||||
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
|
||||
rope_config_validation(self)
|
||||
|
||||
super().__init__(
|
||||
pad_token_id=pad_token_id,
|
||||
bos_token_id=bos_token_id,
|
||||
eos_token_id=eos_token_id,
|
||||
tie_word_embeddings=tie_word_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
__all__ = ["DeepseekV3Config"]
|
||||
@ -34,9 +34,12 @@ class StaticCache(transformers.StaticCache):
|
||||
self.max_batch_size = max_batch_size
|
||||
self.max_cache_len = config.max_position_embeddings if max_cache_len is None else max_cache_len
|
||||
# Some model define a custom `head_dim` != config.hidden_size // config.num_attention_heads
|
||||
self.head_dim = (
|
||||
config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads
|
||||
)
|
||||
if config.architectures[0] == "DeepseekV3ForCausalLM":
|
||||
self.head_dim = config.qk_rope_head_dim
|
||||
else:
|
||||
self.head_dim = (
|
||||
config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads
|
||||
)
|
||||
|
||||
self.dtype = dtype if dtype is not None else torch.float32
|
||||
self.num_key_value_heads = (
|
||||
@ -46,7 +49,7 @@ class StaticCache(transformers.StaticCache):
|
||||
self.key_cache: List[torch.Tensor] = []
|
||||
self.value_cache: List[torch.Tensor] = []
|
||||
cache_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, self.head_dim)
|
||||
if config.architectures[0] == "DeepseekV2ForCausalLM":
|
||||
if config.architectures[0] == "DeepseekV2ForCausalLM" or config.architectures[0] == "DeepseekV3ForCausalLM":
|
||||
# TODO: for deepseek, cache_shape is different whether using Absorbed MLA, check it automatically
|
||||
# key_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, config.qk_rope_head_dim + config.qk_nope_head_dim)
|
||||
# value_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, config.v_head_dim)
|
||||
@ -132,3 +135,7 @@ class StaticCache(transformers.StaticCache):
|
||||
# In-place ops prevent breaking the static address
|
||||
self.key_cache[layer_idx].zero_()
|
||||
self.value_cache[layer_idx].zero_()
|
||||
|
||||
def get_max_cache_shape(self) -> Tuple[int, int, int, int]:
|
||||
"""Returns the maximum shape of the cache."""
|
||||
return self.max_cache_len
|
||||
1936
ktransformers/models/modeling_deepseek_v3.py
Normal file
1936
ktransformers/models/modeling_deepseek_v3.py
Normal file
File diff suppressed because it is too large
Load Diff
@ -12,15 +12,21 @@ from ktransformers.models.modeling_llama import (
|
||||
LlamaLinearScalingRotaryEmbedding,
|
||||
LlamaDynamicNTKScalingRotaryEmbedding,
|
||||
)
|
||||
from ktransformers.models.modeling_deepseek_v3 import (
|
||||
DeepseekV3RotaryEmbedding
|
||||
)
|
||||
from ktransformers.models.modeling_deepseek import (
|
||||
DeepseekV2YarnRotaryEmbedding,
|
||||
DeepseekV2RotaryEmbedding,
|
||||
yarn_get_mscale,
|
||||
yarn_linear_ramp_mask,
|
||||
yarn_find_correction_range
|
||||
)
|
||||
from ktransformers.operators.base_operator import BaseInjectedModule
|
||||
from ktransformers.util.custom_gguf import GGUFLoader
|
||||
from ktransformers.util.utils import InferenceState
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
|
||||
import torch
|
||||
|
||||
# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Qwen2Moe
|
||||
class RotaryEmbedding(BaseInjectedModule, DeepseekV2RotaryEmbedding):
|
||||
@ -53,6 +59,57 @@ class RotaryEmbedding(BaseInjectedModule, DeepseekV2RotaryEmbedding):
|
||||
)
|
||||
|
||||
|
||||
class RotaryEmbeddingV3(BaseInjectedModule):
|
||||
def __init__(
|
||||
self,
|
||||
key: str,
|
||||
gguf_loader: GGUFLoader,
|
||||
config: PretrainedConfig,
|
||||
orig_module: nn.Module,
|
||||
# device: str = "cuda",
|
||||
generate_device: str = "cuda",
|
||||
prefill_device: str = "cuda",
|
||||
**kwargs,
|
||||
):
|
||||
BaseInjectedModule.__init__(
|
||||
self, key, gguf_loader, config, orig_module, generate_device, **kwargs
|
||||
)
|
||||
self.generate_device = generate_device
|
||||
self.prefill_device = prefill_device
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(self, x, position_ids):
|
||||
# x: [bs, num_attention_heads, seq_len, head_size]
|
||||
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
|
||||
position_ids_expanded = position_ids[:, None, :].float()
|
||||
# Force float32 since bfloat16 loses precision on long contexts
|
||||
# See https://github.com/huggingface/transformers/pull/29285
|
||||
device_type = x.device.type
|
||||
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
|
||||
with torch.autocast(device_type=device_type, enabled=False):
|
||||
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
||||
emb = torch.cat((freqs, freqs), dim=-1)
|
||||
cos = emb.cos()
|
||||
sin = emb.sin()
|
||||
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
||||
|
||||
def load(self):
|
||||
self._init(
|
||||
dim=self.config.qk_rope_head_dim,
|
||||
max_position_embeddings=self.config.max_position_embeddings,
|
||||
base=self.config.rope_theta,
|
||||
device=self.device,
|
||||
)
|
||||
def _init(self, dim, max_position_embeddings, base, device, scaling_factor=1.0):
|
||||
self.scaling_factor = scaling_factor
|
||||
self.dim = dim
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.base = base
|
||||
self.inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
|
||||
# self.register_buffer("inv_freq", inv_freq, persistent=False)
|
||||
# For BC we register cos and sin cached
|
||||
self.max_seq_len_cached = max_position_embeddings
|
||||
|
||||
class RotaryEmbeddingV2(BaseInjectedModule, LlamaRotaryEmbedding):
|
||||
def __init__(
|
||||
self,
|
||||
@ -134,6 +191,137 @@ class YarnRotaryEmbedding(BaseInjectedModule, DeepseekV2YarnRotaryEmbedding):
|
||||
self.orig_module.mscale_all_dim,
|
||||
)
|
||||
|
||||
# class DeepSeekV3YarnRotaryEmbedding(BaseInjectedModule, DeepseekV3RotaryEmbedding):
|
||||
# def __init__(
|
||||
# self,
|
||||
# key: str,
|
||||
# gguf_loader: GGUFLoader,
|
||||
# config: PretrainedConfig,
|
||||
# orig_module: nn.Module,
|
||||
# # device: str = "cuda",
|
||||
# generate_device: str = "cuda",
|
||||
# prefill_device: str = "cuda",
|
||||
# **kwargs,
|
||||
# ):
|
||||
# BaseInjectedModule.__init__(
|
||||
# self, key, gguf_loader, config, orig_module, generate_device, **kwargs
|
||||
# )
|
||||
# self.generate_device = generate_device
|
||||
# self.prefill_device = prefill_device
|
||||
|
||||
# def load(self):
|
||||
# # TODO support perlayer prefill
|
||||
# self.orig_module.__init__(
|
||||
# self.config,
|
||||
# device=self.generate_device
|
||||
# )
|
||||
# return
|
||||
|
||||
class YarnRotaryEmbeddingV3(BaseInjectedModule):
|
||||
def __init__(
|
||||
self,
|
||||
key: str,
|
||||
gguf_loader: GGUFLoader,
|
||||
config: PretrainedConfig,
|
||||
orig_module: nn.Module,
|
||||
# device: str = "cuda",
|
||||
generate_device: str = "cuda",
|
||||
prefill_device: str = "cuda",
|
||||
**kwargs,
|
||||
):
|
||||
BaseInjectedModule.__init__(
|
||||
self, key, gguf_loader, config, orig_module, generate_device, **kwargs
|
||||
)
|
||||
self.generate_device = generate_device
|
||||
self.prefill_device = prefill_device
|
||||
|
||||
def load(self):
|
||||
kwargs = {
|
||||
key: self.config.rope_scaling[key]
|
||||
for key in [
|
||||
"original_max_position_embeddings",
|
||||
"beta_fast",
|
||||
"beta_slow",
|
||||
"mscale",
|
||||
"mscale_all_dim",
|
||||
]
|
||||
if key in self.config.rope_scaling
|
||||
}
|
||||
self._init(
|
||||
dim=self.config.qk_rope_head_dim,
|
||||
max_position_embeddings=self.config.max_position_embeddings,
|
||||
base=self.config.rope_theta,
|
||||
device=self.device,
|
||||
scaling_factor=self.config.rope_scaling["factor"],
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(self, x, position_ids):
|
||||
# x: [bs, num_attention_heads, seq_len, head_size]
|
||||
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
|
||||
position_ids_expanded = position_ids[:, None, :].float()
|
||||
# Force float32 since bfloat16 loses precision on long contexts
|
||||
# See https://github.com/huggingface/transformers/pull/29285
|
||||
device_type = x.device.type
|
||||
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
|
||||
with torch.autocast(device_type=device_type, enabled=False):
|
||||
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
||||
emb = torch.cat((freqs, freqs), dim=-1)
|
||||
cos = emb.cos()* self._mscale
|
||||
sin = emb.sin()* self._mscale
|
||||
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
||||
|
||||
def _init(
|
||||
self,
|
||||
dim,
|
||||
max_position_embeddings=2048,
|
||||
base=10000,
|
||||
device=None,
|
||||
scaling_factor=1.0,
|
||||
original_max_position_embeddings=4096,
|
||||
beta_fast=32,
|
||||
beta_slow=1,
|
||||
mscale=1,
|
||||
mscale_all_dim=0,
|
||||
):
|
||||
self.original_max_position_embeddings = original_max_position_embeddings
|
||||
self.beta_fast = beta_fast
|
||||
self.beta_slow = beta_slow
|
||||
self.mscale = mscale
|
||||
self.mscale_all_dim = mscale_all_dim
|
||||
self.scaling_factor = scaling_factor
|
||||
self.dim = dim
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.base = base
|
||||
|
||||
freq_extra = 1.0 / (
|
||||
self.base
|
||||
** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
|
||||
)
|
||||
freq_inter = 1.0 / (
|
||||
self.scaling_factor
|
||||
* self.base
|
||||
** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
|
||||
)
|
||||
|
||||
low, high = yarn_find_correction_range(
|
||||
self.beta_fast,
|
||||
self.beta_slow,
|
||||
dim,
|
||||
self.base,
|
||||
self.original_max_position_embeddings,
|
||||
)
|
||||
inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(
|
||||
device=device, dtype=torch.float32
|
||||
)
|
||||
self.inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
|
||||
self._mscale = float(
|
||||
yarn_get_mscale(self.scaling_factor, self.mscale)
|
||||
/ yarn_get_mscale(self.scaling_factor, self.mscale_all_dim)
|
||||
)
|
||||
# For BC we register cos and sin cached
|
||||
self.max_seq_len_cached = max_position_embeddings
|
||||
|
||||
class DynamicNTKScalingRotaryEmbedding(
|
||||
BaseInjectedModule, LlamaDynamicNTKScalingRotaryEmbedding
|
||||
|
||||
@ -13,6 +13,8 @@ from ktransformers.models.configuration_deepseek import DeepseekV2Config
|
||||
from ktransformers.models.configuration_llama import LlamaConfig
|
||||
from ktransformers.models.modeling_llama import LlamaRotaryEmbedding
|
||||
from ktransformers.models.modeling_deepseek import DeepseekV2Attention, apply_rotary_pos_emb
|
||||
from ktransformers.models.modeling_deepseek_v3 import DeepseekV3Attention
|
||||
from ktransformers.models.modeling_deepseek_v3 import apply_rotary_pos_emb as apply_rotary_pos_emb_v3
|
||||
from typing import Optional, Tuple
|
||||
from ktransformers.operators.base_operator import BaseInjectedModule
|
||||
from ktransformers.util.custom_gguf import GGUFLoader
|
||||
@ -20,6 +22,206 @@ import logging
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from transformers.cache_utils import Cache
|
||||
logger = logging.getLogger("attention")
|
||||
|
||||
class KDeepseekV3Attention(BaseInjectedModule, DeepseekV3Attention):
|
||||
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
||||
attn_mask: Optional[torch.Tensor] = None
|
||||
|
||||
def __init__(self,
|
||||
key: str,
|
||||
gguf_loader : GGUFLoader,
|
||||
config: PretrainedConfig,
|
||||
orig_module: nn.Module,
|
||||
device: str = "cuda",
|
||||
chunck_size: int = 1000,
|
||||
**kwargs):
|
||||
BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs)
|
||||
self.orig_module.__init__(orig_module.config,
|
||||
orig_module.layer_idx)
|
||||
self.chunck_size = chunck_size # TODO, generate chunck_size automatically.
|
||||
self.softmax_scale = self.q_head_dim ** (-0.5)
|
||||
|
||||
def get_absorbed(self) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
if not (hasattr(self, 'q_absorb') and hasattr(self, 'out_absorb')):
|
||||
kv_b_proj = self.kv_b_proj.weight.view(self.num_heads, -1, self.kv_lora_rank)
|
||||
q_absorb = kv_b_proj[:, :self.qk_nope_head_dim, :].reshape(-1, self.kv_lora_rank)
|
||||
out_absorb = kv_b_proj[:, self.qk_nope_head_dim:, :].reshape(-1, self.kv_lora_rank)
|
||||
self.q_absorb = nn.Linear(self.kv_lora_rank, self.num_heads * self.qk_nope_head_dim,
|
||||
bias=False, dtype=q_absorb.dtype, device=q_absorb.device)
|
||||
self.q_absorb.weight.data = q_absorb
|
||||
self.out_absorb = nn.Linear(self.kv_lora_rank, self.num_heads * self.v_head_dim,
|
||||
bias=False, dtype=out_absorb.dtype, device=out_absorb.device)
|
||||
self.out_absorb.weight.data = out_absorb
|
||||
del self.orig_module.kv_b_proj
|
||||
q_absorb = self.q_absorb.weight.view(self.num_heads, self.qk_nope_head_dim, self.kv_lora_rank)
|
||||
out_absorb = self.out_absorb.weight.view(self.num_heads, self.v_head_dim, self.kv_lora_rank)
|
||||
return q_absorb, out_absorb
|
||||
|
||||
def forward_chunck(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Cache] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**kwargs
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
if self.q_lora_rank is None:
|
||||
q = self.q_proj(hidden_states)
|
||||
else:
|
||||
q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
|
||||
q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
|
||||
q_nope, q_pe = torch.split(
|
||||
q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
|
||||
)
|
||||
|
||||
compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
|
||||
compressed_kv, k_pe = torch.split(
|
||||
compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
|
||||
)
|
||||
compressed_kv = self.kv_a_layernorm(compressed_kv)
|
||||
k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
|
||||
|
||||
kv_seq_len = k_pe.shape[-2]
|
||||
if past_key_value is not None:
|
||||
if self.layer_idx is None:
|
||||
raise ValueError(
|
||||
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
|
||||
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
|
||||
"with a layer index."
|
||||
)
|
||||
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
||||
|
||||
cos, sin = self.rotary_emb(q_pe, position_ids)
|
||||
q_pe, k_pe = apply_rotary_pos_emb_v3(q_pe, k_pe, cos, sin)
|
||||
|
||||
if past_key_value is not None:
|
||||
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
|
||||
compressed_kv = compressed_kv.unsqueeze(1)
|
||||
k_pe, compressed_kv = past_key_value.update(k_pe, compressed_kv, self.layer_idx, cache_kwargs)
|
||||
compressed_kv = compressed_kv.squeeze(1)
|
||||
#if cache_position is not None:
|
||||
# compressed_kv = compressed_kv[:,: cache_position[-1] + 1,:]
|
||||
# k_pe = k_pe[:,:,: cache_position[-1] + 1,:]
|
||||
q_absorb, out_absorb = self.get_absorbed()
|
||||
|
||||
q_nope = torch.matmul(q_nope, q_absorb)
|
||||
attn_weights = (torch.matmul(q_pe, k_pe.mT) + torch.matmul(q_nope, compressed_kv.unsqueeze(-3).mT)) * self.softmax_scale
|
||||
"""
|
||||
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
||||
raise ValueError(
|
||||
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
||||
f" {attn_weights.size()}"
|
||||
)
|
||||
assert attention_mask is not None
|
||||
"""
|
||||
if attention_mask is not None:
|
||||
"""
|
||||
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
||||
raise ValueError(
|
||||
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
||||
)
|
||||
"""
|
||||
#causal_mask = attention_mask[:, :, :, : kv_seq_len]
|
||||
attn_weights = attn_weights + attention_mask
|
||||
|
||||
# upcast attention to fp32
|
||||
attn_weights = nn.functional.softmax(
|
||||
attn_weights, dim=-1, dtype=torch.float32
|
||||
).to(q_pe.dtype)
|
||||
attn_weights = nn.functional.dropout(
|
||||
attn_weights, p=self.attention_dropout, training=self.training
|
||||
)
|
||||
attn_output = torch.einsum('bhql,blc->bhqc', attn_weights, compressed_kv)
|
||||
|
||||
attn_output = torch.matmul(attn_output, out_absorb.mT)
|
||||
|
||||
if attn_output.size() != (bsz, self.num_heads, q_len, self.v_head_dim):
|
||||
raise ValueError(
|
||||
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.v_head_dim)}, but is"
|
||||
f" {attn_output.size()}"
|
||||
)
|
||||
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim)
|
||||
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
return attn_output, attn_weights, past_key_value
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Cache] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**kwargs,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
if "padding_mask" in kwargs:
|
||||
warnings.warn(
|
||||
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
||||
)
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
|
||||
if q_len <= self.chunck_size:
|
||||
return self.forward_chunck(
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
position_ids,
|
||||
past_key_value,
|
||||
output_attentions,
|
||||
use_cache,
|
||||
cache_position,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
assert output_attentions == False, "output_attentions is not supported when using chunked attention"
|
||||
attn_output = None
|
||||
attn_weight = None
|
||||
cur_idx = 0
|
||||
while cur_idx < q_len:
|
||||
if attention_mask is not None:
|
||||
chunk_mask = attention_mask[:, :, cur_idx:min(cur_idx + self.chunck_size, q_len), ...]
|
||||
else:
|
||||
# generate chunk_mask automatically.
|
||||
self.attn_mask = \
|
||||
torch.zeros(1, 1, self.chunck_size, past_key_value.max_cache_len, device=hidden_states.device) \
|
||||
if self.attn_mask is None \
|
||||
else self.attn_mask
|
||||
self.attn_mask[:, :, :, cur_idx:min(cur_idx+self.chunck_size, past_key_value.max_cache_len)] = \
|
||||
-1e+38 * torch.triu(torch.ones(self.chunck_size, self.chunck_size, device=hidden_states.device), diagonal=1)\
|
||||
[:,:min(self.chunck_size, min(past_key_value.max_cache_len-cur_idx, self.chunck_size))]
|
||||
self.attn_mask[:, :, :, cur_idx+self.chunck_size:] = -1e+38
|
||||
self.attn_mask[:, :, :, :cur_idx] = 0
|
||||
chunk_mask = torch.narrow(self.attn_mask, 2, 0, min(self.chunck_size, q_len-cur_idx))
|
||||
|
||||
cur_output, cur_attn_weight = self.forward_chunck(
|
||||
hidden_states[:, cur_idx:min(cur_idx + self.chunck_size, q_len), ...],
|
||||
chunk_mask,
|
||||
position_ids[:, cur_idx:min(cur_idx + self.chunck_size, q_len)],
|
||||
past_key_value,
|
||||
output_attentions,
|
||||
use_cache,
|
||||
cache_position[cur_idx:min(cur_idx + self.chunck_size, q_len)],
|
||||
**kwargs
|
||||
)
|
||||
cur_idx += self.chunck_size
|
||||
if attn_output is None:
|
||||
attn_output = cur_output
|
||||
attn_weight = cur_attn_weight
|
||||
else:
|
||||
attn_output = torch.cat((attn_output, cur_output), dim=-2)
|
||||
attn_weight = torch.cat((attn_weight, cur_attn_weight), dim=-2)
|
||||
|
||||
return attn_output, attn_weight, past_key_value
|
||||
|
||||
class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
|
||||
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
||||
attn_mask: Optional[torch.Tensor] = None
|
||||
|
||||
@ -302,13 +302,13 @@ class KExpertsMarlin(KExpertsBase):
|
||||
if w is None: w = self.load_weights()[self.key]
|
||||
|
||||
if isinstance(w, dict):
|
||||
self.gate = nn.Parameter(torch.from_numpy(w["gate"]))
|
||||
self.up = nn.Parameter(torch.from_numpy(w["up"]))
|
||||
self.down = nn.Parameter(torch.from_numpy(w["down"]))
|
||||
self.gate = w["gate"]
|
||||
self.up = (w["up"])
|
||||
self.down = (w["down"])
|
||||
for i in range(self.expert_num):
|
||||
self.up_projs[i].load(self.up[i,...], device=device)
|
||||
self.gate_projs[i].load(self.gate[i,...], device=device)
|
||||
self.down_projs[i].load(self.down[i,...], device=device)
|
||||
self.up_projs[i].load(nn.Parameter(self.up[i,...]), device=device)
|
||||
self.gate_projs[i].load(nn.Parameter(self.gate[i,...]), device=device)
|
||||
self.down_projs[i].load(nn.Parameter(self.down[i,...]), device=device)
|
||||
self.loaded_experts_idx.append(i)
|
||||
return
|
||||
|
||||
@ -342,23 +342,45 @@ class KExpertsMarlin(KExpertsBase):
|
||||
up_type = self.gguf_loader.tensor_info[key + ".ffn_up_exps.weight"]["ggml_type"]
|
||||
down_type = self.gguf_loader.tensor_info[key + ".ffn_down_exps.weight"]["ggml_type"]
|
||||
# tensors = self.load_multi(key, [".ffn_gate_exps.weight", ".ffn_up_exps.weight", ".ffn_down_exps.weight"])
|
||||
res = {key:{"gate": gate, "up": up, "down": down, "gate_type": gate_type, "up_type": up_type, "down_type": down_type}}
|
||||
res = {key:{"gate": nn.Parameter(gate), "up": nn.Parameter(up), "down": nn.Parameter(down), "gate_type": gate_type, "up_type": up_type, "down_type": down_type}}
|
||||
return res
|
||||
|
||||
def forward(self, input_tensor:torch.Tensor, expert_ids, weights):
|
||||
# forward
|
||||
device = input_tensor.device
|
||||
input_tensor = input_tensor.to("cuda")
|
||||
outs = torch.zeros_like(input_tensor)
|
||||
for expert_idx in range(expert_ids.size(0)):
|
||||
down_proj = self.down_projs[expert_idx]
|
||||
gate_proj = self.gate_projs[expert_idx]
|
||||
up_proj = self.up_projs[expert_idx]
|
||||
def forward(self, hidden_states_cpu: torch.Tensor, selected_experts_cpu: torch.Tensor, routing_weights_cpu: torch.Tensor) -> torch.Tensor:
|
||||
org_dtype = hidden_states_cpu.dtype
|
||||
org_device = hidden_states_cpu.device
|
||||
hidden_states_cpu = hidden_states_cpu.to(self.device)
|
||||
selected_experts_cpu = selected_experts_cpu.to(self.device)
|
||||
routing_weights_cpu = routing_weights_cpu.to(self.device).to(org_dtype)
|
||||
|
||||
batch_sequence_length, hidden_dim = hidden_states_cpu.size()
|
||||
|
||||
outs += down_proj(self.act_fn(gate_proj(input_tensor)) * up_proj(input_tensor)) * weights[expert_idx]
|
||||
outs = outs.to(device)
|
||||
return outs
|
||||
final_hidden_states = torch.zeros(
|
||||
(batch_sequence_length, hidden_dim), dtype=hidden_states_cpu.dtype, device=hidden_states_cpu.device
|
||||
)
|
||||
# One hot encode the selected experts to create an expert mask
|
||||
# this will be used to easily index which expert is going to be sollicitated
|
||||
expert_mask = torch.nn.functional.one_hot(selected_experts_cpu, num_classes=self.expert_num).permute(2, 1, 0)
|
||||
|
||||
# Loop over all available experts in the model and perform the computation on each expert
|
||||
for expert_idx in range(self.expert_num):
|
||||
if not expert_mask[expert_idx].any():
|
||||
continue
|
||||
idx, top_x = torch.where(expert_mask[expert_idx])
|
||||
# Index the correct hidden states and compute the expert hidden state for
|
||||
# the current expert. We need to make sure to multiply the output hidden
|
||||
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
|
||||
current_state = hidden_states_cpu[None, top_x].reshape(-1, hidden_dim)
|
||||
G = self.gate_projs[expert_idx].forward(current_state)
|
||||
A = self.act_fn(G)
|
||||
U = self.up_projs[expert_idx].forward(current_state)
|
||||
H = A * U # Element-wise multiplication
|
||||
current_hidden_states = self.down_projs[expert_idx].forward(H) * routing_weights_cpu[top_x, idx, None]
|
||||
# However `index_add_` only support torch tensors for indexing so we'll use
|
||||
# the `top_x` tensor here.
|
||||
final_hidden_states.index_add_(0, top_x, current_hidden_states)
|
||||
|
||||
return final_hidden_states.to(dtype=org_dtype, device=org_device)
|
||||
|
||||
class KExpertsTorch(KExpertsBase):
|
||||
expert_num: int
|
||||
loaded_experts_idx: list[int]
|
||||
@ -519,6 +541,7 @@ class KTransformersExperts(BaseInjectedModule, KExpertsBase):
|
||||
|
||||
|
||||
from ktransformers.models.modeling_deepseek import DeepseekV2MoE
|
||||
from ktransformers.models.modeling_deepseek_v3 import DeepseekV3MoE
|
||||
from ktransformers.models.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock
|
||||
from ktransformers.models.modeling_mixtral import MixtralSparseMoeBlock
|
||||
|
||||
@ -727,6 +750,107 @@ class KDeepseekV2MoE(BaseInjectedModule, DeepseekV2MoE):
|
||||
)
|
||||
return final_out
|
||||
|
||||
class KDeepseekV3MoE(BaseInjectedModule, DeepseekV3MoE):
|
||||
|
||||
def forward(self, hidden_states):
|
||||
identity = hidden_states
|
||||
orig_shape = hidden_states.shape
|
||||
sequence_length = orig_shape[1]
|
||||
topk_idx, topk_weight = self.gate(hidden_states)
|
||||
hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
|
||||
|
||||
# only for generate phase
|
||||
if sequence_length == 1 and hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_current_stream_capturing():
|
||||
self.experts.generate_experts.submit_for_one_decode(hidden_states[0], topk_idx[0], topk_weight[0])
|
||||
if self.config.n_shared_experts is not None:
|
||||
y_ = self.shared_experts(identity).squeeze(0)
|
||||
y = self.experts.generate_experts.sync_for_one_decode().unsqueeze(0)
|
||||
y += y_
|
||||
y.resize_(*orig_shape)
|
||||
return y
|
||||
|
||||
if self.config.n_shared_experts is not None:
|
||||
y_ = self.shared_experts(identity).squeeze(0)
|
||||
|
||||
if isinstance(self.experts, KExpertsBase):
|
||||
y = self.moe_on_cpuinfer(hidden_states, topk_idx, topk_weight).view(*orig_shape).to(device=hidden_states.device)
|
||||
elif hidden_states.size(0) > 10:
|
||||
# TODO may bugs here
|
||||
y = (
|
||||
self.moe_infer(hidden_states, topk_idx, topk_weight)
|
||||
.view(*orig_shape)
|
||||
.to(device=hidden_states.device)
|
||||
)
|
||||
else:
|
||||
# TODO may bugs here
|
||||
y = (
|
||||
self.moe_infer_simple(hidden_states, topk_idx, topk_weight)
|
||||
.view(*orig_shape)
|
||||
.to(device=hidden_states.device)
|
||||
)
|
||||
if self.config.n_shared_experts is not None:
|
||||
y += y_
|
||||
return y
|
||||
|
||||
@torch.no_grad()
|
||||
def moe_on_cpuinfer(self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor) -> torch.Tensor:
|
||||
outs = torch.empty_like(x)
|
||||
outs = self.experts(x, topk_ids, topk_weight)
|
||||
return outs
|
||||
|
||||
@torch.no_grad()
|
||||
# TODO may bugs here
|
||||
def moe_infer_simple(
|
||||
self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
x: [num_tokens, hidden_size]
|
||||
topk_ids, topk_weight: [num_tokens, num_selected_experts]
|
||||
"""
|
||||
outs = torch.zeros_like(x)
|
||||
for token_idx in range(topk_ids.size(0)):
|
||||
for expert_idx in range(topk_ids.size(1)):
|
||||
expert = self.experts[topk_ids[token_idx, expert_idx]]
|
||||
outs[token_idx] += (
|
||||
expert.forward(x[token_idx]) * topk_weight[token_idx, expert_idx]
|
||||
)
|
||||
return outs
|
||||
|
||||
@torch.no_grad()
|
||||
# TODO may bugs here
|
||||
def moe_infer(self, x, topk_ids, topk_weight):
|
||||
cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
|
||||
cnts.scatter_(1, topk_ids, 1)
|
||||
tokens_per_expert = cnts.sum(dim=0)
|
||||
idxs = topk_ids.view(-1).argsort()
|
||||
sorted_tokens = x[idxs // topk_ids.shape[1]]
|
||||
tokens_per_expert = tokens_per_expert.cpu().numpy()
|
||||
|
||||
outputs = []
|
||||
start_idx = 0
|
||||
for i, num_tokens in enumerate(tokens_per_expert):
|
||||
end_idx = start_idx + num_tokens
|
||||
if num_tokens == 0:
|
||||
continue
|
||||
expert = self.experts[i + self.ep_rank * self.experts_per_rank]
|
||||
tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
|
||||
expert_out = expert.forward(tokens_for_this_expert)
|
||||
outputs.append(expert_out)
|
||||
start_idx = end_idx
|
||||
|
||||
outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
|
||||
|
||||
new_x = torch.empty_like(outs)
|
||||
new_x[idxs] = outs
|
||||
final_out = (
|
||||
new_x.view(*topk_ids.shape, -1)
|
||||
.type(topk_weight.dtype)
|
||||
.mul_(topk_weight.unsqueeze(dim=-1))
|
||||
.sum(dim=1)
|
||||
.type(new_x.dtype)
|
||||
)
|
||||
return final_out
|
||||
|
||||
class KMistralSparseMoEBlock(BaseInjectedModule, MixtralSparseMoeBlock):
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
|
||||
126
ktransformers/operators/gate.py
Normal file
126
ktransformers/operators/gate.py
Normal file
@ -0,0 +1,126 @@
|
||||
|
||||
from typing import Any, Union
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
from torch import Tensor, nn
|
||||
import torch.nn.functional as F
|
||||
import torch
|
||||
import sys, os
|
||||
from ktransformers.operators.base_operator import BaseInjectedModule
|
||||
|
||||
sys.path.append(os.path.join(os.path.dirname(__file__), "..", "ktransformers_ext", "build"))
|
||||
sys.path.append(os.path.join(os.path.dirname(__file__), "..", "ktransformers_ext", "build", "Release"))
|
||||
sys.path.append(os.path.join(os.path.dirname(__file__), "..", "ktransformers_ext", "build", "Debug"))
|
||||
import cpuinfer_ext
|
||||
from cpuinfer_ext.moe import MOEConfig, MOE
|
||||
import ctypes
|
||||
from ktransformers.operators.base_operator import BaseInjectedModule
|
||||
from ktransformers.util.custom_gguf import GGUFLoader
|
||||
from transformers.activations import ACT2FN
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from abc import ABC, abstractmethod
|
||||
import time
|
||||
|
||||
|
||||
# class Base(BaseInjectedModule, ABC):
|
||||
class KMoEGateBase(ABC):
|
||||
def __init__(self,
|
||||
key: str,
|
||||
gguf_loader: GGUFLoader,
|
||||
config: PretrainedConfig,
|
||||
orig_module: nn.Module,
|
||||
device: str = "cuda",
|
||||
**kwargs):
|
||||
# super().__init__(key, gguf_loader, config, orig_module, device, **kwargs)
|
||||
super().__init__()
|
||||
self.key = key
|
||||
self.gguf_loader = gguf_loader
|
||||
self.config = config
|
||||
self.device = device
|
||||
self.orig_module = orig_module
|
||||
|
||||
@abstractmethod
|
||||
def forward(self, input_tensor, expert_ids, weights):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def load(self, w: dict | nn.Parameter | tuple | None = None, device: str = "cpu", warmup: bool = False):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def unload():
|
||||
pass
|
||||
|
||||
def load_weights(self, override_key: str | None = None, device: str = "cpu"):
|
||||
res = {}
|
||||
if override_key is not None:
|
||||
keys = override_key
|
||||
else:
|
||||
keys = [self.key]
|
||||
|
||||
gate = None
|
||||
up = None
|
||||
down = None
|
||||
gate_type = None
|
||||
up_type = None
|
||||
down_type = None
|
||||
|
||||
for key in keys:
|
||||
key = ".".join(key.split(".")[:-1])
|
||||
if key + ".ffn_gate_inp.weight" in self.gguf_loader.tensor_info:
|
||||
targets = [".ffn_gate_inp.weight", ".exp_probs_b.bias"]
|
||||
tensors = self.load_multi(key, targets, device=device)
|
||||
weight = tensors[".ffn_gate_inp.weight"]
|
||||
e_score_correction_bias = tensors[".exp_probs_b.bias"]
|
||||
weight_type = self.gguf_loader.tensor_info[key + ".ffn_gate_inp.weight"]["ggml_type"]
|
||||
e_score_correction_bias_type = self.gguf_loader.tensor_info[key + ".exp_probs_b.bias"]["ggml_type"]
|
||||
else:
|
||||
raise ValueError(f"Experts {key} not found in gguf_loader")
|
||||
res = {"weight": weight, "e_score_correction_bias": e_score_correction_bias, "weight_type": weight_type, "e_score_correction_bias_type": e_score_correction_bias_type}
|
||||
return res
|
||||
|
||||
def load_multi(self, key: str, keys: list[str], device: str = "cpu"):
|
||||
tensors = {}
|
||||
for k in keys:
|
||||
tensors[k] = self.gguf_loader.load_gguf_tensor(key + k, device=device)
|
||||
return tensors
|
||||
|
||||
|
||||
class KMoEGate(BaseInjectedModule, KMoEGateBase):
|
||||
def __init__(
|
||||
self,
|
||||
key: str,
|
||||
gguf_loader: GGUFLoader,
|
||||
config: PretrainedConfig,
|
||||
orig_module: nn.Module = None,
|
||||
generate_device: str = "cuda",
|
||||
prefill_device: str = "cuda",
|
||||
**kwargs,
|
||||
):
|
||||
BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, generate_device, **kwargs)
|
||||
KMoEGateBase.__init__(self, key, gguf_loader, config, orig_module, generate_device, **kwargs)
|
||||
self.generate_device = generate_device
|
||||
self.prefill_device = prefill_device
|
||||
|
||||
def forward(self, hidden_states) -> torch.Tensor:
|
||||
return self.orig_module.forward(hidden_states)
|
||||
|
||||
def load(self, w: dict | nn.Parameter | tuple | None = None, device: str|None = None):
|
||||
if device is None: device = self.device
|
||||
if w is None: w = self.load_weights(device=device)
|
||||
|
||||
if isinstance(w, dict):
|
||||
self.weight_type = w["weight_type"]
|
||||
self.e_score_correction_bias_type = w["e_score_correction_bias_type"]
|
||||
self.orig_module.weight = nn.Parameter(w["weight"])
|
||||
self.orig_module.e_score_correction_bias = nn.Parameter(w["e_score_correction_bias"])
|
||||
else:
|
||||
raise ValueError("Invalid weight type")
|
||||
self.orig_module.weight = self.orig_module.weight.to(device)
|
||||
self.orig_module.e_score_correction_bias = self.orig_module.e_score_correction_bias.to(device)
|
||||
|
||||
def unload(self):
|
||||
if self.weight is not None:
|
||||
self.weight = None
|
||||
if self.e_score_correction_bias is not None:
|
||||
self.e_score_correction_bias = None
|
||||
@ -54,15 +54,15 @@ class KLinearBase(ABC):
|
||||
|
||||
self.has_bias = False
|
||||
self.dtype = torch.get_default_dtype()
|
||||
if orig_module is not None:
|
||||
self.in_features = orig_module.in_features
|
||||
self.out_features = orig_module.out_features
|
||||
else:
|
||||
shape = self.gguf_loader.tensor_info[key + ".weight"]["shape"]
|
||||
if len(shape) == 1:
|
||||
print("Warning: orig_module is not set, but has in_features or out_features equals to 1, can't get in_features and out_features from GGUF")
|
||||
self.in_features = self.gguf_loader.tensor_info[key + ".weight"]["shape"][0]
|
||||
self.out_features = self.gguf_loader.tensor_info[key + ".weight"]["shape"][1]
|
||||
# if orig_module is not None:
|
||||
# self.in_features = orig_module.in_features
|
||||
# self.out_features = orig_module.out_features
|
||||
# else:
|
||||
shape = self.gguf_loader.tensor_info[key + ".weight"]["shape"]
|
||||
if len(shape) == 1:
|
||||
print("Warning: orig_module is not set, but has in_features or out_features equals to 1, can't get in_features and out_features from GGUF")
|
||||
self.in_features = self.gguf_loader.tensor_info[key + ".weight"]["shape"][0]
|
||||
self.out_features = self.gguf_loader.tensor_info[key + ".weight"]["shape"][1]
|
||||
|
||||
@abstractmethod
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
@ -138,10 +138,10 @@ class KLinearTorch(KLinearBase):
|
||||
if w is None: w = self.load_weight(device=device)
|
||||
|
||||
if isinstance(w, nn.Parameter):
|
||||
self.w = w.to(dtype=self.dtype).view(self.out_features, self.in_features).T
|
||||
self.w = w.to(dtype=self.dtype).T
|
||||
self.has_bias = False
|
||||
elif isinstance(w, tuple):
|
||||
self.w = w[0].to(dtype=self.dtype).view(self.out_features, self.in_features).T
|
||||
self.w = w[0].to(dtype=self.dtype).T
|
||||
self.bias = w[1].to(dtype=self.dtype)
|
||||
self.has_bias = True
|
||||
else:
|
||||
@ -222,7 +222,7 @@ class KLinearMarlin(KLinearBase):
|
||||
x = x.to(self.device)
|
||||
orig_shape = list(x.shape)
|
||||
orig_dtype = x.dtype
|
||||
x = x.reshape(-1, x.shape[-1])
|
||||
x = x.reshape(-1, orig_shape[-1])
|
||||
marlin_s = self.marlin_s.to(x.dtype)
|
||||
x = KTransformersOps.gptq_marlin_gemm(
|
||||
x,
|
||||
|
||||
@ -625,6 +625,13 @@ class KDeepseekV2Model(BaseInjectedModule):
|
||||
if use_legacy_cache:
|
||||
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
||||
past_key_values_length = past_key_values.get_usable_length(seq_length)
|
||||
|
||||
if inputs_embeds is None:
|
||||
org_device = input_ids.device
|
||||
# TODO move to embed_tokens's device, not hard code to cpu
|
||||
input_ids = input_ids.to("cpu")
|
||||
inputs_embeds = self.embed_tokens(input_ids).to(org_device)
|
||||
input_ids = input_ids.to(org_device)
|
||||
|
||||
if cache_position is None:
|
||||
past_seen_tokens = (
|
||||
@ -639,12 +646,6 @@ class KDeepseekV2Model(BaseInjectedModule):
|
||||
if position_ids is None:
|
||||
position_ids = cache_position.unsqueeze(0)
|
||||
|
||||
if inputs_embeds is None:
|
||||
org_device = input_ids.device
|
||||
input_ids = input_ids.to("cpu")
|
||||
inputs_embeds = self.embed_tokens(input_ids)
|
||||
input_ids = input_ids.to(org_device)
|
||||
|
||||
if per_layer_prefill_flag:
|
||||
causal_mask = None
|
||||
else:
|
||||
@ -716,6 +717,8 @@ class KDeepseekV2Model(BaseInjectedModule):
|
||||
self.load_layer_to(decoder_layer, InferenceState.PREFILL)
|
||||
torch.cuda.empty_cache()
|
||||
t4 = time.time()
|
||||
# with open("log.txt", "a") as f:
|
||||
# f.write(f"@@@@@@@@@@@@@@@@@layer {i}@@@@@@@@@@@@@@@@@@@@ \n")
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=causal_mask,
|
||||
@ -737,6 +740,7 @@ class KDeepseekV2Model(BaseInjectedModule):
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
# @@@@@@@ TODO open this notes, tmp close to fit deepseekv3
|
||||
if use_cache:
|
||||
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
||||
|
||||
@ -744,6 +748,10 @@ class KDeepseekV2Model(BaseInjectedModule):
|
||||
all_self_attns += (layer_outputs[1],)
|
||||
|
||||
hidden_states = self.norm(hidden_states)
|
||||
# with open("log.txt", "a") as f:
|
||||
# f.write(f"@@@After layers\n")
|
||||
# f.write(f"hidden_states={hidden_states}\n")
|
||||
# f.write(f"hidden_states.shape={hidden_states.shape}\n")
|
||||
|
||||
if per_layer_prefill_flag:
|
||||
t6 = time.time()
|
||||
|
||||
@ -0,0 +1,143 @@
|
||||
- match:
|
||||
name: "^model.embed_tokens"
|
||||
replace:
|
||||
class: "default"
|
||||
kwargs:
|
||||
generate_device: "cpu"
|
||||
prefill_device: "cpu"
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\."
|
||||
class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
|
||||
replace:
|
||||
class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
|
||||
kwargs:
|
||||
generate_device: "cuda:0"
|
||||
prefill_device: "cuda:0"
|
||||
- match:
|
||||
name: "^model\\.layers\\.([3456][0-9])\\."
|
||||
class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
|
||||
replace:
|
||||
class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
|
||||
kwargs:
|
||||
generate_device: "cuda:1"
|
||||
prefill_device: "cuda:1"
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.(?!self_attn\\.kv_b_proj).*$" # regular expression
|
||||
class: torch.nn.Linear # only match modules matching name and class simultaneously
|
||||
replace:
|
||||
class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
|
||||
kwargs:
|
||||
generate_device: "cuda:0"
|
||||
prefill_device: "cuda:0"
|
||||
generate_op: "KLinearMarlin"
|
||||
prefill_op: "KLinearTorch"
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.([3456][0-9])\\.(?!self_attn\\.kv_b_proj).*$" # regular expression
|
||||
class: torch.nn.Linear # only match modules matching name and class simultaneously
|
||||
replace:
|
||||
class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
|
||||
kwargs:
|
||||
generate_device: "cuda:1"
|
||||
prefill_device: "cuda:1"
|
||||
generate_op: "KLinearMarlin"
|
||||
prefill_op: "KLinearTorch"
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp$"
|
||||
class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
|
||||
replace:
|
||||
class: ktransformers.operators.experts.KDeepseekV3MoE # mlp module with custom forward function
|
||||
kwargs:
|
||||
generate_device: "cuda:0"
|
||||
prefill_device: "cuda:0"
|
||||
- match:
|
||||
name: "^model\\.layers\\.([3456][0-9])\\.mlp$"
|
||||
class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
|
||||
replace:
|
||||
class: ktransformers.operators.experts.KDeepseekV3MoE # mlp module with custom forward function
|
||||
kwargs:
|
||||
generate_device: "cuda:1"
|
||||
prefill_device: "cuda:1"
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp\\.gate$"
|
||||
class: ktransformers.models.modeling_deepseek_v3.MoEGate
|
||||
replace:
|
||||
class: ktransformers.operators.gate.KMoEGate
|
||||
kwargs:
|
||||
generate_device: "cuda:0"
|
||||
prefill_device: "cuda:0"
|
||||
- match:
|
||||
name: "^model\\.layers\\.([3456][0-9])\\.mlp\\.gate$"
|
||||
class: ktransformers.models.modeling_deepseek_v3.MoEGate
|
||||
replace:
|
||||
class: ktransformers.operators.gate.KMoEGate # mlp module with custom forward function
|
||||
kwargs:
|
||||
generate_device: "cuda:1"
|
||||
prefill_device: "cuda:1"
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp\\.experts$"
|
||||
replace:
|
||||
class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
|
||||
kwargs:
|
||||
prefill_device: "cuda:0"
|
||||
prefill_op: "KExpertsTorch"
|
||||
generate_device: "cpu"
|
||||
generate_op: "KExpertsCPU"
|
||||
out_device: "cuda:0"
|
||||
recursive: False # don't recursively inject submodules of this module
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.([3456][0-9])\\.mlp\\.experts$"
|
||||
replace:
|
||||
class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
|
||||
kwargs:
|
||||
prefill_device: "cuda:1"
|
||||
prefill_op: "KExpertsTorch"
|
||||
generate_device: "cpu"
|
||||
generate_op: "KExpertsCPU"
|
||||
out_device: "cuda:1"
|
||||
recursive: False # don't recursively inject submodules of this module
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.self_attn$"
|
||||
replace:
|
||||
class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
|
||||
kwargs:
|
||||
generate_device: "cuda:0"
|
||||
prefill_device: "cuda:0"
|
||||
- match:
|
||||
name: "^model\\.layers\\.([3456][0-9])\\.self_attn$"
|
||||
replace:
|
||||
class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
|
||||
kwargs:
|
||||
generate_device: "cuda:1"
|
||||
prefill_device: "cuda:1"
|
||||
- match:
|
||||
name: "^model$"
|
||||
replace:
|
||||
class: "ktransformers.operators.models.KDeepseekV2Model"
|
||||
kwargs:
|
||||
per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
|
||||
transfer_map:
|
||||
30: "cuda:1"
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\."
|
||||
replace:
|
||||
class: "default"
|
||||
kwargs:
|
||||
generate_device: "cuda:0"
|
||||
prefill_device: "cuda:0"
|
||||
|
||||
- match:
|
||||
name: "(^model\\.layers\\.([3456][0-9])\\.)|(model.norm)|(lm_head)"
|
||||
replace:
|
||||
class: "default"
|
||||
kwargs:
|
||||
generate_device: "cuda:1"
|
||||
prefill_device: "cuda:1"
|
||||
@ -0,0 +1,143 @@
|
||||
- match:
|
||||
name: "^model.embed_tokens"
|
||||
replace:
|
||||
class: "default"
|
||||
kwargs:
|
||||
generate_device: "cpu"
|
||||
prefill_device: "cpu"
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\."
|
||||
class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
|
||||
replace:
|
||||
class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
|
||||
kwargs:
|
||||
generate_device: "cuda:0"
|
||||
prefill_device: "cuda:0"
|
||||
- match:
|
||||
name: "^model\\.layers\\.([3456][0-9])\\."
|
||||
class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
|
||||
replace:
|
||||
class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
|
||||
kwargs:
|
||||
generate_device: "cuda:1"
|
||||
prefill_device: "cuda:1"
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.(?!self_attn\\.kv_b_proj).*$" # regular expression
|
||||
class: torch.nn.Linear # only match modules matching name and class simultaneously
|
||||
replace:
|
||||
class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
|
||||
kwargs:
|
||||
generate_device: "cuda:0"
|
||||
prefill_device: "cuda:0"
|
||||
generate_op: "KLinearMarlin"
|
||||
prefill_op: "KLinearTorch"
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.([3456][0-9])\\.(?!self_attn\\.kv_b_proj).*$" # regular expression
|
||||
class: torch.nn.Linear # only match modules matching name and class simultaneously
|
||||
replace:
|
||||
class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
|
||||
kwargs:
|
||||
generate_device: "cuda:1"
|
||||
prefill_device: "cuda:1"
|
||||
generate_op: "KLinearMarlin"
|
||||
prefill_op: "KLinearTorch"
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp$"
|
||||
class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
|
||||
replace:
|
||||
class: ktransformers.operators.experts.KDeepseekV3MoE # mlp module with custom forward function
|
||||
kwargs:
|
||||
generate_device: "cuda:0"
|
||||
prefill_device: "cuda:0"
|
||||
- match:
|
||||
name: "^model\\.layers\\.([3456][0-9])\\.mlp$"
|
||||
class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
|
||||
replace:
|
||||
class: ktransformers.operators.experts.KDeepseekV3MoE # mlp module with custom forward function
|
||||
kwargs:
|
||||
generate_device: "cuda:1"
|
||||
prefill_device: "cuda:1"
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp\\.gate$"
|
||||
class: ktransformers.models.modeling_deepseek_v3.MoEGate
|
||||
replace:
|
||||
class: ktransformers.operators.gate.KMoEGate
|
||||
kwargs:
|
||||
generate_device: "cuda:0"
|
||||
prefill_device: "cuda:0"
|
||||
- match:
|
||||
name: "^model\\.layers\\.([3456][0-9])\\.mlp\\.gate$"
|
||||
class: ktransformers.models.modeling_deepseek_v3.MoEGate
|
||||
replace:
|
||||
class: ktransformers.operators.gate.KMoEGate # mlp module with custom forward function
|
||||
kwargs:
|
||||
generate_device: "cuda:1"
|
||||
prefill_device: "cuda:1"
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp\\.experts$"
|
||||
replace:
|
||||
class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
|
||||
kwargs:
|
||||
prefill_device: "cuda:0"
|
||||
prefill_op: "KExpertsTorch"
|
||||
generate_device: "cpu"
|
||||
generate_op: "KExpertsCPU"
|
||||
out_device: "cuda:0"
|
||||
recursive: False # don't recursively inject submodules of this module
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.([3456][0-9])\\.mlp\\.experts$"
|
||||
replace:
|
||||
class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
|
||||
kwargs:
|
||||
prefill_device: "cuda:1"
|
||||
prefill_op: "KExpertsTorch"
|
||||
generate_device: "cpu"
|
||||
generate_op: "KExpertsCPU"
|
||||
out_device: "cuda:1"
|
||||
recursive: False # don't recursively inject submodules of this module
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.self_attn$"
|
||||
replace:
|
||||
class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
|
||||
kwargs:
|
||||
generate_device: "cuda:0"
|
||||
prefill_device: "cuda:0"
|
||||
- match:
|
||||
name: "^model\\.layers\\.([3456][0-9])\\.self_attn$"
|
||||
replace:
|
||||
class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
|
||||
kwargs:
|
||||
generate_device: "cuda:1"
|
||||
prefill_device: "cuda:1"
|
||||
- match:
|
||||
name: "^model$"
|
||||
replace:
|
||||
class: "ktransformers.operators.models.KDeepseekV2Model"
|
||||
kwargs:
|
||||
per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
|
||||
transfer_map:
|
||||
30: "cuda:1"
|
||||
|
||||
- match:
|
||||
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\."
|
||||
replace:
|
||||
class: "default"
|
||||
kwargs:
|
||||
generate_device: "cuda:0"
|
||||
prefill_device: "cuda:0"
|
||||
|
||||
- match:
|
||||
name: "(^model\\.layers\\.([3456][0-9])\\.)|(model.norm)|(lm_head)"
|
||||
replace:
|
||||
class: "default"
|
||||
kwargs:
|
||||
generate_device: "cuda:1"
|
||||
prefill_device: "cuda:1"
|
||||
63
ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml
Normal file
63
ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml
Normal file
@ -0,0 +1,63 @@
|
||||
- match:
|
||||
class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
|
||||
replace:
|
||||
class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
|
||||
kwargs:
|
||||
generate_device: "cuda"
|
||||
prefill_device: "cuda"
|
||||
- match:
|
||||
name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$" # regular expression
|
||||
class: torch.nn.Linear # only match modules matching name and class simultaneously
|
||||
replace:
|
||||
class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
|
||||
kwargs:
|
||||
generate_device: "cuda"
|
||||
prefill_device: "cuda"
|
||||
generate_op: "KLinearMarlin"
|
||||
prefill_op: "KLinearTorch"
|
||||
- match:
|
||||
name: "^model\\.layers\\..*\\.mlp$"
|
||||
class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
|
||||
replace:
|
||||
class: ktransformers.operators.experts.KDeepseekV3MoE # mlp module with custom forward function
|
||||
kwargs:
|
||||
generate_device: "cuda"
|
||||
prefill_device: "cuda"
|
||||
- match:
|
||||
class: ktransformers.models.modeling_deepseek_v3.MoEGate
|
||||
replace:
|
||||
class: ktransformers.operators.gate.KMoEGate
|
||||
kwargs:
|
||||
generate_device: "cuda:0"
|
||||
prefill_device: "cuda:0"
|
||||
- match:
|
||||
name: "^model\\.layers\\..*\\.mlp\\.experts$"
|
||||
replace:
|
||||
class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
|
||||
kwargs:
|
||||
prefill_device: "cuda"
|
||||
prefill_op: "KExpertsTorch"
|
||||
generate_device: "cpu"
|
||||
generate_op: "KExpertsCPU"
|
||||
out_device: "cuda"
|
||||
recursive: False # don't recursively inject submodules of this module
|
||||
- match:
|
||||
name: "^model\\.layers\\..*\\.self_attn$"
|
||||
replace:
|
||||
class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
|
||||
kwargs:
|
||||
generate_device: "cuda"
|
||||
prefill_device: "cuda"
|
||||
- match:
|
||||
name: "^model$"
|
||||
replace:
|
||||
class: "ktransformers.operators.models.KDeepseekV2Model"
|
||||
kwargs:
|
||||
per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
|
||||
- match:
|
||||
name: "^model.embed_tokens"
|
||||
replace:
|
||||
class: "default"
|
||||
kwargs:
|
||||
generate_device: "cpu"
|
||||
prefill_device: "cpu"
|
||||
@ -24,8 +24,8 @@ class KTransformersInterface(TransformersInterface):
|
||||
self.args = args
|
||||
torch.set_default_dtype(torch.bfloat16)
|
||||
torch.set_grad_enabled(False)
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(args.model_dir, device=args.device)
|
||||
config = AutoConfig.from_pretrained(args.model_dir, trust_remote_code=True)
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(args.model_dir, device=args.device, trust_remote_code=args.trust_remote_code)
|
||||
config = AutoConfig.from_pretrained(args.model_dir, trust_remote_code=args.trust_remote_code)
|
||||
if config.architectures[0] == "Qwen2MoeForCausalLM":
|
||||
config._attn_implementation = "flash_attention_2"
|
||||
|
||||
@ -46,51 +46,61 @@ class KTransformersInterface(TransformersInterface):
|
||||
)
|
||||
optimize_and_load_gguf(self.model, optimize_rule_path, gguf_path, config)
|
||||
|
||||
device_map = self.model.gguf_loader.tensor_device_map
|
||||
logger.info(f"{args.model_name} loaded from {args.model_dir} to {device_map}")
|
||||
self.device_map = self.model.gguf_loader.tensor_device_map
|
||||
# logger.info(f"{args.model_name} loaded from {args.model_dir} to {self.device_map}")
|
||||
self.cache = StaticCache(
|
||||
config=self.model.config,
|
||||
max_batch_size=args.batch_size,
|
||||
max_cache_len=args.cache_lens,
|
||||
device=device_map,
|
||||
device=self.device_map,
|
||||
dtype=self.model.dtype,
|
||||
)
|
||||
logger.info(f"StaticCache (length={args.cache_lens}) created at {device_map}, batch size:{args.batch_size}")
|
||||
self.model.generation_config = GenerationConfig.from_pretrained(args.model_dir)
|
||||
# logger.info(f"StaticCache (length={args.cache_lens}), batch size:{args.batch_size}")
|
||||
try:
|
||||
self.model.generation_config = GenerationConfig.from_pretrained(args.model_dir)
|
||||
except:
|
||||
gen_config = GenerationConfig(
|
||||
max_length=128,
|
||||
temperature=0.7,
|
||||
top_p=0.9,
|
||||
do_sample=True
|
||||
)
|
||||
self.model.generation_config = gen_config
|
||||
if self.model.generation_config.pad_token_id is None:
|
||||
self.model.generation_config.pad_token_id = self.model.generation_config.eos_token_id
|
||||
self.streamer = TextStreamer(self.tokenizer)
|
||||
|
||||
def decode_one_tokens(self):
|
||||
if not hasattr(self, "cuda_graph_runner"):
|
||||
device_map = self.model.gguf_loader.tensor_device_map
|
||||
torch_device = get_device("blk.0.self_attn", device_map)
|
||||
torch_device = "cuda:0" if torch_device == "cuda" else torch_device
|
||||
self.cuda_graph_runner = CUDAGraphRunner()
|
||||
self.cuda_graph_runner.capture(
|
||||
self.model,
|
||||
self.current_ids,
|
||||
self.active_cache_position.unsqueeze(0),
|
||||
self.active_cache_position,
|
||||
self.cache,
|
||||
main_device=torch_device,
|
||||
return_dict=False,
|
||||
use_cache=True,
|
||||
)
|
||||
device_map = self.model.gguf_loader.tensor_device_map
|
||||
torch_device = get_device("blk.0.self_attn", device_map)
|
||||
torch_device = "cuda:0" if torch_device == "cuda" else torch_device
|
||||
if self.args.use_cuda_graph:
|
||||
if not hasattr(self, "cuda_graph_runner"):
|
||||
self.cuda_graph_runner = CUDAGraphRunner()
|
||||
self.cuda_graph_runner.capture(
|
||||
self.model,
|
||||
self.current_ids,
|
||||
self.active_cache_position.unsqueeze(0),
|
||||
self.active_cache_position,
|
||||
self.cache,
|
||||
main_device=torch_device,
|
||||
return_dict=False,
|
||||
use_cache=True,
|
||||
)
|
||||
|
||||
if hasattr(self, "cuda_graph_runner"):
|
||||
logits = self.cuda_graph_runner(
|
||||
self.current_ids, self.active_cache_position.unsqueeze(0), self.active_cache_position
|
||||
)
|
||||
self.cache.change_seq_length(1)
|
||||
torch.cuda.synchronize()
|
||||
logits = logits[0, -1, :]
|
||||
return self.logits_to_token(logits)
|
||||
if hasattr(self, "cuda_graph_runner"):
|
||||
logits = self.cuda_graph_runner(
|
||||
self.current_ids, self.active_cache_position.unsqueeze(0), self.active_cache_position
|
||||
)
|
||||
self.cache.change_seq_length(1)
|
||||
torch.cuda.synchronize()
|
||||
logits = logits[0, -1, :]
|
||||
return self.logits_to_token(logits)
|
||||
|
||||
if self.use_static_cache:
|
||||
mask = torch.ones((1, self.seq_length)).to(torch_device)
|
||||
logits = self.model(
|
||||
self.current_ids,
|
||||
self.current_ids.to(torch_device),
|
||||
cache_position=self.active_cache_position,
|
||||
past_key_values=self.cache,
|
||||
attention_mask=mask,
|
||||
@ -102,3 +112,63 @@ class KTransformersInterface(TransformersInterface):
|
||||
logits = logits[0, -1, :]
|
||||
|
||||
return self.logits_to_token(logits)
|
||||
|
||||
|
||||
|
||||
@torch.no_grad
|
||||
def prefill(self, input_ids: torch.Tensor, is_new: bool):
|
||||
input_ids_length = input_ids.shape[-1]
|
||||
self.profiler.set_counter("prefill", input_ids_length)
|
||||
logger.debug(f"input_ids: {input_ids.shape}")
|
||||
|
||||
device = self.device_map.get("blk.0.self_attn", {}).get("generate_device", "cuda:0")
|
||||
|
||||
if is_new:
|
||||
self.cache.reset()
|
||||
self.ever_generated_ids.clear()
|
||||
former_seq_length = 0
|
||||
self.seq_length = input_ids_length
|
||||
self.generated_ids = torch.zeros(
|
||||
self.args.batch_size,
|
||||
self.seq_length + self.args.max_new_tokens + 1,
|
||||
dtype=torch.int,
|
||||
device=self.args.device,
|
||||
)
|
||||
else:
|
||||
logger.debug(f"generate_ids: {self.generated_ids.shape}")
|
||||
former_seq_length = self.seq_length
|
||||
self.seq_length += input_ids_length
|
||||
expected_length = self.seq_length + self.args.max_new_tokens + 1
|
||||
delta_length = expected_length - self.generated_ids.shape[-1]
|
||||
if delta_length > 0:
|
||||
new_generate_ids = torch.zeros(
|
||||
self.args.batch_size, delta_length, dtype=torch.int, device=self.args.device
|
||||
)
|
||||
self.generated_ids = torch.cat([self.generated_ids, new_generate_ids], dim=-1)
|
||||
logger.debug(f"cache position: {former_seq_length} to {self.seq_length}")
|
||||
cache_position = torch.arange(former_seq_length, self.seq_length, device=device)
|
||||
self.generated_ids[:, cache_position] = input_ids.to(self.args.device).to(torch.int)
|
||||
|
||||
mask = torch.ones((1, self.seq_length)).to(device)
|
||||
if not (type(self) is TransformersInterface):
|
||||
input_ids = input_ids.to("cpu")
|
||||
inputs_embeds = self.model.model.embed_tokens(input_ids).to(device)
|
||||
if self.use_static_cache:
|
||||
logits = self.model(
|
||||
inputs_embeds=inputs_embeds,
|
||||
cache_position=cache_position,
|
||||
past_key_values=self.cache,
|
||||
return_dict=False,
|
||||
use_cache=True,
|
||||
attention_mask=mask,
|
||||
)[0]
|
||||
else:
|
||||
logits = self.model(inputs_embeds=inputs_embeds, return_dict=False)[0]
|
||||
|
||||
next_token = self.logits_to_token(logits[0, -1, :])
|
||||
yield self.append_new_tokens(next_token)
|
||||
|
||||
@property
|
||||
def active_cache_position(self):
|
||||
device = self.device_map.get("blk.0.self_attn", {}).get("generate_device", "cuda:0")
|
||||
return torch.tensor([self.seq_length - 1], device=device)
|
||||
@ -134,7 +134,7 @@ class TransformersInterface(BackendInterfaceBase):
|
||||
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(args.model_dir)
|
||||
self.model = AutoModelForCausalLM.from_pretrained(args.model_dir, device_map=args.device, use_safetensors=True)
|
||||
logger.info(f"{args.model_name} loaded from {args.model_dir} to {args.device}")
|
||||
# logger.info(f"{args.model_name} loaded from {args.model_dir} to {args.device}")
|
||||
|
||||
self.cache = StaticCache(
|
||||
config=self.model.config,
|
||||
@ -143,7 +143,7 @@ class TransformersInterface(BackendInterfaceBase):
|
||||
device=args.device,
|
||||
dtype=self.model.dtype,
|
||||
)
|
||||
logger.info(f"StaticCache (length={args.cache_lens}) created at {args.device}, batch size:{args.batch_size}")
|
||||
# logger.info(f"StaticCache (length={args.cache_lens}) created at {args.device}, batch size:{args.batch_size}")
|
||||
|
||||
self.streamer = TextStreamer(self.tokenizer)
|
||||
|
||||
@ -198,7 +198,7 @@ class TransformersInterface(BackendInterfaceBase):
|
||||
return self.streamer.put(new_tokens)
|
||||
|
||||
def logits_to_token(self, logits: torch.Tensor):
|
||||
logits = logits / self.args.temperature
|
||||
logits = logits / self.args.temperature if self.args.temperature!=0 else logits
|
||||
|
||||
for token_idx in self.ever_generated_ids:
|
||||
if logits[token_idx] < 0:
|
||||
@ -318,7 +318,9 @@ class TransformersInterface(BackendInterfaceBase):
|
||||
if isinstance(local_messages, List):
|
||||
input_ids = self.format_and_tokenize_input_ids(thread_id, local_messages)
|
||||
elif isinstance(local_messages, str):
|
||||
#local_messages = local_messages[0]['content']
|
||||
input_ids = self.tokenize_prompt(local_messages)
|
||||
#input_ids = torch.tensor([[6366]], device=input_ids.device)
|
||||
else:
|
||||
raise ValueError("local_messages should be List or str")
|
||||
|
||||
@ -327,14 +329,14 @@ class TransformersInterface(BackendInterfaceBase):
|
||||
self.profiler.create_and_start_timer("prefill")
|
||||
for t in self.prefill(input_ids, self.check_is_new(thread_id)):
|
||||
if t is not None:
|
||||
print(t, end="")
|
||||
print(t, end="",flush=True)
|
||||
yield t
|
||||
self.profiler.pause_timer("prefill")
|
||||
|
||||
self.profiler.create_and_start_timer("decode")
|
||||
for t in self.generate():
|
||||
if t is not None:
|
||||
print(t, end="")
|
||||
print(t, end="",flush=True)
|
||||
yield t
|
||||
print("")
|
||||
self.profiler.pause_timer("decode")
|
||||
|
||||
@ -93,6 +93,8 @@ class Config(metaclass=Singleton):
|
||||
self.model_name: str = self.model.get("name", "")
|
||||
self.model_device: str = self.model.get("device", "cuda:0")
|
||||
self.gguf_path: Optional[str] = self.model.get("gguf_path", None)
|
||||
self.use_cuda_graph = self.model.get("use_cuda_graph", True)
|
||||
self.trust_remote_code = self.model.get("trust_remote_code", True)
|
||||
# self.model_cache_lens = self.model.get("cache_lens")
|
||||
self.optimize_config_path: Optional[str] = self.model.get(
|
||||
"optimize_config_path", None
|
||||
@ -102,7 +104,7 @@ class Config(metaclass=Singleton):
|
||||
self.total_context = self.model.get("total_context", 2**18)
|
||||
self.max_batch_size = self.model.get("max_batch_size", 20 if self.paged else 1)
|
||||
self.max_chunk_size = self.model.get("max_chunk_size", 2048)
|
||||
self.max_new_tokens = self.model.get("max_new_tokens", 500)
|
||||
self.max_new_tokens = self.model.get("max_new_tokens", 2000)
|
||||
self.json_mode = self.model.get("json_mode", False)
|
||||
self.healing = self.model.get("healing", False)
|
||||
self.ban_strings: Optional[list] = self.model.get("ban_strings", None)
|
||||
|
||||
592
ktransformers/util/modeling_rope_utils.py
Normal file
592
ktransformers/util/modeling_rope_utils.py
Normal file
@ -0,0 +1,592 @@
|
||||
# Copyright 2024 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import math
|
||||
from typing import Optional, Tuple
|
||||
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from transformers.utils import is_torch_available, logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
|
||||
def _compute_default_rope_parameters(
|
||||
config: Optional[PretrainedConfig] = None,
|
||||
device: Optional["torch.device"] = None,
|
||||
seq_len: Optional[int] = None,
|
||||
**rope_kwargs,
|
||||
) -> Tuple["torch.Tensor", float]:
|
||||
"""
|
||||
Computes the inverse frequencies according to the original RoPE implementation
|
||||
Args:
|
||||
config ([`~transformers.PretrainedConfig`]):
|
||||
The model configuration.
|
||||
device (`torch.device`):
|
||||
The device to use for initialization of the inverse frequencies.
|
||||
seq_len (`int`, *optional*):
|
||||
The current sequence length. Unused for this type of RoPE.
|
||||
rope_kwargs (`Dict`, *optional*):
|
||||
BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
|
||||
Returns:
|
||||
Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
|
||||
post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
|
||||
"""
|
||||
if config is not None and len(rope_kwargs) > 0:
|
||||
raise ValueError(
|
||||
"Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
|
||||
f"`_compute_default_rope_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
|
||||
)
|
||||
if len(rope_kwargs) > 0:
|
||||
base = rope_kwargs["base"]
|
||||
dim = rope_kwargs["dim"]
|
||||
elif config is not None:
|
||||
base = config.rope_theta
|
||||
partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
|
||||
head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
||||
dim = int(head_dim * partial_rotary_factor)
|
||||
|
||||
attention_factor = 1.0 # Unused in this type of RoPE
|
||||
|
||||
# Compute the inverse frequencies
|
||||
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
|
||||
return inv_freq, attention_factor
|
||||
|
||||
|
||||
def _compute_linear_scaling_rope_parameters(
|
||||
config: Optional[PretrainedConfig] = None,
|
||||
device: Optional["torch.device"] = None,
|
||||
seq_len: Optional[int] = None,
|
||||
**rope_kwargs,
|
||||
) -> Tuple["torch.Tensor", float]:
|
||||
"""
|
||||
Computes the inverse frequencies with linear scaling. Credits to the Reddit user /u/kaiokendev
|
||||
Args:
|
||||
config ([`~transformers.PretrainedConfig`]):
|
||||
The model configuration.
|
||||
device (`torch.device`):
|
||||
The device to use for initialization of the inverse frequencies.
|
||||
seq_len (`int`, *optional*):
|
||||
The current sequence length. Unused for this type of RoPE.
|
||||
rope_kwargs (`Dict`, *optional*):
|
||||
BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
|
||||
Returns:
|
||||
Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
|
||||
post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
|
||||
"""
|
||||
if config is not None and len(rope_kwargs) > 0:
|
||||
raise ValueError(
|
||||
"Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
|
||||
f"`_compute_linear_scaling_rope_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
|
||||
)
|
||||
if len(rope_kwargs) > 0:
|
||||
factor = rope_kwargs["factor"]
|
||||
elif config is not None:
|
||||
factor = config.rope_scaling["factor"]
|
||||
|
||||
# Gets the default RoPE parameters
|
||||
inv_freq, attention_factor = _compute_default_rope_parameters(config, device, seq_len, **rope_kwargs)
|
||||
|
||||
# Then applies linear scaling to the frequencies.
|
||||
# NOTE: originally, scaling was applied to the position_ids. However, we get `embs = inv_freq @ position_ids`, so
|
||||
# applying scaling to the inverse frequencies is equivalent.
|
||||
inv_freq /= factor
|
||||
return inv_freq, attention_factor
|
||||
|
||||
|
||||
def _compute_dynamic_ntk_parameters(
|
||||
config: Optional[PretrainedConfig] = None,
|
||||
device: Optional["torch.device"] = None,
|
||||
seq_len: Optional[int] = None,
|
||||
**rope_kwargs,
|
||||
) -> Tuple["torch.Tensor", float]:
|
||||
"""
|
||||
Computes the inverse frequencies with NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla
|
||||
Args:
|
||||
config ([`~transformers.PretrainedConfig`]):
|
||||
The model configuration.
|
||||
device (`torch.device`):
|
||||
The device to use for initialization of the inverse frequencies.
|
||||
seq_len (`int`, *optional*):
|
||||
The current sequence length, used to update the dynamic RoPE at inference time.
|
||||
rope_kwargs (`Dict`, *optional*):
|
||||
BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
|
||||
Returns:
|
||||
Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
|
||||
post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
|
||||
"""
|
||||
# TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
|
||||
if config is not None and len(rope_kwargs) > 0:
|
||||
raise ValueError(
|
||||
"Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
|
||||
f"`_compute_dynamic_ntk_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
|
||||
)
|
||||
if len(rope_kwargs) > 0:
|
||||
base = rope_kwargs["base"]
|
||||
dim = rope_kwargs["dim"]
|
||||
max_position_embeddings = rope_kwargs["max_position_embeddings"]
|
||||
factor = rope_kwargs["factor"]
|
||||
elif config is not None:
|
||||
base = config.rope_theta
|
||||
partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
|
||||
head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
||||
dim = int(head_dim * partial_rotary_factor)
|
||||
max_position_embeddings = config.max_position_embeddings
|
||||
factor = config.rope_scaling["factor"]
|
||||
|
||||
attention_factor = 1.0 # Unused in this type of RoPE
|
||||
|
||||
# seq_len: default to max_position_embeddings, e.g. at init time
|
||||
seq_len = seq_len if seq_len is not None and seq_len > max_position_embeddings else max_position_embeddings
|
||||
|
||||
# Compute the inverse frequencies
|
||||
base = base * ((factor * seq_len / max_position_embeddings) - (factor - 1)) ** (dim / (dim - 2))
|
||||
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
|
||||
return inv_freq, attention_factor
|
||||
|
||||
|
||||
def _compute_yarn_parameters(
|
||||
config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
|
||||
) -> Tuple["torch.Tensor", float]:
|
||||
"""
|
||||
Computes the inverse frequencies with NTK scaling. Please refer to the
|
||||
[original paper](https://arxiv.org/abs/2309.00071)
|
||||
Args:
|
||||
config ([`~transformers.PretrainedConfig`]):
|
||||
The model configuration.
|
||||
device (`torch.device`):
|
||||
The device to use for initialization of the inverse frequencies.
|
||||
seq_len (`int`, *optional*):
|
||||
The current sequence length. Unused for this type of RoPE.
|
||||
rope_kwargs (`Dict`, *optional*):
|
||||
BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
|
||||
Returns:
|
||||
Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
|
||||
post-processing scaling factor applied to the computed cos/sin.
|
||||
"""
|
||||
# No need to keep BC with yarn, unreleased when this new pattern was created.
|
||||
if len(rope_kwargs) > 0:
|
||||
raise ValueError(
|
||||
f"Unexpected arguments: `**rope_kwargs` should be unset in `_compute_yarn_parameters`, got {rope_kwargs}"
|
||||
)
|
||||
|
||||
base = config.rope_theta
|
||||
partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
|
||||
head_dim = getattr(config, "qk_rope_head_dim", config.hidden_size // config.num_attention_heads)
|
||||
dim = int(head_dim * partial_rotary_factor)
|
||||
factor = config.rope_scaling["factor"]
|
||||
attention_factor = config.rope_scaling.get("attention_factor")
|
||||
mscale = config.rope_scaling.get("mscale")
|
||||
mscale_all_dim = config.rope_scaling.get("mscale_all_dim")
|
||||
|
||||
# NOTE: DeekSeek-V3 (and potentially other models) modify `max_position_embeddings` and have a
|
||||
# `original_max_position_embeddings` field containing the pretrained value. They use the ratio between these two
|
||||
# values to compute the default attention scaling factor, instead of using `factor`.
|
||||
if "original_max_position_embeddings" in config.rope_scaling:
|
||||
original_max_position_embeddings = config.rope_scaling["original_max_position_embeddings"]
|
||||
factor = config.max_position_embeddings / original_max_position_embeddings
|
||||
else:
|
||||
original_max_position_embeddings = config.max_position_embeddings
|
||||
|
||||
def get_mscale(scale, mscale=1):
|
||||
if scale <= 1:
|
||||
return 1.0
|
||||
return 0.1 * mscale * math.log(scale) + 1.0
|
||||
|
||||
# Sets the attention factor as suggested in the paper
|
||||
if attention_factor is None:
|
||||
if mscale and mscale_all_dim:
|
||||
attention_factor = float(get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dim))
|
||||
else:
|
||||
attention_factor = get_mscale(factor)
|
||||
|
||||
# Optional config options
|
||||
# beta_fast/beta_slow: as suggested in the paper, default to 32/1 (correspondingly)
|
||||
beta_fast = config.rope_scaling.get("beta_fast") or 32
|
||||
beta_slow = config.rope_scaling.get("beta_slow") or 1
|
||||
|
||||
# Compute the inverse frequencies
|
||||
def find_correction_dim(num_rotations, dim, base, max_position_embeddings):
|
||||
"""Inverse dimension formula to find the dimension based on the number of rotations"""
|
||||
return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (2 * math.log(base))
|
||||
|
||||
def find_correction_range(low_rot, high_rot, dim, base, max_position_embeddings):
|
||||
"""Find dimension range bounds based on rotations"""
|
||||
low = math.floor(find_correction_dim(low_rot, dim, base, max_position_embeddings))
|
||||
high = math.ceil(find_correction_dim(high_rot, dim, base, max_position_embeddings))
|
||||
return max(low, 0), min(high, dim - 1)
|
||||
|
||||
def linear_ramp_factor(min, max, dim):
|
||||
if min == max:
|
||||
max += 0.001 # Prevent singularity
|
||||
|
||||
linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
|
||||
ramp_func = torch.clamp(linear_func, 0, 1)
|
||||
return ramp_func
|
||||
|
||||
# Note on variable naming: "interpolation" comes from the original technique, where we interpolate the position IDs
|
||||
# to expand the possible context length. In other words, interpolation = apply scaling factor.
|
||||
pos_freqs = base ** (torch.arange(0, dim, 2).float().to(device) / dim)
|
||||
inv_freq_extrapolation = 1.0 / pos_freqs
|
||||
inv_freq_interpolation = 1.0 / (factor * pos_freqs)
|
||||
|
||||
low, high = find_correction_range(beta_fast, beta_slow, dim, base, original_max_position_embeddings)
|
||||
|
||||
# Get n-dimensional rotational scaling corrected for extrapolation
|
||||
inv_freq_extrapolation_factor = 1 - linear_ramp_factor(low, high, dim // 2).float().to(device)
|
||||
inv_freq = (
|
||||
inv_freq_interpolation * (1 - inv_freq_extrapolation_factor)
|
||||
+ inv_freq_extrapolation * inv_freq_extrapolation_factor
|
||||
)
|
||||
return inv_freq, attention_factor
|
||||
|
||||
|
||||
def _compute_longrope_parameters(
|
||||
config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
|
||||
) -> Tuple["torch.Tensor", float]:
|
||||
"""
|
||||
Computes the inverse frequencies with LongRoPE scaling. Please refer to the
|
||||
[original implementation](https://github.com/microsoft/LongRoPE)
|
||||
Args:
|
||||
config ([`~transformers.PretrainedConfig`]):
|
||||
The model configuration.
|
||||
device (`torch.device`):
|
||||
The device to use for initialization of the inverse frequencies.
|
||||
seq_len (`int`, *optional*):
|
||||
The current sequence length.
|
||||
rope_kwargs (`Dict`, *optional*):
|
||||
BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
|
||||
Returns:
|
||||
Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
|
||||
post-processing scaling factor applied to the computed cos/sin.
|
||||
"""
|
||||
# TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
|
||||
# No need to keep BC with longrope, unreleased when this new pattern was created.
|
||||
if len(rope_kwargs) > 0:
|
||||
raise ValueError(
|
||||
"Unexpected arguments: `**rope_kwargs` should be unset in `_compute_longrope_parameters`, got "
|
||||
f"{rope_kwargs}"
|
||||
)
|
||||
|
||||
base = config.rope_theta
|
||||
partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
|
||||
head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
||||
dim = int(head_dim * partial_rotary_factor)
|
||||
long_factor = config.rope_scaling["long_factor"]
|
||||
short_factor = config.rope_scaling["short_factor"]
|
||||
factor = config.rope_scaling.get("factor")
|
||||
attention_factor = config.rope_scaling.get("attention_factor")
|
||||
|
||||
# NOTE: Phi3 (and potentially other models) modify `max_position_embeddings` and have a
|
||||
# `original_max_position_embeddings` field containing the pretrained value. They use the ratio between these two
|
||||
# values to compute the default attention scaling factor, instead of using `factor`.
|
||||
if hasattr(config, "original_max_position_embeddings"):
|
||||
original_max_position_embeddings = config.original_max_position_embeddings
|
||||
factor = config.max_position_embeddings / config.original_max_position_embeddings
|
||||
else:
|
||||
original_max_position_embeddings = config.max_position_embeddings
|
||||
|
||||
# Sets the attention factor as suggested in the paper
|
||||
if attention_factor is None:
|
||||
if factor <= 1.0:
|
||||
attention_factor = 1.0
|
||||
else:
|
||||
attention_factor = math.sqrt(1 + math.log(factor) / math.log(original_max_position_embeddings))
|
||||
|
||||
# Compute the inverse frequencies -- scaled based on the target sequence length
|
||||
if seq_len and seq_len > original_max_position_embeddings:
|
||||
ext_factors = torch.tensor(long_factor, dtype=torch.float32, device=device)
|
||||
else:
|
||||
ext_factors = torch.tensor(short_factor, dtype=torch.float32, device=device)
|
||||
inv_freq_shape = torch.arange(0, dim, 2, dtype=torch.int64, device=device).float() / dim
|
||||
inv_freq = 1.0 / (ext_factors * base**inv_freq_shape)
|
||||
|
||||
return inv_freq, attention_factor
|
||||
|
||||
|
||||
def _compute_llama3_parameters(
|
||||
config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
|
||||
) -> Tuple["torch.Tensor", float]:
|
||||
"""
|
||||
Computes the inverse frequencies for llama 3.1.
|
||||
|
||||
Args:
|
||||
config ([`~transformers.PretrainedConfig`]):
|
||||
The model configuration.
|
||||
device (`torch.device`):
|
||||
The device to use for initialization of the inverse frequencies.
|
||||
seq_len (`int`, *optional*):
|
||||
The current sequence length. Unused for this type of RoPE.
|
||||
rope_kwargs (`Dict`, *optional*):
|
||||
BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
|
||||
Returns:
|
||||
Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
|
||||
post-processing scaling factor applied to the computed cos/sin.
|
||||
"""
|
||||
# Gets the default RoPE parameters
|
||||
inv_freq, attention_factor = _compute_default_rope_parameters(config, device, seq_len, **rope_kwargs)
|
||||
|
||||
factor = config.rope_scaling["factor"] # `8` in the original implementation
|
||||
low_freq_factor = config.rope_scaling["low_freq_factor"] # `1` in the original implementation
|
||||
high_freq_factor = config.rope_scaling["high_freq_factor"] # `4` in the original implementation
|
||||
old_context_len = config.rope_scaling["original_max_position_embeddings"] # `8192` in the original implementation
|
||||
|
||||
low_freq_wavelen = old_context_len / low_freq_factor
|
||||
high_freq_wavelen = old_context_len / high_freq_factor
|
||||
|
||||
wavelen = 2 * math.pi / inv_freq
|
||||
# wavelen < high_freq_wavelen: do nothing
|
||||
# wavelen > low_freq_wavelen: divide by factor
|
||||
inv_freq_llama = torch.where(wavelen > low_freq_wavelen, inv_freq / factor, inv_freq)
|
||||
# otherwise: interpolate between the two, using a smooth factor
|
||||
smooth_factor = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
|
||||
smoothed_inv_freq = (1 - smooth_factor) * inv_freq_llama / factor + smooth_factor * inv_freq_llama
|
||||
is_medium_freq = ~(wavelen < high_freq_wavelen) * ~(wavelen > low_freq_wavelen)
|
||||
inv_freq_llama = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)
|
||||
|
||||
return inv_freq_llama, attention_factor
|
||||
|
||||
|
||||
# This maps the "rope_type" string field in rope config to the corresponding function to compute the RoPE parameters
|
||||
# from the model config. You can append new {'rope_type': callable} pairs to this dictionary to enable custom RoPE
|
||||
# parameterizations, as long as the callable has the same signature.
|
||||
ROPE_INIT_FUNCTIONS = {
|
||||
"default": _compute_default_rope_parameters,
|
||||
"linear": _compute_linear_scaling_rope_parameters,
|
||||
"dynamic": _compute_dynamic_ntk_parameters,
|
||||
"yarn": _compute_yarn_parameters,
|
||||
"longrope": _compute_longrope_parameters,
|
||||
"llama3": _compute_llama3_parameters,
|
||||
}
|
||||
|
||||
|
||||
def _check_received_keys(
|
||||
rope_type: str,
|
||||
received_keys: set,
|
||||
required_keys: set,
|
||||
optional_keys: Optional[set] = None,
|
||||
ignore_keys: Optional[set] = None,
|
||||
):
|
||||
"""Compare the received keys in `config.rope_scaling` against the expected and optional keys"""
|
||||
# BC: "rope_type" was originally "type" -- let's check for "rope_type" when "type" is present
|
||||
if "type" in received_keys:
|
||||
received_keys -= {"type"}
|
||||
required_keys.add("rope_type")
|
||||
|
||||
# Some models need to store model-specific keys, and we don't want to throw warning at them
|
||||
if ignore_keys is not None:
|
||||
received_keys -= ignore_keys
|
||||
|
||||
missing_keys = required_keys - received_keys
|
||||
if missing_keys:
|
||||
raise KeyError(f"Missing required keys in `rope_scaling` for 'rope_type'='{rope_type}': {missing_keys}")
|
||||
|
||||
if optional_keys is not None:
|
||||
unused_keys = received_keys - required_keys - optional_keys
|
||||
else:
|
||||
unused_keys = received_keys - required_keys
|
||||
if unused_keys:
|
||||
logger.warning(f"Unrecognized keys in `rope_scaling` for 'rope_type'='{rope_type}': {unused_keys}")
|
||||
|
||||
|
||||
def _validate_default_rope_parameters(config: PretrainedConfig, ignore_keys: Optional[set] = None):
|
||||
rope_scaling = config.rope_scaling
|
||||
rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None)) # BC: "rope_type" was originally "type"
|
||||
required_keys = {"rope_type"}
|
||||
received_keys = set(rope_scaling.keys())
|
||||
_check_received_keys(rope_type, received_keys, required_keys, ignore_keys=ignore_keys)
|
||||
|
||||
|
||||
def _validate_linear_scaling_rope_parameters(config: PretrainedConfig, ignore_keys: Optional[set] = None):
|
||||
rope_scaling = config.rope_scaling
|
||||
rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None)) # BC: "rope_type" was originally "type"
|
||||
required_keys = {"rope_type", "factor"}
|
||||
received_keys = set(rope_scaling.keys())
|
||||
_check_received_keys(rope_type, received_keys, required_keys, ignore_keys=ignore_keys)
|
||||
|
||||
factor = rope_scaling["factor"]
|
||||
if factor is None or not isinstance(factor, float) or factor < 1.0:
|
||||
logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
|
||||
|
||||
|
||||
def _validate_dynamic_scaling_rope_parameters(config: PretrainedConfig, ignore_keys: Optional[set] = None):
|
||||
rope_scaling = config.rope_scaling
|
||||
rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None)) # BC: "rope_type" was originally "type"
|
||||
required_keys = {"rope_type", "factor"}
|
||||
# TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
|
||||
optional_keys = {"original_max_position_embeddings"}
|
||||
received_keys = set(rope_scaling.keys())
|
||||
_check_received_keys(rope_type, received_keys, required_keys, optional_keys, ignore_keys=ignore_keys)
|
||||
|
||||
factor = rope_scaling["factor"]
|
||||
if factor is None or not isinstance(factor, float) or factor < 1.0:
|
||||
logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
|
||||
|
||||
|
||||
def _validate_yarn_parameters(config: PretrainedConfig, ignore_keys: Optional[set] = None):
|
||||
rope_scaling = config.rope_scaling
|
||||
rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None)) # BC: "rope_type" was originally "type"
|
||||
required_keys = {"rope_type", "factor"}
|
||||
optional_keys = {
|
||||
"attention_factor",
|
||||
"beta_fast",
|
||||
"beta_slow",
|
||||
"original_max_position_embeddings",
|
||||
"mscale",
|
||||
"mscale_all_dim",
|
||||
}
|
||||
received_keys = set(rope_scaling.keys())
|
||||
_check_received_keys(rope_type, received_keys, required_keys, optional_keys, ignore_keys=ignore_keys)
|
||||
|
||||
factor = rope_scaling["factor"]
|
||||
if factor is None or not isinstance(factor, float) or factor < 1.0:
|
||||
logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
|
||||
|
||||
attention_factor = rope_scaling.get("attention_factor")
|
||||
if attention_factor is not None and (not isinstance(attention_factor, float) or attention_factor < 0):
|
||||
logger.warning(
|
||||
f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
|
||||
)
|
||||
beta_fast = rope_scaling.get("beta_fast")
|
||||
if beta_fast is not None and not isinstance(beta_fast, float):
|
||||
logger.warning(f"`rope_scaling`'s beta_fast field must be a float, got {beta_fast}")
|
||||
beta_slow = rope_scaling.get("beta_slow")
|
||||
if beta_slow is not None and not isinstance(beta_slow, float):
|
||||
logger.warning(f"`rope_scaling`'s beta_slow field must be a float, got {beta_slow}")
|
||||
|
||||
if (beta_fast or 32) < (beta_slow or 1):
|
||||
logger.warning(
|
||||
f"`rope_scaling`'s beta_fast field must be greater than beta_slow, got beta_fast={beta_fast} "
|
||||
f"(defaults to 32 if None) and beta_slow={beta_slow} (defaults to 1 if None)"
|
||||
)
|
||||
|
||||
|
||||
def _validate_longrope_parameters(config: PretrainedConfig, ignore_keys: Optional[set] = None):
|
||||
rope_scaling = config.rope_scaling
|
||||
rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None)) # BC: "rope_type" was originally "type"
|
||||
required_keys = {"rope_type", "short_factor", "long_factor"}
|
||||
# TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
|
||||
optional_keys = {"attention_factor", "factor", "original_max_position_embeddings"}
|
||||
received_keys = set(rope_scaling.keys())
|
||||
_check_received_keys(rope_type, received_keys, required_keys, optional_keys, ignore_keys=ignore_keys)
|
||||
|
||||
partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
|
||||
head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
||||
dim = int(head_dim * partial_rotary_factor)
|
||||
|
||||
short_factor = rope_scaling.get("short_factor")
|
||||
if not isinstance(short_factor, list) and all(isinstance(x, (int, float)) for x in short_factor):
|
||||
logger.warning(f"`rope_scaling`'s short_factor field must be a list of numbers, got {short_factor}")
|
||||
if not len(short_factor) == dim // 2:
|
||||
logger.warning(f"`rope_scaling`'s short_factor field must have length {dim // 2}, got {len(short_factor)}")
|
||||
|
||||
long_factor = rope_scaling.get("long_factor")
|
||||
if not isinstance(long_factor, list) and all(isinstance(x, (int, float)) for x in long_factor):
|
||||
logger.warning(f"`rope_scaling`'s long_factor field must be a list of numbers, got {long_factor}")
|
||||
if not len(long_factor) == dim // 2:
|
||||
logger.warning(f"`rope_scaling`'s long_factor field must have length {dim // 2}, got {len(long_factor)}")
|
||||
|
||||
# Handle Phi3 divergence: prefer the use of `attention_factor` and/or `factor` over
|
||||
# `original_max_position_embeddings` to compute internal variables. The latter lives outside `rope_scaling` and is
|
||||
# unique to longrope (= undesirable)
|
||||
if hasattr(config, "original_max_position_embeddings"):
|
||||
logger.warning_once(
|
||||
"This model has set a `original_max_position_embeddings` field, to be used together with "
|
||||
"`max_position_embeddings` to determine a scaling factor. Please set the `factor` field of `rope_scaling`"
|
||||
"with this ratio instead -- we recommend the use of this field over `original_max_position_embeddings`, "
|
||||
"as it is compatible with most model architectures."
|
||||
)
|
||||
else:
|
||||
factor = rope_scaling.get("factor")
|
||||
if factor is None:
|
||||
logger.warning("Missing required keys in `rope_scaling`: 'factor'")
|
||||
elif not isinstance(factor, float) or factor < 1.0:
|
||||
logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
|
||||
|
||||
attention_factor = rope_scaling.get("attention_factor")
|
||||
if attention_factor is not None:
|
||||
if not isinstance(attention_factor, float) or attention_factor < 0.0:
|
||||
logger.warning(
|
||||
f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
|
||||
)
|
||||
|
||||
|
||||
def _validate_llama3_parameters(config: PretrainedConfig, ignore_keys: Optional[set] = None):
|
||||
rope_scaling = config.rope_scaling
|
||||
rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None)) # BC: "rope_type" was originally "type"
|
||||
required_keys = {"rope_type", "factor", "original_max_position_embeddings", "low_freq_factor", "high_freq_factor"}
|
||||
received_keys = set(rope_scaling.keys())
|
||||
_check_received_keys(rope_type, received_keys, required_keys, ignore_keys=ignore_keys)
|
||||
|
||||
factor = rope_scaling["factor"]
|
||||
if factor is None or not isinstance(factor, float) or factor < 1.0:
|
||||
logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
|
||||
|
||||
low_freq_factor = rope_scaling["low_freq_factor"]
|
||||
high_freq_factor = rope_scaling["high_freq_factor"]
|
||||
if low_freq_factor is None or not isinstance(low_freq_factor, float):
|
||||
logger.warning(f"`rope_scaling`'s low_freq_factor field must be a float, got {low_freq_factor}")
|
||||
if high_freq_factor is None or not isinstance(high_freq_factor, float):
|
||||
logger.warning(f"`rope_scaling`'s high_freq_factor field must be a float, got {high_freq_factor}")
|
||||
if high_freq_factor <= low_freq_factor:
|
||||
logger.warning(
|
||||
"`rope_scaling`'s high_freq_factor field must be greater than low_freq_factor, got high_freq_factor="
|
||||
f"{high_freq_factor} and low_freq_factor={low_freq_factor}"
|
||||
)
|
||||
|
||||
original_max_position_embeddings = rope_scaling["original_max_position_embeddings"]
|
||||
if original_max_position_embeddings is None or not isinstance(original_max_position_embeddings, int):
|
||||
logger.warning(
|
||||
"`rope_scaling`'s original_max_position_embeddings field must be an integer, got "
|
||||
f"{original_max_position_embeddings}"
|
||||
)
|
||||
if original_max_position_embeddings >= config.max_position_embeddings:
|
||||
logger.warning(
|
||||
"`rope_scaling`'s original_max_position_embeddings field must be less than max_position_embeddings, got "
|
||||
f"{original_max_position_embeddings} and max_position_embeddings={config.max_position_embeddings}"
|
||||
)
|
||||
|
||||
|
||||
# Like `ROPE_INIT_FUNCTIONS`, this validation function mapping can be dynamically updated for custom RoPE types.
|
||||
ROPE_VALIDATION_FUNCTIONS = {
|
||||
"default": _validate_default_rope_parameters,
|
||||
"linear": _validate_linear_scaling_rope_parameters,
|
||||
"dynamic": _validate_dynamic_scaling_rope_parameters,
|
||||
"yarn": _validate_yarn_parameters,
|
||||
"longrope": _validate_longrope_parameters,
|
||||
"llama3": _validate_llama3_parameters,
|
||||
}
|
||||
|
||||
|
||||
def rope_config_validation(config: PretrainedConfig, ignore_keys: Optional[set] = None):
|
||||
"""
|
||||
Validate the RoPE config arguments, given a `PretrainedConfig` object
|
||||
"""
|
||||
rope_scaling = getattr(config, "rope_scaling", None) # not a default parameter in `PretrainedConfig`
|
||||
if rope_scaling is None:
|
||||
return
|
||||
|
||||
# BC: "rope_type" was originally "type"
|
||||
rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", "default"))
|
||||
validation_fn = ROPE_VALIDATION_FUNCTIONS.get(rope_type)
|
||||
if validation_fn is not None:
|
||||
validation_fn(config, ignore_keys=ignore_keys)
|
||||
else:
|
||||
logger.warning(
|
||||
f"Missing validation function mapping in `ROPE_VALIDATION_FUNCTIONS` for 'rope_type'='{rope_type}'"
|
||||
)
|
||||
@ -1,5 +1,5 @@
|
||||
fire
|
||||
transformers
|
||||
transformers==4.43.2
|
||||
numpy
|
||||
torch>=2.3.0
|
||||
packaging
|
||||
|
||||
8
setup.py
8
setup.py
@ -278,13 +278,15 @@ class CMakeBuild(BuildExtension):
|
||||
if "CMAKE_BUILD_PARALLEL_LEVEL" not in os.environ:
|
||||
if hasattr(self, "parallel") and self.parallel:
|
||||
build_args += [f"-j{self.parallel}"]
|
||||
|
||||
print("CMake args:", cmake_args)
|
||||
build_temp = Path(ext.sourcedir) / "build"
|
||||
if not build_temp.exists():
|
||||
build_temp.mkdir(parents=True)
|
||||
subprocess.run(
|
||||
["cmake", ext.sourcedir, *cmake_args], cwd=build_temp, check=True
|
||||
result = subprocess.run(
|
||||
["cmake", ext.sourcedir, *cmake_args], cwd=build_temp, check=True , capture_output=True
|
||||
)
|
||||
print("Standard output:", result.stdout)
|
||||
print("Standard error:", result.stderr)
|
||||
subprocess.run(
|
||||
["cmake", "--build", ".", *build_args], cwd=build_temp, check=True
|
||||
)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user