diff --git a/.github/workflows/package_wheel_release.yml b/.github/workflows/package_wheel_release.yml index 8028d59..dfbfde4 100644 --- a/.github/workflows/package_wheel_release.yml +++ b/.github/workflows/package_wheel_release.yml @@ -142,11 +142,11 @@ jobs: - name: Setup Mamba if: matrix.cuda != '' - uses: conda-incubator/setup-miniconda@v2.3.0 + uses: conda-incubator/setup-miniconda@v3 with: activate-environment: "ktransformers" python-version: ${{ matrix.pyver }} - miniforge-variant: Mambaforge + miniforge-variant: Miniforge3 miniforge-version: latest use-mamba: true add-pip-as-python-dependency: true diff --git a/.github/workflows/package_wheel_test.yml b/.github/workflows/package_wheel_test.yml index 35636db..cd8db62 100644 --- a/.github/workflows/package_wheel_test.yml +++ b/.github/workflows/package_wheel_test.yml @@ -54,11 +54,11 @@ jobs: - name: Setup Mamba if: matrix.cuda != '' - uses: conda-incubator/setup-miniconda@v2.3.0 + uses: conda-incubator/setup-miniconda@v3 with: activate-environment: "ktransformers" python-version: ${{ matrix.pyver }} - miniforge-variant: Mambaforge + miniforge-variant: Miniforge3 miniforge-version: latest use-mamba: true add-pip-as-python-dependency: true diff --git a/.gitignore b/.gitignore index c33a95d..d45e956 100644 --- a/.gitignore +++ b/.gitignore @@ -18,4 +18,7 @@ compile_commands.json ktransformers/server/local_store/ ktransformers/server_test1.db *.patch -img/ \ No newline at end of file +img/ +tmp1.txt +test_65_300_1536.txt +test.txt diff --git a/Makefile b/Makefile index dbf771d..f8633a9 100644 --- a/Makefile +++ b/Makefile @@ -17,5 +17,5 @@ dev_install: pip install -r requirements-local_chat.txt echo "Installing ktransformers" - KTRANSFORMERS_FORCE_BUILD=TRUE pip install -e . --no-build-isolation + KTRANSFORMERS_FORCE_BUILD=TRUE pip install -e . -v --no-build-isolation echo "Installation completed successfully" \ No newline at end of file diff --git a/README.md b/README.md index eb23bf8..d06163d 100644 --- a/README.md +++ b/README.md @@ -23,6 +23,7 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin

🔥 Updates

+* **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) * **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). * **Aug 28, 2024**: Decrease DeepseekV2's required VRAM from 21G to 11G. * **Aug 15, 2024**: Update detailed [TUTORIAL](doc/en/injection_tutorial.md) for injection and multi-GPU. @@ -30,7 +31,44 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin * **Aug 12, 2024**: Support multiple GPU; Support new model: mixtral 8\*7B and 8\*22B; Support q2k, q3k, q5k dequant on gpu. * **Aug 9, 2024**: Support windows native. -

🔥 Show Cases

+

🌟 Show Cases

+ +
+

GPT-4/o1-level Local VSCode Copilot on a Desktop with only 24GB VRAM

+
+ +https://github.com/user-attachments/assets/ebd70bfa-b2c1-4abb-ae3b-296ed38aa285 + +

+ +- **[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**. + - Upcoming Open Source Release: + - AMX optimizations and selective expert activation will be open-sourced in V0.3. + - 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). + +- **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). + +

+ + DeepSeek-Coder-V2 Score + +

+ +- **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. + +

+ +https://github.com/user-attachments/assets/4c6a8a38-05aa-497d-8eb1-3a5b3918429c + +

+

1M Context Local Inference on a Desktop with Only 24GB VRAM

@@ -54,30 +92,7 @@ https://github.com/user-attachments/assets/a865e5e4-bca3-401e-94b8-af3c080e6c12 * **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). -

-

GPT-4-level Local VSCode Copilot on a Desktop with only 24GB VRAM

-
-https://github.com/user-attachments/assets/0b9fa2da-66f0-48eb-b4b9-f0e1f06f8927 - -

- -- **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). - -

- - DeepSeek-Coder-V2 Score - -

- -- **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. - -

- -https://github.com/user-attachments/assets/4c6a8a38-05aa-497d-8eb1-3a5b3918429c - -

More advanced features will coming soon, so stay tuned! diff --git a/doc/en/DeepseekR1_V3_tutorial.md b/doc/en/DeepseekR1_V3_tutorial.md new file mode 100644 index 0000000..0282ba1 --- /dev/null +++ b/doc/en/DeepseekR1_V3_tutorial.md @@ -0,0 +1,139 @@ +# GPT-4/o1-level Local VSCode Copilot on a Desktop with only 24GB VRAM +# SUMMARY + +> **Fed 10, 2025**: Support DeepseekR1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~64x speedup.
+ +Hi, we're the KTransformers team (formerly known for our local CPU/GPU hybrid inference open source project with DeepSeek-V2). + +We've heard your requests for DeepSeek-R1/V3 support—and we're excited to finally deliver! +Apologies for the wait, but we've been cooking up something truly amazing! + +Today, we're proud to announce that we not only support DeepSeek-R1/V3, as showcased in the video below: + +https://github.com/user-attachments/assets/ebd70bfa-b2c1-4abb-ae3b-296ed38aa285 + +

+ +- **[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**. + + +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. +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) + + +## Prerequisites +We run our best performance tests (V0.2) on
+CPU: Intel (R) Xeon (R) Gold 6454S 1T DRAM (2 NUMA nodes)
+GPU: 4090D 24G VRAM
+## Bench Result +### V0.2 +#### Settings +- Model: DeepseekV3-q4km (int4)
+- CPU: cpu_model_name: Intel (R) Xeon (R) Gold 6454S, 32 cores per socket, 2 sockets, 2 numa nodes +- GPU: 4090D 24G VRAM +- We test after enough warm up +#### Memory consumption: + - Single socket: 382G DRAM, at least 14GB VRAM + - Dual socket: 1T DRAM, at least 14GB VRAM + +#### Benchmark Results + +"6 experts" case is part of V0.3's preview + +| Prompt
(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) | +| --- | --- | --- | --- | --- | --- | +| Prefill token/s | 97.32 | 82.94 | 65.14 | 54.21 | 10.31 | +| Decode token/s | 13.69 | 12.208 | 10.303 | 8.73 |4.51 | + +**The highest speedup reaches up to 3.03x in decoding and 9.44x in prefill.** + +### V0.3-Preview +#### Settings +- Model: DeepseekV3-BF16 (online quant into int8 for CPU and int4 for GPU) +- CPU: cpu_model_name: Intel (R) Xeon (R) Gold 6454S, 32 cores per socket, 2 socket, 2 numa nodes +- GPU: (1~4)x 4090D 24GVRAM (requires more VRAM for longer prompt) + +#### Memory consumptions: +- 644GB DRAM, at least 14GB VRAM + +#### Benchmark results +| Prompt length | 1K | 2K | 4K | 8K | +|---------------|-----|-----|-----|-----| +| KTrans (8 experts) Prefill token/s | 185.96 | 255.26 | 252.58 | 195.62 | +| KTrans (6 experts) Prefill token/s | 203.70 | 286.55 | 271.08 | 207.20 | + +**The prefill of KTrans V0.3 is up to 3.45x times faster than KTrans V0.2, and is up to 63.53x times faster than llama.cpp.** +**The decoding speed is the same as KTrans V0.2 (6 experts version) so it is omitted** + +The main acceleration comes from +- Intel AMX instruction set and our specially designed cache friendly memory layout +- Expert selection strategy that selects fewer experts based on offline profile results of out of domain data + + +*From our research on DeepSeekV2, DeepSeekV3 and DeepSeekR1, +when we slightly decrease the activation experts num in inference, +the output quality doesn't change. But the speed of decoding and prefill +is speed up which is inspiring. So our showcase makes use of this finding* + +## How to Run +### V0.2 Showcase +#### Single socket version (32 cores) +Our local_chat test command is: +``` shell +git clone https://github.com/kvcache-ai/ktransformers.git +cd ktransformers +numactl -N 1 -m 1 python ./ktransformers/local_chat.py --model_path --gguf_path --prompt_file --cpu_infer 33 --cache_lens 1536 + +``` +\ 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)
+\ can also be online, but as its large we recommend you download it and quantize the model to what you want
+The command numactl -N 1 -m 1 aims to advoid data transfer between numa nodes +#### Dual socket version (64 cores) +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)
+Our local_chat test command is: +``` shell +git clone https://github.com/kvcache-ai/ktransformers.git +cd ktransformers +export USE_NUMA=1 +make dev_install # or sh ./install.sh +python ./ktransformers/local_chat.py --model_path --gguf_path --prompt_file --cpu_infer 65 --cache_lens 1536 + +``` +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 +pip install ./ktransformers-0.3.0rc0+cu126torch26fancy-cp311-cp311-linux_x86_64.whl +python -m ktransformers.local_chat --model_path --gguf_path --prompt_file --cpu_infer 65 --cache_lens 1536 + +``` +The parameters' meaning is the same with V0.2. But As we use dual socket, we set cpu_infer to 65 + +## Some Explanations +1. Also we want to make further use of our two NUMA nodes on Xeon Gold cpu. +To avoid the cost of data transfer between nodes, we "copy" the critical matrix on +both nodes which takes more memory consumption but accelerates the prefill and decoding process. +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~
+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)
+ +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. \ No newline at end of file diff --git a/ktransformers/__init__.py b/ktransformers/__init__.py index 2c7b4dc..8c5108b 100644 --- a/ktransformers/__init__.py +++ b/ktransformers/__init__.py @@ -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" \ No newline at end of file +__version__ = "0.2.0" \ No newline at end of file diff --git a/ktransformers/configs/config.yaml b/ktransformers/configs/config.yaml index 7bde376..80de09a 100644 --- a/ktransformers/configs/config.yaml +++ b/ktransformers/configs/config.yaml @@ -54,4 +54,4 @@ long_context: token_step: local_chat: - prompt_file: "./ktransformers/p.txt" \ No newline at end of file + prompt_file: "" \ No newline at end of file diff --git a/ktransformers/ktransformers_ext/CMakeLists.txt b/ktransformers/ktransformers_ext/CMakeLists.txt index 1ef9823..d9ecd7a 100644 --- a/ktransformers/ktransformers_ext/CMakeLists.txt +++ b/ktransformers/ktransformers_ext/CMakeLists.txt @@ -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() diff --git a/ktransformers/ktransformers_ext/cpu_backend/backend.cpp b/ktransformers/ktransformers_ext/cpu_backend/backend.cpp index 16693f0..5980ba3 100644 --- a/ktransformers/ktransformers_ext/cpu_backend/backend.cpp +++ b/ktransformers/ktransformers_ext/cpu_backend/backend.cpp @@ -10,6 +10,13 @@ #include "backend.h" +#ifdef USE_NUMA +#include +#include + +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); } diff --git a/ktransformers/ktransformers_ext/cpu_backend/backend.h b/ktransformers/ktransformers_ext/cpu_backend/backend.h index 80ff7f9..7a95f27 100644 --- a/ktransformers/ktransformers_ext/cpu_backend/backend.h +++ b/ktransformers/ktransformers_ext/cpu_backend/backend.h @@ -38,6 +38,9 @@ class Backend { void do_work_stealing_job(int, std::function, std::function, std::function); + #ifdef USE_NUMA + static thread_local int numa_node; + #endif static thread_local int thread_local_id; private: diff --git a/ktransformers/ktransformers_ext/operators/llamafile/moe.cpp b/ktransformers/ktransformers_ext/operators/llamafile/moe.cpp index 0fcf9df..35c144f 100644 --- a/ktransformers/ktransformers_ext/operators/llamafile/moe.cpp +++ b/ktransformers/ktransformers_ext/operators/llamafile/moe.cpp @@ -11,11 +11,41 @@ #include #include +#ifdef USE_NUMA +#include +#include +#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> 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); diff --git a/ktransformers/ktransformers_ext/operators/llamafile/moe.h b/ktransformers/ktransformers_ext/operators/llamafile/moe.h index a1470aa..a39e21d 100644 --- a/ktransformers/ktransformers_ext/operators/llamafile/moe.h +++ b/ktransformers/ktransformers_ext/operators/llamafile/moe.h @@ -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 gate_proj_numa_; // [numa_num, expert_num * intermediate_size * hidden_size ( /32 if quantized)] + std::vector up_proj_numa_; // [numa_num, expert_num * intermediate_size * hidden_size ( /32 if quantized)] + std::vector 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)] diff --git a/ktransformers/local_chat.py b/ktransformers/local_chat.py index 41f98a1..5f17c21 100644 --- a/ktransformers/local_chat.py +++ b/ktransformers/local_chat.py @@ -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) \ No newline at end of file diff --git a/ktransformers/models/configuration_deepseek_v3.py b/ktransformers/models/configuration_deepseek_v3.py new file mode 100644 index 0000000..6227092 --- /dev/null +++ b/ktransformers/models/configuration_deepseek_v3.py @@ -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"] \ No newline at end of file diff --git a/ktransformers/models/custom_cache.py b/ktransformers/models/custom_cache.py index dbaea57..e402506 100644 --- a/ktransformers/models/custom_cache.py +++ b/ktransformers/models/custom_cache.py @@ -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 \ No newline at end of file diff --git a/ktransformers/models/modeling_deepseek_v3.py b/ktransformers/models/modeling_deepseek_v3.py new file mode 100644 index 0000000..10b8766 --- /dev/null +++ b/ktransformers/models/modeling_deepseek_v3.py @@ -0,0 +1,1936 @@ +# coding=utf-8 +# Copyright 2023 DeepSeek-AI and The HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# 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. +""" PyTorch DeepSeek model.""" +import math +import warnings +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache, DynamicCache, StaticCache +from transformers.modeling_attn_mask_utils import ( + AttentionMaskConverter, + _prepare_4d_attention_mask, + _prepare_4d_causal_attention_mask, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + SequenceClassifierOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.pytorch_utils import ( + ALL_LAYERNORM_LAYERS, + is_torch_greater_or_equal_than_1_13, +) +from transformers.utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_2_available, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) +from transformers.utils.import_utils import is_torch_fx_available +from .configuration_deepseek_v3 import DeepseekV3Config +import torch.distributed as dist +import numpy as np + +if is_flash_attn_2_available(): + from flash_attn import flash_attn_func, flash_attn_varlen_func + from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa + + +# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph. +# It means that the function will not be traced through and simply appear as a node in the graph. +if is_torch_fx_available(): + if not is_torch_greater_or_equal_than_1_13: + import torch.fx + + _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask) + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "DeepseekV3Config" + + +def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad( + torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0) + ) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + +class DeepseekV3RMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + DeepseekV3RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + +ALL_LAYERNORM_LAYERS.append(DeepseekV3RMSNorm) + + +class DeepseekV3RotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / ( + self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim) + ) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, + device=self.inv_freq.device, + dtype=torch.get_default_dtype(), + ) + self.max_seq_len_cached = None + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange( + self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype + ) + + freqs = torch.outer(t, self.inv_freq.to(t.device)) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if self.max_seq_len_cached is None or seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:seq_len].to(dtype=x.dtype), + self.sin_cached[:seq_len].to(dtype=x.dtype), + ) + + +# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->DeepseekV3 +class DeepseekV3LinearScalingRotaryEmbedding(DeepseekV3RotaryEmbedding): + """DeepseekV3RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" + + def __init__( + self, + dim, + max_position_embeddings=2048, + base=10000, + device=None, + scaling_factor=1.0, + ): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange( + self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype + ) + t = t / self.scaling_factor + + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + +# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->DeepseekV3 +class DeepseekV3DynamicNTKScalingRotaryEmbedding(DeepseekV3RotaryEmbedding): + """DeepseekV3RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" + + def __init__( + self, + dim, + max_position_embeddings=2048, + base=10000, + device=None, + scaling_factor=1.0, + ): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + + if seq_len > self.max_position_embeddings: + base = self.base * ( + (self.scaling_factor * seq_len / self.max_position_embeddings) + - (self.scaling_factor - 1) + ) ** (self.dim / (self.dim - 2)) + inv_freq = 1.0 / ( + base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim) + ) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + t = torch.arange( + self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype + ) + + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + +# Inverse dim formula to find dim based on number of rotations +def yarn_find_correction_dim( + num_rotations, dim, base=10000, max_position_embeddings=2048 +): + return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / ( + 2 * math.log(base) + ) + + +# Find dim range bounds based on rotations +def yarn_find_correction_range( + low_rot, high_rot, dim, base=10000, max_position_embeddings=2048 +): + low = math.floor( + yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings) + ) + high = math.ceil( + yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings) + ) + return max(low, 0), min(high, dim - 1) # Clamp values just in case + + +def yarn_get_mscale(scale=1, mscale=1): + if scale <= 1: + return 1.0 + return 0.1 * mscale * math.log(scale) + 1.0 + + +def yarn_linear_ramp_mask(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 + + +class DeepseekV3YarnRotaryEmbedding(DeepseekV3RotaryEmbedding): + + 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.scaling_factor = scaling_factor + 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 + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + dim = self.dim + + 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 + ) + inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask + self.register_buffer("inv_freq", inv_freq, persistent=False) + + t = torch.arange(seq_len, device=device, dtype=torch.float32) + + freqs = torch.outer(t, inv_freq) + + _mscale = float( + yarn_get_mscale(self.scaling_factor, self.mscale) + / yarn_get_mscale(self.scaling_factor, self.mscale_all_dim) + ) + + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer( + "cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False + ) + self.register_buffer( + "sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False + ) + + +# Copied from transformers.models.llama.modeling_llama.rotate_half +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb +def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`): + The position indices of the tokens corresponding to the query and key tensors. For example, this can be + used to pass offsetted position ids when working with a KV-cache. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos[position_ids].unsqueeze(unsqueeze_dim) + sin = sin[position_ids].unsqueeze(unsqueeze_dim) + + b, h, s, d = q.shape + q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d) + + b, h, s, d = k.shape + k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d) + + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class DeepseekV3MLP(nn.Module): + def __init__(self, config, hidden_size=None, intermediate_size=None): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size if hidden_size is None else hidden_size + self.intermediate_size = ( + config.intermediate_size if intermediate_size is None else intermediate_size + ) + + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class MoEGate(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.top_k = config.num_experts_per_tok + self.n_routed_experts = config.n_routed_experts + self.routed_scaling_factor = config.routed_scaling_factor + self.scoring_func = config.scoring_func + self.seq_aux = config.seq_aux + self.topk_method = config.topk_method + self.n_group = config.n_group + self.topk_group = config.topk_group + + # topk selection algorithm + self.norm_topk_prob = config.norm_topk_prob + self.gating_dim = config.hidden_size + self.weight = nn.Parameter( + torch.empty((self.n_routed_experts, self.gating_dim)) + ) + if self.topk_method == "noaux_tc": + self.e_score_correction_bias = nn.Parameter( + torch.empty((self.n_routed_experts)) + ) + self.reset_parameters() + + def reset_parameters(self) -> None: + import torch.nn.init as init + + init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + + def forward(self, hidden_states): + bsz, seq_len, h = hidden_states.shape + ### compute gating score + hidden_states = hidden_states.view(-1, h) + logits = F.linear( + hidden_states.type(torch.float32), self.weight.type(torch.float32), None + ) + if self.scoring_func == "sigmoid": + scores = logits.sigmoid() + else: + raise NotImplementedError( + f"insupportable scoring function for MoE gating: {self.scoring_func}" + ) + + ### select top-k experts + if self.topk_method == "noaux_tc": + scores_for_choice = scores.view(bsz * seq_len, -1) + self.e_score_correction_bias.unsqueeze(0) + group_scores = ( + scores_for_choice.view(bsz * seq_len, self.n_group, -1).topk(2, dim=-1)[0].sum(dim = -1) + ) # [n, n_group] + group_idx = torch.topk( + group_scores, k=self.topk_group, dim=-1, sorted=False + )[ + 1 + ] # [n, top_k_group] + group_mask = torch.zeros_like(group_scores) # [n, n_group] + group_mask.scatter_(1, group_idx, 1) # [n, n_group] + score_mask = ( + group_mask.unsqueeze(-1) + .expand( + bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group + ) + .reshape(bsz * seq_len, -1) + ) # [n, e] + tmp_scores = scores_for_choice.masked_fill(~score_mask.bool(), 0.0) # [n, e] + _, topk_idx = torch.topk( + tmp_scores, k=self.top_k, dim=-1, sorted=False + ) + topk_weight = scores.gather(1, topk_idx) + else: + raise NotImplementedError( + f"insupportable TopK function for MoE gating: {self.topk_method}" + ) + + ### norm gate to sum 1 + if self.top_k > 1 and self.norm_topk_prob: + denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 + topk_weight = topk_weight / denominator + topk_weight = topk_weight * self.routed_scaling_factor # must multiply the scaling factor + + return topk_idx, topk_weight + +class DeepseekV3MoE(nn.Module): + """ + A mixed expert module containing shared experts. + """ + + def __init__(self, config): + super().__init__() + self.config = config + self.num_experts_per_tok = config.num_experts_per_tok + + if hasattr(config, "ep_size") and config.ep_size > 1: + assert config.ep_size == dist.get_world_size() + self.ep_size = config.ep_size + self.experts_per_rank = config.n_routed_experts // config.ep_size + self.ep_rank = dist.get_rank() + self.experts = nn.ModuleList( + [ + ( + DeepseekV3MLP( + config, intermediate_size=config.moe_intermediate_size + ) + if i >= self.ep_rank * self.experts_per_rank + and i < (self.ep_rank + 1) * self.experts_per_rank + else None + ) + for i in range(config.n_routed_experts) + ] + ) + else: + self.ep_size = 1 + self.experts_per_rank = config.n_routed_experts + self.ep_rank = 0 + self.experts = nn.ModuleList( + [ + DeepseekV3MLP( + config, intermediate_size=config.moe_intermediate_size + ) + for i in range(config.n_routed_experts) + ] + ) + self.gate = MoEGate(config) + if config.n_shared_experts is not None: + intermediate_size = config.moe_intermediate_size * config.n_shared_experts + self.shared_experts = DeepseekV3MLP( + config=config, intermediate_size=intermediate_size + ) + + def forward(self, hidden_states): + identity = hidden_states + orig_shape = hidden_states.shape + topk_idx, topk_weight = self.gate(hidden_states) + hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) + flat_topk_idx = topk_idx.view(-1) + if not self.training: + y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape) + if self.config.n_shared_experts is not None: + y = y + self.shared_experts(identity) + return y + + @torch.no_grad() + 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]] + sorted_tokens_shape = sorted_tokens.shape + if self.ep_size > 1: + tokens_per_ep_rank = tokens_per_expert.view(self.ep_size, -1).sum(dim=1) + tokens_per_expert_group = tokens_per_expert.new_empty( + tokens_per_expert.shape[0] + ) + dist.all_to_all_single(tokens_per_expert_group, tokens_per_expert) + output_splits = ( + tokens_per_expert_group.view(self.ep_size, -1) + .sum(1) + .cpu() + .numpy() + .tolist() + ) + gathered_tokens = sorted_tokens.new_empty( + tokens_per_expert_group.sum(dim=0).cpu().item(), sorted_tokens.shape[1] + ) + input_split_sizes = tokens_per_ep_rank.cpu().numpy().tolist() + dist.all_to_all( + list(gathered_tokens.split(output_splits)), + list(sorted_tokens.split(input_split_sizes)), + ) + tokens_per_expert_post_gather = tokens_per_expert_group.view( + self.ep_size, self.experts_per_rank + ).sum(dim=0) + gatherd_idxs = np.zeros(shape=(gathered_tokens.shape[0],), dtype=np.int32) + s = 0 + for i, k in enumerate(tokens_per_expert_group.cpu().numpy()): + gatherd_idxs[s : s + k] = i % self.experts_per_rank + s += k + gatherd_idxs = gatherd_idxs.argsort() + sorted_tokens = gathered_tokens[gatherd_idxs] + tokens_per_expert = tokens_per_expert_post_gather + 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(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) + if self.ep_size > 1: + new_x = torch.empty_like(outs) + new_x[gatherd_idxs] = outs + gathered_tokens = new_x.new_empty(*sorted_tokens_shape) + dist.all_to_all( + list(gathered_tokens.split(input_split_sizes)), + list(new_x.split(output_splits)), + ) + outs = gathered_tokens + + 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 + + +# Copied from transformers.models.llama.modeling_llama.repeat_kv +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand( + batch, num_key_value_heads, n_rep, slen, head_dim + ) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DeepseekV3 +class DeepseekV3Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: DeepseekV3Config, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " + "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.attention_dropout = config.attention_dropout + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + + self.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + self.q_lora_rank = config.q_lora_rank + self.qk_rope_head_dim = config.qk_rope_head_dim + self.kv_lora_rank = config.kv_lora_rank + self.v_head_dim = config.v_head_dim + self.qk_nope_head_dim = config.qk_nope_head_dim + self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim + + self.is_causal = True + + if self.q_lora_rank is None: + self.q_proj = nn.Linear( + self.hidden_size, self.num_heads * self.q_head_dim, bias=False + ) + else: + self.q_a_proj = nn.Linear( + self.hidden_size, config.q_lora_rank, bias=config.attention_bias + ) + self.q_a_layernorm = DeepseekV3RMSNorm(config.q_lora_rank) + self.q_b_proj = nn.Linear( + config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False + ) + + self.kv_a_proj_with_mqa = nn.Linear( + self.hidden_size, + config.kv_lora_rank + config.qk_rope_head_dim, + bias=config.attention_bias, + ) + self.kv_a_layernorm = DeepseekV3RMSNorm(config.kv_lora_rank) + self.kv_b_proj = nn.Linear( + config.kv_lora_rank, + self.num_heads + * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim), + bias=False, + ) + + self.o_proj = nn.Linear( + self.num_heads * self.v_head_dim, + self.hidden_size, + bias=config.attention_bias, + ) + self._init_rope() + + self.softmax_scale = self.q_head_dim ** (-0.5) + if self.config.rope_scaling is not None: + mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0) + scaling_factor = self.config.rope_scaling["factor"] + if mscale_all_dim: + mscale = yarn_get_mscale(scaling_factor, mscale_all_dim) + self.softmax_scale = self.softmax_scale * mscale * mscale + + def _init_rope(self): + if self.config.rope_scaling is None: + self.rotary_emb = DeepseekV3RotaryEmbedding( + self.qk_rope_head_dim, + max_position_embeddings=self.max_position_embeddings, + base=self.rope_theta, + ) + else: + scaling_type = self.config.rope_scaling["type"] + scaling_factor = self.config.rope_scaling["factor"] + if scaling_type == "linear": + self.rotary_emb = DeepseekV3LinearScalingRotaryEmbedding( + self.qk_rope_head_dim, + max_position_embeddings=self.max_position_embeddings, + scaling_factor=scaling_factor, + base=self.rope_theta, + ) + elif scaling_type == "dynamic": + self.rotary_emb = DeepseekV3DynamicNTKScalingRotaryEmbedding( + self.qk_rope_head_dim, + max_position_embeddings=self.max_position_embeddings, + scaling_factor=scaling_factor, + base=self.rope_theta, + ) + elif scaling_type == "yarn": + 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.rotary_emb = DeepseekV3YarnRotaryEmbedding( + self.qk_rope_head_dim, + max_position_embeddings=self.max_position_embeddings, + scaling_factor=scaling_factor, + base=self.rope_theta, + **kwargs, + ) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return ( + tensor.view(bsz, seq_len, self.num_heads, self.v_head_dim) + .transpose(1, 2) + .contiguous() + ) + + 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 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 + ) + k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2) + kv = ( + self.kv_b_proj(self.kv_a_layernorm(compressed_kv)) + .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim) + .transpose(1, 2) + ) + + k_nope, value_states = torch.split( + kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1 + ) + kv_seq_len = value_states.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(value_states, seq_len=kv_seq_len) + + q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids) + + query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim) + query_states[:, :, :, : self.qk_nope_head_dim] = q_nope + query_states[:, :, :, self.qk_nope_head_dim :] = q_pe + + key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim) + key_states[:, :, :, : self.qk_nope_head_dim] = k_nope + key_states[:, :, :, self.qk_nope_head_dim :] = k_pe + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models + key_states, value_states = past_key_value.update( + key_states, value_states, self.layer_idx, cache_kwargs + ) + + attn_weights = ( + torch.matmul(query_states, key_states.transpose(2, 3)) * 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()}" + ) + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax( + attn_weights, dim=-1, dtype=torch.float32 + ).to(query_states.dtype) + attn_weights = nn.functional.dropout( + attn_weights, p=self.attention_dropout, training=self.training + ) + attn_output = torch.matmul(attn_weights, value_states) + + 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) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->DeepseekV3 +class DeepseekV3FlashAttention2(DeepseekV3Attention): + """ + DeepseekV3 flash attention module. This module inherits from `DeepseekV3Attention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.LongTensor] = 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]]]: + # DeepseekV3FlashAttention2 attention does not support output_attentions + 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.`" + ) + + # overwrite attention_mask with padding_mask + attention_mask = kwargs.pop("padding_mask") + + output_attentions = False + + 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 + ) + + # Flash attention requires the input to have the shape + # batch_size x seq_length x head_dim x hidden_dim + # therefore we just need to keep the original shape + 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 + ) + k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2) + kv = ( + self.kv_b_proj(self.kv_a_layernorm(compressed_kv)) + .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim) + .transpose(1, 2) + ) + + k_nope, value_states = torch.split( + kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1 + ) + kv_seq_len = value_states.shape[-2] + + kv_seq_len = value_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids) + + query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim) + query_states[:, :, :, : self.qk_nope_head_dim] = q_nope + query_states[:, :, :, self.qk_nope_head_dim :] = q_pe + + key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim) + key_states[:, :, :, : self.qk_nope_head_dim] = k_nope + key_states[:, :, :, self.qk_nope_head_dim :] = k_pe + + if self.q_head_dim != self.v_head_dim: + value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim]) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models + key_states, value_states = past_key_value.update( + key_states, value_states, self.layer_idx, cache_kwargs + ) + + # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache + # to be able to avoid many of these transpose/reshape/view. + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + dropout_rate = self.attention_dropout if self.training else 0.0 + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in the correct dtype just to be sure everything works as expected. + # This might slowdown training & inference so it is recommended to not cast the LayerNorms + # in fp32. (DeepseekV3RMSNorm handles it correctly) + + input_dtype = query_states.dtype + if input_dtype == torch.float32: + # Handle the case where the model is quantized + if hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + elif torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + else: + target_dtype = ( + self.q_proj.weight.dtype + if self.q_lora_rank is None + else self.q_a_proj.weight.dtype + ) + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + attn_output = self._flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + softmax_scale=self.softmax_scale, + ) + if self.q_head_dim != self.v_head_dim: + attn_output = attn_output[:, :, :, : self.v_head_dim] + + attn_output = attn_output.reshape( + bsz, q_len, self.num_heads * self.v_head_dim + ).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + def _flash_attention_forward( + self, + query_states, + key_states, + value_states, + attention_mask, + query_length, + dropout=0.0, + softmax_scale=None, + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`int`, *optional*): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + """ + if not self._flash_attn_uses_top_left_mask: + causal = self.is_causal + else: + # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in DeepseekV3FlashAttention2 __init__. + causal = self.is_causal and query_length != 1 + + # Contains at least one padding token in the sequence + if attention_mask is not None: + batch_size = query_states.shape[0] + ( + query_states, + key_states, + value_states, + indices_q, + cu_seq_lens, + max_seq_lens, + ) = self._upad_input( + query_states, key_states, value_states, attention_mask, query_length + ) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + + attn_output = pad_input( + attn_output_unpad, indices_q, batch_size, query_length + ) + else: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + + return attn_output + + def _upad_input( + self, query_layer, key_layer, value_layer, attention_mask, query_length + ): + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape + + key_layer = index_first_axis( + key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + value_layer = index_first_axis( + value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), + indices_k, + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( + query_layer, attention_mask + ) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +ATTENTION_CLASSES = { + "eager": DeepseekV3Attention, + "flash_attention_2": DeepseekV3FlashAttention2, +} + + +class DeepseekV3DecoderLayer(nn.Module): + def __init__(self, config: DeepseekV3Config, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = ATTENTION_CLASSES[config._attn_implementation]( + config=config, layer_idx=layer_idx + ) + + self.mlp = ( + DeepseekV3MoE(config) + if ( + config.n_routed_experts is not None + and layer_idx >= config.first_k_dense_replace + and layer_idx % config.moe_layer_freq == 0 + ) + else DeepseekV3MLP(config) + ) + self.input_layernorm = DeepseekV3RMSNorm( + config.hidden_size, eps=config.rms_norm_eps + ) + self.post_attention_layernorm = DeepseekV3RMSNorm( + config.hidden_size, eps=config.rms_norm_eps + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, + **kwargs, + ) -> Tuple[ + torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] + ]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): + attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, + query_sequence_length, key_sequence_length)` if default attention is used. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + 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.`" + ) + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +DeepseekV3_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`DeepseekV3Config`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare DeepseekV3 Model outputting raw hidden-states without any specific head on top.", + DeepseekV3_START_DOCSTRING, +) +class DeepseekV3PreTrainedModel(PreTrainedModel): + config_class = DeepseekV3Config + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["DeepseekV3DecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_cache_class = True + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +DeepseekV3_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare DeepseekV3 Model outputting raw hidden-states without any specific head on top.", + DeepseekV3_START_DOCSTRING, +) +class DeepseekV3Model(DeepseekV3PreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV3DecoderLayer`] + + Args: + config: DeepseekV3Config + """ + + def __init__(self, config: DeepseekV3Config): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding( + config.vocab_size, config.hidden_size, self.padding_idx + ) + self.layers = nn.ModuleList( + [ + DeepseekV3DecoderLayer(config, layer_idx) + for layer_idx in range(config.num_hidden_layers) + ] + ) + self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" + self.norm = DeepseekV3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + batch_size, seq_length = input_ids.shape[:2] + elif inputs_embeds is not None: + batch_size, seq_length = inputs_embeds.shape[:2] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + past_key_values_length = 0 + if use_cache: + use_legacy_cache = not isinstance(past_key_values, Cache) + 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 position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, + seq_length + past_key_values_length, + dtype=torch.long, + device=device, + ) + position_ids = position_ids.unsqueeze(0) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if self._use_flash_attention_2: + # 2d mask is passed through the layers + attention_mask = ( + attention_mask + if (attention_mask is not None and 0 in attention_mask) + else None + ) + else: + # 4d mask is passed through the layers + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + ) + + # embed positions + hidden_states = inputs_embeds + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = None + if use_cache: + next_cache = ( + next_decoder_cache.to_legacy_cache() + if use_legacy_cache + else next_decoder_cache + ) + if not return_dict: + return tuple( + v + for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] + if v is not None + ) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask + def _update_causal_mask( + self, + attention_mask: torch.Tensor, + input_tensor: torch.Tensor, + cache_position: torch.Tensor, + past_key_values: Cache, + output_attentions: bool, + ): + # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static + # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes. + # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using + # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114 + + if self.config._attn_implementation == "flash_attention_2": + if attention_mask is not None and 0.0 in attention_mask: + return attention_mask + return None + + # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in + # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail + # to infer the attention mask. + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + using_static_cache = isinstance(past_key_values, StaticCache) + + # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward + if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: + if AttentionMaskConverter._ignore_causal_mask_sdpa( + attention_mask, + inputs_embeds=input_tensor, + past_key_values_length=past_seen_tokens, + is_training=self.training, + ): + return None + + dtype, device = input_tensor.dtype, input_tensor.device + min_dtype = torch.finfo(dtype).min + sequence_length = input_tensor.shape[1] + if using_static_cache: + target_length = past_key_values.get_max_length() + else: + target_length = ( + attention_mask.shape[-1] + if isinstance(attention_mask, torch.Tensor) + else past_seen_tokens + sequence_length + 1 + ) + + if attention_mask is not None and attention_mask.dim() == 4: + # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing + if attention_mask.max() != 0: + raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`") + causal_mask = attention_mask + else: + causal_mask = torch.full( + (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device + ) + if sequence_length != 1: + causal_mask = torch.triu(causal_mask, diagonal=1) + causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) + causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1) + if attention_mask is not None: + causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + if ( + self.config._attn_implementation == "sdpa" + and attention_mask is not None + and attention_mask.device.type == "cuda" + and not output_attentions + ): + # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when + # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. + # Details: https://github.com/pytorch/pytorch/issues/110213 + causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) + + return causal_mask + +class DeepseekV3ForCausalLM(DeepseekV3PreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = DeepseekV3Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING) + @replace_return_docstrings( + output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC + ) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, DeepseekV3ForCausalLM + + >>> model = DeepseekV3ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + cache_position=cache_position, + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + **kwargs, + ): + if past_key_values is not None: + if isinstance(past_key_values, Cache): + cache_length = past_key_values.get_seq_length() + past_length = past_key_values.seen_tokens + max_cache_length = past_key_values.get_max_length() + else: + cache_length = past_length = past_key_values[0][0].shape[2] + max_cache_length = None + + # Keep only the unprocessed tokens: + # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where + # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as + # input) + if ( + attention_mask is not None + and attention_mask.shape[1] > input_ids.shape[1] + ): + input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] + # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard + # input_ids based on the past_length. + elif past_length < input_ids.shape[1]: + input_ids = input_ids[:, past_length:] + # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. + + # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. + if ( + max_cache_length is not None + and attention_mask is not None + and cache_length + input_ids.shape[1] > max_cache_length + ): + attention_mask = attention_mask[:, -max_cache_length:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -input_ids.shape[1] :] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple( + past_state.index_select(0, beam_idx.to(past_state.device)) + for past_state in layer_past + ), + ) + return reordered_past + + +@add_start_docstrings( + """ + The DeepseekV3 Model transformer with a sequence classification head on top (linear layer). + + [`DeepseekV3ForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + DeepseekV3_START_DOCSTRING, +) +class DeepseekV3ForSequenceClassification(DeepseekV3PreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = DeepseekV3Model(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, transformers., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError( + "Cannot handle batch sizes > 1 if no padding token is defined." + ) + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = ( + torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 + ).to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[ + torch.arange(batch_size, device=logits.device), sequence_lengths + ] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and ( + labels.dtype == torch.long or labels.dtype == torch.int + ): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct( + pooled_logits.view(-1, self.num_labels), labels.view(-1) + ) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) \ No newline at end of file diff --git a/ktransformers/operators/RoPE.py b/ktransformers/operators/RoPE.py index dca441d..dc5902c 100644 --- a/ktransformers/operators/RoPE.py +++ b/ktransformers/operators/RoPE.py @@ -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 diff --git a/ktransformers/operators/attention.py b/ktransformers/operators/attention.py index ff2d644..9b47b89 100644 --- a/ktransformers/operators/attention.py +++ b/ktransformers/operators/attention.py @@ -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 diff --git a/ktransformers/operators/experts.py b/ktransformers/operators/experts.py index 81135ea..274a3ca 100644 --- a/ktransformers/operators/experts.py +++ b/ktransformers/operators/experts.py @@ -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: diff --git a/ktransformers/operators/gate.py b/ktransformers/operators/gate.py new file mode 100644 index 0000000..ab7d0b2 --- /dev/null +++ b/ktransformers/operators/gate.py @@ -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 diff --git a/ktransformers/operators/linear.py b/ktransformers/operators/linear.py index 7cdb204..9e35e8d 100644 --- a/ktransformers/operators/linear.py +++ b/ktransformers/operators/linear.py @@ -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, diff --git a/ktransformers/operators/models.py b/ktransformers/operators/models.py index f6e85c0..5d2e911 100644 --- a/ktransformers/operators/models.py +++ b/ktransformers/operators/models.py @@ -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() diff --git a/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-marlin.yaml b/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-marlin.yaml new file mode 100644 index 0000000..06ab4db --- /dev/null +++ b/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-marlin.yaml @@ -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$" + replace: + class: "ktransformers.operators.models.KDeepseekV2Model" + kwargs: + per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill + transfer_map: + 30: "cuda:1" + +- match: + name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\." + replace: + class: "default" + kwargs: + generate_device: "cuda:0" + prefill_device: "cuda:0" + +- match: + name: "(^model\\.layers\\.([3456][0-9])\\.)|(model.norm)|(lm_head)" + replace: + class: "default" + kwargs: + generate_device: "cuda:1" + prefill_device: "cuda:1" diff --git a/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu.yaml b/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu.yaml new file mode 100644 index 0000000..06ab4db --- /dev/null +++ b/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu.yaml @@ -0,0 +1,143 @@ +- match: + name: "^model.embed_tokens" + replace: + class: "default" + kwargs: + generate_device: "cpu" + prefill_device: "cpu" + +- match: + name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\." + class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding + replace: + class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3 + kwargs: + generate_device: "cuda:0" + prefill_device: "cuda:0" +- match: + name: "^model\\.layers\\.([3456][0-9])\\." + class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding + replace: + class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3 + kwargs: + generate_device: "cuda:1" + prefill_device: "cuda:1" + +- match: + name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.(?!self_attn\\.kv_b_proj).*$" # 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" diff --git a/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml b/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml new file mode 100644 index 0000000..7a44c5d --- /dev/null +++ b/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml @@ -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" \ No newline at end of file diff --git a/ktransformers/server/backend/interfaces/ktransformers.py b/ktransformers/server/backend/interfaces/ktransformers.py index 420f37e..d228b64 100644 --- a/ktransformers/server/backend/interfaces/ktransformers.py +++ b/ktransformers/server/backend/interfaces/ktransformers.py @@ -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) \ No newline at end of file diff --git a/ktransformers/server/backend/interfaces/transformers.py b/ktransformers/server/backend/interfaces/transformers.py index f205ac5..81fa6e5 100644 --- a/ktransformers/server/backend/interfaces/transformers.py +++ b/ktransformers/server/backend/interfaces/transformers.py @@ -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") diff --git a/ktransformers/server/config/config.py b/ktransformers/server/config/config.py index 27b788f..7ce616b 100644 --- a/ktransformers/server/config/config.py +++ b/ktransformers/server/config/config.py @@ -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) diff --git a/ktransformers/util/modeling_rope_utils.py b/ktransformers/util/modeling_rope_utils.py new file mode 100644 index 0000000..4fec4bc --- /dev/null +++ b/ktransformers/util/modeling_rope_utils.py @@ -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}'" + ) \ No newline at end of file diff --git a/requirements-local_chat.txt b/requirements-local_chat.txt index 50b1f65..0479d36 100644 --- a/requirements-local_chat.txt +++ b/requirements-local_chat.txt @@ -1,5 +1,5 @@ fire -transformers +transformers==4.43.2 numpy torch>=2.3.0 packaging diff --git a/setup.py b/setup.py index 2a09b48..d24db14 100644 --- a/setup.py +++ b/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 )